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  • Turning Headwinds Into Wins: How Brands Can Navigate Price, Share, and Visibility Amid Tariff Disruption

    Turning Headwinds Into Wins: How Brands Can Navigate Price, Share, and Visibility Amid Tariff Disruption

    Disruption Is Now the Baseline

    Tariffs can spike landed costs overnight, regulations rewrite labelling rules, and competitors slash prices before your team finishes its daily stand-up. And yet, some consumer brands thrive.

    The winning brands see changes early, decide quickly, and execute flawlessly across the digital shelf. This post blends three decades of pricing and merchandising expertise with timely digital shelf insights from DataWeave, offering a clear path forward for brands navigating today’s volatile retail environment.

    From Cost Shock to Chronic Uncertainty

    Tariffs are no longer just one-off headlines; they’ve become an unpredictable, ongoing variable in the global marketplace. The true challenge isn’t always the duty rate itself, but the constant whiplash of not knowing if, when, or how much that duty will change. This pervasive uncertainty is having a tangible impact:

    • Market Uncertainty: Tariff talk alone disrupts planning and fuels market instability.
    • Operational cost inflation: Shifting trade rules raise expenses across sourcing, freight, and distribution.
    • Compromised SKU-level Margin: The profitability of individual products is under constant threat.
    • Shrinkflation: Brands shrink product quantities to mask rising costs, risking consumer trust.

    Unpredictable Competitive Response: Delaying price moves while watching competitors can erode margins as much as tariffs.

    To stay ahead, pricing decisions must be stress-tested against multiple tariff scenarios and aligned with likely competitor reactions. Timing matters as much as accuracy, move too soon or too late, and margins suffer either way.

    The Tariff Math No One Can Afford to Get Wrong

    When it comes to tariff disruption, the difference between profit and loss often hinges on a precise understanding of a three-step process. Get any part of this chain wrong, and the financial ripple effect can undermine pricing and promotions. The duty you pay, therefore, is the direct result of the following three critical steps:

    Step 1: Harmonized System (HS) Code

    • What it is: A six- to ten-digit classifier that drills down to product sub-types.
    • Why it matters: A single digit change can shift an item into a higher-tariff bracket.

    Step 2: Country of Origin

    • What it is: The nation in which the imported item was made.
    • Why it matters: Mis-tagging the origin can lead to mis-pricing and inaccurate margin calculations.

    Step 3: Trade-Agreement Overlay

    • What it is: Differentiation between the World Trade Organization (WTO) baseline tariffs and special trade agreements (e.g., USMCAUnited States-Mexico-Canada Agreement).
    • Why it matters: The same HS code can result in significantly different duties, up to a 10% swing, depending on the originating country (see the example below).

    This isn’t just about paying the correct duty; it’s about safeguarding your bottom line in a global marketplace where every digit and every designation carries substantial weight.

    The wrong origin, the wrong rule, the wrong margin.

    Hard Numbers: Where Prices Are Already Climbing

    DataWeave’s latest digital shelf analysis shows import-driven price inflation diverging sharply by source country.

    The intricate dance of HS codes, country of origin, and trade agreements directly translates into the prices consumers see. And the data doesn’t lie. Below, we delve into the hard numbers: where prices are already climbing, as illuminated by DataWeave’s latest digital shelf monitoring, showing significant import-driven price inflation by source country.

    • China: Products sourced from China are up 5%. This is largely attributable to the numerous tariffs currently imposed on Chinese goods.
    • Mexico: Prices for products from Mexico have risen by 3%.
    • United States: Interestingly, even U.S.-sourced products show a 3% increase.
    tariff price increases

    This rise in U.S. product prices might seem counterintuitive if tariffs are solely focused on imports. However, the reality lies in the global supply chain for many products.

    Consider guacamole as an example: While the final product might be “Made in the USA,” its components often come from various international sources. Avocados might be imported from Mexico, lime juice from Central America, and seasonings from India or China. Even packaging could originate in Asia. Each of these imported components can be subject to tariffs. Therefore, even if an item is assembled in the U.S., the tariffs on its constituent parts contribute to an overall price increase, explaining the rising rates for U.S.-sourced goods.

    Action step: Map tariff exposure at both finished-goods and component-level to avoid “Made in USA” blind spots.

    Timing Is a Competitive Weapon

    With duty tables and competitor reactions changing fast, the question is: move first or follow? Early movers recoup cost fastest but risk overshooting if tariffs ease; laggards may enjoy a brief price advantage but suffer sudden margin compression.

    The Strategic Dilemma

    The table below illustrates this strategic choice and its potential outcomes:

    Shrinkflation: Margin Patch or Trust Erosion?

    Beyond direct price adjustments, many brands are turning to shrinkflation to manage tariff-driven cost pressure, shaving net weight instead of hiking prices. DataWeave’s analysis reveals an average package reduction of 5 – 6%, with extreme cases reaching 15 – 25%, sometimes even coupled with a shelf-price increase.

    While this can cushion immediate margin, it comes at a significant cost: brand credibility. Savvy shoppers quickly spot these changes, sharing “before-and-after” photos online and fueling consumer frustration. What begins as a margin patch can rapidly erode trust and damage long-term loyalty.

    Ultimately, navigating this volatile environment requires dynamic intelligence and a holistic pricing strategy that balances profitability with market share and, crucially, consumer trust.

    Price Hikes May be Inevitable, But You Can Still Run Your Digital Shelf

    Tariff‑driven cost pressure can force list‑price increases, but it does not dictate how well your products show up, sell through, or satisfy shoppers online. Those outcomes still hinge on five levers that live entirely inside your control. Master them and you cushion margin hits while protecting (or even expanding) share.

    The Five Levers of Digital‑Shelf Control

    • Inventory Depth – Maintain online in‑stock rates above 95 percent for high‑velocity SKUs and flag substitute logic when unavoidable out‑of‑stocks occur.
    • Content Quality & Accuracy – Keep titles keyword‑rich, imagery crisp, and attributes complete so search filters never bury you.
    • Ratings & Reviews Cadence – Proactively request fresh reviews to earn retailer search boosts and reassure value‑conscious shoppers.
    • Retail‑Media Precision – Bid where pages are healthy and in‑stock; pause spend on broken listings that leak conversion and ROAS.
    • Fulfillment Excellence – Monitor pick‑pack accuracy, on‑time delivery, and substitution rates; each one influences retailer algorithmic visibility.

    Content Hygiene Keeps You Visible, Compliant, and Conversion-Ready

    Missing or incorrect product attributes (e.g., “gluten-free,” “caffeine content”) can swiftly jeopardize both regulatory compliance and your product’s fundamental search visibility. Simply put, if it’s not labeled right, it won’t be found.

    This impact plays out in two crucial areas:

    1. Retailer Search Visibility: Filter logic on major e-commerce platforms like Target.com, Walmart.com, and Instacart is increasingly driven by precise attribute tags (e.g., “gluten-free,” “BPA-free,” “0g added sugar”). Fail to provide or correctly format these claims, and your product will simply never appear when shoppers apply these critical search filters. You become invisible to a motivated audience.
    2. Regulatory Compliance: Global regulatory bodies, including the U.S. FDA and EU authorities, now treat online product detail pages as officially regulated labeling space. This means that a single missing allergen statement or an inaccurate nutritional claim can trigger severe consequences, from product takedowns and hefty fines to a devastating “straight-to-zero” share of search. Non-compliance isn’t just a legal risk; it’s a direct threat to your market presence (see example below).

    The Hygiene Playbook: Audit → Score → Fix → Grow

    Your Product Detail Pages (PDPs) are your digital storefronts, and they need to be impeccable. Modern content-intelligence tools are like vigilant auditors, constantly scanning, structuring, and scoring every PDP across your retail network.

    Tools like DataWeave do the heavy lifting by:

    • Surfacing critical gaps: They’ll pinpoint issues like blurry images, inaccurate titles, or missing nutrition information.
    • Optimizing for search: They ensure your product attributes align with live search filters, turning claims into clicks.
    • Flagging compliance risks: You’ll know about potential issues before regulators or retail partners ever do.
    • Quantifying your impact: Get a clear Content Quality Score that your teams can own and improve, week after week.

    When you execute this well, it’s not just about tidying up; it’s a powerful growth engine. This proactive approach fuels every step of the digital customer journey – from getting found, to winning the click, converting the cart, and ultimately, capturing reviews that boost your search rankings.

    A Case Study: Bush’s Beans Converts Visibility into Revenue

    Before Bush’s Beans achieved rapid success with their “audit → scorecard → rapid-fix” approach, they confronted a significant hurdle. Here’s how they overcame it to drive impressive revenue growth.

    The Challenge

    Bush’s Beans saw its e-commerce contribution stall at just 1.5 percent while competition in canned goods intensified. A quick audit revealed three root causes:

    1. Dipping online sales that signalled slipping visibility and conversion.
    2. Fragmented product content across major retailer sites as images, titles, and claims were inconsistent or missing altogether.
    3. Heavier category competition  making it harder to hold first-page search positions.

    The Fix

    The brand adopted DataWeave’s Digital Shelf Analytics to create a single source of truth for every PDP. A lean internal team then:

    • Ran content audits across priority retailers to surface incomplete or non-compliant attributes.
    • Prioritized quick wins focusing on high-velocity SKUs where simple edits (e.g., adding pack-size keywords or allergy statements) would unlock search filters.
    • Tracked progress weekly using an automated scorecard to keep everyone focused on the next set of fixes.

    The Win

    Twelve months later the numbers told the story:

    Bush’s Beans transformed their product data into a strategic asset, significantly improving online visibility, safeguarding brand reputation, and driving sustained revenue growth. Accurate and complete product pages ensured compliance and boosted search rankings, directly increasing sales. While you can’t control external factors like tariffs, you can control the quality and compliance of your product pages and that control directly translates margin pressure into market share gains.

    Unified Insight: Turning Signals into Sustained Advantage

    Imagine one living dashboard where every digital shelf signal like timely price moves, share-of-search shifts, retail media spend, on-shelf availability gaps, compliance flags, MAP breaches, plus content and review health flows together. With that single lens, the “whose numbers are right?” debate disappears and cross-functional teams can act in minutes rather than days.

    A consolidated feed lets you:

    • Build market awareness: Spot competitor price changes as they happen, understand who owns first-page search, and measure the true lift of retail media campaigns.
    • Mitigate emerging risks: Surface impending out-of-stocks before rank erodes, catch claim or label errors ahead of audits, and receive instant alerts when a seller breaks MAP.
    • Activate growth levers: Prioritize content edits that open search filters and use ratings and reviews trends to fine-tune messaging and assortment.

    Brands that weave these signals into one workflow move faster than the disruption. That’s the connective tissue highlighted in our recent post on pairing Digital Shelf Analytics with Marketing-Mix Modelling: when granular shelf data sits beside strategic performance metrics, smarter decisions follow.

    A platform like DataWeave brings the pieces together quietly ingesting millions of price checks, availability reads, and PDP audits each day, then presenting only the next best actions. The payoff is simple: sharper market awareness, lower operational risk, and growth that compounds with every iteration.

    Keep Moving, Keep Winning

    Tariffs, evolving regulations, and agile competitors are no longer storms; they are the climate. Brands that pair a clear, shared insight stream with rapid execution turn volatility into durable advantage. Keep your data united, keep iterating on the five digital-shelf levers, and every new headwind becomes another step ahead.

  • Bridging the Gap: How Digital Shelf Analytics Empowers Marketing Mix Modelling for Smarter Brand Decisions

    Bridging the Gap: How Digital Shelf Analytics Empowers Marketing Mix Modelling for Smarter Brand Decisions

    Marketing Mix Modeling (MMM) has been a cornerstone of marketing analytics for decades: first as a service offered by large consultancies like Nielsen and IRI, and later as software solutions from NielsenIQ and Ekimetrics. By 2024, some 64% of senior marketing leaders had already adopted and used MMM solutions.

    However, despite this widespread adoption, MMM faces significant limitations in our fast-moving digital marketplace. According to Gartner, opaque pricing models and siloed data integration remain substantial barriers to actionable insights from these tools. Most critically, traditional MMM often misses vital variables influencing consumer behavior, such as:

    • Competitor price drops and promotions
    • Product availability issues and stockouts
    • Negative review trends and sentiment shifts
    • Search ranking fluctuations

    These blind spots must be addressed to unlock the full value of MMM investments and make truly informed marketing decisions.

    The Critical Data Gap In Traditional MMM

    Traditional MMM solutions expose brands to considerable risk, especially in the CPG and retail space. The fundamental challenge lies in MMM’s reliance on lagging indicators for essential metrics like historical sales and ad spend. Data inputs may be months or quarters old before they’re used for scenario analysis.

    That’s like making million-dollar marketing decisions while only looking in the rearview mirror when you need to watch the road ahead simultaneously.

    MMM tools also typically overlook external market factors that can dramatically impact performance. In today’s retail landscape, where market conditions change rapidly, being blind to real-time competitive dynamics creates significant vulnerability. Key external factors that traditional MMMs fail to capture include:

    • Competitor moves: Price changes, promotions, content updates
    • Consumer sentiment: Review trends, ratings, social engagement
    • Market dynamics: Stockouts, search ranking shifts, category growth

    How Digital Shelf Analytics Completes The Picture

    This is where Digital Shelf Analytics (DSA) plays a crucial complementary role. Brands and retailers leveraging DSA gain insights into real-time market dynamics that MMM alone cannot provide. However, brands using DSA in isolation often struggle to quantify how digital shelf improvements directly impact revenue. Answering questions like “Did better product content drive sales, or was it the influencer campaign?” remains challenging.

    Bridging these disconnected platforms requires intentional integration and a DSA platform that can feed intensively cleaned and organized data into existing MMM platforms. With the right data inputs, companies establish a powerful feedback loop for agile, data-driven decisions.

    A comprehensive DSA solution like DataWeave provides granular, actionable data on critical external variables such as:

    • Daily or weekly competitor pricing movements and promotional activity
    • Product content standardization and optimization across retailers
    • Review sentiment trends and potential reputation issues
    • Share of search/shelf performance relative to competitors

    When merged with established MMM capabilities, DSA creates a complete picture that fills the blind spots holding marketing teams back from maximizing ROI.

    The DSA + MMM Advantage in Retail Media

    The popularity of retail media networks has further amplified the need for integrated DSA and MMM approaches. These advertising platforms, operated by retailers, allow brands to display targeted ads to shoppers across digital properties based on first-party customer data and purchase insights.

    The retail media revolution has transformed e-commerce pages into sophisticated search engines for product discovery. This evolution has been so impactful that retail media ad revenue surged 16.3% in 2023, reaching $43.7B in the U.S., with continued growth projected.

    Major platforms like Walmart have expanded their retail media networks to capitalize on closed-loop attribution. Since retailers own the entire customer journey, they can track everything from ad impression to purchase on their e-commerce sites. This creates a significant advantage through accurate ROI measurement, unlike traditional advertising where attribution remains challenging.

    How DSA Enhances Retail Media Optimization

    With retail media emerging as a top-performing sales channel, brands need sophisticated optimization strategies. Every brand wants to maximize visibility and performance across individual eCommerce sites, just as they optimize for Google or emerging AI platforms.

    Integrating digital shelf analytics into marketing mix models enables brands to:

    • Allocate ad spend more intelligently using real-time competitive insights
    • Identify timely campaign activation opportunities in response to market changes
    • Monitor organic ranking trends to strategically time paid promotional activities
    • Measure true campaign impact on digital shelf performance metrics

    For example, when a competitor launches an aggressive price drop in your category, DSA provides visibility into this change. This intelligence can trigger recommended campaign adjustments, such as increased sponsored ad bidding in affected categories. Traditional MMM alone cannot deliver this level of responsive optimization.

    How to Integrate DSA into MMM: A 3-Step Framework

    Digital Shelf Analytics for Marketing Mix Modeling - 3 Step Framework

    Here’s how to integrate your Digital Shelf Analytics into your Marketing Mix Models to start making better data-driven decisions for your brand.

    Step 1: Map DSA Variables to MMM Inputs

    Begin by mapping specific DSA variables to your static MMM inputs. Ensure that competitors are properly configured for monitoring in your DSA platform and that metrics like price changes and search ranking positions are linked with your MMM’s models.

    This integration is crucial because traditional MMM models rely exclusively on historical data for forecasting. Adding real-time inputs delivers several benefits:

    • More accurate elasticity curves reflecting current market conditions
    • Better understanding of root causes behind demand shifts
    • Prevention of misattributing sales changes to your marketing activities when external factors may be responsible

    At DataWeave, our comprehensive coverage spans 500+ billion data points, 400,000 brands, and 1,500+ websites, ensuring brands never miss a competitor move and maintain complete visibility across the connected e-commerce landscape.

    Step 2: Feed High-Quality DSA Data into MMM Platforms

    Next, integrate critical digital shelf metrics into your MMM framework:

    • Review and sentiment scores and trends
    • Content quality measurements
    • Competitive positioning data
    • Price gap analytics
    • Search ranking performance

    DataWeave employs a rigorous data accuracy validation process to ensure teams work with the cleanest, most reliable data possible. Our sophisticated processing pipeline removes anomalies and standardizes information across retailers, providing the consistent, high-integrity data foundation that robust marketing mix modeling demands.

    Step 3: Validate and Iterate

    A powerful DSA solution helps measure whether your marketing efforts achieved their intended impact on the digital shelf. Use your DSA platform to assess campaigns’ actual effect on key performance indicators:

    • Do promo-driven sales lifts correlate with improved search rankings?
    • How do content improvements impact conversion rates?
    • What is the relationship between paid media and organic visibility?

    DataWeave enables users to correlate metrics across the entire consumer journey, from awareness through post-purchase. Rather than focusing solely on short-term spikes, brands can measure lasting impacts on digital shelf health. This end-to-end visibility empowers teams to make increasingly informed decisions with each campaign cycle.

    Executive Decision Support in Uncertain Times

    It is no surprise to anyone that we are living through volatile times. Executives may be uncomfortable if they cannot provide their teams with strategic direction based on data or the tools they need to accelerate their workdays.

    By integrating DSA with MMM, companies gain early warning signals about market shifts, enabling smarter resource allocation during budget constraints. This integration helps organizations move from tactical execution to strategic direction by:

    • Providing cross-channel impact analysis to understand the full marketing ecosystem
    • Equipping category managers with tactical optimization tools that support broader strategic objectives
    • Identifying competitive threats before they impact sales
    • Forecasting potential ROI impacts across various spending scenarios

    These capabilities help prevent wasted ad spend, missed opportunities, and lost sales.

    Future-Proofing with DSA-Driven MMM

    Several emerging trends highlight the growing importance of DSA-enhanced marketing mix modeling:

    • Trend 1: Navigating Economic Volatility – Brands can use DSA to track how competitors adjust pricing in response to cost shocks like tariffs and inflation. This real-time intelligence directly improves MMM’s inflation modeling accuracy.
    • Trend 2: AI-Powered Predictive Insights – Combining DSA trend detection (such as viral product reviews or sudden inventory fluctuations) with MMM helps forecast demand spikes from otherwise unforeseen events.
    • Trend 3: Automated Optimization – Smart campaign activations and adjustments based on real-time DSA triggers drive efficiency. DataWeave’s vision includes an automated retail media intelligence layer that optimizes spend across channels based on integrated insights.

    DataWeave’s Unique Advantage

    At DataWeave, we’ve seen our digital shelf analytics customers significantly improve their organic search rankings because of better-sponsored ad campaigns. What makes our approach to DSA-MMM integration uniquely powerful? Our platform is specifically designed to address the challenges of modern marketing mix modeling:

    • Superior data refresh rates ensure timely insights when they matter most
    • Unmatched marketplace coverage across more than 1,500 eCommerce sites globally
    • Advanced data normalization that standardizes metrics across disparate categories and retailers
    • API-first architecture enabling flexible data access and utilization

    Conclusion – From Hindsight to Foresight

    In the past, companies relied primarily on historical data for their marketing mix models. Today’s market leaders are incorporating digital shelf analytics to unlock superior insights, improve decision accuracy, and drive measurable ROI.

    DataWeave serves as the essential bridge between MMM systems and real-time, comprehensive market intelligence. When DSA and MMM work together, brands gain a complete picture: MMM shows precisely what happened, while DSA explains why it happened—and together, they reveal what’s coming next.

    Ready to transform your marketing mix modeling from hindsight to foresight? Contact us today to discover how our Digital Shelf Analytics can enhance your existing MMM investments and drive measurable business results.

  • Standard Reporting vs. Competitive Intelligence: What Retail Leaders Need to Know

    Standard Reporting vs. Competitive Intelligence: What Retail Leaders Need to Know

    Back in the day, pricing strategies were a lot easier. These days, not only do teams need to have robust standard price reporting workflows, but they also need to have the know-how and tools to gain and act on competitive intelligence. Retail leaders should prioritize automation and strategic thinking and ensure their teams have the tools, processes, and methodologies required to monitor the competition at scale and over the long term.

    Retail leaders who recognize the distinction between standard reporting and competitive intelligence are more likely to gain team buy-in, especially when developing pricing strategies that drive results. You can’t be everywhere at once, but you can optimize pricing strategies to stay ahead of the competition.

    This article has everything you need to know about the differences between standard reporting and competitive intelligence and how to use both to make your teams more effective than ever!

    Understanding the Distinction

    Standard price reporting is much like checking the weather to see if it’s stormy before grabbing a raincoat or sunhat. You need to do it to make essential, everyday choices, but it will not help you predict when the next storm is coming. Standard price reporting deals more with the short-term and immediate actions needed as opposed to long-term strategy.

    Don’t get us wrong, standard price reporting is still an essential responsibility of a pricing team’s function—but there’s more to it. It is also lower-tech than a competitive intelligence strategy and can rely on route heuristics.

    Think of it as data-in, data-out. It deals with pricing operations like:

    • Weekly price movements: Seeing which competitors, product categories, and individual items had pricing shifts in the short-term
    • Basic price indices: Outlining benchmarks to watch how your own, and your competitors’, products are trending in the market
    • Price competitiveness metrics: Setting thresholds that show whether your products are priced below, above, or equal to your competition for general trend reporting

    Standard price reporting is fundamental for operational teams that manage price adjustments in the short term. It can also help teams remain agile and reactive to market condition changes.

    It’s likely that your team already has standard reporting strategies or tools to help them with tactical execution. But are they harnessing competitive intelligence correctly with your help?

    Characteristics of Competitive Intelligence

    While standard price reporting is like checking the weather, competitive intelligence is like being a meteorologist who measures atmospheric changes, predicts storms, and scientifically analyzes weather patterns to keep everyone informed and in the know.

    Competitive intelligence goes well beyond simply tracking price movements and benchmarking them against a single set of standards. Competitive intelligence helps steer teams in a strategic direction based on insights from the market. It can drive long-term business success and is one of your best tools to ‘steer the ship’ as a retail leader.

    Here are some of the essential elements of competitive intelligence:

    • Strategic insights: Including but not limited to understanding your competitors’ pricing strategy, promotions, and product positioning
    • Market-wide patterns: Identifying trends based on geography, product category, or individual SKU across retailers to inform broader strategies
    • Long-term trends: Taking historical market and competitor data and combining it with real-time retail data to predict future price movements as shifts in consumer behavior to inform pricing strategies

    The pricing team serves as a critical strategic partner to senior leadership, delivering the cross-functional insights and market analysis needed to inform C-suite decision-making. By equipping executives with a holistic view of the competitive landscape, pricing gaps, and emerging trends, the team empowers leadership to align pricing strategies with broader business objectives.

    This partnership enables senior leaders to guide day-to-day pricing operations with confidence—ensuring tactical execution aligns with corporate goals, monitoring strategy effectiveness, and maintaining competitive agility. Through ongoing market intelligence and scenario modeling, the pricing function helps leadership proactively position the brand, capitalize on untapped opportunities, and future-proof revenue streams.

    Different Audiences, Different Needs

    As mentioned, there is a place for both standard price reporting and competitive intelligence. They have different roles to play, and different teams find them valuable. Since standard reporting mainly focuses on day-to-day shifts and being able to react to real-time changes, operational teams find it most useful.

    On the other hand, competitive intelligence is a tool that leadership can use to shape overarching pricing strategies. The insights from competitive intelligence drive operational activities over months and quarters, whereas standard reporting drives actions daily.

    To succeed in pricing, you need to rely on a combination of tactical standard reporting and competitive intelligence for long-term planning. With both, you can successfully navigate the ever-fluctuating retail market.

    Price Reporting for Operational Teams

    Your operational team is responsible for making pricing adjustments that directly impact sales volume. Automated data aggregation and AI-powered analytics can make this process faster and more accurate by eliminating the need for manual intervention.

    Instead of spending hours identifying changes, standard reporting tools surface the most critical areas that need attention and recommend adjustments. This helps operational teams react fast to shifting market conditions.

    Key functions of standard price reporting include:

    • Daily/weekly pricing decisions: Frequent price adjustments based on market trends will help your company remain competitive across entire product categories. With automated, real-time dashboards, your pricing team can monitor broad category-level pricing shifts and make necessary adjustments accordingly.
    • Individual SKU management: Not all pricing changes happen at the category level. Standard reporting also allows teams to view price and promotion changes on individual SKUs down to the zip code. It’s important to have targeted, granular insights when a change occurs even on a single SKU, especially because these individual changes are easy to miss. Advanced product matching algorithms can tie together exact products across retailers to monitor items conjointly. By incorporating similar product matching technologies beyond standard reporting, your teams can monitor individual price changes on comparable products.
    • Immediate action items: The best standard reporting tools alert pricing teams when there has been a change in competitor pricing and give them recommendations for what to change. If a competitor launches a flash sale or an aggressive discount program, your team should know as fast as possible which product to adjust. Without this functionality, teams can miss important changes or experience a delay in action that results in lost sales or customer perception.

    Competitive Intelligence for Leadership

    For Senior Retail Executives, Category Directors, and Pricing Strategy Leaders, pricing cannot only be about reacting to individual competitor price changes. Instead, you must proactively think about your market positioning and brand perception. Doing this without a complete competitive intelligence strategy can feel like throwing darts while blindfolded. Sometimes, you’ll hit the target, but mostly, you’ll miss or only come close. Competitive intelligence tools can help you hit that target every time. They leverage big data, artificial intelligence (AI), and predictive modeling to help you derive holistic insights to understand your current positioning relative to the current and future pricing landscape.

    Core strategic functions of competitive intelligence include:

    • Strategic planning: Competitive intelligence tools can help you forecast competitor behavior, economic shifts, and category-specific patterns you’d otherwise overlook (ex, price drops before new releases, subscription or bundling trends, or seasonable price cycles). Instead of reacting to a change, your team can already have made changes or at least know what playbook to implement.
    • Market positioning: Geographic pricing intelligence built into competitive intelligence tools can help you understand variations across locations and optimize multiple channels simultaneously. This can be the foundation of regional pricing strategies that factor in local economies and consumer perception.
    • Long-term decision-making: You can use competitive intelligence technology to align your pricing strategy with upcoming seasonal trends isolated using historical data, predicted economic shifts, and changes in customer purchasing behavior. This aggregate view of the pricing landscape will help you step out of the weeds and make better company decisions.

    From Data to Strategy – Transforming Basic Price Data

    Shifting your focus from isolated, reactive data to broader market trends is the key to going from basic price reporting to real competitive intelligence. Never forget the importance of real-time data, but know it’s your responsibility as a leader to bring a broader viewpoint to operations.

    Transforming from basic price data to competitive intelligence involves:

    1. Harnessing the data
      • Pattern recognition: Your solution should help you identify repeat pricing behaviors and competitor strategies
    2. Figuring out what to do with the data
      • Strategic implications: It should help you understand how your pricing changes will affect customer perception of your brand
    3. Doing something with the insights from your data
      • Action planning: The solution should help you create proactive strategies that position you as a market leader, leaving your competition to try to keep up with you instead of vice versa

    Leveraging Technology for Competitive Intelligence

    Technology is at the heart of leveling up your standard price reporting game. If you want industry-leading competitive intelligence, you can leverage DataWeave’s comprehensive pricing intelligence solution with built-in competitive intelligence capabilities and features for your operational teams.

    You can also uncover gaps and stay competitive in the dynamic world of eCommerce. It provides brands with the competitive intelligence they need to promptly adapt to market demand and competitors’ pricing. Stay ahead of market shifts by configuring your own alerts for price fluctuations on important SKUs, categories, or brands, all time-stamped and down to the zip.

    And since our platform relies on human-backed AI technology, you can have complete confidence in your data’s accuracy at any scale. If you want to bring a new strategic mindset to your pricing team, consider adding competitive intelligence to your tech stack. If you want to learn more, connect with our team at DataWeave today.

  • Preparing for Tariff Impact: A Retailer’s Guide to Price Intelligence

    Preparing for Tariff Impact: A Retailer’s Guide to Price Intelligence

    The power to impose tariffs on foreign countries is one of the most impactful measures a government has at their disposal. The government can use this power for various reasons: to punish rivals, equalize trade, give domestic products a comparative advantage, or collect more funds for the federal government.

    Whatever the reason, tariffs have real-world impacts on brands and retailers selling in a global economy. They effectively make products more expensive for some and comparatively cheaper for others. Since tariffs can be added or removed at the drop of a hat, retail executives, category managers, and pricing teams trying to keep up have their work cut out for them.

    You’ve come to the right place if you’re wondering how to prepare for and respond to potential tariffs. The answer lies in technology that will make you flexible when you need to react to policy changes. Establishing workflows and processes embedded with pricing intelligence can help you stay competitive even when global politics intercepts your business.

    Understanding Tariff Impact

    Before diving into tariffs’ implications on pricing strategies, we need to understand how tariffs work and the current economic environment. Tariffs are a government’s tax on products a foreign country sells to domestic buyers. You might remember President Trump’s expanded tariff policy in September 2018. It placed a 10% tax on $200 billion worth of Chinese imports for three months before raising to a rate of 25% in January 2019. At that time, an American buyer would pay the original price of the goods plus the tax to the American government. Many additional tariffs and counter-tariffs by other countries were enacted during Trump’s first term in office, including the European Union, Canada, Mexico, Brazil, and Argentina, resulting in a trade war.

    Announcements of when, where, and on what new tariffs will be imposed are unpredictable. The only predictable thing is that this type of market volatility is here to stay. Pricing teams should adjust their mindsets to assume that volatility may always be on the horizon. This is because tariffs have many cost implications. Besides the flat rate imposed by the government on a certain product, tariffs have historically raised the price of all goods.

    In economic terms, tariffs create a multiplier effect. Consider a tariff placed on gasoline imported from Canada. This measure may encourage American drilling but will have immediate ripple effects throughout the economy. Everything that relies on ground transportation will increase in price, at least in the short term.

    This means that a fashion brand that sources and manufactures its entire line domestically will incur more costs since transportation will be more expensive. If fashion companies act like most companies, they will pass that added tax burden on to the consumer through higher prices. The company will make this decision based on how sensitive its consumers are to price increases, i.e., the elasticity of demand. These interwoven relationships extend across industries and products, affecting most retailers somehow.

    Of course, category exposure varies by industry and sector. Tariffs are known to impact specific industries more than others. For example, steel, electronics, and agriculture products are at risk of price fluctuations based on their reliance on imported components. These have high category exposure. Some industries reliant on domestic production with stable input costs are less prone to category exposure. These include domestic power grids, natural gas, real estate, and handmade goods. No matter which industry you’re in, however, expect some spill over.

    Preparation Strategies

    Strategies to battle disruption in retail

    Forward-thinking leaders can help position their teams for success in the face of pricing volatility brought on by tariffs. The key is to enable teams to sense disruptions quickly and provide a way to take corrective action that doesn’t diminish sales. Here are three strategies you can implement ahead of time that will help keep you competitive during tariff disruption.

    Cost Monitoring

    Start by getting a firm handle on internal and external costs. Understand and analyze fluctuations in the cost of raw materials, production, and supply chain for your business to operate. Make sure that your products are priced with pre-defined logic so changes in price on one SKU don’t create confusion with another. For example, faux leather costs rise while genuine leather stays the same. In that case, a leather version of a product should be raised to reflect the price increase in the pleather variation, not to devalue the perception of luxury.

    Next, you will want to understand historical pricing trends as well as pricing indexes across your categories. These insights can help your teams anticipate cost fluctuations before they even arise and mitigate the risk that economic shifts create, even unexpected tariffs.

    Competition Tracking

    Tracking your competition is likely already a strategy you have in mind. But how well are your teams executing this important task? If they’re trying to watch for market shifts and adjust pricing in real time without the help of technology, things are likely slipping through the cracks.

    Competitive intelligence solutions help retailers discover all competitive SKUs across the e-commerce market, monitor for real-time pricing shifts, and take action to mitigate risk. You need an “always-on” competitive pricing strategy now so that the second a tariff is announced, you can see how it’s affecting your market. This way, you can maintain price competitiveness and avoid margin erosion when competitors’ pricing changes in response to a tariff or other market shift.

    Consumer Impact Assessment

    The multiplier effect is felt throughout the supply chain when tariffs are implemented. The effect can affect consumers in a number of ways and cause them to become spending averse in certain areas. Often, during times of economic hardship, grocery items remain relatively inelastic. This is because consumers continue to purchase essentials regardless of price changes. Conversely, the price of eating out or home delivery becomes more elastic since consumers cut back on dining expenses when costs rise across their shopping basket.

    You need to establish clear visibility into the results of your pricing changes. The goal should be to monitor progress and measure the ROI on specific and broad pricing changes across your assortment. Conducting market share impact analysis will also help you determine if you are losing out on potential customers or whether a decline in sales is being felt across your competition. Impact analysis tools can help your company check actual deployed price changes in real time.

    Response Framework

    Tariff response action plan for retailers

    Once you’ve prepared your team with strategies and technologies to set them up for success, it’s time to think about what to do once a tariff is announced or implemented. Here are three real-time decision-making strategies you should consider before your feet are to the fire. Having these in your back pocket will help you avoid financial disruption.

    Price Adjustment Strategies

    Think about how you strategically adjust prices. These could include percentage increases, flat rate increases, or absorbed via other strategies like bundling. You should also determine a cost increase threshold that you’re willing to absorb before raising prices. Think about the importance of remaining price attractive to consumers and weigh the risk of increasing prices past consumers’ ability or willingness to pay.

    Promotion Planning

    Folding increased costs into value-added offerings for consumers can be a good way to retain customer sentiment and sales volume without negatively affecting profit margins. You can leverage discounts, promotions, or bundling options to sell more of an item to a customer at a lower per-unit cost.

    What you don’t want to do is panic-adjust prices in response to tariffs of competitor moves. Instead, you can use a tool competitor intelligence solutions to watch if your competition is holding prices steady or adjusting. With full information about pricing at your disposal, you can make better decisions on your promotional strategy and not undercut yourself or lose customer loyalty.

    Alternative Sourcing

    Let’s face it: putting all your eggs in one basket is bad for business. Instead of relying solely on a single supplier for production, you should have a diverse set of suppliers ready and able to shift production when tariffs are announced. If a tariff impacts Chinese exports, having a backup supplier in Vietnam can prevent added costs entirely. You can also consider strategies like bulk pricing, set pricing, or shifting entirely to domestic suppliers.

    Forward Buying

    Proactively stockpile inventory by purchasing large quantities of at-risk products before tariffs take effect. This strategy locks in lower costs and ensures supply continuity during disruptions. However, balance this with careful demand forecasting to avoid overstocking, which ties up cash flow and incurs storage costs. Use historical sales data and tariff implementation timelines to optimize order volumes—this is especially effective for products with stable demand or long shelf lives.

    Market Intelligence Requirements

    Preparing your pricing teams and giving them a framework upon which to act when tariffs are announced doesn’t have to be complicated. You can get access to the right data on costs, competitors, and consumer behavior with DataWeave’s pricing intelligence capability.

    We provide retailers with insights on pricing trends, category exposure, and competitor adjustments. Our AI-powered competitor intelligence solutions allow you to get timely alerts whenever a significant change happens. This can include changes to competitor pricing and category-level shifts that you’d otherwise react to when it’s too late.

    These automated insights can also help you track historical pricing trends, elasticity, and margin impact to construct a clear response framework in an emergency. Additionally, our analytics capabilities can help you identify patterns to power pre-emptive pricing and promotional strategies.

    Getting the right pricing intelligence strategy in place now can prevent disaster later. Think through your preparedness strategy and how you want your teams to respond in the event of a new tariff, and consider how much easier reacting accurately would be with all the data needed at your fingertips. Reach out to us to know more.

  • Beyond MAP Pricing: Strategic Approaches for Brands and Retailers

    Beyond MAP Pricing: Strategic Approaches for Brands and Retailers

    Many retailers view minimum advertised pricing (MAP) policies as a necessary evil since they present several challenges for competitive positioning. In an idealistic free market, there wouldn’t be a need for MAP policies, and healthy competition would do the work of setting the final advertised price.

    However, MAP policies aren’t beneficial only for brands; they also greatly benefit retailers. This article will examine why MAP pricing can be a strategic advantage for both brands and retailers. We’ll also look at ways brand managers and retail pricing teams can navigate MAP requirements to maintain profitability and safeguard customer trust.

    Understanding MAP Fundamentals

    Minimum Advertised Price (MAP) is a policy set by brands that requires their sales channels to price the brand’s products at a minimum dollar value. Retailers are free to price the items higher, but the advertised price is never to exceed the minimum threshold.

    This agreement is established at the outset of a relationship or new product launch and can change at the brand’s discretion. Consumers typically see only the minimum advertised price when they search for a product across competing retailers. This means retailers need to find other ways to differentiate themselves beyond offering the lowest price.

    But a retailer can still effectively price the product at a lower cost to win sales away from the competition. This comes in the form of discounts applied at checkout, bundled deals, or other promotions that affect the final cart but not the advertised price. Only the advertised price must remain within MAP guidelines. This gives retailers a way to set themselves apart from the competition while still protecting the brand.

    A minimum advertised price has three central values: one for the brand, one for the retailer, and one for both.

    1. Brand or manufacturer: A MAP policy protects the brand’s value and prevents price erosion. If a retailer consistently undercuts a product’s price to make it more competitive, customers may begin to perceive the brand as lower in value over time. It can cause the brand to appear less premium than if prices hold steady. If a customer pays full price one day and then sees the same item advertised at a lower base price the next, it can weaken brand loyalty and cause dissatisfaction.
    2. Retailer: Minimum advertised pricing policies prevent retailers from engaging in a pricing war with one another, driving the price of an item down and hurting margins. This race to the bottom is bad for business. Apart from reducing profits, it discourages sellers from investing in marketing and other activities that drive sales. It also means that smaller retailers can compete with larger retailers, effectively leveling the playing field across the market.
    3. All parties: The issue of counterfeit and unauthorized sellers on the grey market plagues retailers and brands. One of the most straightforward ways to identify these sellers that undercut prices and damage brand perception is to track who is pricing products outside of agreements. Unauthorized or counterfeit sellers can be identified by establishing a MAP policy and monitoring who sells at the wrong price. Then, official legal action can be taken to prevent those merchants from selling the product.

    Brand Perspective

    Developing a clear and precise MAP policy is an important option for brands looking to stay competitive. Make sure you outline the minimum advertised price for each product for each sales channel and do so by geography. Write clear instructions on how discounts, promotions, and sales can be applied to the advertised price to avoid misunderstandings later. Ensure you work with your legal team to fill in any gaps before presenting them to retailers.

    If you find sellers acting outside the MAP policy, you must act swiftly to enforce your MAP policy. Cease and desist orders are the most common enforcement strategy a brand can use on unauthorized sellers and counterfeiters. But there are legal considerations for authorized sellers, too. You may need to fine the retailer for damages, restrict inventory replenishment until prices have been adjusted, remove seller authorization by terminating the relationship entirely, or escalate to your legal team.

    Open communication between the brand and retailer is in everyone’s best interest to ensure minimum pricing is being used. Have explanatory documents available for your retailers’ non-legal teams to reference while they set prices. These can take the form of checklists, video explainers, or even well-informed brand representatives working closely with retail pricing teams. It’s likely that some MAP violations will occur from time to time. The importance your retail partners place on fixing those errors will help you determine how much goodwill you will give them in the future.

    Brands can consider rewarding retailers that consistently adhere to minimum advertised price policies. Rewards often take the form of more lenient promotion policies, especially during major holidays like Christmas, Prime Day, or Black Friday. However, it’s never advisable to relax the actual MAP policy to allow one retailer to advertise a lower price year-round.

    Retailer Strategies

    A retailer can take several approaches to complying with a brand’s MAP policy while still maximizing sales. First, you need a dedicated compliance process spearheaded by compliance specialists or, better yet, enabled by technology. Embedding a process that checks for MAP violations into daily or weekly operations will prevent problems before brands become aware.

    Automated price tracking tools can help discover discrepancies so that you don’t accidentally violate a MAP agreement. Make sure MAP training extends beyond your pricing team and includes marketing. Anyone who participates in promotions or events should be made aware of the agreements made with specific brands. Determine if there are alternative promotion methods available to attract customers. You could offer free shipping on certain items, bundle giveaways, or apply cart-wide discounts at checkout.

    Monitoring your competition in real time will also help you stay ahead. If you discover a competitor undercutting your prices, bring this to the attention of your brand representative. This can build loyalty with the brand and help prevent lost sales due to market share loss.

    Digital Implementation for MAP Compliance

    Pricing teams at brands and retailers manually attempting to manage MAP pricing will lag behind the competition without help. They must discover, monitor, and enforce MAP compliance simply and effectively.

    Over the past several years, there has been a seemingly exponential proliferation of online sellers, complicating the industry and making it nearly impossible to find and discover all instances of every product you sell. It’s further complicated by marketplaces like Amazon, Walmart, and eBay, which are full of individual unauthorized sellers and resellers.

    Implementing a digital tool is the first step to effectively discovering and monitoring MAP compliance, even across these marketplaces. This tool should monitor all competition for you and discover imbalances in pricing parity.

    DataWeave’s MAP Violations Merchant Analytics solution has AI-backed software that scours the web for your products. It uses identifiers like UPCs and product titles and compares imagery to find where the product is sold. Our AI-powered image recognition capabilities are especially helpful in identifying inauthentic listings that may be counterfeit products or unauthorized sellers. It also has built-in geographic and channel-specific MAP monitoring capabilities to help with localized enforcement.

    The tool can aggregate all this data and present dashboard views of your own and competitors’ pricing that are easy to digest and act on. After all, retailers need to monitor their own MAP compliance as well as the competition’s. Brands can also track competitor sellers’ networks to explore potential new retail partnerships and grow their network reach.

    The MAP Violations Merchant Analytics solution has automated violation alerts and advanced reporting built into it. This means you can get real-time alerts instead of pouring through dashboards searching for exceptions each week. For deeper insights, the dashboards provide time-stamped proof of which sellers are undercutting MAP minimums, so you have all the information you need to make a case against them. Discovering repeat offenders is easy with historical trends dashboards that show which sellers have a history of violations.

    With all this information on who is violating what—and when—enforcement becomes much more manageable. Send cease and desist orders to unauthorized sellers and start having conversations with authorized sellers acting outside of your agreement. Acting quickly will help prevent hits to your brand’s reputation, price erosion, and lost sales.

    DataWeave’s MAP solution gives you the competitive edge to effectively discover MAP violations, monitor market activity, and act quickly when an issue is discovered.

    Make MAP Compliance a Strategic Advantage

    Basic MAP compliance and enforcement isn’t simply about setting pricing policies anymore. These policies are foundational to brand strategies, maintaining good relationships with retailers, and establishing long-term profitability for your business.

    When you let MAP violations go unchecked, it can erode your margins, damage how your customers perceive your brand, and create confusion across channels. Discovering, monitoring, and acting on MAP violations is much easier with the help of tools like DataWeave’s AI-enabled MAP Violations Merchant Analytics.

    Ready to take control of MAP pricing at your company? Request a MAP policy assessment from DataWeave today!

  • Portfolio Enhancement Through Price Relationship Management: Building Coherent Pricing Across Product Lines

    Portfolio Enhancement Through Price Relationship Management: Building Coherent Pricing Across Product Lines

    Do you remember when the movie Super Size Me came out? If you missed it, it was about the harmful effects of eating fast food too often. One aspect of the film that stands out is McDonald’s clever use of pricing to encourage consumers to buy bigger—and therefore more expensive—meals.

    Hungry patrons could upgrade their meal to a Super Size version for only a few cents more. In doing so, McDonald’s was able to capitalize on perceived value, i.e., getting more product for an apparent lower total price for the volume. It encouraged restaurant-goers to spend a little more while feeling like they got a great deal. It was a smart use of strategic pricing.

    There are hundreds of pricing relationship types like this one that pricing leaders need to be aware of and can use to their advantage when creating their team’s pricing strategy and workflows. You need to maintain profitable and logical price relationships across your entire product portfolio while keeping up with the competition. After all, the gimmick to Super Size would never have worked if the upgrade had been of less value than just buying another burger, for example.

    In this article, we’ll examine more real-world examples of pricing challenges so you can consider the best ways to manage complex price relationships. We’ll examine things like package sizes, brands, and product lines and how they’re intertwined in systematic price relationship management. Read on to learn how to prevent margin erosion, improve customer perception of your brand, and keep your pricing consistent and competitive.

    The Price Relationship Challenge

    Pricing is one of the most challenging aspects of managing a retail brand. This is especially true if you are dealing with a large assortment of products, including private label items, the same products of differing sizes, and hundreds, or even thousands, of competing products to link. Inconsistencies in your price relationship management can confuse customers, erode trust, and harm your bottom line.

    Let’s take a look at a few common pitfalls in portfolio pricing that you might run into in real life to better understand the impact on customer perception, trust, and sales.

    Pricing Relationship Challenges Retailers Need to Account For

    Private Label vs. Premium Product Pricing

    Let’s consider a nuanced scenario where price relationships between a retailer’s private label and premium branded products create an unexpected customer perception. Imagine you’re in a supermarket, comparing prices on peanut butter. You’ve always opted for the store’s private-label brand, “Best Choice,” because it’s typically the more affordable option. Here’s what you find:

    • Best Choice (Private Label) 16 oz – $3.50
    • Jif (National Brand) 16 oz – $3.25

    At first glance, this pricing feels off—shouldn’t the private label be the cheaper option? If a customer has been conditioned to expect savings with private-label products, seeing a national brand undercut that price could make them pause.
    This kind of pricing misalignment can erode trust in private-label value and even push customers toward the national brand. When price relationships don’t follow consumer expectations, they create friction in the shopping experience and may lead to lost sales for the retailer’s own brand.

    Value Size Relationships

    A strong value-size relationship ensures that customers receive logical pricing as they move between different sizes of the same product. When this relationship is misaligned, customers may feel confused or misled, which can lead to lost sales and eroded trust.

    Let’s look at a real-world example using a well-known branded product—salad dressing. Imagine you’re shopping for Hidden Valley Ranch (HVR) dressing and see the following pricing on the shelf:

    • HVR 16 oz – $3.99
    • HVR 24 oz – $6.49
    • HVR 36 oz – $8.99

    At first glance, you might assume that buying a larger size offers better value. However, a quick calculation shows that the price per ounce actually increases with size:

    • 16 oz = $0.25 per ounce
    • 24 oz = $0.27 per ounce
    • 36 oz = $0.25 per ounce

    Customers expecting a discount for buying in bulk may feel misled or frustrated when they realize the mid-size option (24 oz) is actually the most expensive per ounce. This mispricing could drive shoppers to purchase the smallest size instead of the intended larger, more profitable unit—or worse, lead them to a competitor with clearer pricing structures.

    Retailers must maintain logical price progression by ensuring that price per unit decreases as the product size increases. This not only improves customer trust but also encourages higher-volume purchases, driving profitability while maintaining a fair value perception.

    Price Link Relationships

    A well-structured price link relationship ensures customers can easily compare similar offerings of the same product and size. When the pricing across different versions or variations of the same item isn’t clear or consistent, it can confuse customers and damage trust, ultimately leading to missed sales and a negative brand perception.

    Let’s break this down with an example of a popular product—coffee. Imagine you’re shopping for a bag of Starbucks coffee and you see the following pricing on the shelf:

    • Starbucks Classic Coffee, 12 oz – $7.99
    • Starbucks Coffee, Mocha, 12 oz – $9.99
    • Starbucks Ground Coffee, Pumpkin Spice, 12 oz – $12.99

    At first glance, the product is the same size (12 oz) across all options, but the prices vary significantly. One might assume that the price difference is due to differences in quality or features, but what if there’s no clear indication of why the different flavors are priced higher than the standard?

    After investigating, you may realize that the only differences are related to different variants—like “Mocha” or “Pumpkin Spice” rather than any significant changes in the product’s core attributes. When customers realize they’re paying a premium for just different flavors, without any tangible difference in product quality, it can lead to feelings of confusion and frustration.

    Retailers must ensure that price links between similar offerings are justifiable by clearly communicating what differentiates each product. This avoids the perception that customers are being charged extra for little added value, building trust and encouraging repeat purchases. By maintaining transparent price link relationships, businesses can foster customer loyalty, increase sales, and drive better overall satisfaction.

    What is the Foundational Process to Tackle the Price Relationship Challenge?

    Now that we’ve reviewed several challenges brands face when pricing their products, what can be done about them?

    If you’re a pricing leader, you must create a robust pricing strategy that considers customer expectations, competitive data, sizing, and the overall value progressions of your product assortment. These are the three foundational steps to solve your price relationship challenges.

    1. First, you need to group products together accurately.
    2. Second, you need to establish price management rules around the group of related items.
    3. Third, you should set in place a process to review your assortment each week to see if anything is out of tolerance.

    This process is difficult, if not impossible, to manage manually. To effectively set up and execute these steps, you’ll need the help of an advanced pricing intelligence system.

    Implementation Strategy

    Want to know how to roll out a price relationship management strategy? Follow this implementation strategy for a practical way to get started.

    1. Set up price relationship rules: Determine which of your products go together, such as same products with different sizes or color options. Assign different product assortment groups and determine tolerances for scaling prices based on volume or unit counts.
    2. Monitoring and maintenance: Establish rules to alert the appropriate party when something is out of tolerance or a price change has been discovered with a competitive product.
    3. Exception management: Only spend time actioning the exceptions instead of pouring through clean data each week, looking for discrepancies. This will save your team time and help address the most significant opportunities first.
    4. Change management considerations: Think about the current processes you have in place. How will this affect the individuals on your team who have managed pricing operations? Establish a methodology for rolling this new strategy and technology out over select product assortments or brands one at a time to build trust with internal players.

    DataWeave offers features specifically built to help pricing teams manage pricing strategies. These applications can help you optimize profit margins and improve your overall market positioning for long-term success. A concerted effort to create brand hierarchies within your own product assortment from the get-go, followed by routine monitoring and real-time updates, can make all the difference in your pricing efforts.

    Within DataWeave, you can create price links between your products (value sizing) and those of the competition. These will alert you to exceptions when discrepancies are discovered outside your established tolerance levels. If a linked set of your products in different sizes shows inconsistent pricing based on scaled volumes, your team can quickly know how to make changes. If a competitor’s price drops significantly, you can react to that change before you lose sales.

    DataWeave even offers AI-driven similar product matching capabilities, which can help you manage pricing for private label products by finding and analyzing similar products across the market.

    If you want to learn more about price relationship management, connect with our team at DataWeave today.

  • Maximizing Competitive Match Rates: The Foundation of Effective Price Intelligence

    Maximizing Competitive Match Rates: The Foundation of Effective Price Intelligence

    Merchants make countless pricing decisions every day. Whether you’re a brand selling online, a traditional brick-and-mortar retailer, or another seller attempting to navigate the vast world of commerce, figuring out the most effective price intelligence strategy is essential. Having your plan in place will help you price your products in the sweet spot that enhances your price image and maximizes profits.

    For the best chance of success, your overall pricing strategy must include competitive intelligence.

    Many retailers focus their efforts on just collecting the data. But that’s only a portion of the puzzle. The real value lies in match accuracy and knowing exactly which competitor products to compare against. In this article, we will dive deeper into cutting-edge approaches that combine the traditional matching techniques you already leverage with AI to improve your match rates dramatically.

    If you’re a pricing director, category manager, commercial leader, or anyone else who deals with pricing intelligence, this article will help you understand why competitive match rates matter and how you can improve yours.

    Change your mindset from tactical to strategic and see the benefits in your bottom line.

    The Match Rate Challenge

    To the layman, tracking and comparing prices against the competition seems easy. Just match up two products and see which ones are the same! In reality, it’s much more challenging. There are thousands of products to discover, analyze, compare, and derive subjective comparisons from. Not only that, product catalogs across the market are constantly evolving and growing, so keeping up becomes a race of attrition with your competitors.

    Let’s put it into focus. Imagine you’re trying to price a 12-pack of Coca-Cola. This is a well-known product that, hypothetically, should be easy to identify across the web. However, every retailer uses their own description in their listing. Some examples include:

    How product names differ on websites - Amazon Example
    Why matching products is a challenge - Naming conventions on Target
    Match Rate Challenge - how product names differ on retailers - Wamlart
    • Retailer A lists it as “Coca-Cola 12 Fl. Oz 12 Pack”
    • Retailer B shows “Coca Cola Classic Soda Pop Fridge Pack, 12 Fl. Oz Cans, 12-Pack”
    • Retailer C has “Coca-Cola Soda – 12pk/12 fl oz Cans”

    While a human can easily deduce that these are the same product, the automated system you probably have in place right now is most likely struggling. It cannot tell the difference between the retailers’ unique naming conventions, including brand name, description, bundle, unit count, special characters, or sizing.

    This has real-world business impacts if your tools cannot accurately compare the price of a Coca-Cola 12-pack across the market.

    Why Match Rates Matter

    If your competitive match rates are poor, you aren’t seeing the whole picture and are either overcharging, undercharging, or reacting to market shifts too slowly.

    Overcharging can result in lost sales, while undercharging may result in out-of-stock due to spikes in demand you haven’t accounted for. Both are recipes to lose out on potential revenue, disappoint customers, and drive business to your competitors.

    What you need is a sophisticated matching capability that can handle the tracking of millions of competitive prices each week. It needs to be able to compare using hundreds of possible permutations, something that is impossible for pricing teams to do manually, especially at scale. With technology to make this connection, you aren’t missing out on essential competitive intelligence.

    The Business Impact

    Besides the bottom-line savings, accurately matching competitor products for pricing intelligence has other business impacts that can help your business. Adding technology to your workflow to improve match rates can help identify blind spots, improve decision quality, and improve operational efficiency.

    • Pricing Blind Spots
      • Missing competitor prices on key products
      • Inability to detect competitive threats
      • Delayed response to market changes
    • Decision Quality
      • Incomplete competitive coverage leads to suboptimal pricing
      • Risk of pricing decisions based on wrong product comparisons
    • Operational Efficiency
      • Manual verification costs
      • Time spent reconciling mismatched products
      • Resources needed to maintain price position

    Current Industry Challenges

    As mentioned, the #1 reason businesses like yours probably aren’t already finding the most accurate matches is that not all sites carry comparable product codes. If every listing had a consistent product code, it would be very easy to match that code to your code base. In fact, most retailers currently only achieve 60-70% match rates using their traditional methods.

    Different product naming conventions, constantly changing product catalogs, and regional product variations contribute to the industry challenges, not to mention the difficulty of finding brand equivalencies and private label comparisons across the competition. So, if you’re struggling, just know everyone else is as well. However, there is a significant opportunity to get ahead of your competition if you can improve your match rates with technology.

    The Matching Hierarchy

    • Direct Code Matching: There are a number of ways to start finding matches across the market. The base tier of the hierarchy of most accurate approaches is Direct Code matching. Most likely, your team already has a process in place that can compare UPC to UPC, for example. When no standard codes are listed, your team is left with a blind spot. This poses limitations in modern retail but is an essential first step to identifying the “low-hanging fruit” to start getting matches.
    • Non-Code-Based Matching: The next level of the hierarchy is implementing non-code-based matching strategies. This is when there are no UPCs, DPCIs, ASINs, or other known codes that make it easy to do one-to-one comparisons. These tools can analyze complex metrics like direct size comparisons, unique product descriptions, and features to find more accurate matches. They can look deep into the listing to extract data points beyond a code, even going as far as analyzing images and video content to help find matches. Advanced technologies for competitive matching can help pricing teams by adding different comparison metrics to their arsenal beyond code-based. 
    • Private Label Conversions: Up until this level of the hierarchy, comparisons relied on direct comparisons. Finding identical codes and features and naming similarities is excellent for figuring out one-to-one comparisons, but when there is no similar product to compare with for pricing intelligence, things get more complicated. This is the third tier of the matching hierarchy. It’s the ability to find similar product matches for ‘like’ products. This can be used for private label conversions and to create meaningful comparisons without direct matches.
    • Similar Size Mappings: This final rung on the matching hierarchy adds another layer of advanced calculations to the comparison capability. Often, retailers and merchants list a product with different sizing values. One may choose to bundle products, break apart packs to sell as single items or offer a special-sized product manufactured just for them. 
    Similar Size Mappings - product matching hierarchy - Walmart
    Similar Size Mappings - product matching hierarchy - Costco

    While at the end of the day, the actual product is the same, when there are unusual size permutations, it can be hard to identify the similarities. Technology can help with value size relationships, package variation handling, size equalization, and unit normalization.

    The AI Advantage

    AI is the natural solution for efficiently executing competitive product matching at scale. DataWeave offers solutions for pricing teams to help them reach over 95% product match accuracy. The tools leverage the most modern Natural Language Processing models for ingesting and analyzing product descriptions. Image recognition capabilities apply methods such as object detection, background removal, and image quality enhancement to focus on an individual product’s key features to improve match accuracy.

    Deep learning models have been trained on years of data to perform pattern recognition in product attributes and to learn from historical matches. All of these capabilities, and others, automate the attribute matching process, from code to image to feature description, to help pricing teams build the most accurate profile of products across the market for highly accurate pricing intelligence.

    Implementation Strategy

    We understand that moving away from manual product comparison methods can be challenging. Every organization is different, but some fundamental steps can be followed for success when leveling up your pricing teams’ workflow.

    1. First, conduct a baseline assessment. Figure out where you are on the Matching hierarchy. Are you still only doing direct code-based comparisons? Has your team branched out to compare other non-code-based identifiers?
    2. Next, establish clear match rate targets for yourself. If your current match rate is aligned with industry norms, strive to significantly improve it, aiming for a high alignment that supports maximizing the match rate. Break this down into achievable milestones across different stages of the implementation process.
    3. Work with your vendor on quality control processes. It may be worth running your current process in tandem to be able to calculate the improvements in real time. With a veteran technology provider like DataWeave, you can rely on the most cutting-edge technology combined with human-in-the-loop checks and balances and a team of knowledgeable support personnel. Additionally, for teams wanting direct control, DataWeave’s Approve/Disapprove Module lets your team review and validate match recommendations before they go live, maintaining full oversight of the matching process.
    4. The more data about your products it has, the better your match rates. DataWeave’s competitive intelligence tools also come with a built-in continuous improvement framework. Part of this is the human element that continually ensures high-quality matches, but another is the AI’s ‘learning’ capabilities. Every time the AI is exposed to a new scenario, it learns for the next time.
    5. The final step, ensure cross-functional alignment is achieved. Every one from the C-Suite down should be able to access the synthesized information useful for their role without complex data to sift through. Customized dashboards and reports can help with this process.

    Future-Proofing Match Rates

    The world of retail is constantly evolving. If you don’t keep up, you’re going to be left behind. There are emerging retail channels, like the TikTok shop, and new product identification methods to leverage, like image comparisons. As more products enter the market along with new retailers, figuring out how to scale needs to be taken into consideration. It’s impossible to keep up with manual processes. Instead, think about maximizing your match rates every week and not letting them degrade over time. A combination of scale, timely action, and highly accurate match rates will help you price your products the most competitively.

    Key Takeaways

    Match rates are the foundation of pricing intelligence. You can evaluate how advanced your match rate strategy is based on the matching hierarchy. If you’re still early in your journey, you’re likely still relying on code-to-code matches. However, using a mix of AI and traditional methods, you can achieve a 95% accuracy rate on product matching, leading to overall higher competitive match rates. As a result, with continuous improvement, you will stay ahead of the competition even as the goalposts change and new variables are introduced to the competitive landscape.

    Starting this process to add AI to your pricing strategy can be overwhelming. At DataWeave, we work with you to make the change easy. Talk to us today to know more.

  • Beyond Basic Price Monitoring: Advanced Applications of Competitive Intelligence

    Beyond Basic Price Monitoring: Advanced Applications of Competitive Intelligence

    It’s up to senior leadership, whether you’re a Chief Strategy Officer, Pricing Executive, or Commercial Director, to think big picture about your company’s competitive intelligence strategy. For more junior team members, it’s easy to get caught in the “this is how we’ve always done it” mindset and continue to go through the motions of price monitoring.

    You don’t have that luxury—it’s up to you to find and implement new ways to move beyond basic price monitoring and usher your company into an era of achieving actionable insights through competitive intelligence. There is much more to gain from competitive data than simple price monitoring.

    How can retailers leverage clean, competitive data to uncover strategic insights beyond basic price comparisons? This article will help you shift your mindset from tactical monitoring to strategic insight generation. We’ll see how sophisticated analysis of clean and refined competitive data can reveal competitor strategies, regional and geographic opportunities, and overall market trends.

    It’s time to shift away from standard reporting, which should be left for your pricing owners and end users, and towards gaining competitive intelligence to shape your holistic company pricing strategy. With the right tools, you can make this shift a reality.

    Regional Price Intelligence

    One significant opportunity you should take advantage of is a greater understanding of regional price intelligence. Understanding the nuances that shape how products, categories, and other retailers’ prices according to geographical differences can set your company up to win customer trust and dollars at checkout.

    Understanding geographic and regional pricing strategies

    Geographic price intelligence helps leaders leverage market opportunities based on where sales are happening. Variations in how products and categories are priced across regions often reflect competitor tactics, local demand, and cost structures.

    Let’s consider an example that impacts a broad geography, such as the entire continental United States – egg prices. Eggs are a staple grocery item and are frequently a loss leader in stores. This means they are products priced below their cost specifically to draw customers into stores.

    However, Avian Flu outbreaks affecting millions of birds have become more common recently. These outbreaks drive the cost of eggs higher as flocks must be culled to prevent the spread of the disease. This means that retailers must act to maintain acceptable margins or losses without frightening away customers or losing their trust.

    Avian Flu has been especially bad in Iowa and California. Retailers in these regions face tough decisions during outbreaks. They need to figure out how to balance the high prices required to cover the supply shortages with maintaining consumer trust that this staple product will not be perceived as ‘overpriced.’ Customers expect retailers to be fair even when supply chain issues make it challenging to keep prices stable.

    Another example impacting the broader USA is credit card defaults. Credit card defaults are reaching levels unseen since the financial crisis of 2008. $46 billion worth of credit card balances were written off in the first nine months of 2024 alone. This unprecedented figure highlights the fact that many Americans are struggling financially. Higher-income earners continue to do ok, but lower-income families are feeling the pressure more than ever.

    Understanding the differences between the geographies you sell in can help you construct your pricing strategies better. This is especially true as consumers brace themselves for more anticipated economic hardship.

    Retailers must set realistic financial targets without overpricing their catalogs. Otherwise, they risk losing customers who would otherwise have bought their products. Competitive intelligence can help retailers understand how economic disparities impact core consumer bases.

    Pricing leaders can leverage insights around geographic variations in supply, demand, and competitor pricing to help in situations like these. With how important eggs are, changes to their price can spill over into other categories. And with credit card defaults affecting hundreds of thousands of Americans, having a way to dive into these topics can help shape overarching strategies.

    Customer perception is a recurring theme in competitive intelligence. It’s not only about the actual value your brand offers but the perceived value based on historical context, current events, and competition.

    Leveraging Regional Price Differences for Strategic Advantage

    On the topic of customer perception, there are strategic ways to use customer perception to your advantage. One of these is detecting cross-market arbitrage opportunities using competitive intelligence and actioning them.

    But what is cross-market arbitrage? It’s the practice of exploiting the differences in price across different markets or regions. As a retailer, you can use cross-market arbitrage to your advantage by finding disparities in market conditions and strategically pricing your products to entice customers or offer more value. These opportunities can be in demand, supply, or competitive pricing. Acting quickly in markets where frequent disruptions happen can drive your business forward.

    DataWeave’s advanced competitive intelligence tools can analyze regional market trends to help you respond to real-time local demand fluctuations or cost pressures.

    Local Market Dynamics

    Pricing isn’t a one-size-fits-all operation. Where and what you’re pricing truly matters. Pricing teams should take price zones into account when constructing pricing strategies. This is because pricing isn’t equivalent across locations; it’s localized. Understanding this fact is critical for category-specific considerations at the macro and micro levels.

    This map shows a retailer’s regional price differentials on a breakfast basket. With the ability to access and refine your data, you can better construct maps like this one to track local market dynamics. Determine where you need to differentiate prices based on locality, understand the strategic stance of your competitors, and plan if you start to see changes in competitive price zones.

    Map shows a retailer's regional price differentials on a breakfast basket

    Competitor Strategy Detection

    As a retailer, you should continuously monitor your competitors, whether they’re succeeding or stagnating. One example of a major retailer that is seeing growth even during this challenging economic time is Costco. Costco is an interesting case because they do not have stores in every major city or even in every state.

    Costco has its brand strategy down, and it is tied to the pricing strategy. Costco has committed to its customers to provide quality items at competitive prices, and they’ve delivered even in a volatile economy. Costco has managed to maintain competitive prices on core merchandise and make strategic pricing adjustments on items that matter most to members. Their private label brand, Kirkland Signature, highlights their value-first approach. They continue to lead with price reductions like:

    • Organic Peanut Butter: $11.49 → $9.99
    • Chicken Stock: $9.99 → $8.99
    • Sauvignon Blanc: $7.49 → $6.99

    Costco has figured out how to balance premium offerings for cost-conscious consumers with standardly priced items filling the shopper’s basket. This demonstrates that they have a pricing strategy that relies on competitive intelligence and market trends.

    With the correct data and tools, any retailer can conduct research to discover more about their competitors and gain usable insights into their implemented pricing strategies. Once established, this can act as an early warning signal so you can take action.

    For example, understanding whether a retailer operates with a stable Everyday Low Price (EDLP) strategy or a more dynamic High/Low pricing approach is crucial when building and maintaining the integrity of your pricing strategy.

    Data should be able to show you things like:

    • When holiday price decreases start to accelerate
    • How quickly a retailer responds to cost increases (especially at the category or item level)
    • The cadence of seasonal campaigns and their impact on pricing behavior

    When we move beyond the numbers, these patterns tell a story about how to win in today’s competitive retail landscape. After all, pricing isn’t just a standard reporting tactic. In its truest form, it’s a strategy rooted in understanding the bigger picture of your consumers, competition, and the economy.

    Actionable Intelligence Framework

    With a practical system to turn insights into action, your company’s pricing strategy is much more likely to drive actual results. Merely collecting data and churning out out-of-date reports won’t cut it. Instead, begin to identify patterns and insights for accurate competitive intelligence. Use this simple framework to start setting up a comprehensive competitive intelligence process.

    • Setting up monitoring systems: Leverage technology to monitor and aggregate data on your competition, market trends, and consumer behavior. Ensure the system chosen can clean and refine the data along the way so it’s ready to be analyzed.
    • Creating action triggers: Define clear thresholds and triggers based on key insights. These can be things like price changes of a certain amount, competitor moves, assortment changes, or regional and geographic trends. These triggers should prompt specific, pre-planned actions for your team to capitalize on opportunities.
    • Response protocol development: Change management is easier with a plan. Work on building out and training your teams on protocols for specific triggers. When something arises, they know the protocol to take advantage of the opportunity or mitigate the challenge effectively.
    • Performance measurement: Measure the impact of your team’s protocol-based actions with the help of pre-determined KPIs and then hone your approach to competitive intelligence based on the results.

    Competitive Intelligence at Your Fingertips

    Shifting from a latent standard reporting and price monitoring mindset to a growth mindset centered around competitive intelligence doesn’t need to be a struggle. The key is to lean on the tools that will accelerate your company’s journey to finding the right insights and putting action behind them quickly.

    Start by conducting a competitive intelligence maturity assessment to evaluate your organization’s current state and identify areas for improvement. Then, create a capability development roadmap for your company to track efficacy and progress toward your goal.

    Want to talk to the experts in competitive pricing intelligence? Click here to speak with the DataWeave team!

  • From Raw Data to Retail Pricing Intelligence: Transforming Competitive Data into Strategic Assets

    From Raw Data to Retail Pricing Intelligence: Transforming Competitive Data into Strategic Assets

    Poor retail data is the bane of Chief Commercial Officers and VPs of Pricing. If you don’t have the correct inputs or enough of them in real time, you can’t make data-driven business decisions regarding pricing.

    Retail data isn’t limited to your product assortment. Price data from your competition is as important as understanding your brand hierarchies and value size progressions. However, the vast and expanding nature of e-commerce means new competitors are around every corner, creating more raw data for your teams.

    Think of competitive price data like crude oil. Crude or unrefined oil is an extremely valuable and sought-after commodity. But in its raw form, crude oil is relatively useless. Simply having it doesn’t benefit the owner. It must be transformed into refined oil before it can be used as fuel. This is the same for competitive data that hasn’t been transformed. Your competitive data needs to be refined into an accurate, consistent, and actionable form to power strategic insights.

    So, how can retailers transform vast amounts of competitive pricing data into actionable business intelligence? Read this article to find out.

    Poor Data Refinement vs. Good Refinement

    Let’s consider a new product launch as an example of poor price data refinement vs. good data refinement, which affects most sellers across industries.

    Retailer A

    Imagine you’re launching a limited-edition sneaker. Sneakerheads online have highly anticipated the launch, and you know your competitors are watching you closely as go-live looms.

    Now, imagine that your pricing data is outdated and unrefined when you go to price your new sneakers. You base your pricing assumptions on last year’s historical data and don’t have a way to account for real-time competitor movements. You price your new product the same as last year’s limited-edition sneaker.

    Your competitor, having learned from last year, anticipates your new product’s price and has a sale lined up to go live mid-launch that undercuts you. Your team discovers this a week later and reacts with a markdown on the new product, fearing demand will lessen without action.

    Customers who have already bought the much-anticipated sneakers feel like they’ve been overcharged now, and backlash on social media is swift. New buyers see the price reduction as proof that your sneakers aren’t popular, and demand decreases. This hurts your brand’s reputation, and the product launch is not deemed a success.

    Retailer B

    Imagine your company had refined competitive data to work with before launch. Your team can see trends in competitors’ promotional activity and can see that a line of sneakers at a major competitor is overdue for sale based on trends. Your team can anticipate that the competitor is planning to lower prices during your launch week in the hope of undercutting you.

    Instead of needing to react retroactively with a markdown, your team comes up with clever ways to bundle accessories with a ‘deal’ during launch week to create value beyond just the price. During launch week, your competitor’s sneakers look like the lesser option while your new sneakers look like the premium choice while still being a good value. Customer loyalty improves, and buzz on social media is positive.

    Here, we can see that refined data drives better decision-making and competitive advantage. It is the missing link in retail price intelligence and can set you ahead of the competition. However, turning raw competitive data into strategic insights is easier said than done. To achieve intelligence from truly refined competitive pricing data, pricing teams need to rely on technology.

    The Hidden Cost of Unrefined Data

    Technology is advancing rapidly, and more sellers are leveraging competitive pricing intelligence tools to make strategic pricing decisions. Retailers that continue to rely on old, manual pricing methods will soon be left behind.

    You might consider your competitive data process to be quite extensive. Perhaps you are successfully gathering vast data about your competitors. But simply having the raw data is just as ineffective as having access to crude oil and making no plan to refine it. Collection alone isn’t enough—you need to transform it into a usable state.

    Attempting to harmonize data using spreadsheets will waste time and give you only limited insights, which are often out of date by the time they’re discovered. Trying to crunch inflexible data will set your team up for failure and impact business decision quality.

    The Two Pillars of Data Refinement

    There are two foundational pillars in data refinement. Neither can truly be achieved manually, even with great effort.

    Competitive Matches

    There are always new sellers and new products being launched in the market. Competitive matching is the process of finding all these equivalent products across the web and tying them together with your products. It goes beyond matching UPCs to link identical products together. Instead, it involves matching products with similar features and characteristics, just as a shopper might decide to compare two similar products on the shelf. For instance private label brands are compared to legacy brands when consumers shop to discern value.

    A retailer using refined competitive matches can quickly and confidently adjust its prices during a promotional event, know where to increase prices in response to demand and availability and stay attractive to sensitive shoppers without undercutting margins.

    Internal Portfolio Matches

    Product matching is a combination of algorithmic and manual techniques that work to recognize and link identical products. This can even be done internally across your product portfolio. Retailers selling thousands or even hundreds of thousands of products know the challenge of consistently pricing items with varying levels of similarity or uniformity. If you must sell a 12oz bottle of shampoo for $3.00 based on its costs, then a 16oz bottle of the same product should not sell for $2.75, even if that aligns with the competition.

    Establishing a process for internal portfolio matching helps to eliminate inefficiencies caused by duplicated or misaligned product data. Instead of discovering discrepancies and having to fire-fight them one by one, an internal portfolio matching feature can help teams preempt this issue.

    Leveraging AI for Enhanced Match Rates

    As product SKUs proliferate and new sellers seem to enter the market at lightning speed, scaling is essential without hiring dozens more pricing experts. That’s where AI comes in. Not only can AI do the job of dozens of experts, but it also does it in a fraction of the time and at an improved match accuracy rate.

    DataWeave’s AI-powered pricing intelligence and price monitoring offerings help retailers uncover gaps and opportunities to stay competitive in the dynamic world of e-commerce. It can gather competitive data from across the market and accurately match competitor products with internal catalogs. It can also internally match your product portfolio, identifying product family trees and setting tolerances to avoid pricing mismatches. The AI synthesizes all this data and links products into a usable format. Teams can easily access reports and dashboards to get their questions answered without manually attempting to refine the data first.

    How AI helps convert raw data to pricing and assortment intelligence

    From Refinement to Business Value

    Refined competitive price data is your team’s foundation to execute these essential pricing functions: price management, price reporting, and competitive intelligence.

    Price Management

    Refined data is the core of accurate price management and product portfolio optimization. Imagine you’re an electronics seller offering a range of laptops and personal computing devices marketed toward college students. Without refined competitive data, you might fail to account for pricing differences based on regionality for similar products. Demand might be greater in one city than in another. By monitoring your competition, you can match your forecasted demand assumptions with competitor pricing trends to better manage your prices and even offer a greater assortment where there is more demand.

    Price Reporting

    Leadership is always looking for new and better market positioning opportunities. This often revolves around how products are priced, whether you’re making a profit, and where. To effectively communicate across departments and with leadership, pricing teams need a convenient way to report on pricing and make changes or updates as new ad hoc requests come through. Spending hours constructing a report on static data will feel like a waste when the C-Suite asks for it again next week but with current metrics. Refined, constantly updated price data nips this problem in the bud.

    Competitive Intelligence

    Unrefined data can’t be used to discover competitive intelligence accurately. You might miss a new player, fail to account for a new competitive product line, or be unable to extract insights quickly enough to be helpful. This can lead to missed opportunities and misinformed strategies. As a seller, your competitive intelligence should be able to fuel predictive scenario modeling. For example, you should be able to anticipate competitor price changes based on seasonal trends. Your outputs will be wrong without the correct inputs.

    Implementation Framework

    As a pricing leader, you can take these steps to begin evaluating your current process and improve your strategy.

    • Assess your current data quality: Determine whether your team is aggregating data across the entire competitive landscape. Ask yourself if all attributes, features, regionality, and other metrics are captured in a single usable format for your analysts to leverage.
    • Setting refinement objectives: If your competitive data isn’t refined, what are your objectives? Do you want to be able to match similar products or product families within your product portfolio?
    • Measuring success through KPIs: Establish a set of KPIs to keep you on track. Measure things like match rate accuracy, how quickly you can react to price changes, assortment overlaps, and price parity.
    • Building cross-functional alignment: Create dashboards and establish methods to build ad hoc reports for external departments. Start the conversation with data to build trust across teams and improve the business.

    What’s Next?

    The time is now to start evaluating your current data refinement process to improve your ability to capture and leverage competitive intelligence. Work with a specialized partner like DataWeave to refine your competitive pricing data using AI and dedicated human-in-the-loop support.

    Want help getting started refining your data fast? Talk to us to get a demo today!

  • How AI Can Drive Superior Data Quality and Coverage in Competitive Insights for Retailers and Brands

    How AI Can Drive Superior Data Quality and Coverage in Competitive Insights for Retailers and Brands

    Managing the endlessly growing competitive data from across your eCommerce landscape can feel like pushing a boulder uphill. The sheer volume can be overwhelming, and ensuring that data meets standards of high accuracy and quality, and the insights are actionable is a constant challenge.

    This article explores the challenges eCommerce companies face in having sustained access to high-quality competitive data and how AI-driven solutions like DataWeave empower brands and retailers with reliable, comprehensive, and timely market intelligence.

    The Data Quality Challenge for Retailers and Brands

    Brands and retailers make innumerable daily business decisions relying on accurate competitive and market data. Pricing changes, catalog expansion, development of new products, and where to go to market are just a few. However, these decisions are only as good as the insights derived from the data. If the data is made up of inaccurate or low-quality inputs, the outputs will also be low-quality.

    Managing eCommerce data at scale gets more complex every year. There are more market entrants, retailers, and copy-cats trying to sell similar or knock-off products. There are millions of SKUs from thousands of retailers in multiple markets. Not only that, the data is constantly changing. Amazon may add a new subcategory definition in an existing space, or Staples might decide to branch out into a new industry like “snack foods for the office”, an established brand might introduce new sizing options in their apparel, or shrinkflation might decrease the size of a product.

    Given this, it is imperative that conventional data collection and validation methods need to be revised. Teams that rely on spreadsheets and manual auditing processes can’t keep up with the scale and speed of change. An algorithm that once could match products easily needs to be updated when trends, categories, or terminology change.

    With SKU proliferation, visually matching product images against the competition becomes impossible. Knowing where to look for comprehensive data becomes impossible with so many new sellers in the market. Luckily, technology has advanced to a place where manual intervention isn’t the main course of action.

    Advanced AI capabilities, like DataWeave’s, tackle these challenges to help gather, categorize, and extract insights that drive impactful business decisions. It performs the millions of actions that your team can’t accomplish with greater accuracy and in near real-time.

    Improving the Accuracy of Product Matching

    Image Matching for Data Quality

    DataWeave’s product matching capabilities rely on an ensemble of text and image-based models with built-in loss functions to determine confidence levels in all insights. These loss functions measure precision and recall. They help in determining how accurate – both in terms of correctness and completeness – the results are so the system can learn and improve over time. The solution’s built-in scoring function provides a confidence metric that brands and retailers can rely on.

    The product matching engine is configurable based on the type of products that we are matching. It uses a “pipelined mode” that first focuses on recall or coverage by maximizing the search space for viable candidates, followed by mechanisms to improve the precision.

    How ‘Embeddings’ Enhance Scoring

    Embeddings are like digital fingerprints. They are dense vector representations that capture the essence of a product in a way that makes it easy to identify similar products. With embeddings, we can codify a more nuanced understanding of the varied relationships between different products. Techniques used to create good embeddings are generic and flexible and work well across product categories. This makes it easier to find similarities across products even with complex terminology, attributes, and semantics.

    These along with advanced scoring mechanisms used across DataWeave’s eCommerce offerings provide the foundation for:

    • Semantic Analysis: Embeddings identify subtle patterns and meanings in text and image data to better align with business contexts.
    • Multimodal Integration: A comprehensive representation of each SKU is created by incorporating embeddings from both text (product descriptions) and images or videos (product visuals)
    • Anomaly Detection: AI models leverage embeddings to identify outliers and inconsistencies to improve the overall score accuracy.
    DataWeave's AI Tech Stack

    Vector Databases for Enhanced Accuracy

    Vector databases play a central role in DataWeave’s AI ecosystem. These databases help with better storage, retrieval, and scoring of embeddings and serve to power real-time applications such as Verification. This process helps pinpoint the closest matches for products, attributes, or categories with the help of similarity algorithms. It can even operate when there is incomplete or noisy data. After identification, the system prioritizes data that exhibits high semantic alignment so that all recommendations are high-quality and relevant.

    Evolution of Embeddings and Scoring: A Multimodal Perspective

    Product listings undergo daily visual and text changes. DataWeave takes a multimodal approach in its AI to ensure that any content shown on a listing is accounted for, including visuals, videos, contextual signals, and text. DataWeave is continually evolving its embedding and scoring models to align with industry advancements and always works within an up-to-date context.

    DataWeave’s AI framework can:

    • Handle Diverse Data Types: The framework captures a holistic view of the digital shelf by integrating insights from multiple sources.
    • Improve Matching Precision: Sophisticated scoring methods refine the accuracy of matches so that brands and retailers can trust the competitive intelligence.
    • Scale Across Markets: Additional, expansive datasets are easy for DataWeave’s capabilities, meaning brands and retailers can scale across markets without pausing.

    Quantified Improvements: Model Accuracy and Stats

    • Since we deployed LLMs and CLIP Embeddings, Product Matching accuracy improved by > 15% from the previous baseline numbers in categories such as Home Improvement, Fashion, and CPG.
    • High precision in certain categories such as Electronics and Fashion. Upwards of 85%.
    • Close to 90% of matches are auto-processed (auto-verified or auto-rejected).
    • Attribute tagging accuracy > 75% and significant improvement for the top 5 categories.

    Business Use Case: Multimodal Matching for Price Leadership

    For example, if you’re a retailer selling consumer electronics, you probably want to maintain your price leadership across your key markets during peak times like Black Friday Cyber Monday. Doing so is a challenge, as all your competitors are changing prices several times a day to steal your sales. To get ahead of them, this retailer could use DataWeave’s multimodal embedding-based scoring framework to:

    • Detect Discrepancies: Isolate SKUs with price mismatches with your competition and take action before revenue is lost.
    • Optimize Coverage: Establish a process to capture complete data across the competition so you can avoid knowledge gaps.
    • Enable Timely Decisions: Address the ‘low-hanging fruit’ by prioritizing products that need pricing adjustments based on confidence scores on high-impact products. Leverage confidence metrics to prioritize pricing adjustments for high-impact products.

    This approach helps retailers stay competitive even as eCommerce evolves around us. By acting fast on complete and reliable data, they can earn and sustain their competitive advantage.

    DataWeave’s AI-Driven Data Quality Framework

    Let’s look at how our AI can gather the most comprehensive data and output the highest-quality insights. Our framework evaluates three critical dimensions:

    • Accuracy: “Is my data correct?” – Ensuring reliable product matches and attribute tracking
    • Coverage: “Do I have the complete picture?” – Maintaining comprehensive market visibility
    • Freshness: “Is my data recent?” – Guaranteeing timely and current market insights
    The 3 pillars to gauge data quality at DataWeave

    Scoring Data Quality

    To maintain the highest levels of data quality, we rely on a robust scoring mechanism across our solutions. Every dataset that is evaluated is done so based on several key parameters. These can include things like accuracy, consistency, timeliness, and completeness of data. Scores are dynamically updated as new data flows in so that insights can be acted upon.

    • Accuracy: Compare gathered data with multiple trusted sources to reduce discrepancies.
    • Consistency: Detect and rectify variations or contradictions across the data with regular audits.
    • Timeliness: Scoring emphasizes data recency, especially for fast-changing markets like eCommerce.
    • Completeness: Ensure all essential data points are included and gaps in coverage are highlighted by analysis.

    Apart from this, we also leverage an evolved quality check framework:

    DataWeave's Data Quality Check framework

    Statistical Process Control

    DataWeave implements a sophisticated system of statistical process control that includes:

    • Anomaly Detection: Using advanced statistical techniques to identify and flag outlier data, particularly for price and stock variations
    • Intelligent Alerting: Automated system for notifying stakeholders of significant deviations
    • Continuous Monitoring: Real-time tracking of data patterns and trends
    • Error Correction: Systematic approach to addressing and rectifying data discrepancies

    Transparent Quality Assurance

    The platform provides complete visibility into data quality through:

    • Comprehensive Data Transparency & Statistics Dashboard: Offering detailed insights into match performance and data freshness
    • Match Distribution Analysis: Tracking both exact and similar matches across retailers and locations as required
    • Product Tracking Metrics: Visibility into the number of products being monitored
    • Autonomous Audit Mechanisms: Giving customers access to cached product pages for transparent, on-demand verification

    Human-in-the-Loop Validation (Véracité)

    DataWeave’s Véracité system combines AI capabilities with human expertise to ensure unmatched accuracy:

    • Expert Validation: Product category specialists who understand industry-specific similarity criteria
    • Continuous Learning: AI models that evolve through ongoing expert feedback
    • Adaptive Matching: Recognition that similarity criteria can vary by category and change over time
    • Detailed Documentation: Comprehensive reasoning for product match decisions

    Together, these elements create a robust framework that delivers accurate, complete, and relevant product data for competitive intelligence. The system’s combination of automated monitoring, statistical validation, and human expertise ensures businesses can make decisions based on reliable, high-quality data.

    In Conclusion

    DataWeave’s AI-driven approach to data quality and coverage empowers retailers and brands to navigate the complexities of eCommerce with confidence. By leveraging advanced techniques such as multimodal embeddings, vector databases, and advanced scoring functions, businesses can ensure accurate, comprehensive, and timely competitive intelligence. These capabilities enable them to optimize pricing, improve product visibility, and stay ahead in an ever-evolving market. As AI continues to refine product matching and data validation processes, brands can rely on DataWeave’s technology to eliminate inefficiencies and drive smarter, more profitable decisions.

    The evolution of AI in competitive intelligence is not just about automation—it’s about precision, scalability, and adaptability. DataWeave’s commitment to high data quality standards, supported by statistical process controls, transparent validation mechanisms, and human-in-the-loop expertise, ensures that insights remain actionable and trustworthy. In a digital landscape where data accuracy directly impacts profitability, investing in AI-powered solutions like DataWeave’s is not just an advantage—it’s a necessity for sustained eCommerce success.

    To learn more, reach out to us today or email us at contact@dataweave.com.

  • Black Friday vs Boxing Day: Which Sale Event Offered Better Deals?

    Black Friday vs Boxing Day: Which Sale Event Offered Better Deals?

    When it comes to shopping events, Black Friday stands out as one of the most anticipated dates for scoring deals. Typically occurring the day after Thanksgiving, the weekend kicks off the holiday shopping season with a frenzy of discounts. But Boxing Day, celebrated on December 26, is also well-known for its post-Christmas clearance sales.

    This Black Friday, US eCommerce sales increased by a hefty 14.6% in 2024, according to Mastercard SpendingPulse. While Black Friday leads in overall revenue generation for retailers, Boxing Day presents unique opportunities for clearing post-holiday inventory.

    For a consumer, which sale event is likely to offer the most attractive deals?

    At DataWeave, we analyzed discounts across retailers and categories to uncover the answer.

    Our Methodology

    For this analysis, we tracked pricing data across major retailers for Black Friday and Boxing Day. To provide a comprehensive analysis of Black Friday pricing strategies, we explored a matched products dataset, comparing identical 14,000+ SKUs across retailers within key categories.

    • Categories included: Consumer Electronics, Home & Furniture, Apparel, Health & Beauty, Grocery
    • Retailers included: Amazon, Target, Walmart, Sephora, Ulta Beauty, Overstock, Home Depot, Best Buy, Saks Fifth Ave, Nordstrom, Macy’s, Bloomingdale’s, Neiman Marcus
    • Timeline: November 26 (Black Friday), December 26 (Boxing Day)

    Average Discounts: Black Friday vs Boxing Day

    Our analysis reveals that Black Friday generally offered steeper discounts across most categories, although Boxing Day wasn’t far behind. Here’s a breakdown:

    Boxing Day Vs. Black Friday - Discounts Across Categories

    While Black Friday led in most categories, Apparel saw a slight edge on Boxing Day, with discounts averaging 40.22% compared to 37.67% on Black Friday. Electronics, Beauty, and Home, however, remained more lucrative during Black Friday.

    Top 5 Products Higher Discounts on Black Friday

    Diving deeper into specific products, here are our top 5 picks offering better discounts during Black Friday.

    Top 5 Products With Higher Discounts on Black Friday
    • Appliances like an Immersion blender set offering a discount of 88.29%, significantly higher than its Boxing Day offer of 86.62%. 
    • High-end electronics like the Microsoft Surface Pro 4 also saw substantial markdowns at 84.60%. 
    • In beauty and fashion, both La Roche Posay’s retinol serum and Vera Bradley’s satchel offered discounts above 80%. 
    • Even everyday essentials like paper towels enjoyed generous discounts, with markdowns reaching 82.35% during Black Friday compared to 76.47% on Boxing Day.

    Top 5 Products With Higher Discounts on Boxing Day

    Boxing Day revealed some remarkable deals across diverse categories, with certain products offering significantly better value than their Black Friday counterparts.

    Top 5 Products With Higher Discounts on Boxing Day
    • The JBL Go 2 portable speaker emerged as the standout, with an extraordinary 82% Boxing Day discount compared to just 20% on Black Friday—a dramatic 62% difference.
    • Home furnishings showed strong Boxing Day performance, with the Costway accent armchair set reaching 80.30% off.

    In Conclusion

    Black Friday reigns supreme in driving early holiday sales, offering deeper discounts and drawing larger crowds. However, Boxing Day remains critical for retailers to offload surplus inventory and attract post-holiday shoppers.

    By combining insights from both events, retailers can refine their strategies to maximize revenue and enhance customer satisfaction. For shoppers, the decision comes down to timing—shop early for better deals or wait to capitalize on clearance markdowns. The products and categories with more attractive offers tend to vary between these two sale events. Hence, as a shopper, it’s a good idea to keep track of prices all through the holiday season to take advantage of the best deals.

    Check out our comprehensive coverage of Black Friday 2024 deals and discounts across categories.

    For a deeper dive into the world of competitive pricing intelligence and to explore how our solutions can benefit apparel retailers and brands, reach out to us today!

  • Enterprise Data Security at DataWeave: Empowering Smarter Decisions with Seamless, Secure Data Management and Integration

    Enterprise Data Security at DataWeave: Empowering Smarter Decisions with Seamless, Secure Data Management and Integration

    At DataWeave, data security isn’t just about compliance—it’s about enabling peace of mind and better decision-making for our customers. Our customers rely on us not just for competitive and market intelligence but also for the seamless integration of critical data sources into their decision-making frameworks. To achieve this, we have built a security-first infrastructure that ensures organizations can confidently leverage both external and internal data without compromising privacy or protection.

    Secure Data Integration: The Foundation of Smarter Decisions

    Effective decision-making in today’s digital commerce landscape depends on combining multiple data sources—including first-party customer data, pricing intelligence, and business rules—into a unified framework. However, without the right security measures in place, businesses often struggle to operationalize this data effectively.

    At DataWeave, we eliminate this challenge by offering:

    • Integration with Leading Data Storage Solutions: Our platform seamlessly connects with data lakes and warehouses like AWS S3 and Snowflake, ensuring that businesses can easily ingest and analyze our data in real time.
    DataWeave's Data Security Framework
    • Support for Sandboxed Environments & Data Clean Rooms: Organizations can securely merge internal and external datasets without compromising confidentiality, unlocking deeper insights for pricing and business strategies.
    • Automated Data Ingestion & Management: We simplify the process of integrating first-party data alongside competitive insights, allowing customers to focus on execution rather than infrastructure management.

    Our Purpose-Built Security Framework

    Handling millions of data points daily demands a security framework that is not only robust but also scalable and adaptable to evolving threats. DataWeave’s multi-tenant architecture ensures seamless data security without compromising operational efficiency.

    • Multi-Tenant Architecture: Our system allows multiple customers to share the same application infrastructure while maintaining complete data isolation and security.
      • Tenants share infrastructure and computing resources but remain logically isolated.
      • Application-level controls ensure privacy while maximizing cost efficiency.
      • Centralized updates, maintenance, and easy scalability for new tenants.
    • End-to-End Encryption & Access Controls: Every piece of data is encrypted both in transit and at rest. Role-based access controls (RBAC) restrict visibility to only authorized personnel, ensuring minimal risk of unauthorized data access.

    Active Monitoring & Automated Compliance Management: We leverage automated access controls that adjust permissions dynamically as organizational roles evolve, ensuring that compliance is continuously maintained.

    Certifications That Inspire Confidence

    Data security is at the core of everything we do. Our compliance with the highest industry standards ensures that businesses can trust us with their sensitive data.

    SOC 2 Type II Certification: DataWeave’s SOC 2 compliance is a testament to our commitment to stringent security protocols. This certification guarantees that we adhere to strict standards in data protection, availability, and confidentiality.

    We implement a phased approach to security improvement:

    • Prioritizing Critical Systems: To maximize impact, we prioritized systems that had the highest data security relevance and expanded the coverage thereafter. By addressing these priority areas, we were able to make meaningful security improvements early in the process.
    • Automating Monitoring and Compliance: Partnering with Sprinto streamlined the compliance journey by automating key processes. This included real-time monitoring of our cloud environments, automated generation of audit-ready evidence, and integration with critical systems like AWS, Bitbucket, and Jira. These enhancements ensured efficient management of compliance requirements while reducing the burden on our teams.
    SOC 2 Compliance at DataWeave
    • Fostering a Culture of Shared Responsibility: We conducted organization-wide training sessions to embed compliance as a shared responsibility across all teams. By educating employees on the importance of security practices and providing them with the tools to manage compliance autonomously, we established a security-first mindset throughout the company.

    This systematic method allowed us to deliver immediate improvements while aligning long-term practices with industry’s best standards.

    What This Means for Our Customers

    By combining robust security with seamless data integration, DataWeave empowers businesses to:

    • Optimize Price Management & Modelling: With secure access to real-time data, organizations can make informed pricing decisions that enhance profitability and market competitiveness.
    • Run Advanced Simulations & Testing: Reliable, secure data enables businesses to model various pricing and assortment strategies before implementation, reducing risks and maximizing returns.
    • Uncompromised Data Security: SOC 2 Type II compliance ensures stringent protocols to protect your data at every stage.
    • Simplified Vendor Processes: Verified security certifications reduce friction during due diligence and onboarding, making it easier to partner with us.
    • Aligned Standards: Our adherence to industry benchmarks reflects our commitment to meeting your expectations as a trusted technology partner.
    • Scalable Operations: Expand across regions while maintaining full confidence in data privacy and security.
    • Secure Collaboration: Share insights across teams with tools designed to protect sensitive information.

    Our customers are increasingly looking to integrate their internal datasets with the external competitive intelligence provided by DataWeave. This can be a complex and risky process without the right security measures in place. We remove these roadblocks by providing a secure, scalable infrastructure designed to help businesses unify data without security concerns.

    By ensuring seamless compatibility with key data storage platforms, such as Snowflake and AWS S3, we enable organizations to consolidate valuable first-party data with timely market insights. This integration empowers businesses to refine their pricing, assortment, and digital shelf strategies, thereby driving superior customer experiences—without the headaches of data security risks.

    Security remains a top priority in everything we do. Our SOC 2 Type II-certified framework enforces rigorous encryption, access controls, and real-time compliance monitoring. We take on the burden of data security so our customers can focus on innovation and growth.

    With DataWeave, businesses can confidently leverage secure data-driven decision-making to unlock new opportunities, optimize operations, and scale without compromise.

    To learn more, write to us at contact@dataweave.com or request a consultation here.

  • Black Friday 2024 in Canada: Insights on Consumer Electronics and Home & Furniture

    Black Friday 2024 in Canada: Insights on Consumer Electronics and Home & Furniture

    Black Friday and Cyber Monday are major retail events in Canada, with 43% and 29% of the population making purchases during these sales respectively, according to a YouGov report. Consumer electronics continue to lead the Canadian retail market during these events, with 55% of surveyed shoppers choosing to buy tech products on Black Friday. Household appliances come in second, with 25% of shoppers opting for these items, while 18% prefer to shop for furniture deals.

    These statistics highlight the importance of delivering value during the Thanksgiving sales week. Retailers must cater to shoppers’ expectations with competitive pricing, attractive deals, and a seamless shopping experience. So, what unique offerings did Canadian retailers present to shoppers this season?

    To understand the pricing and discount dynamics during BFCM 2024 in Canada, DataWeave analyzed discounts across leading consumer electronics and home & furniture retailers. Using our AI-powered pricing intelligence platform, we analyzed 37,108 SKUs across these categories for major retailers including Amazon, Walmart, Best Buy, Home Depot, and Canadian Tire from the 10th to 29th November. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “sofa” and “wearables”.

    In the following insights, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.

    Also check out our detailed analysis of discounts and pricing for the consumer electronics, apparel, health & beauty, grocery, and home & furniture categories across major US retailers this Black Friday.

    Consumer Electronics

    Retailers in Focus

    Consumer electronics saw robust participation from major retailers, with Amazon, Best Buy, and Walmart leading the charge. Here’s how they stacked up in terms of discounts:

    Pricing Trends Across Leading Consumer Electronics Retailers in Canada - Black Friday Cyber Monday 2024
    • Best Buy emerged as the frontrunner in absolute discounts at 31.2%, while Amazon impressed with a notable 19.7% additional discount, indicating a strong Black Friday-specific markdown strategy.
    • Walmart offered steady competition, particularly in audio and video products, which reached an average absolute discount of 37.2%. However, it’s average additional discount was only 3.1%, indicating muted BFCM-specific price reductions in this category.

    Subcategory Insights

    Diving deeper into consumer electronics subcategories, we observed varied discounting strategies.

    Pricing Trends Across Leading Canadian Consumer Electronics Retailer Subcategories - Black Friday Cyber Monday 2024
    • Audio & Video stood out as the most discounted subcategory, with Walmart leading at 37.2%.
    • In Wearables, Walmart again took the top spot with 36.4%, while Amazon offered higher additional discounts (22.4%).
    • Discounting for computers and gaming was less aggressive, highlighting strategic pricing to maintain profitability in these high-demand segments.

    Brand Performance

    Brand-level data highlighted how key players used Black Friday to drive visibility and sales.

    Pricing Trends Across Leading Canadian Consumer Electronics Brands - Black Friday Cyber Monday 2024
    • Dell led in average absolute discounts (36.7%) followed by Samsung at 36.68%
    • Audio brand JBL offered significant absolute discounts at 35.9%.
    • Apple and Lenovo offered comparatively fewer discounts but maintained strong visibility, as seen in their increase in the Share of Search during the sale period.
    Visibility Trends Across Leading Canadian Consumer Electronics Brands - Share of Search - Black Friday Cyber Monday 2024
    • MSI (laptop brand) and Bose (audio and earphone brand) experienced significant increases in visibility, with Share of Search increases of 5% and 3.6%, respectively.
    • Notably, HP faced a decline (-3.2%) in the Share of Search, suggesting missed opportunities to align promotions with consumer interest.

    Home & Furniture

    Retailers in Focus

    The home and furniture category saw competitive discounting, with Walmart, Canadian Tire, and Home Depot vying for consumer attention.

    Black Friday - Cyber Monday Trends Across Leading Canadian Home & Furniture Retailers
    • Walmart took the lead with the highest absolute discounts at 36.8%. The retailer’s additional discounts were more conservative at 3.6%. This is similar to their discount levels in Consumer Electronics.
    • Canadian Tire offered stiff competition, providing 31.6% absolute discounts and 25% additional discounts.
    • Home Depot matched its absolute and additional discounts, maintaining consistency at 24.1%.

    Subcategory Insights

    Home and furniture subcategories revealed targeted discount strategies.

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Subcategories - Canada
    • Bedding emerged as the most discounted subcategory at Walmart (50.6%) and Canadian Tire (35.3%).
    • Kitchenware saw competitive pricing, with Walmart leading at 42.9%, followed by Canadian Tire at 33.9%.
    • Canadian Tire focused on lighting, offering the highest absolute discounts in this subcategory (38.2%)

    Brand Performance

    Brand-level analysis revealed stark contrasts in discounting approaches.

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Brands - Canada
    • Furniture brands Homcom led in absolute discounts (36.4%), while South Shore stood out with the highest additional discounts (30.2%).
    • Value-oriented brands like furnishings brand Mainstays and mattress and bedding brand Zinus offered more modest discounts, focusing on consistent affordability.
    Black Friday - Cyber Monday Trends Across Leading Canadian Home & Furniture Brands - Share of Search and Visibility
    • Zinus (mattresses and sofa brand) experienced a significant 7.9% increase in the Share of Search, driven by aggressive promotions.
    • Home furnishings brands like Costway and Safavieh faced declines, reflecting the importance of aligning promotional strategies with consumer expectations.

    Insights for Retailers and Brands

    This Black Friday, Canadian retailers effectively balanced deep discounts with category-specific strategies to maximize sales. However, the fluctuating Share of Search highlights the critical need for brands to align promotions with consumer interest.

    For brands and retailers looking to stay ahead of the competition, DataWeave’s pricing intelligence platform offers unparalleled insights to refine discounting strategies and boost visibility. Contact us to learn how we can help you stay competitive in this dynamic retail landscape.

  • A Deep Dive into Consumer Electronics Pricing During Black Friday 2024

    A Deep Dive into Consumer Electronics Pricing During Black Friday 2024

    Americans spent a whopping total of $10.8 billion online this Black Friday. As Thanksgiving Week 2024 wraps up, one thing is clear: the consumer electronics category continues to dominate seasonal shopping trends. Fueled by a blend of enticing deals and high consumer demand, the sector delivered competitive discounts across subcategories like wearables, gaming, and mobile devices.

    At DataWeave, we analyzed discounting trends in the U.S. consumer electronics market during this year’s sales events. Using our AI-powered pricing intelligence platform, we tracked pricing and promotions for 22383 SKUs across Amazon, Walmart, Target, and Best Buy from November 10 to 29. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “gaming” and “apple.” Here’s what we uncovered.

    Also check out our insights on discounts and pricing for health & beauty, grocery, apparel, and home & furniture categories this Black Friday.

    Retailers Battle It Out with Competitive Discounts

    Discount trends reveal clear leaders in terms of markdowns:

    • Walmart offered the deepest average absolute discounts at 36.9%.
    • Amazon and Target followed closely, highlighting a diverse range of deals designed to appeal to budget-conscious shoppers
    • Best Buy, the specialist consumer electronics retailer, offers the lowest discounts this Black Friday at 26.2%.
    Pricing Trends Across Leading Consumer Electronics Retailers - Black Friday Cyber Monday 2024

    Note: The Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.

    Subcategory Spotlight: Where the Best Deals Happened

    From audio & video to wearables, each retailer carved out competitive advantages across subcategories.

    Pricing Trends Across Leading Consumer Electronics Retailer Subcategories - Black Friday Cyber Monday 2024
    • Both Amazon and Walmart offered high discounts in audio & video and wearables, but Walmart led, with discounts up to 46.3%.
    • Best Buy, meanwhile, offered high absolute discounts on Mobile Devices(34%) and Storage (31%), followed by high discounts on wearables and Audio & Video.
    • Amazon maintained a balanced approach, excelling in audio & video and mobile devices.

    Brand-Level Insights: HP and Samsung Dominate

    The biggest winners this year were brands that strategically leveraged Black Friday discounts to boost visibility and sales:

    • HP took the top spot with average discounts of 36.9%, followed by Samsung at 31.4%.
    • Despite its premium reputation, Apple offered an average discount of 29.3%, signaling a shift in strategy to attract deal hunters.
    Pricing Trends Across Leading Consumer Electronics Brands - Black Friday Cyber Monday 2024

    Share of Search: Shifting Consumer Attention

    Search trends reveal how discounts shaped brand visibility:

    • Microsoft saw the largest spike in share of search (+8.6%), thanks to aggressive pricing on gaming consoles and accessories.
    • Marshall and Amazon also saw significant gains in visibility.
    • Surprisingly, HP experienced a sharp decline (-9.8%), indicating missed opportunities despite steep discounts.
    Visibility Trends Across Leading Consumer Electronics Brands - Share of Search - Black Friday Cyber Monday 2024

    Consumer Electronics: Lowest-Priced Retailer Analysis

    In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 340 matched products across retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.

    Here are the key takeaways from this analysis.

    Category-Level Highlights

    Retailers Offering Most Value - Lowest Priced - Consumer Electronics - Black Friday 2024
    • Amazon leads with the highest average discount (41.35%), offering the most value to consumers. It is followed by Target (39.37%) and Walmart (36.15%).
    • Best Buy, the specialist consumer electronics retailer, ranks last with an average discount of 31.53%, emphasizing a less aggressive pricing strategy compared to competitors.

    Subcategory Highlights

    Lowest Priced Retailer Across Major Subcategories- Consumer Electronics - Black Friday 2024
    • Wearables: Amazon offers the steepest discounts (55.40%), followed by Best Buy (50.60%) and Walmart (45.75%).
    • Mobile Devices: Amazon also leads (37.94%), with Walmart (29.30%) in second place and Target trailing at 19.48%.
    • Gaming: Target takes the lead (37.47%), with Amazon and Best Buy offering similar discounts around 30%.
    • Computers: Target again emerges as the leader (39.18%), narrowly surpassing Walmart (36.13%).

    Brand Highlights

    Lowest Priced Retailer Across Leading Brands- Consumer Electronics - Black Friday 2024
    • Apple: Amazon dominates with 53.06%, closely followed by Walmart (50.55%), while Target and Best Buy hover around 43%.
    • Nintendo: Target edges out Amazon (37.62% vs. 36.54%), with Best Buy (33.21%) and Walmart (25.92%) trailing.
    • Beats by Dr. Dre: Amazon leads (46.07%), with Target (37.14%) as the runner-up. Best Buy and Walmart offer comparatively modest discounts around 25%.
    • Bose: Walmart emerges as the value leader (23.90%), surpassing Target (16.09%) and Best Buy (15.29%).
    • Cricut: Amazon sets a high benchmark (54.13%), with Target far behind (36.43%) for this viral portable printer brand. Best Buy (12.32%) and Walmart (10.79%) offer significantly lower discounts.

    What This Means for Retailers and Brands

    Retailers looking to stay competitive should focus on strategic discounting and enhanced brand visibility. Brands must align with consumer expectations by:

    • Leveraging platforms like DataWeave to analyze discount trends.
    • Optimizing pricing and assortment strategies for seasonal demand.

    For more insights into consumer electronics pricing, contact DataWeave to discover how our AI-powered solutions can drive success in today’s fast-paced market. Stay tuned for more category-specific analyses in the coming weeks!

  • The Apparel Market: A Closer Look at Black Friday Discounts

    The Apparel Market: A Closer Look at Black Friday Discounts

    As the holiday shopping season kicked off, savvy shoppers embraced the spirit of the season, drawn by enticing deals. The apparel category is forecasted as the second highest earning category (Source: Statista), expected to generate revenues up to $43.9 billion, closely following consumer electronics. To understand the pricing strategies of top retailers amidst the sale season, DataWeave analyzed the pricing trends for the Apparel category this Black Friday.

    We leveraged our AI-powered data platform to analyze the discounting across key retailers. Our analysis focused on the Apparel category, examining how Amazon, Walmart, Target, Saks Fifth Avenue, Nordstrom, Bloomingdales, Neiman Marcus and Macy’s differentiated themselves through their discounts.

    Also check out our in-depth insights on discounts and pricing for health & beauty, grocery, and home & furniture categories this Black Friday.

    Our Methodology

    For this analysis, we tracked the average discounts of apparel products among leading US retailers during the Thanksgiving weekend sale, including Black Friday. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across during the sale.

    • Sample size: 37,666 SKUs
    • Retailers tracked: Amazon, Walmart, Target, Nordstrom, Macy’s, Bloomingdale’s, Saks Fifth Avenue, Neiman Marcus
    • Subcategories reported on: Footwear, Kid’s Clothing, Men’s Clothing, Women’s Clothing, Activewear, Plus Size Clothing, Accessories
    • Timeline of analysis: 10 to 29 November 2024

    We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “athleisure” and “plus size clothing”. Our methodology distinguished between standard discounts and Black Friday-specific ‘additional discounts’ or price reductions during the sale compared to the week before, to reveal true consumer value.

    Key Findings

    This year’s fashion discounts were unprecedented. Let’s take a look.

    Retailer Level Insights

    Discounts Across Leading Apparel Retailers - Black Friday 2024
    • Nordstrom leads with the highest average absolute discount at 59%, followed by Saks Fifth Avenue at 35.5% and Bloomingdale’s at 41.5%. Macy’s shows the lowest average discount at 24.1%, while Amazon has an average discount of 30.4%.
    • Amazon ranks lower in both average absolute and additional discounts compared to competitors, indicating a more conservative discounting strategy.

    Subcategory Analysis

    Discounts Across Leading Apparel Retailers - Subcategories - Black Friday 2024
    • Kids’ Clothing saw the deep discounts (up to 55% at Nordstrom), reflecting growing pressure on family budgets and heightened competition to attract budget-conscious parents.
    • Plus-Size Clothing emerged as a major focus, with Nordstrom leading at 53.22% average absolute discounts, signaling that retailers are increasingly prioritizing size inclusivity and appealing to a broader consumer base.
    • Footwear experienced robust discounting, particularly at Bloomingdale’s with 37% average absolute discounts, showing a competitive approach to attract customers looking for seasonal footwear deals.
    • Activewear displayed substantial discounts, with Walmart offering up to 41% on average, aligning with the trend of consumers looking for practical and comfortable attire during the winter season.

    Brand Level Insights

    Apparel brands, meanwhile, also offer telling insights.

    Discounts Across Leading Apparel Brands - Black Friday 2024
    • Top Discounting Brands: Aqua leads with an average absolute discount of 44.58%, followed by Boss at 42.33% and Burberry at 37.84%.
    • Lowest Discounts: Athletic Works shows the lowest average absolute discount at 31.23%, with a minimal additional discount of 3.73%.
    • Competitive Advantage: Brands like Ralph Lauren and Boss show strong discounts, indicating aggressive marketing during the sale.

    Share of Search Insights

    Visibility - Share of Search Trend Across Leading Apparel Retailers - Black Friday 2024
    • Top Gainers: Adidas and Nike each saw an increase of 1.20% in their share of search during Black Friday/Cyber Monday, highlighting their strong brand presence and consumer interest.
    • Top Losers: Reebok experienced a sharp decline, losing 2.60% in its share of search, while Levi’s also dropped by 0.60%.
    • Search Trends: The data suggests a strong consumer preference for activewear brands like Nike and Adidas and a decline in interest for traditional apparel brands like Levi’s.

    Who Offered Most Value This Black Friday

    In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 418 matched products across Apparel specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.

    Here are the key takeaways from this analysis.

    Category-Level Analysis

    At the overall category level, Macy’s emerged as the lowest-priced retailer, offering the highest average discount of 28.72%, followed closely by Nordstrom (26.06%). The steep decline in average discounts from Saks Fifth Avenue (14.42%) and Neiman Marcus (7.93%) highlights a clear gap in discounting strategies.

    • Macy’s and Nordstrom are aggressively competitive on pricing in the overall apparel category, likely capturing consumer attention with substantial discounts.
    • Saks Fifth Avenue and Neiman Marcus may rely more on brand perception and luxury positioning rather than heavy discounting.
    Retailers Offering Most Value - Lowest Priced - Apparel - Black Friday 2024

    Subcategory-Level Analysis

    Lowest Priced Retailer Across Major Subcategories- Apparel - Black Friday 2024
    • Neiman Marcus tops the ranking with an impressive 60.85% average discount, outperforming Macy’s (52.86%) and Nordstrom (43.04%) for Men’s Clothing. We see a similar trend with Neiman Marcus offering more value across Women’s Clothing as well, compared to other retailers.
    • The competition in footwear was intense, with Neiman Marcus narrowly securing the top spot at 31.03%, slightly ahead of Saks Fifth Avenue (30.28%) and Macy’s (30.07%).
    • Saks Fifth Avenue led by a significant margin in the Activewear category, offering 39.89% average discounts, indicating a strong push in this growing segment.
    • Macy’s followed at 32.16% in Activewear, while Neiman Marcus and Nordstrom had comparatively lower discounts of 26.40% and 19.52%, respectively.

    Brand-Level Analysis

    Lowest Priced Retailer Across Leading Brands- Apparel - Black Friday 2024
    • Kate Spade New York: Neiman Marcus leads with the highest discount of 55.23%, reflecting strong price leadership in premium fashion, closely followed by Saks Fifth Avenue at 51.66%.
    • Coach: Neiman Marcus dominates with a significant 75.85% discount, showcasing an aggressive promotional strategy for this luxury brand.
    • Spanx: While Neiman Marcus leads with 28.22%, discounts across other retailers like Saks Fifth Avenue, Macy’s, and Nordstrom are clustered within a competitive range of 17–19%.
    • Montblanc: Macy’s takes the lead with 20.32%, signaling its competitiveness even in high-end accessories, with Saks Fifth Avenue and Nordstrom closely behind.
    • Ugg: Saks Fifth Avenue leads with 31.42%, focusing on maintaining price leadership for this popular brand, while other retailers remain competitive with discounts around 25–30%.

    What’s Next

    To win over price-conscious shoppers, retailers need to stay competitive and consistently offer the lowest prices.

    For a deeper dive into the world of competitive pricing intelligence and to explore how our solutions can benefit apparel retailers and brands, reach out to us today!

    Stay tuned to our blog for more insights on different categories this Black Friday and Cyber Monday.


  • Breaking Down Grocery Discounts This Black Friday

    Breaking Down Grocery Discounts This Black Friday

    As shoppers flocked online and to stores during Black Friday and Cyber Monday, the grocery category stood out as a key battleground for retailers. With inflation affecting consumer spending, discounted groceries have become a critical driver for both shopper savings and retailer competitiveness.

    In fact, according to the NRF, one of the top shopping destinations during Thanksgiving weekend were department stores (42%), online (42%),and grocery stores and supermarkets (40%). Clearly, consumers are looking to stock up in bulk on their groceries to maximize their savings.

    To understand the pricing dynamics in the grocery category, DataWeave analyzed grocery discounts across leading grocers, uncovering significant trends that shaped consumer choices during this holiday shopping period.

    Our research encompassed retailers like Amazon, Target, and Walmart, examining their discounting strategies across subcategories, alongside trends in share of search for leading CPG companies.

    Also check out our detailed analysis of discounts and pricing for health & beauty and home & furniture this Black Friday.

    Key Grocery Market Stats for Black Friday-Cyber Monday 2024

    • Retailer Discounts: Walmart offered the highest average absolute discount at 27.6%, followed by Amazon at 20.4% and Target at 14.0%
    • Subcategory Insights: Beverages Category at Walmart saw the deepest discounts, with an average of 33.4%
    • Top Gaining Brands: Cesar experienced the largest increase in share of search during the sales period (+3.89%)

    This blog will dive deeper into grocery discount trends and brand-level strategies, offering insights for retailers looking to stay competitive in the grocery sector.

    Our Methodology

    For this analysis, we tracked the average discounts offered by major U.S. grocery retailers during the Thanksgiving weekend, including Black Friday and Cyber Monday. We focused on key subcategories within the grocery segment, capturing trends in discounting strategies.

    • Sample Size: 18,324 SKUs
    • Retailers Tracked: Amazon, Walmart, Target
    • Subcategories Reported On: Fresh Produce, Dairy & Eggs, Pantry Essentials, Snacks, Frozen Foods, Meat & Seafood, Household Essentials, Beverages, Pet Products, Baby Products
    • Timeline of Analysis: November 10 to 29, 2024

    In the following insights, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.

    Key Findings

    Retailer-Level Insights

    Average Discounts Across Leading Grocery Retailers - Black Friday Cyber Monday 2024
    • Walmart emerged as the leader in grocery discounting, offering the highest average absolute (27.6%) and additional (18%) discounts.
    • Amazon adopted a mid-tier discounting strategy, with average absolute discounts of 20.4%.
    • Target, while more conservative, maintained competitiveness in select subcategories like baby products.

    Subcategory Insights

    Average Discounts Across Leading Grocery Retailer Subcategories - Black Friday Cyber Monday 2024
    • Pantry Essentials saw Walmart leading with an average discount of 31.2%, appealing to budget-conscious consumers stocking up for the holidays.
    • Fresh Produce showed consistent discounting across retailers, with Amazon slightly ahead at 27%.
    • Beverages stood out for significant discounting at Walmart, with an impressive 33.4% average discount.

    Brand-Level Insights

    Average Discounts Across Leading Grocery Brands - Black Friday Cyber Monday 2024
    • Lay’s led in absolute discounts (37.52%) and additional discounts (26.23%) showcasing aggressive pricing in the snacks subcategory.
    • Good & Gather maintained its competitive edge with strong discounts, appealing to price-conscious consumers seeking value.
    • Brands like Blue Buffalo (pet food brand) offered significant absolute discounts, but with a low additional discount of just 2%, the overall impact of the sale event on effective value was limited.

    Share of Search Insights

    Gains and Losses in Share of Search Across Leading Grocery Brands - Black Friday Cyber Monday 2024
    • Cesar (dog food brand), Tide (laundry staple) and Doritos saw significant gains in share of search, reflecting successful promotional strategies.
    • Brands like Pampers (baby diapers brand), Healthy Choice, (frozen foods brand) and Pedigree (pet food brand) experienced a decline, indicating less effective engagement during the sale period.

    Who offered the lowest prices?

    In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 1433 matched products across retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.

    Here are the key takeaways from this analysis.

    Category-Level Analysis

    Retailers Offering Most Value - Lowest Priced - Grocery - Black Friday 2024
    • Walmart is the lowest priced retailer overall for the grocery category, with an impressive average discount of 44.60%. This significant discount advantage makes Walmart a leading option for value-seeking consumers.
    • Target follows with strong discounts of 36.73%, indicating solid pricing in comparison but less aggressive than Walmart.
    • Interestingly, Amazon was the most expensive in Grocery, with an average discount of only 6.3%.

    Subcategory-Level Analysis

    Lowest Priced Retailer Across Major Subcategories- Grocery - Black Friday 2024
    • Walmart leads in various subcategories such as Pet Products (21.12%), Dairy & Eggs (13.79%), Household Essentials (13.05%), Frozen Foods (15.07%), and Meat & Seafood (17.60%), showcasing its extensive value across the board.
    • Target excels in Beverages (14.58%) and Baby Products (15.00%) with competitive discounts, standing out in these specific subcategories.
    • Kroger provides notable value in Pantry Essentials (20.04%) and Fresh Produce (15.85%), although its overall average discount is lower than Walmart’s.
    • Amazon consistently ranks lower in terms of average discounts across most subcategories, highlighting it as less competitive for consumers seeking the lowest prices.

    Brand-Level Analysis

    Lowest Priced Retailer Across Leading Brands- Grocery - Black Friday 2024
    • Walmart also holds the top position for several key brands like Cheetos (14.92%) and Dannon (8.81%), making it the best option for consumers looking for budget-friendly choices across popular brands.
    • Target takes the lead for brands like Betty Crocker (25.20%) and Chobani (11.37%), showing that it can offer value for specific products.
    • Kroger maintains strong discounts for brands such as Delmonte (9.19%), but it does not outpace Walmart in the overall grocery brand comparison.
    • Amazon generally lags behind in average discounts for most brands, with Dannon (1.12%) and Chobani (2.43%) showing significantly lower discounts.

    Walmart is the lowest priced retailer in the grocery category and provides substantial value across a wide range of subcategories and popular brands. This ties in with Walmart’s ELDP pricing strategy. The retailer leads in overall average discounts and maintains its position as the go-to for price-conscious consumers. Target offers strong value in certain subcategories and brands but falls short of Walmart’s broad value based pricing advantages.

    What’s Next

    For grocery retailers, competitive pricing and targeted promotions are critical to driving sales during key shopping events. As consumers continue to prioritize value, staying ahead in the discounting game can significantly impact market share.

    For detailed insights into grocery discounting strategies and to explore how DataWeave’s solutions can help retailers optimize their pricing, contact us today!

    Stay tuned to our blog for further analyses of other categories during Black Friday and Cyber Monday.

  • Black Friday 2024: Home & Furniture Pricing Trends Analyzed

    Black Friday 2024: Home & Furniture Pricing Trends Analyzed

    The Home & Furniture category continues to thrive, propelled by consumer interest in creating personalized and functional living spaces. In 2023, the U.S. furniture and home furnishings market was valued at approximately $641.7 billion in 2023 and is estimated to grow at a CAGR of 5.1% from 2024 to 2032. Black Friday and Cyber Monday play a crucial role in fueling this growth, offering consumers a mix of premium and affordable options across subcategories.

    To better understand market trends and discount strategies this Black Friday, at DataWeave we tracked over 18,149 SKUs across major home & furniture retailers, including Amazon, Walmart, Target, Best Buy, Home Depot, and Overstock, from November 10 to 29, 2024. Using our AI-powered pricing intelligence platform, we focused on the top 500 products in subcategories like kitchenware, furniture, decor, lighting, outdoor items, and bedding.

    In our analysis, the Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the Black Friday sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.

    Also check out our insights on discounts and pricing for the health & beauty category this Black Friday.

    Retailer Performance: Who Led the Discount Race?

    Retailers showed varying discount strategies for Home & Furniture products. Walmart emerged as the leader in absolute discounts (37.5%) while Amazon offered the highest additional discount of 14%. Best Buy maintained competitive pricing across all subcategories, while Overstock and Home Depot offered relatively modest discounts.

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Retailers

    Subcategories in Focus

    Breaking down the discounts by subcategory provides deeper insights into consumer priorities and retailer strategies:

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Subcategories
    • Kitchenware saw strong competition, with Walmart (30.40% absolute discounts) and Amazon (29% absolute discounts) dominating.
    • Lighting became a discount hotspot, with Walmart offering up to 45.8% in absolute discounts and 25.3% additional markdowns.
    • Furniture remained a core focus for Target, delivering an impressive 34% average absolute discount.
    • Bedding stood out at Walmart, where discounts peaked at 49.6%.

    Brand Spotlight: Who Stood Out?

    Among top-performing brands, furniture brand Costway offered the highest discounts, with an average of 48.4%. Meanwhile, Adesso (lighting solutions), Mainstays and Safavieh (both home furnishings brands) balanced discounts and premium appeal.

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Brands

    Search Visibility: The Winners and Losers

    Share of search dynamics revealed significant shifts in brand visibility during Black Friday:

    Black Friday - Cyber Monday Trends Across Leading Home & Furniture Brands - Share of Search and Visbility
    • Furniture brand Costway (+1.2%) and home improvement player Black+Decker (+1.5%) gained visibility.
    • On the flip side, premium brands like Safavieh known for rugs and home furnishings (-16.8%) and furniture brand Burrow ( -1.7%) saw declines.

    Who Offers the Lowest Prices?

    In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 735 matched products across Home & Furniture specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.

    Here are the key takeaways from this analysis.

    Category-Level Highlights

    Retailers Offering Most Value - Lowest Priced - Home & Furniture - Black Friday 2024
    • Amazon emerges as the lowest-priced retailer across Home & Furniture categories, with the highest average discount of 27.50%, closely followed by Walmart (26.09%).
    • Overstock and Wayfair trail with average discounts of 22.93% and 20.71%, respectively, while Home Depot offers the least aggressive pricing at 18.14%. This is notable, as all 3 players are known specialists in the category.

    Subcategory Highlights

    Lowest Priced Retailer Across Major Subcategories- Home & Furniture - Black Friday 2024
    • Amazon stands out as the leader in multiple subcategories, including Appliances, Furniture, Decor, and Outdoor, offering competitive average discounts of around 26-29%.
    • Overstock leads in Bedding and Kitchenware, with strong average discounts of 24.26% and 20.72%, respectively.
    • Wayfair is notable for Lighting, with an average discount of 19.95%, and is also competitive in Outdoor and Furniture categories.
    • Walmart consistently ranks high in several subcategories like Appliances and Bedding, providing solid discounts of around 22-23%.

    What’s Next

    For home & furniture retailers, driving maximum value during mega sale events like Black Friday involves offering bundles and sets to meet customer demands and trend expectations. Gaining insights into competitor discounts and pricing can help furniture retailers get an edge amid this environment.

    Want to know how DataWeave’s intelligence platform can empower your business during peak sales events? Contact us to discover more about competitive insights, price intelligence, and data-driven decision-making.
    Stay tuned to our blog to see more coverage on Black Friday 2024.

  • Health & Beauty Deals on Black Friday 2024: Insights from Top Retailers and Brands

    Health & Beauty Deals on Black Friday 2024: Insights from Top Retailers and Brands

    The U.S. health and beauty retail sector shows remarkable resilience amid economic uncertainties, with the skincare market projected to hit $21.83 billion in 2024. Black Friday data reinforces this trend, with health and beauty products seeing a 14.6% surge in web traffic compared to last year.

    At DataWeave, we conducted an in-depth analysis of Black Friday discounting trends in the U.S. health and beauty sector. DataWeave’s AI-powered pricing intelligence platform was used to monitor pricing and discounts across Sephora, Ulta Beauty, Walmart, Target, and Amazon during Black Friday 2024. The study covered 19985 SKUs from November 10-29. We focused on the top 500 products ranked for each search keyword on each retail site, using targeted terms aligned with categories like “skincare” and “fragrance”.

    The results? Beauty leads across categories in discount depth this year, with some retailers offering significant markdowns.

    The Beauty Boom: More Than Just Looking Good

    If there’s one thing the pandemic taught us, it’s that self-care isn’t just a luxury – it’s a necessity. This Black Friday proved that beauty has become an indispensable part of consumers’ lives, with retailers offering unprecedented discounts and crafting strategic promotions to capture the growing demand.

    The Absolute Discount represents the reduction of the selling price compared to the Manufacturer’s Suggested Retail Price (MSRP). The Additional Discount reflects how much lower the selling price is during Black Friday compared to its price a week before the sale. This metric reveals the actual or effective value of the sale event, beyond the standard discounts typically offered.

    Average Discounts Across Leading Health & Beauty Retailers on Black Friday 2024

    Ulta Beauty led with 45% average discounts, followed by Sephora at 38.1% and Walmart at 35.2%. In terms of additional Black Friday discounts, Ulta maintained dominance at 35%, with Sephora following at 28%.

    Hair care emerged as the standout category, with Ulta Beauty offering up to 56% discounts, reflecting sustained demand for at-home beauty routines. Skincare saw fierce competition, with Sephora emphasizing premium discounts (37%) while Walmart focused on value pricing (32.5%).

    Average Discounts Across Leading Health & Beauty Retailer Subcategories on Black Friday 2024

    Fragrance and Makeup attracted consumers with targeted promotions from Walmart and Ulta Beauty, signaling strong demand for gifting items.

    Average Discounts Across Leading Health & Beauty Brands on Black Friday 2024

    Major beauty brands echoed the sentiment. Premium skincare brand Clinique leads with 50.6% average discounts. Meanwhile, drugstore staples like Revlon (29.1%) and Maybelline (24.4%) balanced accessibility and affordability, driving mass-market appeal. Popular beauty and makeup brand L’Oreal Paris also offered a modest 22.8% average discount, reinforcing its position as a value-oriented brand.

    Share of Search and Visibility Across Leading Health & Beauty Brands on Black Friday 2024

    The more interesting story? The massive shift in brand visibility, as our share of search rankings denote:

    • Shampoo and hair care brand Tresemmé saw an unexpected 5.5% jump in the share of search results
    • Beauty brand Herbal Essences gained 5.1% in share of search well

    Declines in share of search were noted for brands like L’Oreal Paris (-1.8%) and Pantene (-0.6%), indicating missed opportunities in promotional visibility.

    Insight: What’s driving this beauty boom? TikTok and social media continue to fuel beauty purchases, with viral products driving significant search and sales spikes. Plus, the “skinification” of hair care has turned basic shampoo shopping into a full-blown beauty ritual.

    Who Offered the Lowest Prices?

    In the previous analysis, we focused on the top 500 products within each subcategory for each retailer, showcasing the discount strategies for their highlighted or featured items. However, to identify which retailer offered the lowest or highest prices for the same set of products, it’s necessary to match items across retailers. For this, we analyzed a separate dataset of 1133 matched products across Health & Beauty specific retailers to compare their pricing during Black Friday. This approach provides a clearer picture of price leadership and competitiveness across categories.

    Here are the key takeaways from this analysis.

    Retailers Offering Most Value - Lowest Priced - Health and Beauty - Black Friday 2024
    • Bloomingdale’s emerges as the overall leader, offering the highest average discount of 14.87%, closely followed by Bluemercury (12.41%).
    • Ulta Beauty ranks third (10.94%), demonstrating competitiveness across key subcategories, while Sephora trails with the lowest average discount (7.33%), reflecting a more premium positioning.
    Lowest Priced Retailer Across Major Subcategories- Health and Beauty - Black Friday 2024
    • Ulta Beauty leads in Hair Care with the highest discount (22.62%), while Bluemercury dominates in Skin Care (13.81%), Makeup (22.98%), and Fragrance (10.6%).
    • Sephora consistently offers the lowest discounts across all subcategories, reflecting their premium positioning.
    Lowest Priced Retailer Across Leading Brands- Health and Beauty - Black Friday 2024
    • Bluemercury offers the lowest prices for luxury brands like Kiehl (27.02%) and Laura Mercier (34.87%), with Bloomingdale’s closely trailing.
    • Bloomingdale’s leads for Bumble and Bumble (13.59%) and Hourglass (23.41%), showcasing strong promotional efforts.
    • Sephora maintains a more restrained discount strategy, with notable leadership only for Estée Lauder (7.18%).
    • Ulta Beauty shines in offering the steepest discount for Briogeo (33.26%), emphasizing competitiveness in key brands.

    What’s Next for Holiday Discounting?

    For retailers, the message is clear: traditional holiday playbooks need a serious update. For shoppers, it means unprecedented opportunities to score deals in categories that traditionally held firm on pricing.

    Want to stay ahead of retail trends and optimize your holiday shopping strategy? DataWeave’s commerce intelligence platform helps brands and retailers strategically navigate these shifts. Contact us to learn more about how we can help you make data-driven decisions in this rapidly evolving retail landscape.

    Stay tuned to our blog for forthcoming analyses on pricing and discounting trends across a spectrum of shopping categories, as we continue to unravel the intricacies of consumer behavior and market dynamics.

  • Early Black Friday Deals Analyzed: How Top Retailers Stack Up on Discounts

    Early Black Friday Deals Analyzed: How Top Retailers Stack Up on Discounts

    Black Friday, once confined to a single weekend, has evolved into a shopping season that now stretches well before Thanksgiving. With inflation hovering around 3% and consumer confidence showing signs of recovery, retailers are adapting their promotional calendars to capture early-bird shoppers and maintain a competitive edge.

    Major retailers, including Amazon, Walmart, Target, and Best Buy, have capitalized on this trend by launching promotions weeks in advance, signaling the traditional holiday rush is now a month-long event. At DataWeave, we put these deals under a microscope.

    Our Methodology

    Using DataWeave’s advanced, AI-powered pricing intelligence platform, we tracked early Black Friday deals across Consumer Electronics, Home & Furniture, Health & Beauty, and Apparel categories. We monitored dedicated Black Friday deal pages on Amazon, Walmart, Target, Best Buy, Nordstrom, Neiman Marcus, and Sephora to gather and analyze discount data a week prior to Black Friday weekend.

    Who’s Offering the Best Deals Across Categories?

    Our pre- Black Friday analysis reveals a clear pattern of premium brands offering deeper discounts across categories ahead of the holiday. Here are some key findings around retail players:

    • Walmart emerges as the most aggressive discounter across categories, leading in Health & Beauty (57.07%), Apparel (48.97%), and Consumer Electronics (43.35%).
    • Amazon maintains consistent but lower discounts (28-29%) across categories, suggesting potential deeper cuts ahead.
    • Best Buy and Sephora, both category specialists, play it conservative compared to mass retail players.

    Let’s look at each category more closely to get a detailed snapshot of the deals this Thanksgiving week:

    Health & Beauty

    Our analysis reveals that it’s not electronics, but the health & beauty category that leads with the widest discount range pre Black Friday, making it the category to watch out for.

    • Walmart takes the lead with an aggressive 57.1% average discount in this category, capitalizing on its value-oriented reputation.
    • Beauty specialist Sephora holds modest beauty discounts (32.81%) compared to other retailers.
    • Amazon offers the broadest range of SKUs (571) in the category.
    Avg. Discounts Across Retailers Pre Black Friday 2024 - Health & Beauty

    Among the health & beauty brands we analyzed, cosmetics brand Tarte and viral K-Beauty skincare brand COSRX stand out with discounts above 40%, appealing to cost-conscious beauty enthusiasts.

    Brands with Highest Avg. Discounts Before Black Friday 2024 - Health & Beauty

    Consumer Electronics

    Our pre- Black Friday analysis reveals interesting insights about consumer electronics deals this season.

    • Walmart, once again, emerges as the frontrunner in the category with 43.4% average discounts.
    • Best Buy plays it conservative in electronics (30.75%), despite being a category specialist, but offers the most extensive SKU coverage (3030).
    • Amazon’s consistent 29.7% discount across 1,749 SKUs suggests they’re probably holding back their best deals for Prime members during Black Friday.
    Avg. Discounts Across Retailers Pre Black Friday 2024 - Consumer Electronics

    Brand-specific data for the category reveals significant deals on Speck (48.07%) and smart TV brand Insignia (39.22%), making accessories and mid-tier electronics attractive for early shoppers. Core computing (HP at 32.14%) and electronics brands maintain more conservative discounts. It remains to be seen if this changes on Black Friday or Cyber Monday.

    Brands with Highest Avg. Discounts Before Black Friday 2024 - Consumer Electronics

    Apparel

    Our analysis of the apparel category reveals several highlights:

    • In the apparel category too, Walmart dominates with an impressive 49% average discount, effectively targeting price-sensitive shoppers in the fashion segment.
    • Nordstrom and Neiman Marcus, both known for apparel, offer significant discounts at 43.2% and 37.8% respectively.
    • Amazon’s expansive SKU coverage (1344) is countered by a modest 29.5% discount, showing its focus on variety over depth of discounts.
    Avg. Discounts Across Retailers Pre Black Friday 2024 - Apparel

    Premium fashion brands dominate the highest discounts this Black Friday in the apparel category. Vince Camuto leads with over 45.1% average discount. Notably, Levi and Nike’s aggressive 44.43% and 43.50% discounts suggests significant inventory positions or intent to capture market share.

    Brands with Highest Avg. Discounts Before Black Friday 2024 - Apparel

    Home & Furniture

    Our analysis reveals an interesting trend across the category.

    • In the home & furniture category too, Walmart leads at 41.8% average discounts. Target follows closely, but with significantly lesser SKUs on offer.
    • Amazon’s 28.1% discount, though the lowest among major players, spans a substantial 1,982 SKUs, reinforcing its position as a marketplace for diverse needs.
    Avg. Discounts Across Retailers Pre Black Friday 2024 - Home & Furniture

    Top 3 Products With the Highest Discounts Across Retailers

    To provide a clearer picture of the early Black Friday landscape, we analyzed the top 3 products with the most substantial discounts in consumer electronics and health & beauty categories. These insights highlight how retailers are leveraging strategic discounts on high-value items to attract early shoppers.

    Top Discounted Products in Consumer Electronics

    Premium TVs dominate the discount scene, with LG’s 83″ OLED offering up to 44.5% off on Amazon, closely followed by a 44.4% discount on Best Buy, showcasing aggressive competition. The same product has much lower discounting on Walmart, but notably, the product is retailed at $3999.9, at least $1000 less than other retailers, highlighting Walmart’s commitment to offering lowest prices.

    Products With Highest Discounts Pre Black Friday 2024 - Consumer Electronics - TVs
    Products With Highest Discounts Pre Black Friday 2024 - Consumer Electronics - Playstation
    Products With Highest Discounts Pre Black Friday 2024 - Consumer Electronics - Digital Cameras

    Gaming consoles, like the PlayStation 5 Slim Bundle, show moderate discounts (ranging from 15% on Walmart and Target to 25% at Best Buy), appealing to tech-savvy shoppers.

    Notable competition is evident in price matching across major retailers, particularly in TVs and high-value electronics like the Nikon Z 8 camera, where Walmart offers the deepest discount at 13.75%, edging past Amazon and Best Buy.

    Top Discounted Products in Health & Beauty

    Viral skincare staples like Tatcha’s Water Cream show tight discounting consistency, with Walmart offering 19.47% off compared to Amazon’s 20% and Sephora’s 20.83%.

    Products With Highest Discounts Pre Black Friday 2024 - Health & Beauty - Tatcha Water Cream
    Products With Highest Discounts Pre Black Friday 2024 - Health & Beauty - Olaplex Hair Oil
    Products With Highest Discounts Pre Black Friday 2024 - Health & Beauty - Yves Saint Laurent Satin Lipstick

    Trending haircare brand Olaplex displays greater disparity, with Walmart leading with a 33.33% discount, surpassing Amazon and Sephora. Luxury brand, Yves Saint Laurent’s Satin Lipstick is one of the highest discounted items across retailers.

    Looking Ahead

    Our analysis suggests that while some early deals offer genuine value, particularly in premium beauty and high-end electronics, many retailers might be holding their best discounts for Black Friday.

    For shoppers, the key is being selective: jump on premium brand discounts now (since they’re likely to remain the same though the weekend), but wait on mid-range electronics and home goods where better deals are likely to emerge on Black Friday or Cyber Monday.

    For retailers, the imperative is clear: dynamic pricing intelligence is crucial for maintaining a competitive edge while protecting margins. Competitive insights will be critical as the holiday season progresses to balance market share against profitability.

    Stay tuned for our Black Friday Cyber Monday analysis next week, where we’ll track how these early discounts compare to the main event’s deals!

  • Redefining Product Attribute Tagging With AI-Powered Retail Domain Language Models

    Redefining Product Attribute Tagging With AI-Powered Retail Domain Language Models

    In online retail, success hinges on more than just offering quality products at competitive prices. As eCommerce catalogs expand and consumer expectations soar, businesses face an increasingly complex challenge: How do you effectively organize, categorize, and present your vast product assortments in a way that enhances discoverability and drives sales?

    Having complete and correct product catalog data is key. Effective product attribute tagging—a crucial yet frequently undervalued capability—helps in achieving this accuracy and completeness in product catalog data. While traditional methods of tagging product attributes have long struggled with issues of scalability, consistency, accuracy, and speed, a new thinking and fundamentally new ways of addressing these challenges are getting established. These follow from the revolution brought in Large Language Models but they fashion themselves as Small Language Models (SLM) or more precisely as Domain Specific Language Models. These can be potentially considered foundational models as they solve a wide variety of downstream tasks albeit within specific domains. They are a lot more efficient and do a much better job in those tasks compared to an LLM. .

    Retail Domain Language Models (RLMs) have the potential to transform the eCommerce customer journey. As always, it’s never a binary choice. In fact, LLMs can be a great starting point since they provide an enhanced semantic understanding of the world at large: they can be used to mine structured information (e.g., product attributes and values) out of unstructured data (e.g., product descriptions), create baseline domain knowledge (e.g, manufacturer-brand mappings), augment information (e.g., image to prompt), and create first cut training datasets.

    Powered by cutting-edge Generative AI and RLMs, next-generation attribute tagging solutions are transforming how online retailers manage their product catalog data, optimize their assortment, and deliver superior shopping experiences. As a new paradigm in search emerges – based more on intent and outcome, powered by natural language queries and GenAI based Search Agents – the capability to create complete catalog information and rich semantics becomes increasingly critical.

    In this post, we’ll explore the crucial role of attribute tagging in eCommerce, delve into the limitations of conventional tagging methods, and unveil how DataWeave’s innovative AI-driven approach is helping businesses stay ahead in the competitive digital marketplace.

    Why Product Attribute Tagging is Important in eCommerce

    As the eCommerce landscape continues to evolve, the importance of attribute tagging will only grow, making it a pertinent focus for forward-thinking online retailers. By investing in robust attribute tagging systems, businesses can gain a competitive edge through improved product comparisons, more accurate matching, understanding intent, and enhanced customer search experiences.

    Taxonomy Comparison and Assortment Gap Analysis

    Products are categorized and organized differently on different retail websites. Comparing taxonomies helps in understanding focus categories and potential gaps in assortment breadth in relation to one’s competitors: missing product categories, sizes, variants or brands. It also gives insights into the navigation patterns and information architecture of one’s competitors. This can help in making search and navigation experience more efficient by fine tuning product descriptions to include more attributes and/or adding additional relevant filters to category listing pages.

    For instance, check out the different Backpack categories on Amazon and Staples in the images below.

    Product Names and Category Names Differ on Different eCommerce Platforms - Here's an Amazon Example
    Product Names and Category Names Differ on Different eCommerce Platforms - Here's a Staples Example

    Or look at the nomenclature of categories for “Pens” on Amazon (left side of the image) and Staples (right side of the image) in the image below.

    Product Names and Category Names Differ on Different eCommerce Platforms -Here's how Staples Vs. Amazon Categories look for Pens

    Assortment Depth Analysis

    Another big challenge in eCommerce is the lack of standardization in retailer taxonomy. This inconsistency makes it difficult to compare the depth of product assortments across different platforms effectively. For instance, to categorize smartphones,

    • Retailer A might organize it under “Electronics > Mobile Phones > Smartphones”
    • Retailer B could use “Technology > Phones & Accessories > Cell Phones”
    • Retailer C might opt for “Consumer Electronics > Smartphones & Tablets”

    Inconsistent nomenclature and grouping create a significant hurdle for businesses trying to gain a competitive edge through assortment analysis. The challenge is exacerbated if you want to do an in-depth assortment depth analysis for one or more product attributes. For instance, look at the image below to get an idea of the several attribute variations for “Desks” on Amazon and Staples.

    With Multiple Attributes Named in a Variety of Ways, Attribute Tagging is Essential to Ensure Accurate Product Matching

    Custom categorization through attribute tagging is essential for conducting granular assortment comparisons, allowing companies to accurately assess their product offerings against those of competitors.

    Enhancing Product Matching Capabilities

    Accurate product matching across different websites is fundamental for competitive pricing intelligence, especially when matching similar and substitute products. Attribute tagging and extraction play a crucial role in this process by narrowing down potential matches more effectively, enabling matching for both exact and similar products, and tagging attributes such as brand, model, color, size, and technical specifications.

    For instance, when choosing to match similar products in the Sofa category for 2-3 seater sofas from Wayfair and Overstock, tagging attributes like brand, color, size, and more is a must for accurate comparisons.

    Attribute Tagging for Home & Furniture Categories Like Sofas Helps Improve Matching Accuracy
    Attribute Tagging for Home & Furniture Categories Like Sofas Helps Improve Matching Accuracy

    Taking a granular approach not only improves pricing strategies but also helps identify gaps in product offerings and opportunities for expansion.

    Fix Content Gaps and improve Product Detail Page (PDP) Content

    Attribute tagging plays a vital role in enhancing PDP content by ensuring adherence to brand integrity standards and content compliance guidelines across retail platforms. Tagging attributes allows for benchmarking against competitor content, identifying catalog gaps, and enriching listings with precise details.

    This strategic tagging process can highlight missing or incomplete information, enabling targeted optimizations or even complete rewrites of PDP content to improve discoverability and drive conversions. With accurate attribute tagging, businesses can ensure each product page is fully optimized to capture consumer attention and meet retail standards.

    Elevating the Search Experience

    In today’s online retail marketplace, a superior search experience can be the difference between a sale and a lost customer. Through in-depth attribute tagging, vendors can enable more accurate filtering to improve search result relevance and facilitate easier product discovery for consumers.

    By integrating rich product attributes extracted by AI into an in-house search platform, retailers can empower customers with refined and user-friendly search functionality. Enhanced search capabilities not only boost customer satisfaction but also increase the likelihood of conversions by helping shoppers find exactly what they’re looking for more quickly and with minimal effort.

    Pitfalls of Conventional Product Tagging Methods

    Traditional methods of attribute tagging, such as manual and rule-based systems, have been significantly enhanced by the advent of machine learning. While these approaches may have sufficed in the past, they are increasingly proving inadequate in the face of today’s dynamic and expansive online marketplaces.

    Scalability

    As eCommerce catalogs expand to include thousands or even millions of products, the limitations of machine learning and rule-based tagging become glaringly apparent. As new product categories emerge, these systems struggle to keep pace, often requiring extensive revisions to existing tagging structures.

    Inconsistencies and Errors

    Not only is reliance on an entirely human-driven tagging process expensive, but it also introduces a significant margin for error. While machine learning can automate the tagging process, it’s not without its limitations. Errors can occur, particularly when dealing with large and diverse product catalogs.

    As inventories grow more complex to handle diverse product ranges, the likelihood of conflicting or erroneous rules increases. These inconsistencies can result in poor search functionality, inaccurate product matching, and ultimately, a frustrating experience for customers, drawing away the benefits of tagging in the first place.

    Speed

    When product information changes or new attributes need to be added, manually updating tags across a large catalog is a time-consuming process. Slow tagging processes make it difficult for businesses to quickly adapt to emerging market trends causing significant delays in listing new products, potentially missing crucial market opportunities.

    How DataWeave’s Advanced AI Capabilities Revolutionize Product Tagging

    Advanced solutions leveraging RLMs and Generative AI offer promising alternatives capable of overcoming these challenges and unlocking new levels of efficiency and accuracy in product tagging.

    DataWeave automates product tagging to address many of the pitfalls of other conventional methods. We offer a powerful suite of capabilities that empower businesses to take their product tagging to new heights of accuracy and scalability with our unparalleled expertise.

    Our sophisticated AI system brings an advanced level of intelligence to the tagging process.

    RLMs for Enhanced Semantic Understanding

    Semantic Understanding of Product Descriptions

    RLMs analyze the meaning and context of product descriptions rather than relying on keyword matching.
    Example: “Smartphone with a 6.5-inch display” and “Phone with a 6.5-inch screen” are semantically similar, though phrased differently.

    Attribute Extraction

    RLMs can identify important product attributes (e.g., brand, size, color, model) even from noisy or unstructured data.
    Example: Extracting “Apple” as a brand, “128GB” as storage, and “Pink” as the color from a mixed description.

    Identifying Implicit Relationships

    RLMs find implicit relationships between products that traditional rule-based systems miss.
    Example: Recognizing that “iPhone 12 Pro” and “Apple iPhone 12” are part of the same product family.

    Synonym Recognition in Product Descriptions

    Synonym Matching with Context

    RLMs identify when different words or phrases describe the same product.
    Examples: “Sneakers” = “Running Shoes”, “Memory” = “RAM” (in electronics)
    Even subtle differences in wording, like “rose gold” vs “pink” are interpreted correctly.

    Overcoming Brand-Specific Terminology

    Some brands use their own terminologies (e.g., “Retina Display” for Apple).
    RLMs can map proprietary terms to more generic ones (e.g., Retina Display = High-Resolution Display).

    Dealing with Ambiguities

    RLMs analyze surrounding text to resolve ambiguities in product descriptions.
    Example: Resolving “charger” to mean a “phone charger” when matched with mobile phones.

    Contextual Understanding for Improved Accuracy and Precision

    By leveraging advanced natural language processing (NLP), DataWeave’s AI can process and understand the context of lengthy product descriptions and customer reviews, minimizing errors that often arise at human touch points. The solution processes and interprets information to extract key information to dramatically improve the overall accuracy of product tags.

    It excels at grasping the subtle differences between similar products, sizes, colors and identifying and tagging minute differences between items, ensuring that each product is uniquely and accurately represented in a retailer’s catalog.

    This has a major impact on product and similarity-based matching that can even help optimize similar and substitute product matching to enhance consumer search. At the same time, our AI can understand that the same term might have different meanings in various product categories, adapting its tagging approach based on the specific context of each item.

    This deep comprehension ensures that even nuanced product attributes are accurately captured and tagged for easy discoverability by consumers.

    Case Study: Niche Jewelry Attributes

    DataWeave’s advanced AI can assist in labeling the subtle attributes of jewelry by analyzing product images and generating prompts to describe the image. In this example, our AI identifies the unique shapes and materials of each item in the prompts.

    The RLM can then extract key attributes from the prompt to generate tags. This assists in accurate product matching for searches as well as enhanced product recommendations based on similarities.

    DataWeave's AI assists in extracting contextual attributes for accuracy in product matching

    This multi-model approach provides the flexibility to adapt as product catalogs expand while remaining consistent with tagging to yield more robust results for consumers.

    Unparalleled Scalability

    DataWeave can rapidly scale tagging for new categories. The solution is built to handle the demands of even the largest eCommerce catalogs enabling:

    • Effortless management of extensive product catalogs: We can process and tag millions of products without compromising on speed or accuracy, allowing businesses to scale without limitations.
    • Automated bulk tagging: New product lines or entire categories can be tagged automatically, significantly reducing the time and resources required for catalog expansion.

    Normalizing Size and Color in Fashion

    Style, color, and size are the core attributes in the fashion and apparel categories. Style attributes, which include design, appearance, and overall aesthetics, can be highly specific to individual product categories.

    Normalizing Size and Color in Fashion for Product Matching

    Our product matching engine can easily handle color and sizing complexity via our AI-driven approach combined with human verification. By leveraging advanced technology to identify and normalize identical and similar products from competitors, you can optimize your pricing strategy and product assortment to remain competitive. Using Generative AI in normalizing color and size in fashion is key to powering competitive pricing intelligence at DataWeave.

    Continuous Adaptation and Learning

    Our solution evolves with your business, improving continuously through feedback and customization for retailers’ specific product categories. The system can be fine-tuned to understand and apply specialized tagging for niche or industry-specific product categories. This ensures that tags remain relevant and accurate across diverse catalogs and as trends emerge.

    The AI in our platform also continuously learns from user interactions and feedback, refining its tagging algorithms to improve accuracy over time.

    Stay Ahead of the Competition With Accurate Attribute Tagging

    In the current landscape, the ability to accurately and consistently tag product attributes is no longer a luxury—it’s essential for staying competitive. With advancements in Generative AI, companies like DataWeave are revolutionizing the way product tagging is handled, ensuring that every item in a retailer’s catalog is presented with precision and depth. As shoppers demand a more intuitive, seamless experience, next-generation tagging solutions are empowering businesses to meet these expectations head-on.

    DataWeave’s innovative approach to attribute tagging is more than just a technical improvement; it’s a strategic advantage in an increasingly competitive market. By leveraging AI to scale and automate tagging processes, online retailers can keep pace with expansive product assortments, manage content more effectively, and adapt swiftly to changes in consumer behavior. In doing so, they can maintain a competitive edge.

    To learn more, talk to us today!

  • Mastering Grocery Pricing Intelligence: A Strategic Approach for Modern Retailers

    Mastering Grocery Pricing Intelligence: A Strategic Approach for Modern Retailers

    When egg prices surged 70% during the 2023 avian flu outbreak, grocery retailers faced a critical dilemma: maintain margins and risk losing customers, or absorb costs and watch profits evaporate. Similarly, rising olive oil and chocolate prices also had domino effects, cascading down from retailers to consumers. In each of these scenarios, those with sophisticated pricing intelligence systems adapted swiftly, finding the sweet spot between competitiveness and profitability. Others weren’t so fortunate.

    This scenario continues to play out daily across thousands of products in the grocery sector. From breakfast cereals to fresh produce to bottled water, retailers must orchestrate pricing across a variety of categories – each with its own competitive dynamics, margin requirements, and price sensitivity patterns.

    The Evolution of Grocery Pricing Intelligence

    Imagine these scenarios in the grocery industry:

    • Milk prices spike during a supply shortage.
    • Your competitor drops egg prices by 20%.
    • Fresh produce costs fluctuate with an unseasonable frost.

    For grocery retailers, these aren’t occasional challenges—they’re Tuesday. Reacting to each pricing crisis as it comes isn’t just exhausting—it’s a recipe for shrinking margins and missed opportunities.

    Think of it this way: If you’re constantly playing defense with your pricing strategy, you’re already two steps behind. Commoditized items like milk and eggs face intense price competition, while seasonal products and fresh produce demand constant attention. Simply matching competitor prices or adjusting for cost changes isn’t enough anymore. What’s needed is a proactive approach that anticipates market shifts before they happen and turns pricing challenges into competitive advantages. This is where price management comes in.

    Price management has transformed from simple competitor checks into a strategic power play that can make or break a retailer’s market position. Weekly manual adjustments have given way to a long-term strategic view, driven by data analytics and market intelligence. Here are the basics of how price management in grocery retail works today.

    Three Pillars of Grocery Price Management

    1. Smart Data Collection: Building Your Foundation

    The journey begins with comprehensive data collection and storage across your entire product ecosystem. This means:

    • Complete Coverage Of All SKUs Across All Stores: Tracking prices for all SKUs across all stores, with particular attention to high-velocity items and volatile categories.
    • Dynamic Monitoring: Tracking prices across different time frequencies as grocery prices are highly volatile for different categories. So daily tracking for volatile items like dairy and produce, and weekly for more stable categories may be needed.
    • Competitive Intelligence: Gathering data not just on prices, but on promotions, pack sizes, and private label alternatives.
    • Infrastructure to Support Large Volumes of Data: Partnering with external data and analytics providers to bridge the gap when retailers struggle with the scale of digital infrastructure these data sets require.

    2. Intelligent Data Refinement: Making Sense of the Numbers

    Raw data alone isn’t enough—it needs context and structure to become actionable intelligence. This is called Data Refinement—the process of establishing meaningful relationships within the data to facilitate the extraction of valuable insights. This refinement stage is closely tied to the data collection strategy, as the quality and depth of the insights derived depend on the accuracy and coverage of the collected data.

    Data refinement includes several key processes:

    Advanced Product Matching

    Picture this: You’re tracking a competitor’s pricing on organic apples. Simple, right? Not quite. Yes, Universal Product Codes (UPCs) and Price Lookup Codes (PLUs) are present in Grocery to standardize product identification across different retailers—unlike the fashion industry’s endless style variations. Still, product matching isn’t as straightforward as scanning barcodes.

    Grocery Pricing Intelligence data faces a challenge when product names, weights, and details differ

    Here’s the catch: many retailer websites don’t display them. Then there’s the private label puzzle—your “Store’s Best” organic apples need to match against competitors’ house brands, each with their own unique UPC. Throw in different sizes (4 Apples vs. 1Kg of Apples), regional product names (fancy naming for plain old arugula), and international brand variations (like the name for Sprite in the USA and China), and you’ve got yourself a complex matching challenge that would make conventional pricing intelligence providers sweat.

    Grocery Pricing Intelligence data faces a challenge when different naming conventions and languages are used in different geographies

    Custom Product Relationships for Consistent Pricing and Competitive Positioning

    Think like a shopper browsing the dairy aisle. You regularly buy your family’s favorite organic yogurt, the 24oz tub. But today, you notice the larger 32oz size is on sale – except the 24oz isn’t. As you stand there, confused, you wonder: Is the sale only for the bigger size? Did I miss a promotion? Should I buy the 32oz even though it’s more than I need?

    For shoppers, this inconsistent pricing across product variations creates a frustrating experience. Establishing clear relationships between related items in your catalog is essential for maintaining consistent pricing and a coherent competitive strategy.

    Grocery Pricing Intelligence data refinement involves Custom Product Relationships for Consistent Pricing and Competitive Positioning

    Start by linking products based on attributes like size, brand, and packaging. That way, when you adjust the price of the 32oz yogurt, the 24oz version automatically updates too – no more scrambling to ensure uniform pricing across your assortment. Similarly, products of the same brand but with flavor variations should be connected to keep pricing consistent.

    Taking this one step further, mapping your competitors’ exact and similar products is crucial for comprehensive competitive intelligence. Distinguishing between premium and private label tiers, national brands, and regional players gives you a holistic view of the landscape. With this understanding, you can hone your pricing strategies to maintain a clear, compelling position across your entire category lineup.

    Consistent pricing, whether across your own product variations or against competitors, provides clarity and accuracy in your overall competitive positioning. By establishing these logical connections, you avoid the customer confusion of seemingly random, inconsistent discounts – and ensure your pricing strategies work in harmony, not disarray.

    The Role of AI and Data Sciences in Data Refinement

    On the surface, linking products based on attributes like size, brand, and packaging seems like a no-brainer. But developing and maintaining the systems to accurately and automatically identify these connections? That’s a whole different animal.

    Think about it – you’re not just dealing with text-based product titles and UPCs. There are images, videos, regional variations, private labels, and a whole host of other data types and industry nuances to account for.

    Luckily, DataWeave is one of the few companies that’s truly cracked the code. Our multimodal AI models are trained to process all those diverse data formats – from granular product specs to zany regional produce names. And it’s not just about technology; we also harness the power of human intelligence.

    See, in the grocery world, category managers are the real decision makers. They know their shelves inside and out and can spot those tricky connections in product matching, especially when they are not UPC-based. That’s why DataWeave built in a Human-in-the-Loop (HITL) process, where their AI systems continuously learn from expert feedback. It’s a feedback loop that allows our customers to pitch in and keep product relationships accurate, reliable, and always adapting to new market realities.

    So while product mapping may seem straightforward on the surface, the reality is it takes some serious horsepower to do it right. Thankfully, DataWeave has both the technical chops and the grocery industry know-how to make it happen. Because when it comes to pricing intelligence, getting those product connections right is half the battle.

    3. Strategic Implementation: Turning Insights into Action

    The true value of pricing intelligence (PI) is realized through its strategic application. Although many view PI as a technical function, its strategic significance is increasing, particularly in the context of recent economic pressures like inflation. Here’s why:

    Tactical vs Strategic Use of Data: From Standard Reporting to Competitive Analysis

    Pricing intelligence has come a long way from the days of simply reacting to daily price changes. These days, it’s not just about firefighting—it’s about driving long-term strategy.

    You can use pricing data to make quick, tactical adjustments, like matching a competitor’s sudden price drop on milk. Or, you can leverage that same data to predict market trends, optimize your product lineup, and shape your overall pricing strategy. Retailers who take that strategic view can get out ahead of the curve, anticipating shifts instead of just chasing them.

    DataWeave supports both of these approaches. Our Standard Reporting tools give pricing managers the nitty-gritty details they need—current practices, historical patterns, and operational KPIs. It’s all the insights you’d expect for making those tactical, day-to-day tweaks.

    In addition, DataWeave offers something more powerful: Competitive analysis. This is where pricing intelligence becomes a true strategic weapon. By providing a high-level view of market positioning, competitor moves, and untapped opportunities, competitive analysis empowers leadership to make proactive, big-picture decisions.

    Armed with this broader perspective, retailers can start taking a more surgical approach. Maybe you need to adjust pricing zones to better meet customer demands. Or rethink your overall strategies to stay ahead of the competition, not just keep pace. It’s the difference between constantly putting out fires and systematically fortifying your entire pricing fortress.

    Beyond Pricing: Comprehensive Data for Broader Insights

    Pricing intelligence is just the tip of the iceberg. When you really start to refine and harness your data, the possibilities for grocery retailers expand far beyond simple price comparisons. Think about it – all that information you’re collecting on products, markets, and consumer behavior? That’s a goldmine waiting to be tapped. Sure, you can use it to keep a pulse on competitor pricing. But why stop there?

    What if you could leverage that data to optimize your product assortment, making sure you’re stocking the right mix to meet customer demands? Or tap into predictive analytics to get a glimpse of future market shifts, so you can get out ahead of the curve? How about using it to streamline your supply chain, identify availability inefficiencies, and get products to shelves faster?

    Sure, pricing intelligence will always be mission-critical. But when you couple it with these other data-driven insights, that’s when grocery retailing gets really interesting. It’s about evolving from a price-matching robot to a true strategic visionary, armed with the intelligence to take your business to new heights.

    Looking Ahead: The Future of Grocery Pricing Intelligence

    The grocery pricing landscape continues to evolve, driven by:

    • Integration of AI and machine learning for predictive pricing
    • Enhanced focus on omnichannel pricing consistency
    • Growing importance of personalization in pricing strategies

    Pricing intelligence isn’t just about having data—it’s about having the right data and knowing how to use it strategically. Success requires a comprehensive approach that combines robust data collection, sophisticated analysis, and strategic implementation.

    By embracing modern pricing intelligence tools and strategies, grocery retailers can navigate market volatility, maintain competitive positioning, and drive sustainable growth. The key lies in building a pricing ecosystem that’s both sophisticated enough to handle complex data and flexible enough to adapt to changing market conditions.

    Ready to transform your pricing strategy? Check out our grocery price tracker to get month-on-month updates on grocery prices in the real world. Contact us to learn how our advanced pricing intelligence solutions can help your business stay ahead in the competitive grocery market.

  • 10 SEO Tactics to Help Retail Brands Win More Search Visibility on Amazon

    10 SEO Tactics to Help Retail Brands Win More Search Visibility on Amazon

    Today, the first name that comes to anybody’s mind when they hear about online shopping is Amazon. In the US alone, Amazon accounted for over 37.6 percent of total online retail sales in 2023 with the second place Walmart not even managing to win double-digit numbers on the same scale.

    Amazon leads retail eCommerce in the USA

    With such a phenomenal market share, it is not surprising that any retail brand would want to have their products listed on Amazon for sale. However, as enticing as the potential exposure could be, the overwhelming presence of brands selling similar products on Amazon is so huge that getting fair visibility for your products may require some heavy-lifting support.

    Will the Same SEO You Use for Google Work with Amazon?

    Unfortunately, no, as Google and Amazon have different objectives when it comes to search rankings on their respective customer platforms. Google makes the lion’s share of its revenue from search advertising, whereas Amazon makes money when customers buy products listed on its platform by sellers.

    Relying on traditional search engine optimization (SEO) techniques may not get the desired results as they are more optimized for search engines like Google. Amazon embraces its unique DNA when it comes to product display rankings on its search option.

    How Does SEO Work in Amazon?

    Over the years, Amazon amassed data about shopping experiences that billions of customers globally had on its platform. With this data, they developed their custom search algorithm named A9. Contrary to the gazillion objectives that Google has for its intelligent search algorithms, Amazon has tasked A9 with just a simple straightforward target—when a customer keys in a search query, provide the best choice of products that they will most probably purchase, as search results.

    A9 works to fulfill the mission of guiding shoppers to the right product without worrying about semantics, context, intent, mind mapping, etc. of the search query in contrast to what Google does. As with Google search, Amazon does have paid advertising and sponsored results options such as Amazon PPC, Headline ads, etc. but their SEO algorithms are aware of how to support and boost search rankings of genuine products and brands that have taken an effort to follow best practices in Amazon SEO as well as have a great offering with attractive prices.

    As additional knowledge, Amazon also has clear guidelines on what it prioritizes for search rankings. Known in the SEO world as Amazon ranking signals, these are core factors that influence how a product is ranked for search queries. Some of the top Amazon ranking signals that carry heavy influence on search rankings include on-page signals, off-page signals, sales rank, best sellers rank, etc.

    What Brands Need to Strategize to Master the Amazon SEO Algorithms

    From a broad perspective, we can classify the actions brands need to take in this regard in 3 core stages:

    Pre-Optimization

    This deals with getting first-hand knowledge about both customers who are likely to purchase your product and the competitors who are vying for sales from these very same customers. Filtering your target customer or audience is essential to ensure that you get the most ROI from marketing initiatives and that sales cycles are accelerated. For example, if your product is a premium scented candle, there is no point in wasting advertising dollars trying to win attention from customers who are not likely to ever spend on luxury home décor items.

    Knowing how your competitors are performing on Amazon search, the keywords, and SEO strategies they have adapted is critical to ensure that you stay one step ahead.

    Product Listing Page Optimization

    This includes strategies that a brand can adopt so that its product description page gets the much-needed content optimizations to sync with Amazon’s A9 algorithm. It has a mix of keyword-integrated content, relevant images, descriptions in easy-to-understand language, localized content flavors to resonate with target buyers, etc. For example, a kitchen tool like a grater might be used for different kinds of food preparation techniques in different regions of the same country.

    Product Listing Optimization For Amazon SEO

    The brand must ensure that the description adequately localizes the linguistic or usage preference representation of the target audience. If the grater is used for grating coconut shells to extract the fibrous pulp in the Midlands and for grating ginger skin in the Far East, both use cases should be part of the product description if the target customers are from both regions.

    Sales Optimization

    This deals with options that have more sales strategies integrated into their core. For example, blogs on popular websites with the Amazon purchase link embedded in the content, collaboration with social media influencers, paid advertising on Amazon itself as well as on search engines, video ads, banner and display ads, etc.

    The key intent here is to drive organic and inorganic traffic to the Amazon product listing page and ultimately win sales.

    How Can Your Products Rank High in Amazon Search Results? Top 10 Tactics

    Now that you have a clear understanding of the strategies that help in mastering Amazon’s ranking algorithms, here are some great tips to help achieve higher search rankings for your products on Amazon search:

    1. Target Relevant Keywords

    You need to figure out the best keywords that match what customers put as queries into the Amazon search bar. Your brand needs to clearly understand customer behavior when they arrive on Amazon to search for a product or category of products. The best place to begin looking for the same would be on competitor pages on Amazon. The keywords that helped them rank well on Amazon can help you as well. Manually investigating such a large pool of competitors is nearly impossible but with the right tools, you can easily embrace capabilities to know which keywords can help you in mimicking the success of your competitors.

    2. Focus on Product Titles

    Every single part of the content in your brand’s Amazon storefront or product page needs dedicated focus. Beginning with the product titles, effort needs to be made to ensure that they include the brand name, key product category or features, and other relevant keyword information.

    Product Title Optimized for Amazon SEO

    In other words, product titles must be optimized for searchability. This searchability for product titles needs to be optimized for both mobile and desktop screens.

    3. Create Product Descriptions that Resonate with the Audience

    For product descriptions on your Amazon webpage, you need to figure out the optimal quality levels needed for the intended audience. Effective content can help achieve better search ranking visibility and convince the incoming traffic of shoppers to make a purchase. It is important to periodically review and modify your page content to suit the interests of visitors from both web and mobile devices.

    Product Description Optimized for Amazon SEO

    Leveraging solutions like DataWeave can help with regular content audits to ensure you are putting out the best product content that will delight shoppers and deliver on sales conversion targets.

    4. Use High-Quality Media Assets like Images and Videos

    Promoting your product doesn’t have to be restricted to just textual content in Amazon product description sections. You can use other multimedia assets of high quality. These include images, videos, brochure images, etc. Every content asset must aim to educate shoppers on why your product should be their number one choice. For example, look at this detailed product description for the viral K-Beauty product COSRX Mucin Essence.

    Product Description with Images Optimized for Amazon SEO

    Moreover, images can help attract more attention span from visitors, thereby increasing the probability of purchases.

    5. Strengthen the Backend Keywords As Well

    Amazon also supports hidden backend keywords that sellers add to their product listings. They help add more relevance to products similar to meta descriptions and titles in traditional SEO for search engines like Google. A typical backend keyword may comprise synonyms, misspelled keywords, textual variations, etc. However, knowing how to pick the right ones is crucial. By analyzing your keyword rankings against competitors and higher-ranking product results in search, the platform can help you consistently optimize your content backend to help grow visibility.

    6. Focus on Reviews and Ratings

    Reviews and ratings on product pages are key insights that help customers with their purchasing decisions. So, it is natural for brands to keep a close eye on how their products are faring in this regard. Reviews and ratings are a direct indication of the trustworthiness of your product. When previous buyers rate you high and leave favorable reviews on your product, it will directly promote trust and help you secure a better rapport with new customers.

    Reviews with Videos and Images Optimized for Amazon SEO
    Requesting reviews or leveraging user generated reviews and ratings to optimize Amazon SEO

    This upfront advantage can help boost sales conversions better. Leveraging solutions like DataWeave can help you understand the sentiments that customers have for your products by intelligently analyzing reviews and ratings.

    7. Implement Competitive Pricing Strategies

    The goal of most customers when shopping online is to get their desired product at the most affordable prices. The eCommerce price wars every year are growing in scale today and getting your product pricing right is crucial for sales. However, there is a need to gain comprehensive insights into how your competitors are pricing their offerings and how the market responds to specific price ranges. Solutions like DataWeave help your brand access specific insights into pricing. By analyzing competitor pricing, you can create a winning price model that is sustainable for your brand and favorable for target customers.

    8. Track Share of Search

    Content and other SEO activities will help improve your search rankings on Amazon. However, it is equally important to know how well your products are performing periodically against your competitors for the same set of specific keyword searches. You need to understand the share of search that your products are achieving to formulate improvement strategies. DataWeave’s Digital Shelf Analytics solution provides share of search insights helping you uncover deep knowledge on your discoverability on Amazon (and other marketplaces) for your vital search keywords.

    9. Ensure Stock Availability

    To achieve better ranking results, brands need to ensure that the relevant products matching the search keywords are available for quick delivery at the desired ZIP codes where users are more likely to search and order them. Out-of-stock items seldom show up high on search results. Certain products, especially if they’re popular, can get stocked out frequently in certain locations. Keeping a close eye on your stock availability across the map can help minimize these scenarios.

    10. Optimize Your Brand Presence

    While optimizing content and other key areas within the Amazon webpage for your product is critical, there are other avenues to help boost search rankings. One such option includes registering in the Amazon Brand Registry, which provides more beneficial features like protection against counterfeits and ensuring that your brand page is optimized according to Amazon storefront standards.

    The Bottom Line

    Winning the top spot in Amazon search ranking is crucial for brands that aim to capitalize on online sales revenue to grow their business. Knowing your workaround for Amazon’s proprietary SEO frameworks and algorithms is the first step to succeeding. The key element of success is your ability to gain granular insights into the areas we covered in this blog post such as competitor prices, sentiments of customers, market preferences, and content optimization requirements.

    This is where DataWeave’s Digital Shelf Analytics solution becomes the biggest asset for your eCommerce business. Contact us to explore how we can empower your business to build the most visible and discoverable Amazon storefront that guarantees higher search rankings and ultimately increased sales. Talk to us for a demo today.

  • Normalizing Size and Color in Fashion Using AI to Power Competitive Price Intelligence

    Normalizing Size and Color in Fashion Using AI to Power Competitive Price Intelligence

    Fashion is as dynamic a market as any—and more competitive than most others. Consumer trends and customer needs are always evolving, making it challenging for fashion and apparel brands to keep up.

    Despite the inherent difficulties fashion and apparel sellers face, this industry is one of the largest grossing markets in the world, estimated at $1.79 trillion in 2024. Global revenue for apparel is expected to grow at an annual rate of about 3.3% over the next four years. That means companies in this space stand to make significant revenue if they can competitively price their products, keep up with the competition, and win customer loyalty with consistent product availability.

    There are three main categories in fashion and apparel. These include:

    • Apparel and clothing (i.e., shirts, pants, dresses, and other apparel)
    • Footwear (i.e., sneakers, sandals, heels, and other products)
    • Accessories (i.e., bags, belts, watches, and so on)

    If you look at all of these product types across all sorts of retailers, there is a massive amount of overlapping data based on product attributes like style and size that are difficult to normalize.

    Fashion Attributes

    Style, color, and size are the main attribute categories in fashion and apparel. Style attributes include things like design, look, and overall aesthetics of the product. They’re very dependent on the actual product category of fashion as well. A shirt might have a slim fit attribute associated with it, whereas a belt might have a length. All these different attributes are usually labeled within a product listing and affect the consumer’s decision-making process:

    • Color (red, blue, sea green, etc.)
    • Pattern (solid, striped, checked, floral, etc.)
    • Material (cotton, polyester, leather, denim, silk, etc.)
    • Fit (regular, slim, relaxed, oversized, tailored, etc.)
    • Type (casual, formal, sporty, vintage, streetwear)

    Color Complexity in Fashion

    Color is perhaps the most visually distinctive attribute in fashion, yet it presents unique challenges for retailers. This is because color naming can vary across retailers and marketplaces. There are several major differences in color convention:

    • A single color can be labeled differently across brands (e.g., “navy,” “midnight blue,” “deep blue”)
    • Seasonal color names (e.g., “summer sage” vs. “forest green”)
    • Marketing-driven names (e.g., “sunset coral” vs. “pale orange”)
    Differences in color naming - challenges faced by fashion retail intelligence systems

    Size: The Other Critical Dimension

    Size in fashion refers to the dimensions or measurements that determine how fashion products fit. Depending on whether the product is a clothing item, shoes, or a hat, there will be different sizing options. Types of sizes include:

    • Standard sizes (XS, S, M, L, XL, XXL, XXL)
    • Custom sizes (based on brand, retailer, country, etc.)

    A single type of product may have different sizing labels. For instance, one pants listing may use traditional S, M, L, XL sizing, while another pants listing may use 24, 25, or 26, to refer to the waist measurement.

    Size Variations - challenges faced by fashion retail intelligence systems
    Size Variations - challenges faced by fashion retail intelligence systems
    Size Variations - challenges faced by fashion retail intelligence systems

    Size is a dynamic attribute that changes based on current trends. For example, there has recently been a significant shift towards inclusive sizing. Size inclusivity refers to the practice of selling apparel in a wide range of sizes to accommodate people of all body types. Consumers are more aware of this trend and are demanding a broader range of sizing offerings from the brands they shop from.

    In the US market, in particular, some 67% of American women wear a size 14 or above and may be interested in purchasing plus-size clothing. There is a growing demand in the plus-size market for more options and a wider selection. Many brands are considering expanding their sizes to accommodate more shoppers and tap into this growing revenue channel.

    Pricing Based on Size and Color

    Many fashion products are priced differently based on size and color. Let’s take a look at an example of what this can look like.

    Different colors may retail at different price points.

    A popular beauty brand (see image) is known for its viral lip tint. While most of the color variants are priced at $9.90 on Amazon, a specific colorway option, featuring less pigmented options, is priced at $9.57. This price differential is driven by both material costs and market demand.

    Different colorways (any of a range of combinations of colors in which a style or design is available) of the same product often command different prices also. This is based on:

    • Dye costs (some colors require more expensive processes)
    • Seasonal demand (traditional colors vs. trend colors)
    • Exclusivity (limited edition colors)

    An example of price variations by size is a women’s shirt that is being sold on Amazon as shown below. For this product, there are no style attributes to choose from. The only parameter the shopper has to select is the size they’d like to purchase. They can choose from S to XL. On the top, we can see that the product in size S is ₹389. Below, the size XL version of this same shirt is ₹399. This price increase is correlated to the change in size.

    Different sizes may retail at different price points.
    Different sizes may retail at different price points.

    So why are these same products priced differently? In an analysis of One Six, a plus-size clothing brand, several reasons for this difference in plus-size clothing were determined.

    • Extra material is needed, hence an increase in production costs
    • Extra stitching costs, hence an increase in production costs
    • Production of plus-size clothing often means acquiring specialized machinery
    • Smaller scale production runs for plus-size clothing means these initiatives often don’t benefit from cost savings

    Some sizes are sold more than others, meaning that in-demand sizes for certain apparel can affect pricing as well. Brands want to be able to charge as much as possible for their listing without risking losing a sale to a competitor.

    The Competitive Pricing Challenge: Normalizing Product Attributes Across Competitors in Apparel and Fashion

    There are hundreds of possible attribute permutations for every single apparel product. Some retailers may only sell core sizes and basic colors; some may sell a mix of sizes for multiple style types. Most retailers also sell multiple color variants for all styles they have on catalog. Other retailers may only sell a single, in-demand size of the product. Also, when other retailers are selling the product, it’s unlikely that their naming conventions, color options, style options, and sizing match yours one-for-one.

    In one analysis, it was found that there were 800+ unique values for heel sizes and 1000+ unique values for shirts and tops at a single retailer! If you’re looking to compare prices, the effort involved in setting up and managing lookup tables to identify discrepancies when one retailer uses European sizes and another uses USA sizes, for example, is simply too onerous to contemplate doing. Colors only add to the complexity – as similar colors may have new names in different regions and locations as well!

    Even if you managed to find all the discrepancies between product attributes, you would still need to update them any time a competitor changed a convention.

    Still, monitoring your competitors and strategically pricing your listings is essential to maintain and grow market share. So what do you do? You can’t simply eyeball your competitor’s website to check their pricing and naming conventions. Instead, you need advanced algorithms to scan the entire marketplace, identify individual products being sold, and normalize their data and attributes for analysis.

    Getting Color and Size Level Pricing Intelligence

    With DataWeave, size and color are just two of several dimensions of a product instead of an impossible big data problem for teams. Our product matching engine can easily handle color and sizing complexity via our AI-driven approach combined with human verification.

    This works by using AI built on more than 10 years of product catalog data across thousands of retail websites. It matches common identifiers, like UPC, SKU code, and other attributes for harmonization before employing a large language model (LLM) prompts to normalize color variations and sizing to a single standard.

    The data flow DataWeave uses for product sizing and color normalization

    For example, if a competitor has the smallest size listed as Sm but has your smallest listing identified as S, DataWeave can match those two attributes using AI. Similar classification can be performed on color as well.

    Complex LLM prompts are pre-established so that this process is fast and efficient, taking minutes rather than weeks of manual effort.

    Harmonizing products along with their color and sizing data across different retailers for further analysis has several benefits. Most importantly, product matching helps teams conduct better competitive analysis, allowing them to stay informed about market trends, competitors’ offerings, and how those competitors are pricing various permutations of the same product. It helps ensure that you’re offering the most competitive assortment of sizing in several colors to win more market share as well. Overall, it’s easier for teams to gain insights and exploit their findings when all the data is clean and available at their fingertips.

    Product Matching Size and Color in Apparel and Fashion

    Color and size are crucial attributes for retailers and brands in the apparel and fashion industry. It adds a level of complexity that can’t be overstated. While it’s a necessity to win consumers (more colors and sizes will mean a wider potential reach), the more permutations you add to your listing, the more complicated it will be to track it against your competition. However, This challenge is worth undertaking as long as you have the right solutions at your disposal.

    With a strategy backed by advanced technology to discover identical and similar products across the competitive landscape and normalize their color and sizing attributes, you can ensure that you are competitively pricing your products and offering the best assortment possible. Employing DataWeave’s AI technology to find competitor listings, match products across variants, and track pricing regularly is the way to go.

    Interested in learning more about DataWeave? Click here to get in touch!

  • Mastering Fuel Price Competitiveness: How First-Party Data Outperforms Third-Party in Pricing Accuracy

    Mastering Fuel Price Competitiveness: How First-Party Data Outperforms Third-Party in Pricing Accuracy

    Fuel retailers today operate in a highly competitive and volatile market. Consumer behavior is increasingly driven by price sensitivity, particularly in industries like fuel where small changes in price can significantly influence where consumers choose to fill up. The stakes are even higher when you consider the razor-thin margins many fuel retailers work with, making every cent count.

    For years, retailers have relied on third-party apps and services to provide them with location-based competitive fuel price data. These services collect pricing data based on customer transactions. While these platforms offer a convenient way for consumers to find cheaper fuel prices, their value to retailers is limited. The data they provide is often riddled with inaccuracies, lags, and incomplete coverage, leaving retailers vulnerable to missed pricing opportunities.

    In this rapidly shifting landscape, retailers need data that is not only accurate but also real-time. Solving this involves directly tapping into retailers’ own data sources (first-party or 1P data) —such as websites and apps. This is believed to be the most comprehensive and reliable source of fuel price data in the market.

    To validate this hypothesis, we conducted a comprehensive analysis comparing first-party and third-party (3P) fuel price data. Our analysis compared pricing (at the same time of the day) across more than 40 gas stations—including major players like Circle K, Costco, Speedway, and Wawa. The data was captured several times a day for over a week.

    Accurate Pricing Matters More Than Ever

    Our analysis revealed that nearly a quarter (24.4%) of the fuel pricing data provided by third-party sources was inaccurate when compared to first-party data. On average, these inaccuracies amounted to a price difference of 10.9%.

    Such discrepancies, though seemingly minor, can significantly affect consumer behavior. Inaccurate prices could drive customers to competitors who are listed with lower prices—even if the real difference is negligible. For fuel retailers, this leads to lost revenue, missed opportunities, and reduced market share.

    First-party vs Third-party Fuel Price Comparison

    The implications are clear: relying on third-party competitive data alone puts retailers at risk. With inaccurate data, retailers may fail to adjust their prices in time to respond to market changes, losing customers to competitors.

    The Core Challenges of Third-Party Data

    Third-party data comes with inherent limitations. The way this data is collected presents significant challenges for fuel retailers looking to optimize pricing strategies. Here are the main issues:

    • Inconsistent Data Frequency: Third-party pricing data is often gathered through customer card transactions. As a result, pricing data updates only when and where transactions occur. This can lead to irregular data availability, particularly in stations with lower transaction volumes. For instance, in rural areas or during off-peak hours, fewer transactions lead to fewer updates. Retailers are left with outdated data, making it difficult to keep pace with real-time price fluctuations.
    • Limited Geographic Coverage: Regions with lower transaction volumes are particularly affected by data gaps. While urban centers may enjoy more frequent updates, rural and less-frequented stations often suffer from a lack of data. This limited geographic coverage creates blind spots, making it impossible for retailers in these regions to stay competitive.
    • Potential Data Inaccuracies Across Fuel Types: Our analysis showed that inaccuracies in third-party pricing data were most pronounced for Unleaded fuel, with errors occurring nearly 80% of the time. While Diesel prices fared slightly better, inaccuracies were still frequent. This inconsistency across fuel types further complicates the challenge for retailers relying on third-party data.
    First-party vs Third-party Fuel Price Comparison by Fuel Type

    Leveraging First-Party Data

    At DataWeave, our Fuel Pricing Intelligence solution leverages real-time 1P data directly from fuel retailers’ websites and mobile apps, ensuring that retailers always have access to the most up-to-the-minute and accurate pricing information.

    Here’s why first-party data stands out:

    • Real-Time Updates: Our solution provides near-instantaneous updates across more than 30,000 ZIP codes, ensuring that retailers always have the most up-to-date pricing information. This real-time accuracy is essential for making dynamic pricing adjustments in a highly competitive market.
    • Wide Geographic Coverage: DataWeave’s first-party solution captures data across a broad geographic range, ensuring no blind spots in coverage. Retailers in rural or less-frequented areas benefit from the same level of insight as their urban counterparts, giving them the ability to optimize pricing in real-time.
    • Complementary to Existing Solutions: For retailers already using third-party data, DataWeave’s first-party solution can complement and enhance their current systems. By filling in data gaps and providing more frequent updates, our solution ensures that retailers are never left in the dark when it comes to competitive pricing.

    Retailer-Wise Variances

    Among the retailers analyzed, we found that some were more affected by third-party data inaccuracies than others. Speedway and Wawa, for instance, experienced inaccuracies in up to 28% of third-party price data. In contrast, Circle K exhibited fewer discrepancies, but even they were not immune to the challenges posed by third-party data.

    For their competition, relying on third-party data alone presents a significant risk. By switching to first-party data sources, or complementing their existing third-party data with DataWeave’s first-party solution, retailers can ensure they stay competitive in the eyes of price-sensitive consumers.

    First-party vs Third-party Fuel Price Comparison by Retailer

    In an industry as price-sensitive as fuel retail, accurate data is a strategic asset. Leveraging first-party data allows fuel retailers to:

    • Maximize Revenue: By using real-time, accurate data, retailers can avoid under- or over-pricing their fuel, ensuring they capitalize on high-demand periods while minimizing losses during low-demand times.
    • Enhance Margins: First-party data provides the precision needed to fine-tune margins, ensuring profitability even in fiercely competitive markets.
    • Boost Customer Retention: Competitive pricing fosters customer loyalty. With better data, retailers can maintain customer trust and retention, even during volatile market shifts.

    Shift into High Gear with DataWeave

    As the fuel retail industry becomes increasingly competitive, the need for accurate, real-time pricing data has never been more important. DataWeave’s Fuel Pricing Intelligence solution empowers retailers with the insights they need to stay ahead of the competition, optimize pricing strategies, and boost profitability.


    With first-party data, fuel retailers can eliminate the blind spots and inaccuracies associated with third-party sources. This shift toward data-driven pricing strategies ensures that every price adjustment is backed by real-time insights, giving retailers the edge they need to succeed.

    To learn more, talk to us today!

  • Mastering Retail Media Metrics: A Deep Dive into Share of Media

    Mastering Retail Media Metrics: A Deep Dive into Share of Media

    Brands are investing millions of dollars in digital retail media to make their products stand out amid unrelenting competition.

    The ad spend on digital retail media worldwide was estimated at USD 114.4 billion in 2022, and the current projections indicate that it will grow to USD 176 billion by 2028. This amounts to a 54% increase in just six years.

    The current surge in digital retail media advertising has led brands to find an effective way to monitor the efficacy of their ad spend. While Share of Search has long been used to measure brand visibility effectively, the metrics often missed tracking ads on retail sites.

    DataWeave’s Share of Media solution helps solve this problem.

    What is the Share of Media?

    At DataWeave, Share of Media is a metric used to measure a brand’s presence in sponsored listings and banner ads on eCommerce platforms. It captures how often a brand appears in paid promotions compared to competitors, offering insights into advertising visibility and effectiveness.

    These days most marketplaces seamlessly blend banner ads and sponsored listings into organic search results. Let’s take a closer look.

    Banner Advertising

    Banner advertising strategically places creative banners across websites—often at the top, bottom, or sides. Some eCommerce platforms also integrate these banners into product search listings.

    Banner Advertising on Amazon_Share of Media Analytics to win the digital shelf

    What makes banner ads so special is the unique ability to allow marketers to use various types of media in a single ad, such as images, auto-play videos, and animations. Brands can also present curated collections of products. This flexibility provides marketers with creative opportunities to differentiate from competitors, capture customer interest, and encourage conversions.

    Sponsored Listings

    Sponsored listings are paid placements within search engine results or eCommerce platforms. They are usually marked as ‘sponsored’ or ‘ad,’ and they often appear at the top of search results and alongside organic product listing results.

    Sponsored Product Listings on Amazon_Share of Media Analytics to win the digital shelf

    Unlike organic search results, sponsored listings are prioritized based on the advertiser’s bid amount and relevance to users’ search queries.

    Sponsored listings offer a strategic advantage by enabling businesses to connect directly with consumers who are actively searching for their products. This targeted approach ensures that marketing efforts are focused on individuals with high intent of making a purchase, maximizing the potential return on investment.

    The Power of Banner Ads and Sponsored Listings

    Banner ads and sponsored listings are great choices for boosting customer engagement and product sales. Here are four key advantages they offer:

    • Enhanced Visibility: Digital retail media strategically places your brand where it will stand out—outshining competitors and grabbing the attention of high-purchase-intent consumers.
    • Precision in Reach: These ads target specific keywords or categories, allowing for highly focused advertising based on demographics and search intent.
    • Minimal Conversion Friction: Smooth transitions from ads to a brand’s native store or product listing on the marketplace keep conversion friction to a minimum.
    • Brand Awareness and Recall: Consistent exposure to your brand through banner ads and sponsored product listings can leave lasting impressions and build brand recognition.

    The bottom line is that it’s increasingly important for brands to monitor their Share of Media.

    How to Monitor Your Brand’s Share of Media

    DataWeave’s Digital Shelf Analytics (DSA) platform extends beyond the traditional Share of Search metrics and provides robust support for monitoring the Share of Media.

    DataWeave monitors the Share of Media in two ways: keywords and product categories. Users can view Share of Media insights through aggregated views, trend charts, and detailed tables. The views are designed to show brand visibility and the overall competitive landscape. For example, the screenshot below, taken from DataWeave’s dashboard, showcases the Share of Media across keywords, categories, and retailers.

    Share of Media by Keyword

    The Share of Media metric captures a brand’s advertising presence within search listings for a designated keyword. This provides a comprehensive view of a brand’s visibility and promotional efforts across retail platforms, helping brands validate and gauge the effectiveness of their ad spend.

    For example, the screenshot below shows the trend of manufacturer’s Share of Media by keyword—‘baby food.’

    Share of media by keyword_Share of Media Analytics to win the digital shelf

    Share of Media by Category

    The Share of Media metric measures the presence of brands’ banner ads and sponsored listings across product categories on retail sites. This helps brands see which product categories require more investment, making it easier for them to spend their ad budget wisely.

    The screenshot below illustrates manufacturers’ Share of Media by category across retailers.

    Share of Media: An Essential Ecommerce Metric

    As retail media continues to evolve, our analytics must follow—after all, knowledge is a competitive advantage. In the dynamic world of eCommerce, where competition is fierce and consumer attention is scarce, understanding your share of media is crucial.

    Analyzing the Share of Media can give brands a competitive edge. By regularly monitoring and analyzing this metric, you can make data-driven decisions to improve your brand’s visibility, attract more customers, and ultimately drive sales growth. With a deeper understanding of their target audience and market dynamics, brands can refine promotional efforts to drive more effective results and optimize return on ad spend (ROAS).

    For more information on how Digital Shelf Analytics can enhance your brand’s digital shelf presence, request a demo or contact us at contact@dataweave.com.

  • The Complete Guide to Competitive Pricing Strategies in Retail and E-commerce

    The Complete Guide to Competitive Pricing Strategies in Retail and E-commerce

    Your budget-conscious customers are hunting for value and won’t hesitate to switch brands or shop at other retailers.

    In saturated and fiercely competitive markets, how can you retain customers? And better yet, how can you attract more customers and grow your market share? One thing you can do as a brand or retailer is to set the right prices for your products.

    Competitive or competition-based pricing can help you get there.

    So what exactly is competitive pricing? Let’s dive into this strategy, its advantages and disadvantages, and how it can be used to stay ahead of the competition.

    What is Competitive Pricing?

    Competitive or competition-based pricing is a strategy where brands and retailers set product prices based on what their competitors charge. This method focuses entirely on the market landscape and sets aside the cost of production or consumer demand.

    It is a good pricing model for businesses operating in saturated markets, such as consumer packaged goods (CPGs) or retail.

    Competitive Pricing Models

    Competitive pricing isn’t a one-size-fits-all strategy. The approach includes various pricing models that can be customized to fit your business goals and market positioning.

    Here’s a closer look at five of the most common competition-based pricing models:

    Price Skimming

    If you have a new product entering the market, you can initially set a high price. Price skimming allows you to maximize margins when competition is minimal.

    This strategy taps into early adopters’ willingness to pay a premium for new project categories. As competitors enter the market, you can gradually reduce the price to maintain competitiveness.

    Premium Pricing

    Premium pricing lets you position your product as high-quality or luxurious goods.

    When you charge more than your competitors, you’re not just selling a product—you’re selling status and an experience. This strategy is effective when your offering is of superior quality or has unique features that justify a higher price point.

    Price Matching

    Price matching—also known as parity pricing—is a defensive pricing tactic.

    By consistently matching your competitors’ prices, you can retain customers who might otherwise, be tempted to switch to an alternative.

    This approach signals your customers that they don’t need to look elsewhere for what they need and can feel comfortable remaining loyal to your brand.

    Penetration Pricing

    Penetration pricing is when you set a low price for a new product to gain market share quickly. The opposite of price skimming, this strategy can be particularly effective in price-sensitive or highly competitive industries.

    By attracting customers early, you can also deter some competitors from entering the market. This bold move can establish your product as a market leader from the get-go.

    Loss Leader Pricing

    Loss leader pricing is a strategic sacrifice that can lead to greater gains in the long run.

    By offering a product at a low price—sometimes even below cost—you can attract new customers to your brand and strengthen your current customers’ loyalty.

    Eventually, you can cross-sell other higher-margin products to your loyal customer base to cover the loss from your loss leader pricing and increase sales of other more profitable products.

    Key Advantages of Competitive Pricing

    Although it’s not the only pricing strategy available, competitive pricing has some significant advantages.

    It is Responsive

    Agility is synonymous with profit in industries where consumer preferences and market conditions shift rapidly.

    Competitive pricing allows you to adapt quickly—if a competitor lowers their prices, you can respond promptly to maintain your positioning.

    It is Simple to Execute and Manage

    Competitive pricing is straightforward, unlike cost-based pricing, which requires complex calculations and spans various factors and facets.

    By closely monitoring competitors’ prices and adjusting your prices accordingly, you can implement this pricing strategy with relative ease and speed.

    It Can Be Combined with Other Pricing Strategies

    Competitive pricing is not a standalone strategy—it’s a versatile approach that can easily be combined with other pricing strategies. For example, say you want to use competitive pricing without losing money on a product. In this case, you could use cost-plus pricing to determine a base price that you won’t go below, then use competitive pricing as long as the price stays above your base price.

    Key Disadvantages of Competitive Pricing

    While competition-based pricing has its advantages, it’s not without its pitfalls. Here are some potential disadvantages of competitive pricing.

    It De-emphasizes Consumer Demand

    If you focus solely on what competitors are charging, you could overlook consumer demand.

    For example, you could underprice items that consumers could be willing to purchase for more. Or, you might overprice items that consumers perceive as low-value, which can reduce sales.

    You Risk Price Wars

    If you and your competition undercut each other for customer acquisition and loyalty, you will eventually erode profit margins and harm the industry’s overall profitability. It’s a slippery slope where everyone loses in the end.

    There’s Potential for Complacency

    When you base your prices on beating those of competitors, you might neglect to differentiate your offerings through innovation and product improvements. Over time, this can weaken your brand’s position and lead to a loss of market share. Staying competitive means more than just matching prices—it means continuously evolving and adding value for the consumer.

    4 Tips for a Successful Competitive Pricing Strategy in Retail

    Here are four competition-based pricing tips for retailers:

    Retailer Tip #1. Know Where to Position Your Products in the Market

    For competitive pricing to work, you must understand your optimal product positioning in the overall market. To gain this understanding, you must regularly compare your offerings and prices with those of your key competitors, especially for high-demand products.

    Then, you can decide which competition-based pricing model is suitable for you.

    Retailer Tip #2. Price Dynamically

    Dynamic pricing is a tactic with which you automatically adjust prices on your chosen variables, such as market conditions, competitor actions, or consumer demand.

    When it comes to competitive pricing, a dynamic pricing system can track your competitors’ price changes and update yours in lockstep.

    Price-monitoring tools like DataWeave allow you to stay ahead of the game with seasonal and historical pricing trend data.

    Retailer Tip #3. Combine Competitive Pricing with Other Pricing Strategies

    Competitive pricing can be powerful, but it doesn’t have to stand alone. You can enhance its benefits with complementary marketing tactics.

    To illustrate, you can bundle products to offer greater value than what your competitors are offering. You can also leverage loyalty programs to offer exclusive discounts or rewards so customers keep returning, even when your competitors offer them lower prices.

    Retailer Tip #4. Stay in Tune with Consumer Demand

    Competition-based pricing aligns you with your competitor, but don’t lose sight of what your customers want. Routinely test your pricing strategy against consumer behavior to ensure that your prices reflect the actual value of your offerings.

    5 Tips for a Successful Competitive Pricing Strategy for Consumer Brands

    If you’re thinking about how to create a competitive pricing strategy for your brand, consider these five tips:

    Brand Tip #1. Identify Competing Products for Accurate Comparisons

    The first step in competitive pricing is to know the value of what you’re selling and how it compares to that of your competitors’ products. This extends to private-label products, similar but not identical products, and use-case products.

    Product matching ensures your pricing decisions are based on accurate like-for-like comparisons, allowing you to compete effectively.

    Brand Tip #2. Understand Your Product’s Relative Value

    Knowing how your product competes on value is key to setting the right price. If your product offers higher value, price it higher; if it offers less, price it accordingly. This ensures your pricing strategy reflects your product’s market placement.

    Brand Tip #3. Consider Brand Perception

    Even if your product is virtually the same as a competitor’s, your brand’s perceived value may be different, which plays a crucial role in pricing.

    If your brand is perceived as premium, you can justify higher prices. Conversely, if customers perceive you as a value brand, your pricing should reflect affordability.

    Brand Tip #4. Leverage Value-Based Differentiation

    When your prices are similar to competitors’, you must differentiate your products by expressing your product value through branding, packaging, quality, or something else entirely.

    This differentiation will compel consumers to choose your product over other similarly priced options.

    Brand Tip #5. Stay Vigilant with Price Monitoring

    Your competitors will update their pricing repeatedly, and you will, too.

    It can be difficult and time-consuming to monitor your competitive pricing, so you’ll need a system like DataWeave to monitor competitors’ pricing and manage dynamic pricing changes.

    This vigilance ensures your brand remains competitive and relevant in real time.

    4 Essential Capabilities You Need to Implement Successful Competition-Based Pricing

    You’ll need four key capabilities to implement a competitive pricing strategy effectively.

    AI-Driven Product Matching

    Product matching means you’ll compare many products (sometimes tens or hundreds) with varying details across multiple platforms. Accurate product matching at that scale requires AI.

    For instance, AI can identify similar smartphones to yours by analyzing features like screen size and processor type. DataWeave’s AI product matches start with 80–90% matching accuracy, and then human oversight can fine-tune the data for near-perfect matches.

    You can make informed pricing decisions once you know which competing products to base your prices on.

    Accurate and Comprehensive Data

    A successful competition-based pricing strategy depends on high-quality, comprehensive product and pricing data from many retailers and eCommerce marketplaces.

    By tracking prices on large online platforms and niche eCommerce sites across certain regions, you’ll gain a more comprehensive market view, which enables you to make quick and confident price changes.

    Normalized Measurement Units

    Accurate price comparisons are dependent on normalized unit measurements.

    For example, comparing laundry detergent sold in liters to laundry detergent sold in ounces requires converting either or both products to a common base like price-per-liter or price-per-ounce.

    This normalization ensures accurate pricing analysis.

    Timely Actionable Insights

    Timely and actionable pricing insights empower you to make informed pricing decisions.

    With top-tier competitive pricing intelligence systems, you get customized alerts, intuitive dashboards, and detailed reports to help your team quickly act on insights.

    In Conclusion

    Competitive pricing or competition-based pricing is a powerful strategy for businesses navigating crowded markets, but you must balance competitive pricing with your brand’s unique value proposition.

    Competitive pricing should complement innovation and customer-centric strategies, not replace them. To learn more, talk to us today!

  • DataWeave’s AI Evolution: Delivering Greater Value Faster in the Age of AI and LLMs

    DataWeave’s AI Evolution: Delivering Greater Value Faster in the Age of AI and LLMs

    In retail, competition is fierce, and in its ever-evolving landscape, consumer expectations are higher than ever.

    For years, our AI-driven solutions have been the foundation that empowers businesses to sharpen their competitive pricing and optimize digital shelf performance. But in today’s world, evolution is constant—so is innovation. We now find ourselves at the frontier of a new era in AI. With the dawn of Generative AI and the rise of Large Language Models (LLMs), the possibilities for eCommerce companies are expanding at an unprecedented pace.

    These technologies aren’t just a step forward; they’re a leap—propelling our capabilities to new heights. The insights are deeper, the recommendations more precise, and the competitive and market intelligence we provide is sharper than ever. This synergy between our legacy of AI expertise and the advancements of today positions DataWeave to deliver even greater value, thus helping businesses thrive in a fast-paced, data-driven world.

    This article marks the beginning of a series where we will take you through these transformative AI capabilities, each designed to give retailers and brands a competitive edge.

    In this first piece, we’ll offer a snapshot of how DataWeave aggregates and analyzes billions of publicly available data points to help businesses stay agile, informed, and ahead of the curve. These fall into four broad categories:

    • Product Matching
    • Attribute Tagging
    • Content Analysis
    • Promo Banner Analysis
    • Other Specialized Use Cases

    Product Matching

    Dynamic pricing is an indispensable tool for eCommerce stores to remain competitive. A blessing—and a curse—of online shopping is that users can compare prices of similar products in a few clicks, with most shoppers gravitating toward the lowest price. Consequently, retailers can lose sales over minor discrepancies of $1–2 or even less.

    All major eCommerce platforms compare product prices—especially their top selling products—across competing players and adjust prices to match or undercut competitors. A typical product undergoes 20.4 price changes annually, or roughly once every 18 days. Amazon takes it to the extreme, changing prices approximately every 10 minutes. It helps them maintain a healthy price perception among their consumers.

    However, accurate product matching at scale is a prerequisite for the above, and that poses significant challenges. There is no standardized approach to product cataloging, so even identical products bear different product titles, descriptions, and attributes. Information is often incomplete, noisy, or ambiguous. Image data contains even more variability—the same product can be styled using different backgrounds, lighting, orientations, and quality; images can have multiple overlapping objects of interest or extraneous objects, and at times the images and the text on a single page might belong to completely different products!

    DataWeave leverages advanced technologies, including computer vision, natural language processing (NLP), and deep learning, to achieve highly accurate product matching. Our pricing intelligence solution accurately matches products across hundreds of websites and automatically tracks competitor pricing data.

    Here’s how it works:

    Text Preprocessing

    It identifies relevant text features essential for accurate comparison.

    • Metadata Parsing: Extracts product titles, descriptions, attributes (e.g., color, size), and other structured data elements from Product Description Pages (PDP) that can help in accurately identifying and classifying products.
    • Attribute-Value Normalization: Normalize attributes names (e.g. RAM vs Memory) and their values (e.g., 16 giga bytes vs 16 gigs vs 16 GB); brand names (e.g., Benetton vs UCB vs United Colors of Benetton); mapping category hierarchies a standard taxonomy.
    • Noise Removal: Removes stop words and other elements with no descriptive value; this focuses keyword extraction on meaningful terms that contribute to product identification.

    Image Preprocessing

    Image processing algorithms use feature extraction to define visual attributes. For example, when comparing images of a red T-shirt, the algorithm might extract features such as “crew neck,” “red,” or “striped.”

    Image Preprocessing using advanced AI and other tech for product matching in retail analytics.

    Image hashing techniques create a unique representation (or “hash”) of an image, allowing for efficient comparison and matching of product images. This process transforms an image into a concise string or sequence of numbers that captures its essential features even if the image has been resized, rotated, or edited.

    Before we perform these activities there is a need to preprocess images to prepare them for downstream operations. These include object detection to identify objects of interest, background removal, face/skin detection and removal, pose estimation and correction, and so forth.

    Embeddings

    We have built a hybrid or a multimodal product-matching engine that uses image features, text features, and domain heuristics. For every product we process we create and store multiple text and image embeddings in a vector database. These include a combination of basic feature vectors (e.g. tf-idf based, colour histograms, share vectors) to more advanced deep learning algorithms-based embeddings (e.g., BERT, CLIP) to the latest LLM-based embeddings.

    Classification

    Classification algorithms enhance product attribute tagging by designating match types. For example, the product might be identified as an “exact match”, “variant”, “similar”, or “substitute.” The algorithm can also identify identical product combinations or “baskets” of items typically purchased together.

    What is the Business Impact of Product Matching?

    • Pricing Intelligence: Businesses can strategically adjust pricing to remain competitive while maintaining profitability. High-accuracy price comparisons help businesses analyze their competitive price position, identify opportunities to improve pricing, and reclaim market share from competitors.
    • Similarity-Based Matching: Products are matched based on a range of similarity features, such as product type, color, price range, specific features, etc., leading to more accurate matches.
    • Counterfeit Detection: Businesses can identify counterfeit or unauthorized versions of branded products by comparing them against authentic product listings. This helps safeguard brand identity and enables brands to take legal action against counterfeiters.

    Attribute Tagging

    Attribute tagging involves assigning standardized tags for product attributes, such as brand, model, size, color, or material. These naming conventions form the basis for accurate product matching. Tagging detailed attributes, such as specifications, features, and dimensions, helps match products that meet similar criteria. For example, tags like “collar” or “pockets” for apparel ensure high-fidelity product matches for hard-to-distinguish items with minor stylistic variations.

    Attributes that are tagged when images are matched for retail ecommerce analytcis.

    Including tags for synonyms, variants, and long-tail keywords (e.g., “denim” and “jeans”) improves the matching process by recognizing different terms used for similar products. Metadata tags categorize similar items according to SKU numbers, manufacturer details, and other identifiers.

    Altogether, these capabilities provide high-quality product matches and valuable metadata for retailers to classify their products and compare their product assortment to competitors.

    User-Generated Content (UGC) Analysis

    Customer reviews and ratings are rich sources of information, enabling brands to gauge consumer sentiment and identify shortcomings regarding product quality or service delivery. However, while informative, reviews constitute unstructured “noisy” data that is actionable only if parsed correctly.

    Here’s where DataWeave’s UGC analysis capability steps in.

    • Feature Extractor: Automatically pulls specific product attributes mentioned in the review (e.g., “battery life,” “design” and “comfort”)
    • Feature Opinion Pair: Pairs each product attribute with a corresponding sentiment from the review (e.g., “battery life” is “excellent,” “design” is “modern,” and “comfort” is “poor”)
    • Calculate Sentiment: Calculates an overall sentiment score for each product attribute
    The user generated content analysis framework used by DataWeave to calculate sentiment.

    The final output combines the information extracted from each of these features, which looks something like this:

    • Battery life is excellent
    • Design is modern
    • Not satisfied with the comfort

    The algorithm also recognizes spammy reviews and distinguishes subjective reviews (i.e., those fueled by emotion) from objective ones.

    DataWeave's image processing tool also analyses promo banners.

    Promo Banner Analysis

    Our image processing tool can interpret promotional banners and extract information regarding product highlights, discounts, and special offers. This provides insights into pricing strategies and promotional tactics used by other online stores.

    For example, if a competitor offers a 20% discount on a popular product, you can match or exceed this discount to attract more customers.

    The banner reader identifies successful promotional trends and patterns from competitors, such as the timing of discounts, frequently promoted product categories or brands, and the duration of sales events. Ecommerce stores can use this information to optimize their promotion strategies, ensuring they launch compelling and timely offers.

    Other Specialized Use Cases

    While these generalized AI tools are highly useful in various industries, we’ve created other category—and attribute-specific capabilities for specialty goods (e.g., those requiring certifications or approval by federal agencies) and food items. These use cases help our customers adhere to compliance requirements.

    Certification Mark Detector

    This detector lets retailers match items based on official certification marks. These marks represent compliance with industry standards, safety regulations, and quality benchmarks.

    Example:

    • USDA Organic: Certification for organic food production and handling
    • ISO 9001: Quality Management System Certification

    By detecting these certification marks, the system can accurately match products with their certified counterparts. By identifying which competitor products are certified, retailers can identify products that may benefit from certification.

    Image analysis based product matching at DataWeave also detects certificate marks.

    Nutrition Fact Table Reader

    Product attributes alone are insufficient for comparing food items. Differences in nutrition content can influence product category (e.g., “health food” versus regular food items), price point, and consumer choice. DataWeave’s nutrition fact table reader scans nutrition information on packaging, capturing details such as calorie count, macronutrient distribution (proteins, fats, carbohydrates), vitamins, and minerals.

    The solution ensures items with similar nutritional profiles are correctly identified and grouped based on specific dietary requirements or preferences. This helps with price comparisons and enables eCommerce stores to maintain a reliable database of product information and build trust among health-conscious consumers.

    Image processing for product matching also extracts nutrition table data at DataWeave.

    Building Next-Generation Competitive and Market Intelligence

    Moving forward, breakthroughs in generative AI and LLMs have fueled substantial innovation, which has enabled us to introduce powerful new capabilities for our customers.

    How Gen AI and LLMs are used by DataWeave to glean insights for analytics

    These include:

    • Building Enhanced Products, Solutions, and Capabilities: Generative AI and LLMs can significantly elevate the performance of existing solutions by improving the accuracy, relevance, and depth of insights. By leveraging these advanced AI technologies, DataWeave can enhance its product offerings, such as pricing intelligence, product matching, and sentiment analysis. These tools will become more intuitive, allowing for real-time updates and deeper contextual understanding. Additionally, AI can help create entirely new solutions tailored to specific use cases, such as automating competitive analysis or identifying emerging market trends. This positions DataWeave to remain at the forefront of innovation, offering cutting-edge solutions that meet the evolving needs of retailers and brands.
    • Reducing Turnaround Time (TAT) to Go-to-Market Faster: Generative AI and LLMs streamline data processing and analysis workflows, enabling faster decision-making. By automating tasks like data aggregation, sentiment analysis, and report generation, AI dramatically reduces the time required to derive actionable insights. This efficiency means that businesses can respond to market changes more swiftly, adjusting pricing or promotional strategies in near real-time. Faster insights translate into reduced turnaround times for product development, testing, and launch cycles, allowing DataWeave to bring new solutions to market quickly and give clients a competitive advantage.
    • Improving Data Quality to Achieve Higher Performance Metrics: AI-driven technologies are exceptionally skilled at cleaning, organizing, and structuring large datasets. Generative AI and LLMs can refine the data input process, reducing errors and ensuring more accurate, high-quality data across all touchpoints. Improved data quality enhances the precision of insights drawn from it, leading to higher performance metrics like better product matching, more accurate price comparisons, and more effective consumer sentiment analysis. With higher-quality data, businesses can make smarter, more informed decisions, resulting in improved revenue, market share, and customer satisfaction.
    • Augmenting Human Bandwidth with AI to Enhance Productivity: Generative AI and LLMs serve as powerful tools that augment human capabilities by automating routine, time-consuming tasks such as data entry, classification, and preliminary analysis. This allows human teams to focus on more strategic, high-value activities like interpreting insights, building relationships with clients, and developing new business strategies. By offloading these repetitive tasks to AI, human productivity is significantly enhanced. Employees can achieve more in less time, increasing overall efficiency and enabling teams to scale their operations without needing a proportional increase in human resources.

    In our ongoing series, we will dive deep into each of these capabilities, exploring how DataWeave leverages cutting-edge AI technologies like Generative AI and LLMs to solve complex challenges for retailers and brands.

    In the meantime, talk to us to learn more!

  • Less is More in Holiday Pricing: The Case for a Simple, Stable Approach This Holiday Season

    Less is More in Holiday Pricing: The Case for a Simple, Stable Approach This Holiday Season

    With pricing making headlines more frequently than ever, now is the perfect moment for retailers to take a step back and rethink their holiday strategy. The heightened focus on pricing—driven by economic uncertainties, inflationary pressures, and fluctuating supply chain dynamics—presents a unique opportunity for retailers to not only meet customer expectations but to exceed them by rebuilding trust. In today’s climate, where consumer confidence is often fragile, the perception of fair pricing can be a significant differentiator. This is especially true during the holiday season when shoppers are more budget-conscious, and every dollar counts.

    Rather than focusing on the price of individual items, consumers are increasingly concerned with the total amount they spend at checkout. This means the overall basket cost, rather than the price tag on a single product, holds greater sway in determining whether a customer feels they’ve gotten a good deal. Retailers who can maintain steady, predictable basket pricing—despite external pressures such as supply chain disruptions or increased competition—will stand out as reliable and customer-centric.

    Pricing Strategy for the Holiday Season

    Your pricing strategy sets the tone for fostering and maintaining customer trust during the busy holiday season. From establishing initial prices to managing markdowns, having a stable, well-thought-out plan is crucial to balancing profitability with customer satisfaction. Below are several guiding principles to help you navigate this critical time frame:

    holiday-pricing-considerations

    Anchor Your Prices Early on Key Holiday Items

    Identify the products that are likely to drive traffic and sales during the holiday period and set your prices strategically early on. Use these prices as a ceiling that you won’t exceed, allowing customers to trust that they’re getting consistent value. By establishing this anchor price, you create a sense of stability in an otherwise fluctuating market, helping your customers feel confident that they won’t face price hikes on essential holiday items.

    Prepare for Competitive Moves

    The holiday season is notorious for aggressive pricing tactics by competitors, so you’ll need to remain agile. Be prepared to make strategic price reductions when necessary, but ensure you stay below your anchored price to avoid eroding trust. Monitoring competitors closely and adjusting your strategy without undermining your overall value proposition will be key to maintaining a competitive edge.

    To accomplish this, having reliable and timely competitor pricing data is essential. A sophisticated pricing intelligence platform like DataWeave’s can get the job done, which is equipped to handle the scale and speed demanded during the fast-paced holiday season.

    Collaborate with Vendors on Promotions

    Strong vendor relationships are crucial during the holiday season. By working closely with your suppliers, you can develop compelling promotions that not only attract customers but also ensure you have adequate inventory levels to support any reduced pricing strategies. Vendors may offer additional incentives or discounts during this period, and leveraging those to provide deeper savings can help retailers pass along better deals to customers without sacrificing margin.

    Pre-Holiday Markdowns

    Pre-holiday markdowns are an essential tool to clear out older inventory and make room for newer, more seasonal items. Get ahead of these markdowns by tracking trends and data from previous years. This will allow you to anticipate demand and address any overstocking issues early, ensuring that your shelves are stocked with the right products at the right time.

    Post-Holiday Markdowns

    Once the holiday rush subsides, differentiating between products is crucial. For “In and Out” items, which are seasonal or limited-time offers, your goal should be to clear through inventory as quickly as possible to free up valuable shelf space for upcoming product cycles. For products that are part of your regular planogram, the focus should shift to adjusting inventory levels back down to non-holiday norms, ensuring you’re not left with excess stock that could tie up cash flow in the slower months ahead.

    Manage Markdowns at the Store/Item Level

    Not all stores or products will move at the same pace, so it’s essential to manage markdowns on a granular level. Each store has different inventory turnover rates, and customer demand may vary from one location to another. Tailoring markdown strategies to the specific needs of individual stores and products allows for greater flexibility and ensures you’re maximizing sell-through while minimizing excess stock.

    Managing the Rest of the Assortment

    While holiday-specific items will undoubtedly capture much of the attention from customers due to the increased volume and seasonal demand, it’s essential to remember that the rest of the customer’s basket—comprised of non-holiday items—plays a pivotal role in their overall shopping experience. Retailers often focus heavily on optimizing prices for holiday items, but maintaining a consistent and customer-friendly pricing strategy across the entire assortment is equally important. Neglecting non-holiday items can erode trust and diminish the effectiveness of the overall holiday pricing strategy. Customers shop holistically, and their perception of your brand is shaped by the totality of their shopping experience, not just individual product categories.

    While holiday promotions may attract traffic, it’s the consistency and transparency of your broader pricing strategy that will strengthen trust and encourage repeat business. After all, the holiday season is not just about winning a single transaction—it’s about building relationships that extend well into the new year.

    A few critical factors to consider for your non-holiday assortment:

    Minimize Price Increases Unless Absolutely Necessary

    The holiday season is a delicate time when customers are highly sensitive to pricing. Sudden, unexpected price hikes, especially on everyday, non-seasonal items, can quickly erode the trust you’ve worked hard to build. Customers may forgive small fluctuations, but if they perceive a retailer is taking advantage of holiday demand or inflationary pressures to unnecessarily raise prices, that goodwill can evaporate. By maintaining steady pricing, you reinforce the idea that your brand prioritizes fairness over opportunism, especially in a period marked by heightened scrutiny around pricing practices.

    Evaluate Price Gaps Between Product Tiers

    A key element of pricing strategy that retailers should focus on is maintaining appropriate price gaps between product tiers, such as private label and national brands. Ensuring that the price difference between these tiers remains clear and consistent helps reinforce a value proposition for both types of customers: those who seek premium national brands and those who are value-oriented and gravitate toward private label options. If the price gap becomes too narrow, customers may be confused about the differentiation between products, leading to dissatisfaction or lost sales.

    Ensure Accurate Value Sizing

    One of the most effective ways to gain customer trust is through clear, transparent pricing, particularly when it comes to value sizing. Misleading unit pricing, whether intentional or accidental, can quickly frustrate customers, making them feel that they are being deceived. Ensure that unit pricing is visible, logical, and consistent across all product categories, allowing customers to make informed choices without feeling overwhelmed or misled. By offering transparency in this area, you can foster a sense of fairness and accountability, further building your reputation as a customer-first retailer.

    Maintain Price Links Across Your Assortments

    Consistency in pricing across various categories and product lines is crucial to managing customer expectations. Pricing disparities between similar products or across different stores in your chain can create confusion and frustration, leading to negative perceptions of your brand. Customers expect a seamless shopping experience, and this includes consistency in pricing, no matter what they buy or where they buy it. Establishing and maintaining price links within your assortment will ensure that your broader pricing strategy remains aligned with customer expectations, reinforcing reliability.

    Trust is Your Greatest Currency

    In a retail environment where customers are constantly bombarded with news about inflation, price hikes, and economic instability, trust is your greatest asset. Negative perceptions surrounding pricing, whether it’s from the media or personal experiences, can make customers wary and hesitant. By committing to a stable, transparent, and fair pricing strategy—not just for holiday items but across the entire assortment—you can differentiate yourself in the market and foster long-term loyalty. Stability and consistency in your pricing model allow customers to feel confident that they’re getting good value every time they shop with you, regardless of external economic pressures.

    It’s important to prioritize the customer relationship above all else, even if that means sacrificing some immediate short-term gains. Retailers who opt for quick wins through aggressive price changes may see a temporary boost in profits but risk damaging long-term customer loyalty. On the other hand, by focusing on providing a consistent and fair experience, you position your brand as a reliable choice, one that customers will return to not just during the holidays but throughout the entire year.

    In a season where every retailer is vying for the same holiday dollar, your approach to pricing must stand out by emphasizing trust, loyalty, and customer satisfaction. Pricing transparency and fairness are key differentiators, especially in an environment where many retailers will be tempted to capitalize on increased demand by raising prices or reducing promotions. Instead, leading with trust and focusing on stability will allow you to rise above the noise and deliver a superior customer experience.

    In Summary: Stability Wins

    This holiday season, the winning strategy isn’t about pushing for the highest possible margins or taking advantage of seasonal demand spikes. It’s about the bigger picture—building lasting customer relationships that extend well beyond the holidays. By prioritizing consistency in your pricing, maintaining transparency across your assortment, and leading with trust, you’ll not only achieve success during the holiday period but also set the stage for long-term customer loyalty.

    In short, stability wins. Prioritize the customer experience, remain consistent in your approach, and lead with trust. Doing so will ensure that your customers not only choose you during the holidays but continue to choose you long into the future.

    To learn more, reach out and chat with us today!

  • Back-to-School 2024 Pricing Strategies: What Retailers and Brands Need to Know

    Back-to-School 2024 Pricing Strategies: What Retailers and Brands Need to Know

    As summer winds down, families across the US have been gearing up for the annual back-to-school shopping season. The back-to-school season has always been a significant event in the retail calendar, but its importance has grown in recent years. With inflation still impacting many households, parents and guardians are more discerning than ever about their purchases, seeking the best value for their money.

    The National Retail Federation has forecasted that this season could see one of the highest levels of spending in recent years, reaching up to $86.6 billion. As shoppers eagerly stock up on back-to-school and back-to-college essentials, it’s crucial for retailers and brands to refine their pricing strategies in order to capture a larger share of the market.

    To understand how retailers are responding to the back-to-school rush this season, our proprietary analysis delves into pricing trends, discount strategies, and brand visibility across major US retailers, including Amazon, Walmart, Kroger, and Target. By examining 1000 exactly matching products in popular back-to-school categories, our analysis provides valuable insights into the pricing strategies adopted by leading retailers and brands this year.

    Price Changes: A Tale of Moderation

    The most notable trend in our analysis is the much smaller annual price increases this year, in contrast to last year’s sharp price hikes. This shift is a reaction to growing consumer frustration about rising prices. After enduring persistent inflation and steep price growth, which peaked last year, consumers have become increasingly frustrated. As a result, retailers have had to scale back and implement more moderate price increases this year.

    Average Price Increases Across Retailers: Back-to-School 2022-24

    Kroger led the pack with the highest price increases, showing a 5.3% increase this year, which follows a staggering 19.9% rise last year. Walmart’s dramatic price increase of 14.9% is now followed by a muted 3.1% hike. Amazon and Target demonstrated a similar pattern of slowing price hikes, with increases of 2.3% and 2.7% respectively in the latest period. This trend indicates that retailers are still adjusting to increased costs but are also mindful of maintaining customer loyalty in a competitive market.

    Average Price Increases Across Categories 2022-24: Back-to-School USA

    When examining specific product categories, we observe diverse pricing trends. Electronics and apparel saw the largest price increases between 2022 and 2023, likely due to supply chain disruptions and volatile demand. However, the pace of these increases slowed in 2024, indicating a gradual return to more stable market conditions. Notably, backpacks remain an outlier, with prices continuing to rise sharply by 22%.

    Interestingly, some categories, such as office organization and planners, experienced a price decline in 2024. This could signal an oversupply or shifting consumer preferences, presenting potential opportunities for both retailers and shoppers.

    Brand Visibility: The Search for Prominence

    In the digital age, a brand’s visibility in online searches can significantly impact its success during the back-to-school season. Our analysis of the share of search across major retailers provides valuable insights into brand prominence and marketing effectiveness.

    Share of Search of Leading Brands Across Retailers During Back-to-School USA 2024

    Sharpie and Crayola emerged as the strongest performers overall, with particularly high visibility on Target. This suggests strong consumer recognition and demand for these traditional school supply brands. BIC showed strength on Amazon and Target but lagged on Kroger, while Pilot maintained a more balanced presence across most retailers.

    The variation in brand visibility across retailers also hints at potential partnerships or targeted marketing strategies. For instance, Sharpie’s notably high visibility on Target (5.16% share of search) could indicate a specific partnership.

    Talk to us to get more insights on the most prominent brands broken down by specific product categories.

    Navigating the 2024 Back-to-School Landscape

    As we look ahead to the 2024 back-to-school shopping season, several key takeaways emerge for retailers and brands:

    1. Price sensitivity remains high, but the rate of increase is moderating. Retailers should carefully balance the need to cover costs with maintaining competitive pricing.
    2. Strategic discounting can be a powerful tool, especially for lesser-known brands looking to gain market share. However, established brands would need to rely more on quality, visibility, and brand loyalty.
    3. Online visibility is crucial. Brands should invest in strong SEO and retail media strategies, tailored to different retail platforms.
    4. Category-specific strategies are essential. What works for backpacks may not work for writing instruments, so a nuanced approach is key.
    5. Retailers and brands should be prepared for potential shifts in consumer behavior, such as increased demand for value-priced items or changes in category preferences.

    By staying attuned to these trends and remaining flexible in their strategies, businesses can position themselves for success in the competitive back-to-school retail landscape of 2024. As always, the key lies in understanding and responding to consumer needs while maintaining a keen eye on market dynamics.

    Stay tuned to our blog to know more about how retailers can stay aware of changing pricing trends. Reach out to us today to learn more.

  • The Essential Price Management Framework for Retailers

    The Essential Price Management Framework for Retailers

    As a leader with over 20 years of experience leading pricing strategy at a major US grocery chain, I deeply understand the complexities pricing teams face when trying to derive, quantify, and execute corporate pricing initiatives.

    Providing insights into the competitive marketplace in order to ensure the overall success of directed pricing strategies is more than simple reporting.

    That’s what many teams get wrong.

    Reporting is a post-mortem, which is a valuable exercise, but not one that will help you achieve your pricing goals all by itself. After all, your pricing goals can change due to a number of reasons: macroeconomic challenges, regional competition, corporate objectives, along with several other factors.

    Pricing teams need a well-defined process to devise and implement their pricing strategies. This process needs to holistically examine your product base to provide robust price management. It also needs to be backed up by technology powered by the latest advancements because you can be sure your competition is already thinking that way.

    Let’s break down an effective and modern price management process for retailers.

    Data Collection

    The first aspect of any effective price management framework for retailers is a clearly defined product data collection. You need to understand your collection in terms of who to collect pricing data from, what data to collect, where to collect it from, and how often.

    • The who: Consists of both primary competition and others you’d like to keep tabs on
    • The what: Can range from targeted single items like Key Value Items (KVIs) or total portfolio
    • Where: Can range from targeted locations within your market or the total competitive network
    • How often: To be able to support your price management process and for reporting purposes, determining a cadence is essential.

    Data is power and the more data you can acquire, the more insights you’ll gain. Make sure that your collection data is well thought out ahead of time. Leaning on a price management framework built for retailers that can aggregate all your data into representative prices can help.

    For example, if you have multiple competitive stores in a single market, flattening pricing data into a defined representative price will help speed up your analysis. Don’t get confined to a single store when a comprehensive assortment view across your target markets will provide a more accurate understanding.

    Data Refinement

    Competitive Matched Items

    Next, you need to examine your competitive-matched items. These are the products that you want to be priced in direct response to your competitors’ pricing. The goal is to remain closely aligned with their prices so as not to lose market share while simultaneously achieving your corporate strategies.

    Your price management system needs to help you manage your overlapping items. Trying to do so manually will be inefficient and is almost impossible to execute across 100% of your product catalog. 

    The mapping needs to go beyond exact UPC / PLU matches to encompass other match criteria. It needs to be able to incorporate any number of derivatives, including competitor-specific item codes like Amazon’s ASINs or Target’s DPCIs. This will help you overcome the challenge of mapping exact items to a competitor when the competitor’s site doesn’t showcase a UPC. It will also help you map your own private-label items to your competitor’s private-label counterparts.

    A good price management framework will also help you match the same items but with dissimilar sizes (e.g., Cheerios 18 OZ vs. Cheerios 20 OZ), either by letting you match directly within acceptable tolerances or by enabling you to compare prices on a per-unit basis. 

    We need to leverage GenAI to help facilitate matches beyond UPC / PLU exact matches, such as Exact Item with no Competitor Code, Exact Item with Competitive Specific Codes, Similarity Matching on Private Label, Similarity Matching on Size all need to leverage it.

    If you’re playing in a vertical that doesn’t always have a unifying code (restaurants, apparel, etc.) you’ll need to leverage the latest GenAI tools to map items together for price management. The variables are simply too numerous and complex to do manually.

    Unmatched Items and Internal Portfolio

    Not every product will be included in your competitive-matched items collection. Competitive matches in your internal portfolio offer a proxy for building clear and concise price management strategies that are in line with your corporate initiatives.

    However, your unmatched items still need to be factored into your price strategy. If you only manage your competitively priced items, you won’t have a holistic viewpoint of your total product catalog and pricing. It’s critical to ensure that internal portfolio items are effectively mapped and grouped in order to extend overall price management.

    Here are three things you need to consider when managing the pricing of your internal product portfolio. A smart price management framework is your best bet for achieving these results:

    • Value Size Groupings
      Value size groupings allow for the same branded items of different sizes to be priced accordingly to ensure price parity. You don’t want to sell a private label gallon of milk for $4.00 while the half gallon is at $1.75, for example. You need certain mechanisms in place to alert you when price parity is off. This is especially true when some of your items are competitively matched, and others are not.
    • Relationships between Brands
      Relationships between brands are also critical to ensure price parity. There should be well-defined relationships between like-sized products that are from different brands. This will ensure that your private label program is priced ‘at a value’ compared with their national branded counterparts. You need to maintain the balance between different private label tiers along with different national brand tiers.
    • Price Links
      Price Links are also critical to keeping up to date from a consumer perspective. Your customers expect that certain items should be priced together and will be put off if they are not. For example, if you sell an item in different sizes or flavors and scents, their prices should be logically linked.

    For your internal portfolio, there may be items that don’t have a competitive match or simply don’t fall into one of your internal portfolio groupings. These are unique items to your banner and should be considered margin drivers for your brand.

    Leveraging Data for Action

    Now that you have a complete line of sight into both competitively matched items and internal mappings, you can move to fully leveraging your data. Figuring out how to utilize these competitive insights to understand where your price positioning is compared with your competition can be a challenge without a playbook. An effective price management framework will help guide you to the best insights and help you understand how it relates to your corporate strategy.

    If you don’t have a well-defined corporate pricing strategy (competitive or margin) or you need to update it to be more modern, the data sets provided by a price management framework can help you ascertain where you are in your pricing journey. They can also help you identify options for where you want to go.

    Here are some other ways a price management framework can help you improve your pricing strategy:

    • Utilize Competitive Data
      Get competitive insights, identify competitive price zones, and understand your competitors’ pricing philosophy. Figure out if they’re using strategies like:
      • High-Low
      • Everyday Low Price (EDLP)
      • Cost Plus
    • Unravel Competitor Strategy
      See if you can unlock what your competition has planned for pricing strategy and promotions. Try relating what you see in corporate filings and tie back to what you see in your competitive data sets.
    • Assortment Analysis
      Try looking at the data not only from a pricing perspective but also from a competitive assortment, promotion, and supply chain perspective.
    • Proactive Alerts
      Establish alerts for your internal portfolio to ensure that you don’t exceed your tolerance based on price moves.

    Leveraging a Price Management Framework Designed for Retailers

    A price management system designed specifically for you as a retailer is a game changer. An effective one can be configured specifically for the price owners, whether you have a dedicated team for this function or the price is owned by the category management team.

    For category managers, standard reporting offers a clear view of pricing performance and trends. Beyond that, competitive intelligence becomes crucial—using data from various sources like collected pricing data, market filings, social media insights, etc. to provide the senior leadership team with a deeper understanding of competitor strategies and actions. This empowers informed decision-making at the highest levels.

    With these price management insights, retailers can gain a holistic view of the competitive marketplace, uncover gaps and opportunities, and scale their business more effectively. As someone with experience on the retailer’s side of the market, I know first-hand how valuable these insights can be.

    We’d love to talk with you if you’re interested in learning more about DataWeave’s AI-powered price intelligence solution for retailers. Click here to schedule an introductory conversation.

  • Do Amazon’s Competitors Lower Prices During Prime Day?

    Do Amazon’s Competitors Lower Prices During Prime Day?

    As the retail landscape continues to evolve, events like Amazon Prime Day have become more than just shopping extravaganzas—they’ve transformed into strategic battlegrounds where retailers assert their market positions and brand identities. Prime Day 2024 was no exception, serving as a crucial moment for retailers to showcase their pricing prowess, customer loyalty programs, and category expertise.

    In an era where consumer expectations for deals are at an all-time high, the impact of Prime Day extends far beyond Amazon’s ecosystem. Retailers like Walmart, known for its “everyday low prices,” Target with its emphasis on style and value, and Best Buy, the electronics specialist, have all adapted their strategies to compete. These companies didn’t just react to Prime Day; they proactively launched their own pre-emptive sales events, with Target Circle Week, Walmart July Deals and more, effectively extending the shopping bonanza and challenging Amazon’s dominance.

    For Prime Day, we analyzed over 47,000 SKUs across major retailers and product categories to publish insights on Amazon’s pricing strategies as well as the performance of leading consumer brands. Here, we go further to delve into the discounts offered (or not offered) by Amazon’s competitors during Prime Day. Our analysis reveals that some retailers chose to compete on price during the sale for certain categories, while others did not.

    Below, we highlight our findings for each product category. The Absolute Discount is the total discount offered by each retailer during Prime Day compared to the MSRP. These are the discounts consumers are familiar with, displayed on retail websites prominently during sale events. The Additional Discount, on the other hand, is the reduction in price during Prime Day compared to the week prior to the sale, revealing the level of price markdowns by the retailer specific to a sale event.

    Consumer Electronics

    In the Consumer Electronics category, Best Buy stood out as a strong competitor, offering an Additional Discount of 5.9%—the highest among all competitors analyzed. This is unsurprising, as Best Buy is well-known for its focus on consumer electronics and is likely aiming to reinforce its reputation for offering attractive deals in order to maintain its strong consumer perception in the category.

    Discounts offered on the Consumer Electronics category across retailers during Amazon Prime Day USA 2024

    Walmart was a close second with a 4.3% Additional Discount while Target reduced its prices by only 2% during the sale.

    Apparel

    In the Apparel category, Walmart’s Additional Discount was 3.1%, demonstrating its willingness to be priced competitively on a small portion of its assortment during the sale, without compromising much on margins.

    Discounts offered on the Apparel category across retailers during Amazon Prime Day USA 2024

    Target, on the other hand, opted out of competing with Amazon on price during the sale, choosing instead to maintain its Absolute Discount level of around 11%.

    Home & Furniture

    The Home & Furniture category showcased diverse strategies from retailers. Specialty furniture retailers such as Overstock and Home Depot provided Additional Discounts of 3.9% and 2.5%, respectively, compared to Amazon’s 6.9%. This indicates a clear intent to maintain market share and remain top-of-mind for consumers despite Amazon’s competitive pricing.

    Discounts offered on the Home & Furniture Category Across Retailers during Amazon Prime Day USA 2024

    Although Target didn’t significantly lower its prices during the sale, its Absolute Discount remains substantial at 18.9%. This suggests that Target’s markdowns were already steep before the event, which could explain the lack of further reductions during the sale.

    Health & Beauty

    The Health & Beauty category saw minimal participation from Amazon’s competitors, with the exception of Sephora, which reduced prices by 3.7% during Prime Day.

    Discounts offered on the Health & Beauty Category Across Retailers during Amazon Prime Day USA 2024

    Ulta Beauty chose not to adjust its prices, likely reflecting its strategy to uphold a premium brand image. Walmart, on the other hand, offered a modest Additional Discount of 2% on select items. Given Walmart’s generally affordable product range, its total discount remained relatively low, around 3.5%.

    In Conclusion

    During Prime Day, Walmart was the only major retailer that made an effort to compete, albeit modestly. Target, on the other hand, largely chose not to offer any additional markdowns. However, several category-specific retailers, such as Best Buy in Consumer Electronics, Overstock and Home Depot in Furniture, and Sephora in Health & Beauty, aimed to retain market share by providing notable discounts.

    What this means for consumers is that even on Amazon’s Prime Day, it’s not a bad idea to compshop to identify the best deal.

    For retailers, the key takeaway is the importance of quickly analyzing competitor pricing and making agile, data-driven decisions to improve both revenues and margins. By utilizing advanced pricing intelligence solutions like DataWeave, retailers can optimize their discount strategies, better navigate pricing complexities, and drive revenue growth — all while staying prepared for major shopping events and beyond.

    Reach out to us today to learn more!

  • A Guide to Digital Shelf Metrics for Consumer Brands

    A Guide to Digital Shelf Metrics for Consumer Brands

    Our world is increasingly going online. We work online, socialize online, and shop online every day. As a consumer brand, you need to ensure complete awareness of your brand’s online presence across eCommerce platforms, search engines, and media.

    Only by deeply understanding the customer journey can you ensure that your product is reaching your ideal customers and maximizing your brand’s market share. You need data to intrinsically understand your customer journey and make changes where you’re lacking.

    As the old adage goes: ‘You can’t manage what you don’t measure.’

    You need digital shelf metrics to measure and start benchmarking your buyer’s journey. To find several of these types of key performance indicators (KPIs), you need a digital shelf analytics solution. These platforms allow you to track various metrics along the path to purchase from the awareness stage to the post-purchase phase across the entire internet, helping to inform online and offline sales strategies.

    Digital shelf analytics will help you gain insights into how your brand is doing versus the competition, which areas are lagging behind in historical performance, and what activities are driving sales. There are innumerable ways in which you can leverage these valuable insights. But how do you know which KPIs to start tracking with your digital shelf analytics solution?

    Here, we’ve summarized the top metric types your peers report, track and base their decisions on.

    With these KPIs in hand, consumer brands like yours can ensure that their products are consistently visible and appealing to their target audience across online marketplaces, ultimately enhancing conversion rates, market share, and profitability.

    Read this guide to learn more about the top digital shelf metrics consumer brands are tracking and how to use them in your own strategy.

    1. Share of Search

    Share of Search (SoS) is a KPI in digital shelf analytics that measures how frequently a consumer brand’s products appear in search results on eCommerce platforms relative to the competition for specific keywords. A good digital shelf analytics solution will be able to show this metric across all the top marketplaces and retailers, such as Amazon and Walmart, but also more niche marketplaces for industry-specific selling.

    This metric provides brands with a quantifiable way to measure how frequently their products are being “served up” to customers on online marketplaces. Essentially, it measures visibility and discoverability.

    Share of Search exmple_Digital Shelf Metrics

    With Share of Search on DataWeave, you can slice and dice your data in innumerable ways. These are a few important views you can see:

    • Aggregated SoS
    • Organic and Sponsored SoS scores
    • SoS scores across brands, retailers, keywords, cities
    • Historical SoS score trends

    Once you have benchmarked your SoS and category presence relative to your competition, you need to start interpreting the data. Here are some questions you can ask yourself to help interpret your findings:

    Share of Search exmple_Digital Shelf Metrics
    • Which of my key categories have the lowest SoS score?
    • Which products feature low on search results because they are out of stock?
    • Are my competitors’ products faring better due to sponsored searches?
    • Is my SoS low due to poor content quality?

    With insights in hand, you will know which actions to take to drive the biggest impact. For example, you could increase sponsored search results or improve organic reach by optimizing product pages.

    Understanding your SoS is essential to maximizing the awareness phase of your customer journey. It will help you improve your brand visibility and increase product conversions through better search and category presence.

    2. Share of Media

    Share of Media (SoM) is a KPI that is just as impactful, if not more so, than the SoS metric. However, only a limited number of brands track it or use it to drive strategic action. This makes it a perfect opportunity for brands looking to get an edge on the competition.

    But what is SoM in digital shelf analytics? Essentially, it’s a way of measuring retail media advertising activities like brand-sponsored banners, listings, videos, ads, and promotions that sometimes blend into search results. The main types of retail media advertising exist in two categories: banner advertising and sponsored listings.

    Banner advertising involves strategically placing designed banners within websites and search listings. These banners raise brand awareness and drive traffic to online storefronts.

    Sponsored listings are paid placements within search results on search engines or eCommerce platforms. They are prioritized based on the total bid amount and the product’s relevance. These paid listings are marked with “sponsored” or “ad.”

    Sponsored listings on an Amazon webpage

    It’s important to run these types of advertising campaigns on eCommerce platforms to gain customer visibility. In fact, “some 57% of US consumers started their online shopping searches on Amazon as of Q2 2023.” If you aren’t showing up, paying for placement can help.

    These listings serve to enhance your brand’s overall visibility, help you gain more precise reach, increase conversions, and drive better brand awareness and recall with your customers.

    These efforts aren’t free, however, so measuring their effectiveness is critical not only to gain all the listed benefits but to also not waste your valuable marketing budget. The SoM KPI can help a consumer brand answer questions like:

    • Where are the opportunities to increase paid ads?
    • Which categories could benefit from a promotional boost or a strategic and streamlined allocation of ad spend?
    • Which of my competitors have active banners and what is their share of media by keyword?
    • How has my ad spend trended historically in comparison to my competitor?
    Analytics Dashboard on Dataweave

    DataWeave’s digital shelf analytics (DSA) is among the first providers to offer Share of Media KPI tracking and analysis. This is because it requires advanced, multi-modal AI to gather, view, and aggregate listings that encompass text, images, and video. With Share of Media tracking facilitated by DataWeave, consumer brands can track and analyze the effectiveness of their own promotional investments as well as those of their competitors.

    3. Content Quality

    The content quality metric measures how well your product content adheres to the retailer’s specific guidelines, which are in place to steer traffic and sales on their sites.

    With the help of a DSA platform’s AI and ML capabilities, you can measure different elements of your product detail pages (PDPs), such as titles, descriptions, images, videos, and even customer reviews. You need to know which elements are missing, where they are missing, and which ones are negatively affecting sales so you can take corrective action.

    Did you know that the average cart abandonment rate is 69.99%? The quality of your content can significantly impact this number. Ensuring that your content is high-quality will help influence product discoverability, customer engagement, and conversion rates. It will also help position you ahead of the competition. If your content quality is poor, you may find yourself with lower search rankings, a higher return rate, and more abandoned carts.

    Here are some questions you can answer with the help of the content quality digital shelf metric:

    • Is my product content at a retail site exactly what was syndicated?
    • Are there any retailer initiated changes to my product content?
    • Are my product content updates reflected on the retailer platforms?
    • How well does my product content comply with the retailer guidelines?
    • How do I optimize my product content for enhanced discoverability and conversion?

    DataWeave’s content quality digital shelf analysis helps consumer brands ensure that product content on eCommerce platforms is high-quality and benchmark their product listings against the competition. It does this through a combination of AI-driven quality analysis and by presenting brands with actionable recommendations. These optimized suggestions are based on the top-performing products so you can focus your valuable time on the areas that will drive the biggest impact.

    4. Pricing & Promotions

    Your customers can easily shop around to find the best price for the product you’re selling. If your competitor is selling it cheaper, you’ll lose that sale.

    That’s why it’s essential to understand the pricing and promotional landscape for each of your products and categories. This can be a challenge, especially if it’s a common product or comes in multiple pack sizes or variants.

    It’s equally important to track pricing and promotions even at individual, physical stores. Doing so will allow you to remain competitive and responsive to local market dynamics by tailoring your pricing strategies based on regional competition. You don’t want your products to be overpriced (lost sales) or underpriced (lost profit) in specific markets.

    Harmonizing insights when operating an omnichannel consumer brand is extremely difficult without the aid of a digital shelf analytics solution. Insights need to be aggregated between desktop sites, mobile sites, and mobile applications, as well as from physical storefronts.

    Questions you can answer with the help of the pricing & promotions digital shelf metric include:

    • How do my product prices and promotions compare to my competitors?
    • How consistent is my product pricing across retail websites?
    • How does my product pricing vary across regions, ZIPs, and stores?
    • How do price changes influence my sales numbers?
    • Are there regional differences in pricing and promotion effectiveness?

    DataWeave’s digital shelf analytics platform stands out with its sophisticated location-aware capabilities, which enable the aggregation and analysis of localized pricing and promotions. The platform defines locations based on a range of identifiers, such as latitudes and longitudes, regions, states, ZIP codes, or specific store numbers.

    The platform can also extract promotional information, such as credit card-based or volume-based promotions. You can see variances across retailers, split by price groups, brands, and competitors. DataWeave specializes in enabling brands to conduct in-depth analyses across a wide array of attributes so you can answer just about any pricing or promotional question you have.

    Digital shelf pricing insights via Dataweave

    5. Availability

    The availability KPI in digital shelf analytics measures the in-stock and availability rates for a brand’s products across eCommerce and physical locations. Similar to the pricing and promotions metric, it relies heavily on location awareness, down to individual stores. Measuring both online availability and offline in-stock rates will help you understand the big picture and take more informed replenishment action.

    When you start leveraging the availability KPI with the help of digital shelf analytics, you can improve inventory management, boost product discoverability, increase the frequency with which your online product listings convert, and generally drive more sales. This KPI is essential for ensuring your customers can always find and buy the products they want.

    With the availability KPI, you can start answering questions like:

    • What is my overall in-stock rate?
    • Which of my products frequently go out of stock?
    • How does product availability vary across different regions and stores?
    • What is the impact of availability on my conversion rates?
    • Are there any seasonal trends in product availability that I need to address?
    • How quickly are we resolving stockout issues across different locations?
    • What are my biggest opportunities to reduce stockouts?

    DataWeave enables consumer brands to track their product availability metric through automated data collection from various eCommerce platforms in conjunction with physical in-stock rates. The platform provides granular, store-level insights so you can understand regional stock variations and optimize inventory distribution. By tracking historical availability data, you can identify seasonal patterns and predict future demand to pre-empt stockout issues. All of this can be configured with automatic notifications to alert you when there has been a stockout event or when a low stock threshold has been passed, facilitating timely replenishment.

    Graph showing availability across locations

    6. Ratings & Reviews

    The final KPI in our guide is the ratings & reviews digital shelf metric. Consumers rely heavily on genuine feedback from their peers and refer to star ratings, posted comments, and uploaded pictures to inform their buying decisions. This KPI analyzes the impact of customer feedback and reviews on your products’ performance across eCommerce platforms so you can measure overall brand perception and isolate areas of opportunity.

    This metric does something other digital shelf metrics don’t; it can inform your product strategy. It can help you identify repeat complaints that your product team can address with the manufacturer or use for the design of future products.

    Some questions you can answer with this powerful KPI include:

    • What is the overall customer sentiment towards my products based on ratings and reviews?
    • Which product features are frequently mentioned positively or negatively by customers?
    • How do my product ratings and reviews compare to those of my competitors?
    • Are there common issues or complaints that need to be addressed to improve customer satisfaction?
    • Which products have the highest and lowest ratings, and why?

    With DataWeave’s digital ratings and reviews feature, you can keep a pulse on customer sentiment to take short-term action as well as decide long-term strategy. You can leverage reviews to influence product perception, refine products, and enhance overall customer satisfaction.

    DataWeave’s Digital Shelf Metrics

    Each one of these metrics is interconnected and collectively influences a brand’s success. For instance, improving content quality and earning higher ratings can significantly enhance your product’s visibility in search results, thereby boosting the Share of Search digital shelf metric. By focusing on a comprehensive approach that integrates these metrics, brands can ensure their products are consistently visible, competitively priced, well-reviewed, and readily available.

    DataWeave gives consumer brands the means to execute a holistic digital shelf strategy. From a single portal, track and improve digital shelf metrics like Share of Search, Share of Media, Pricing and promotions, Availability, and Ratings and Reviews.

    Our solutions help audit and optimize the most critical KPIs that drive sales and market share for brands so you can stay competitive in a dynamic digital landscape and foster long-term customer satisfaction.

    Ready to get started? Schedule a call with a specialist to see how it can work for your brand.

  • How Digital Shelf Analytics Can Fix Common Revenue Growth Management Challenges for Consumer Brands

    How Digital Shelf Analytics Can Fix Common Revenue Growth Management Challenges for Consumer Brands

    As consumer goods brands increasingly turn to eCommerce marketplaces as a source of profitable growth, it becomes harder for teams to grapple with the complexity of revenue growth management.

    This complexity emerges from multiple fonts: there are hundreds, and even thousands, of competitors to consider when formulating strategies for managing pricing, promotion, and assortment changes. The world is currently experiencing a period of unprecedented supply chain instability, shifting more consumers away from traditional retail and into eCommerce shopping. And finally, consumer buying patterns, preferences, and trends are constantly shifting.

    Revenue growth management (RGM) and net revenue management (NRM) were once less complex processes; but that is no longer the case. Now, some 80% of consumer brand CEOs report that they “aren’t satisfied with their RGM results.”

    Gathering data, analyzing it, and acting on it quickly stand out as major challenges that businesses must overcome to grow their market share, earn more profits, and capitalize on market shifts in real time. In this article, we’ll dive into RGM and NRM, the obstacles business teams face, and explore how using technology for digital shelf analytics can help bridge the gap.

    What is Net Revenue Management (NRM) or Revenue Growth Management (RGM)?

    Every consumer goods company aims to increase profits and grow market share. This requires a concerted effort in RGM and net revenue management (NRM) strategy. Whether a company has a specific team dedicated to this task or relies on the abilities of business analysts or merchandisers, this function is crucial.

    It’s worth mentioning that though the terms NRM and RGM are often used interchangeably, there are subtle differences. While both net revenue management and revenue growth management focus on maximizing overall revenue for the brand, NRM typically has a narrower focus and is specific to optimizing profitability through product pricing, promotion, product mix, and cost management. RGM strategies are a bit broader and tend to look at the top line to grow market share and expand the customer base.

    The Challenges Revenue Teams Face

    Differentiating between ‘good growth’ and ‘bad growth’ is central to NRM and RGM. Net revenue management and revenue growth management teams need the data and tools in place to determine if growth in one area is coming at the expense of another so as not to cannibalize business. Tracking and analyzing extensive data to successfully take action on opportunities and determine whether strategies are working as intended consumes a tremendous amount of mental bandwidth. The fact that these decisions are incredibly time-sensitive only compounds the issue.

    To cope, many teams in charge of NRM or RGM employ digital shelf analytics strategies to help speed up data aggregation and analysis to make sure they’re capitalizing on potential opportunities.

    eCommerce has added a whole new layer of complexity to consumer goods sales. Instead of a few relatively stable prices at big-box stores, a single item for sale may experience high price volatility, with dozens of minute pricing changes occurring online each day. In some cases, consumers become blind to price volatility, letting brands increase prices, but consumer sentiment, the overall price elasticity of the product, and dozens of other factors go into determining the final price of an online product. Net revenue teams need to modernize and adapt to changing eCommerce environments to competitively price, promote, and grow their revenue.

    Here are the top three challenges standing in the way of net revenue management and revenue growth management teams and solutions to address these issues.

    Challenge 1: Incomplete or Inaccurate Data

    Incomplete and inaccurate data are critical for Net Revenue Management and Revenue Growth Management teams to get under control when attempting to modernize in a digital-centric selling environment. As more competitors enter the market, many brands find it hard to make strategic decisions without the complete picture.

    Data may be incomplete or inaccurate because a brand is analyzing only part of the market, such as Amazon or another enterprise-scale eCommerce marketplace. Additionally, they might not be analyzing all types of online media, such as branded ads, sponsored search listings, or sponsored category listings.

    Most importantly, another pitfall is the lack of hyperlocal data. Generalized data across regions, states, ZIPs, and stores can skew the decision-making process and result in poor outcomes.

    Overcoming Incomplete or Inaccurate Data

    In order to get the full picture, consumer brands need to ensure they have a view of the entire competitive landscape across their channels. This includes gathering data down to the case pack, the unique product identifier, and the geography, including ZIP and store. They also need the respective MSRP by SKU, the unit normalized price, and the selling price at a specific moment in time. This is done by aggregating brick-and-mortar store information available online, such as when stores list curbside pickup SKUs and pricing online.

    Individual teams cannot manually gather all this detailed data. The growth in eCommerce means there is simply too much data to find and aggregate. Instead, they can employ digital shelf technology to get more data from more sites. Teams can leverage AI to better match product listings, ads, and even visuals to avoid missing data on listings that lack common attributes, such as UPCs for normalization.

    To add to this, advanced pricing intelligence systems can cache URLs to help teams audit and verify their data, avoiding delays and confusion when ad hoc requests arise.

    Challenge 2: Difficulty in Making Sense of the Competitive Landscape

    Once net revenue management and revenue growth management teams have gathered all of the available data, it’s time to make sense of it. This is a monumental challenge, and ends up being the stage where most NRM and RGM teams flounder. Disparate marketplaces include different product attributes and images. This makes it extremely complicated to sync competitors’ data to ready it for analysis, especially if this analysis is carried out manually in Excel. These are some of the attributes that teams need to harmonize in order to make sense of the competitive landscape:

    • Product identifiers (UPC, SKU, Internal Code)
    • Size, case, pack, volume, bundled offerings
    • Language
    • Currency
    • Stock Status (Whether the product is available or not)
    • Platform-specific attributes such as ‘Amazon’s Choice,’ ‘Best Seller,’ etc.

    Teams also need to group and classify various categories of promotions. These can include sponsored listings, banner ads, coupons, bank offers, and others. Each of these categories needs to be tracked separately. This vast array of data points across hundreds of sites creates a big data problem for teams.

    Making Sense of the Competitive Landscape

    The best way to overcome this challenge is to task a digital shelf analytics system with gathering and harmonizing data automatically across the consumer goods competitive landscape. Competitive and market intelligence tools can help break down an overwhelming amount of data, matching similar products across competing brands and analyzing their various strengths and weaknesses. Once the technology matches complex product attributes and identifiers, it becomes easier for teams to gain insights and exploit findings. In a sense, the data needs to be cleaned before analysis can occur.

    Technology can gather data in multiple ways, and the best systems employ several methods to get the best matches. Data consumption modes include API integrations, CSV and Excel file uploads, and proprietary scrapers that view websites independently of direct inputs. Having all the data in a single place helps net revenue management and revenue growth management teams gain indicative insights on product popularity, pricing, and sales, on their own and competitor products.

    Challenge 3: Lack of Timely Visibility

    The final challenge that many net revenue management and revenue growth management teams face is something of a ‘silent killer’ — timeliness. Even if they successfully gather data across the entire competitive landscape and harmonize that data into a format for easy analysis, a lack of timeliness can render even the best actions irrelevant.

    Speed is of the utmost importance when there are market changes. If a product goes viral and competitors raise prices in response to increased demand, without timely visibility, the trend may be over before a consumer goods brand can successfully increase its prices for the duration of the trend. This can mean lost margins.

    Another example is analyzing data and incorporating lagging promotional and sales data into analyses. This can skew pricing strategies because timely data is not accessible to inform decision-making. Many teams waste time firefighting due to a lack of timely pricing and promotional intelligence data.

    Get Near Real-Time Insights for Faster Decision Making

    Using technology that allows for net revenue management and revenue growth management teams at consumer goods brands to establish update frequencies can be a game changer. Teams can set update frequencies based on their need. They can set up the system to check a fast-moving product daily, while a slow-moving item might only need to be checked weekly, monthly, or even quarterly. This allows teams to focus on the highest-impact products first and address the largest exceptions before they lose out on an opportunity. Managing exceptions with a digital shelf analytics platform saves teams significant time instead of poring over low-impact changes in the data.

    Digital Shelf Analytics for Net Revenue Management

    Modernizing a consumer goods brand’s net revenue management or revenue growth management processes requires advanced digital shelf analytics. DataWeave provides consumer goods companies with the technology they need for quick and accurate pricing, promotional, and assortment intelligence. By tracking over 200 million products each day, users can be sure they get the widest and most timely view of the competitive landscape. DataWeave’s deep industry knowledge is baked into every aspect of its platform.

    Learn more by requesting a demo today!

  • Competitor Price Monitoring in E-commerce: Everything You Need to Know

    Competitor Price Monitoring in E-commerce: Everything You Need to Know

    Picture this: You wake up one morning to discover that your top competitor reduced their prices overnight. And now your shopper traffic has tanked and your sales have taken a hit.

    Unfortunately, this is a common scenario because your customers can compare prices online in seconds—and loyalty lies with the budget.

    So, how can you protect your business? Price monitoring.

    Price monitoring solutions can help you keep abreast of competitor price changes—which, of course, will help you improve your pricing strategies, retain your customers, and maximize your profits.

    How? In this article, we’ll explore:

    • What is price monitoring
    • The key benefits of price monitoring for retailers and brands
    • What a capable price monitoring solution can do

    What Is Price Monitoring?

    Price monitoring is the process of tracking and analyzing your competitor’s prices across various online and offline platforms. By monitoring competitors’ prices, you can understand market price trends and adjust your prices strategically—which, in turn, helps you remain competitive, increase margins, and improve customer retention.

    5 Benefits of Price Monitoring

    Competitor price monitoring can help you:

    1. Gain a competitive edge: Competitor price tracking allows you to adjust your prices to remain attractive to consumers.
    2. Maximize revenue: With timely pricing data, you’re empowered to identify optimum price points that strike a delicate balance between maximizing revenue and maintaining customer loyalty.
    3. Retain customers: Consumers are looking for the most value for their dollar, so maintaining consistently competitive pricing is crucial for retaining loyal customers.
    4. Understand promotional effectiveness: Price monitoring helps businesses evaluate the effectiveness of their promotions and discounts. By comparing the impact of different pricing strategies, businesses can refine their promotional tactics to maximize sales and customer engagement.
    5. Understand market movements: By analyzing historical pricing data, you’re better positioned to anticipate future pricing changes — and adjust your strategies accordingly.

    4 Essential Capabilities of Price Monitoring Software

    Here are four capabilities to look for when choosing a price monitoring system.

    1. AI-Driven Product Matching

    Product matching is the process of identifying identical or similar products across different platforms to ensure accurate price comparisons.

    If your price monitoring solution can’t reliably match your products with competitors’ across various sales channels at scale, you’ll end up with poor data. Inaccurate data will then lead you to make misinformed pricing decisions.

    Product matching needs to be accurate and comprehensive, covering a wide range of products and product variations—even for including private label products.

    For example, AI-driven product matching can recognize a specific brand and model of sneakers across multiple online stores—even if product descriptions and images differ. Here’s how it works in a nutshell:

    • Sophisticated algorithms and deep learning architecture enable AI to identify and match products that aren’t identical but share key characteristics and features.
    • Using unified systems for text and image recognition, the AI matches similar SKUs across hundreds of eCommerce stores and millions of products.
      The AI zeroes in on critical product elements in images, like a t-shirt’s shape, sleeve length, and color.
    • The AI also extracts unique signatures from photos for rapid, efficient identification and grouping across billions of indexed items.

    DataWeave’s AI algorithm can initially match products with 80–90% accuracy. Then, humans can bring contextual judgement and make nuanced decisions that the AI might miss to correct errors quickly and push for accuracy closer to 100%. By integrating AI automation with human validation, you can achieve accurate and reliable product-matching coverage at scale.

    2. Accurate and Comprehensive Data Collection and Aggregation

    The insights you derive are only as good as the data you collect. However, capturing comprehensive pricing data is tough when your competitors operate on multiple platforms.

    For truly effective price monitoring insights, you need consistent, comprehensive, and highly accurate data. This means your chosen price monitoring system should:

    • Scrape data from various sources, such as desktop and mobile sites and mobile applications.
    • Pull data from various online platforms like aggregators, omnichannel retailers, delivery intermediaries, online marketplaces, and more.
    • Handle data from different regions and languages.
    • Collect data at regular intervals to ensure timeliness.

    DataWeave’s online price monitoring software covers all of these bases and more with a fast, automated data source configuration system. It also allows you to painlessly add new data sources to scrape.

    Instead of incomplete or inaccurate data, you’ll have comprehensive and up-to-date data, allowing you to respond quickly to market changes with confidence.

    3. Seamless Normalization of Product Measurement Units

    You can’t compare apples to oranges—or price-per-kilogram to price-per-pound.

    For price monitoring to be accurate, there must be a way to normalize measurement units—so that we’re always comparing price-per-gram to price-per-gram. If we compare prices without taking into account measurement units, our data will be misleading at best.

    Let’s take a closer look. Say that your top competitor sells 12oz cans of beans for $3, and you sell 15oz cans for $3.20. At first glance, your larger cans of beans will appear more expensive—but that’s not true. If we normalize the measurement unit—in this example, an oz—the larger can of beans offers more value to customers.

    Unit of measure normalization facilitates sound price adjustments based on accurate and reliable data. For this reason, every business needs a price tracking tool that can guarantee accurate comparisons by normalizing unit measurements—including weight, volume, and quantity.

    4. Actionable Data and an Intuitive User Experience

    Knowledge is only powerful when applied—and price monitoring insights are only useful when they’re accessible and actionable.

    For this reason, the best price monitoring software doesn’t just provide insights based on accurate and comprehensive data, but it also provides several ways to understand and deploy those insights.

    Ideal price monitoring solutions provide customized pricing alerts, intuitive dashboards, detailed reports, and visuals that are easy to interpret—all tailored to each particular team or a team member’s needs. These features should make it easy for team members to compare prices against those of competitors in specific categories and product groupings.

    Your price tracking tool should also permit flexible API integrations and offer straightforward data export options. This way, you can integrate competitive pricing data with your pricing software, Business Intelligence (BI) tools, or Enterprise Resource Planning (ERP) system.

    4 Ways Retailers Can Leverage Price Monitoring

    Retailers can use price monitoring tools to remain competitive without compromising profitability—here’s how:

    1. Track Competitors’ Prices

    Competitor price monitoring helps you avoid being undercut—and, as a result, maintain market share. By tracking competitor prices in real-time, you can adjust prices to remain competitive, especially in dynamic markets. Ideally, you should monitor both direct competitors selling the same products and indirect competitors selling similar or alternative products. This way, you’ll have a complete picture of market prices and can make more informed pricing adjustments.

    2. Understand Historical and Seasonal Price Trends

    As a retailer, you may want to analyze historical data to identify price patterns and predict future price movements—especially in relation to holidays and seasonal products. Knowing what’s coming, you’re better positioned to plan for pricing changes and promotional campaigns.

    3. Implement Dynamic Pricing

    Dynamic pricing is the process of adjusting prices based on real-time market conditions, product demand, and competitors’ prices—allowing you to respond faster to market changes to maintain optimized prices.

    4. Optimize Promotional Strategies

    Price monitoring tools can track retail promotions across numerous online and offline sales avenues, providing insight into the nature and timing of competitors’ promotions. This data can help you determine which promotions are most effective—and which aren’t—allowing you to improve your own promotions and discounts, and allocate marketing resources where it matters most. This is especially beneficial during peak sales periods.

    3 Ways Brands Can Employ Price Monitoring

    Here are three ways brands can use price monitoring to remain profitable, protect brand equity, and gain a competitive edge.

    1. Maintain Consistent Retail Prices

    Minimum advertised price (MAP) policies are designed to prevent retailers from devaluing a brand while ensuring fair competition among retailers. Price monitoring applications allow your brand to track retailers’ prices to detect MAP policy violations. Data in hand, you can maintain consistent pricing across online sales channels, physical stores, and retail stores’ digital shelves — and, critically, protect your brand equity.

    2. Improve Product and Brand Positioning

    When you understand how your products’ prices compare to those of competitors, you can set prices to improve brand positioning. For example, if you want to position your brand as luxurious and high-quality, you need to set higher product prices than budget-friendly alternative products.

    3. Ensure Product Availability

    You can use a price monitoring solution to track product availability to ensure products are always in stock, even across different physical stores and online marketplaces. If a product is frequently sold out, you can adjust production levels or help retailers to improve their inventory management.

    Key Takeaways: E-commerce Price Monitoring

    Price monitoring software allows you to compare your products’ prices with competitors. This valuable data can help you:

    • Optimize revenue through timely price changes and dynamic pricing
      Avoid being undercut by competitors
    • Improve pricing strategies and promotions to increase sales and retain customers
    • Maintain consistent prices across sales channels

    To learn more, check out our article, What is Competitive Pricing Intelligence: The Ultimate Guide here or reach out and talk to us today!

  • Amazon Prime Day Pricing Trends 2024: Deals and Discounts Galore Across Categories

    Amazon Prime Day Pricing Trends 2024: Deals and Discounts Galore Across Categories

    Amazon Prime Day 2024 has once again shattered records, with more items sold during the two-day event than any previous Prime Day. Prime members worldwide saved billions across all categories, while independent sellers moved an impressive 200 million items.

    At DataWeave, we conducted an extensive analysis of the discounts offered by Amazon across major categories. By examining over 47,000 SKUs, we’ve uncovered compelling insights into pricing strategies, competitive positioning, and emerging trends in the eCommerce space.

    Since products on Amazon and other eCommerce websites are often sold at discounts even on normal days not linked to a sale event, we delved into the real value that Prime Day offers to shoppers by focusing on price reductions or the Additional Discount during the sale compared to the week before. As a result, our approach highlights the genuine benefits of the event for shoppers who count on lower prices during the sale. At the same time, our report also includes the Absolute Discounts offered during Prime Day, which represents the total markdown relative to the MSRP.

    Amazon’s Cross-Category Discount Strategy

    Our analysis reveals that the Electronics category saw the highest discounts with an average absolute discount of 20.4% and additional discounts on Prime Day amounting to 10.4%. Meanwhile the Home & Furniture had the lowest discount at 13.1%.

    Discounts offered Across Key Categories on Amazon Prime Day USA 2024

    The Health & Beauty category saw significant additional discounts during Prime Day, at 9.26%. The Apparel category offered attractive absolute (16.10%) and additional (8.90%) discounts.

    Category Deep Dive

    Consumer Electronics

    Still the star of the show, the electronics category saw the highest markdowns this Prime Day with absolute discounts at 20.40% and across 14.61% of their inventory.

    Discounts offered on Consumer Electronics Subcategories During Amazon Prime Day USA 2024.

    Across Electronics subcategories, Earbuds had the highest markdowns at 34.80%, followed closely by Wireless Headphones at 30.60% and Headphones at 29.00%, with steep additional discounts during Prime Day as well. Apple AirPods Pro, for example, retailed at $168 (down from $249) at a 32% discount.

    Discounts offered on Consumer Electronics Subcategories During Amazon Prime Day USA 2024 Featuring Apple Air Pods

    Meanwhile, smartphones had the lowest markdowns at 9.30%, followed by Laptops at 10.50%. Laptops also had the lowest additional discount during Prime Day at just 1.28%, significantly lower than other subcategories. Speakers (20.80%), Drones (19.10%), and Smartwatches (25.00%) offered moderate to high markdowns.

    Notably, all Amazon products including Kindle, Echo, Echo Earbuds, Alexa, Fire TV, Fire TV Stick, and Fire Tablets, were aggressively discounted upwards of 30% this Prime Day. These products also came with the label “Climate Pledge Friendly.”

    Sustainability Features For Amazon Products During Prime Day USA 2024

    These aspects indicate Amazon’s push to promote its own ecosystem of products to the top, as well as cater to changing consumer preferences.

    Apparel

    Discounts offered this Prime Day increased from 13.2% in 2023 to 16.1% in 2024.

    Discounts offered on Apparel Subcategories During Amazon Prime Day USA 2024

    Amid apparel subcategories, Amazon appears to be pushing Women’s apparel categories more aggressively, particularly in Tops, Shoes, and Athleisure.

    Women’s Shoes lead with the highest discounts at 26.50%, followed by Women’s Tops at 22.50% and Men’s Shoes at 22.80%. Women’s Tops also maintained the highest additional discount at 15.27%, followed by Women’s Athleisure at 13.03% and Men’s Swimwear at 12.44%.

    Similar to 2023, Men’s Innerwear offered significantly lower discounts, with only 1% absolute discount and 0.72% additional discount. Women’s Innerwear also shows low discounts at 3.20% absolute and 2.23% additional.

    Health & Beauty

    Amid health & beauty subcategories, Moisturizes witnessed the highest markdowns at 20.10%, followed by Make Up at 18.90%. The Moisturizer subcategory also offers highest additional discounts at 12.20%, followed closely by Sunscreen at 10.25% and Beard Care at 10.22%.

    Discounts offered on Health & Beauty Subcategories During Amazon Prime Day USA 2024

    The Toothpaste subcategory has the lowest discounts, at 10.90%. The lower discounts on everyday essentials like this might indicate a steady demand or an attempt to maintain margins on frequently purchased items.

    Most Health & Beauty subcategories fall in the 15-18% range for actual discounts and 8-10% range for additional discounts. Electric Toothbrush (16.90% actual, 9.91% additional) and Shampoo (16.50% actual, 8.78% additional) represent the middle of the pack. There were a few highly attractive deals though, such as the Philips Sonicare toothbrush retailing at $122.96 (down from $199.99), with a 39% discount.

    Discounts offered on Health & Beauty Subcategories During Amazon Prime Day USA 2024 Featuring A Philips Electric Toothbrush

    Amazon also offered significant discounts on Open Box products (products that are returned, but unused, out of mint condition boxes) to Prime members.

    Home & Furniture

    This category saw the lowest discounts for this Prime Day event at 13.1%. Across subcategories, Rugs lead with the highest average discount at 21.50%, closely followed by Luggage at 20.90%. Amazon seems to be pushing decorative and organizational items (Rugs, Bookcases) more aggressively, possibly due to higher margins. Rugs also stood out as the subcategory with the highest additional discount of 11.54%.

    Discounts offered on Home & Furniture Subcategories During Amazon Prime Day USA 2024

    Sofas have the lowest additional discount at 2.76%, followed by Dining Tables at 3.21%. Items like Cabinets (15.80% absolute, 6.66% additional) and Coffee Tables (14.40% absolute, 6.25% additional) represent the middle range of discounts.

    Watch Out For More

    As the holiday season approaches, it’s clear that the retail landscape continues to evolve. While Amazon remains a formidable force, there are opportunities for savvy competitors to carve out their niches and attract deal-hungry shoppers. By analyzing these trends and adjusting strategies accordingly, retailers can position themselves for success in the high-stakes world of summer sales events.

    Stay tuned to our blog for more insights on how Amazon’s competitors reacted to Prime Day, and how leading brands across categories fared in terms of their pricing and their visibility during the sale event. Reach out to us today to learn more.

  • Cracking the Code: How Retailers Can Adapt to Plummeting Egg Prices in 2024

    Cracking the Code: How Retailers Can Adapt to Plummeting Egg Prices in 2024

    Virtually every cuisine in the world uses eggs. They’re in your breakfast, lunch, dinner, and dessert — which is perhaps why the global egg market is expected to generate $130.70 billion in revenue in 2024 and is projected to grow to approximately $193.56 billion by 2029.

    More specifically, the United States is the fourth-largest egg producer worldwide. The country’s egg market is projected to generate $15.75 billion in 2024 and increase to $22.51 billion by 2029.

    This growth is driven by several factors, most notably:

    • Health-consciousness among consumers: Consumers value eggs for their essential nutrients and rich protein content.
    • Demand for convenience foods: Consumers’ preferences are shifting toward quick and easy foods, which drives demand for shell eggs and pre-packaged boiled or scrambled eggs.
    • Population Growth: A growing worldwide population increases the demand for eggs.
    • Affordability and accessibility: Eggs are an affordable and accessible nutrient-dense food source for many.

    Despite these factors contributing to the U.S. egg market’s growth, recent times have seen egg prices fall dramatically.

    Based on a sample of 450 SKUs, DataWeave discovered that egg prices in the U.S. fell by 6.7% between April 2023 and April 2024, dipping to its lowest (-12.6%) in December 2023.

    Egg Price Chart: Egg Prices USA Going Down 98.95% between April 2023 and April 2024

    So, what’s causing the decrease in egg prices?

    The Rise and Fall of Egg Prices: A Recent History

    In 2022, avian influenza severely impacted the United States. The disease affected wild birds in nearly every state and devastated commercial flocks in approximately half of the country.

    The 2022 incident was the first major outbreak since 2015 and led to the culling of more than 52.6 million birds, mainly poultry, to prevent the disease from spreading uncontrollably.

    With almost 12 million fewer egg-laying hens, the United States produced around 109.5 billion eggs in 2022 — a drop of nearly two billion from the previous year.

    Consequently, the cost of eggs soared, peaking at $4.82 a dozen — more than double the price of eggs in the previous year.

    The avian flu continues to affect egg-laying hens and other poultry birds across the United States. As of April 2024, farms have killed a total of 85 million poultry birds in an attempt to contain the disease.

    Despite the disease’s effects, production facilities have made significant efforts to repopulate flocks, leading to a steady increase in supply – and a much anticipated decrease in egg prices.

    According to the U.S. Bureau of Labor Statistics, there was an increase in producer egg prices in 2022, reaching a peak in November 2022, at which point they began to fall.

    Retailer’s egg prices followed suit. The egg price chart below depicts retailers’ declining egg prices over one year, from April 2023 to April 2024, with Giant Eagle showing the most significant price reductions and Walmart the least.

    Egg Price Chart Featuring Leading Retailers 2023-2024

    What Does the Future Hold for Egg Prices?

    The USDA reported recent severe avian flu outbreaks in June 2024. These outbreaks are estimated to have affected 6.23 million birds.

    With a reduction in egg-laying hens, egg prices are likely to increase — time will tell.

    Nonetheless, the annual per capita consumption of eggs in the U.S. is projected to reach 284.4 per person in 2024 from 281.3 per person in 2023. So for now, producers and retailers can rest assured of the growing demand for eggs.

    How Can Retailers Adapt to the Unpredictability of Egg Prices?

    Egg prices were down to $2.69 for a dozen in May 2024. However, they are still significantly higher than consumers were used to just a few years ago—eggs were, on average, $1.46 a dozen in early 2020.

    Additionally, while the avian flu puts pressure on producers, inflation and supply chain disruptions exert pressure on retailers.

    With such challenging egg market conditions, what can retailers do to maintain customer loyalty amid reduced consumer spending while maintaining profitability?

    1. Give the Customer What They Want: Increase Offerings of Organic, Cage-Free, and Free-Range Eggs

    As mentioned, Data Bridge Market Research’s trends and forecast report highlighted a significant increase in consumer health consciousness. Additionally, animal welfare increasingly influences consumers’ purchasing decisions when buying meat and dairy products.

    DataWeave data shows that the prices of organic, cage-free, and free-range eggs—such as those by brands like Happy Eggs and Marketside—have fallen less than those of non-organic, caged egg brands.

    Egg Price Chart Featuring Leading Egg Brand Prices 2023-2024

    2. Increase Private-Label Offerings

    Private labels typically offer retailers higher margins than national brands. These margins can shield consumers from sudden wholesale egg price swings, helping to preserve brand trust and consumer loyalty without sacrificing profitability.

    Moreover, eggs are particularly suited to private labeling, given their uniform appearance and taste and the lack of product innovation opportunities.

    Undoubtedly, this is why sales of private-label eggs dwarf sales of national egg brands in the United States. Statista reports that across three months in 2024, private label egg sales amounted to $1.55 billion U.S. dollars, while the combined sales of the top nine national egg brands totaled just $617.88 million U.S. dollars.

    3. Price Intelligently

    With the current and predicted fluctuations in egg prices over the foreseeable future, price competitiveness is paramount to margin management and customer loyalty.

    This is especially true when lower prices are the primary factor influencing the average consumer’s choice of supermarket for daily essentials purchases.

    AI-driven pricing intelligence tools like DataWeave give retailers valuable highly granular and reliable insights on competitor pricing and market dynamics. In today’s data-motivated environment, these insights are necessary for competitiveness and profitability.

    Final Thoughts

    Egg prices have fluctuated significantly due to the impact of avian flu. Despite recent price drops, future egg price increases are possible due to ongoing outbreaks. Retailers should adapt to unstable egg prices by increasing organic, free-range, cage-free, and private-label egg offerings while leveraging AI-driven pricing tools to maintain margins and customer loyalty.

    Speak to us today to learn more!

  • How Healthy is Your Assortment?

    How Healthy is Your Assortment?

    In 2025, both consumers and retailers continue to prioritize better health – albeit with evolving definitions and expectations.

    The pandemic fundamentally transformed how consumers approach wellness, with this shift becoming entrenched in shopping behaviors years later. As shopping habits have permanently altered, retailers now face increased pressure to rapidly adapt their assortments with in-demand health and wellness products that enhance customer experience across various channels – online and offline.

    Let’s explore how leading retailers are keeping consumers – and their own bottom lines – healthy by responding effectively to market trends to drive online sales and market share.

    Health & Wellness Influence The Product Mix Across Categories

    Consumption habits have changed dramatically since the onset of the pandemic. A McKinsey study shows that 82% and 73% of US, and UK consumers respectively now consider health & wellness a top priority. Typically shoppers adjust grocery shopping and meal planning at the start of the year, with many focusing on fresh, organic, and nutrient-rich foods.

    The influential health and wellness mega-trend spans diverse retail channels, including grocery, pharmacy and mass. It extends across numerous categories like:

    • Food and beverage (natural, organic, vegan, plant-based food)
    • Health and personal care
    • Beauty
    • Cleaning products
    • Fitness equipment 
    • Athleisure (apparel)
    • Consumer electronics like health wearables.

    Today’s health movement is so powerful and compelling that retailers have revised their business strategies to better serve health-conscious consumers. For instance, drugstores are reinventing themselves as healthcare destinations, with CVS and Kroger expanding into personalized care delivery and value-based clinics to enhance their health offerings.

    Major retailers like Amazon, Walmart, and Target report robust sales in health and wellness categories. For example, Walmart saw a 4.6% increase in comparable sales in early 2024, driven significantly by grocery, consumables, and health-related products.

    New product categories are gaining traction:

    • Functional foods and beverages are seeing unprecedented growth, with Target launching over 2,000 wellness items in the category, including exclusive products priced under $10.
    • Personalized nutrition and mental health products are surging, including tailored dietary solutions and stress-reducing items.
    • Health wearables and wellness tech continue to rise in popularity, with over 150 new wellness tech items launched at Target this year, including innovative red-light therapy devices.
    • Transparency and sustainability certifications like organic, non-GMO, and vegan labels are increasingly driving purchasing decisions.
    • Clinically proven benefits offered by health & wellness products are gaining traction among Gen Z.

    Retail’s Survival Of The Fittest Moves Online

    As the omnichannel retail sector continues to grow, more shoppers now make purchase decisions within minutes using just a few clicks rather than physically visiting brick-and-mortar stores. In some cases, AI agents like Operator from Chat-GPT or Gemini (Google’s Chatbot) even make personalized, curated lists and reduce the time taken to make purchase decisions. Traditional retail paradigms are rapidly becoming obsolete as consumers grow savvier, more empowered, and better informed than ever before.

    To stay competitive, more retailers are embracing AI-driven data insights to adjust their assortments to reflect consumer demand for health and wellness products.

    According to industry experts, data insights have emerged as a critical retail strategy that continues to gain momentum. This is because retailers can no longer afford to guess how to approach their omnichannel strategy. They need the accuracy, clarity, and efficiency of data insights to guide their assortment and pricing decisions to outmaneuver competitors, maximize sales, and win market share as shopping evolves online.

    Among its retail best practices, Bain & Company recommends retailers “lead with superior assortments that use a customer-centric lens to reduce complexity and increase space for the products customers love.” Insights can help retailers discover the optimal mix of national brands, private labels, limited-time offers, and value-added bundles.

    “Lead with superior assortments …
    increase space for the products consumers love    “
    ~ Bain & Company

    Determining the optimal mix of products also includes bestsellers and unique items that help retailers distinguish their offerings. Assortment insights help retail executives track competitors’ assortment changes and spot gaps in their own product assortment to adapt to emerging consumer trends and in-demand products.

    Why Effective Assortment Planning Matters

    Assortment planning sits at the heart of retail success, directly influencing profitability, customer satisfaction, and competitive differentiation. In today’s health-conscious market, getting your assortment right means:

    • Meeting Customer Expectations: Today’s health-conscious consumers expect relevant, high-quality products that match their wellness goals. A well-planned assortment signals that a retailer understands its customers’ evolving needs.
    • Optimizing Inventory Investment: Strategic assortment planning ensures capital is allocated to products with the highest return potential while minimizing investments in slow-moving items.
    • Creating Competitive Advantage: A distinctive assortment that includes popular health and wellness products alongside unique offerings helps retailers stand out in a crowded marketplace.
    • Reducing Lost Sales: Effective assortment planning minimizes the risk of stockouts on high-demand health and wellness items, preventing customers from shopping elsewhere.
    • Supporting Omnichannel Strategies: Well-executed assortment planning ensures consistency across physical and digital touchpoints, creating a seamless customer experience.
    • Improving Operational Efficiency: A thoughtfully curated assortment reduces complexity throughout the supply chain, from procurement to warehouse management to in-store operations.

    As health and wellness continues to drive consumer spending, retailers who excel at assortment planning can capitalize on these trends more effectively than their competitors, turning market insights into tangible business results.

    AI-Powered Assortment Analytics Driving Retail Success

    The synergy of AI and data analytics into retail assortment planning is changing how businesses approach inventory management. Retailers using AI-driven predictive analytics have achieved a 36% SKU reduction while increasing sales by 1-2%, showcasing the efficiency of data-driven approaches according to a McKinsey report.

    Retailers face several challenges that can hinder strategic assortment planning:

    • Limited Understanding of Competition: Retailers struggle to gain comprehensive insights into their product assortments relative to competitors, often lacking visibility into their strengths and weaknesses across categories.
    • Data Overload: Assortment planning involves handling vast amounts of data, making it challenging for category managers to extract actionable insights without user-friendly tools and visualization.
    • Cross-Channel Consistency: With omnichannel retailing, ensuring consistency across physical stores, e-commerce, and other channels is complex. Misalignment can lead to customer dissatisfaction and loss of loyalty.
    • Adapting to Changing Market Trends: Identifying top-selling products and tracking consumer preferences is challenging. Balancing the right mix of products is crucial; without analytics, retailers risk lost sales or excess slow-moving inventory.
    • Scalability and Efficiency: As retailers expand into new markets or categories, scaling their assortment planning processes efficiently becomes a challenge. Legacy systems and manual methods often fail to support the agility needed for quick decision-making at scale.

    DataWeave’s Assortment Analytics helps retailers address these challenges by providing a robust, easy-to-use platform that delivers actionable insights into product assortments and competitive positioning. With AI-driven, contextual insights and alerts, retailers can effortlessly identify high-demand, unique products, capitalize on catalog strengths, optimize pricing and promotions, improve stock availability, and refine assortments to maintain a competitive edge.

    Beyond Data: Actionable Insights That Drive Results

    DataWeave’s platform provides a comprehensive, insight-led view into assortments through several key dimensions:

    • Stock Insights: Monitor stock changes across retailers to stay updated on availability.
    • Category and Sub-Category Insights: Analyze assortment changes, identify newly introduced or discontinued categories, and track leading retailers in specific segments.
    • Brand Insights: Identify newly introduced, missing, or discontinued brands, as well as leading brands within chosen categories.
    • Product Insights: Identify bestsellers and evaluate their impact on your portfolio, analyzing pricing and promotions.
    • Personalized Recommendations: Receive suggestions tailored to your behavior and user profile to refine decision-making.
    • User-Configured Alerts: Stay informed with alerts designed to highlight significant changes or opportunities.

    The platform addresses data overload by providing an intuitive, insight-driven view of your assortment. Category managers gain a comprehensive, bird’s-eye perspective of key changes within specified timeframes, allowing them to focus on what matters most.

    Preparing for the Future of Retail Health

    To avoid supply chain bottlenecks, inventory shortages, and out-of-stock scenarios, retailers are strategically using data insights to anticipate fluctuations in demand and proactively plan how to manage disruptions that could affect their assortments.

    For variety that satisfies consumers’ diverse product needs, retailers are using data insights to determine whether to collaborate with nimble suppliers to promptly fill any gaps.

    To further strengthen their assortments’ attractiveness, retailers are using AI-powered pricing analytics to offer the right product at the right price. These analytics help retailers know exactly how they compare to rivals’ pricing moves with relevant data so they can keep up with market fluctuations and stay competitive by earning consumer engagement, sales, and trust.

    To Conclude…

    Like nourishing habits that improve consumers’ health, data insights improve retailers’ e-commerce health. Advanced assortment and pricing analytics, powered by artificial intelligence, help retailers make better decisions faster to boost their agility, outmaneuver rivals, and fuel online growth.

    In a retail landscape where consumer preferences for health and wellness continue to evolve rapidly, the retailers who thrive will be those who leverage data and AI to understand, anticipate, and meet these changing demands with the right products at the right time. Reach out to us to know more.

  • How Retailers and Brands Can Navigate Skyrocketing Olive Oil Prices in 2024

    How Retailers and Brands Can Navigate Skyrocketing Olive Oil Prices in 2024

    Olive oil, renowned for its complex flavor and myriad health benefits, holds a significant place in the global market, valued at $14.64 billion in 2023. It is anticipated to reach $19.77 billion by 2032, with a steady compound annual growth rate (CAGR) of 3.42%.

    This growth is fueled by:

    • Increased consumer demand for healthier oils.
    • Olive oil’s rising popularity in skincare products.
    • Greater retail availability.

    Interestingly, this market expansion occurs alongside rising olive oil prices, mainly due to a notable decrease in production. Eight European Union countries, which are the main producers, saw a dramatic drop in output from an average of 2.17 million tons to just 1.50 million tons in 2022—a 30.88% decline. Unfortunately, this drop in production comes as no surprise.

    Erratic weather patterns, rising temperatures, and exacerbating drought conditions in the Mediterranean basin have taken their toll. These climate changes disrupt the growing cycles of olive trees, leading to poorer crop yields and lower-quality olives.

    In the US, where olive oil constitutes 19% of all cooking oils sold and 40% of sales value due to its premium pricing, the market is expected to grow at an impressive CAGR of 11.31% between 2024 and 2032. This forecast is significant despite a recent dip in domestic consumption, which may further decline due to economic pressures. As a result, consumers must make difficult choices as they battle inflation, shrinkflation, and thin budgets.

    DataWeave’s Analysis of Rising Olive Oil Prices

    At DataWeave, we utilized our advanced AI-powered data aggregation and analysis platform to scrutinize the pricing trends of olive oils across key US retailers over the past year. Our analysis covered 130+ SKUs from major chains including Walmart, Kroger, Giant Eagle, and Target.

    The data revealed a notable escalation in olive oil prices, with consumers facing a sharp 25.8% increase from April 2023 to April 2024.

    This trend of rising costs was consistent across all analyzed retailers. Specifically, Walmart and Giant Eagle each reported a substantial 30% increase in their olive oil prices over the past year. In contrast, Target and Kroger experienced somewhat more modest hikes, at 20% and 15% respectively.

    Further investigation into individual brands within our sample highlighted that no brand is immune to the impacts of the ongoing supply shortages. Walmart’s own Great Value brand saw an exceptional 60% surge in prices. Other prominent olive oil brands such as Carapelli, Terra Delysia, and Bertolli also faced significant price increases, ranging from 20% to 50%.

    This across-the-board rise in prices underscores the widespread effect of supply constraints on the olive oil market, affecting both premium and private label brands alike.

    What Strategies Can Retailers and Brands Employ?

    In a market where consumer preferences and price sensitivities are rapidly evolving, retailers and brands must adopt versatile strategies without compromising on profit margins.

    Diversifying Brand Selection

    Retailers can enhance their appeal by offering a diverse range of olive oil brands, thereby stimulating competition among brands based on price, quality, innovation, and customer satisfaction. A well-curated selection that includes well-known brands like Filippo Berio and Bertolli, alongside emerging labels such as Terra Delyss, and premium options like Carapelli, allows retailers to meet a wide array of consumer preferences and budgets.

    For premium outlets, it might be beneficial to introduce more economical options than typically offered to attract budget-conscious consumers. Employing advanced assortment intelligence tools can provide retailers with crucial data, helping them make informed decisions about which brands to stock and promote, ensuring they meet consumer demand effectively while managing inventory costs.

    Data-driven Pricing

    With rising olive oil prices, competitive pricing is more crucial than ever. Retailers must strive to balance competitiveness with margin preservation. It’s essential for retailers to not just passively respond to market price increases but to actively ensure that their offerings are competitively priced relative to the market.

    This involves using sophisticated pricing intelligence tools, such as those provided by DataWeave, which track market trends and competitor pricing actions. These tools enable retailers to implement dynamic pricing strategies that respond promptly to market conditions and consumer demand shifts, helping to optimize sales and profitability.

    Diversifying Sourcing

    The traditional powerhouses of olive oil production, Spain and Italy, are now facing stiff competition from countries like Turkey and Tunisia. This shift is influenced by various factors, including currency fluctuations and changing trade policies, such as the imposition of tariffs on European olive oils by significant importers like the US. Retailers can take advantage of these changes by diversifying their sourcing strategies to include olive oil from non-traditional regions.

    The 2022/2023 season saw remarkable production levels from countries outside the Mediterranean basin, with Iran and China setting new production records. By broadening their supply chains to incorporate these emerging markets, retailers can benefit from lower production costs and introduce unique products to their consumers, enhancing both competitiveness and profit margins.

    Double Down on Private Labels

    Large retailers have successfully used their scale to develop strong private-label brands that can buffer consumers from price hikes in the olive oil market. By focusing on expanding and promoting their private-label offerings, retailers can provide cost-effective alternatives to national brands.

    Private labels generally have lower price points, making them particularly attractive during times of economic pressure and market volatility. Additionally, the development of private labels allows retailers to control more of their supply chain, from pricing to packaging, enabling them to offer high-quality products at competitive prices, thereby retaining customer loyalty and enhancing market share.

    Navigating Market Pressures

    High olive oil prices impact the entire supply chain, presenting varied challenges and opportunities:

    • Producers benefit from higher revenues but face increased pressure to maintain quality and yields in challenging climates. Adapting to these conditions with sustainable practices is crucial.
    • Exporters and Importers navigate tighter margins and greater risks due to tariffs and volume restrictions, requiring agility and strategic planning to adapt to market changes.
    • Retailers must carefully balance competitive pricing with rising procurement costs, affecting consumer affordability and potentially leading to shifts in buying patterns.
    • Consumers may seek cheaper alternatives or reduce their olive oil consumption, which influences overall market demand and pricing stability.

    These dynamics underscore the necessity for retailers and brands to adopt innovative and proactive strategies to navigate the volatile olive oil market effectively. By focusing on adaptive pricing, diversified sourcing, and customer engagement, businesses can enhance their resilience and secure long-term success in this competitive landscape.

    To learn more, talk to us today!

  • Using Siamese Networks to Power Accurate Product Matching in eCommerce

    Using Siamese Networks to Power Accurate Product Matching in eCommerce

    Retailers often compete on price to gain market share in high performance product categories. Brands too must ensure that their in-demand assortment is competitively priced across retailers. Commerce and digital shelf analytics solutions offer competitive pricing insights at both granular and SKU levels. Central to this intelligence gathering is a vital process: product matching.

    Product matching or product mapping involves associating identical or similar products across diverse online platforms or marketplaces. The matching process leverages the capabilities of Artificial Intelligence (AI) to automatically create connections between various representations of identical or similar products. AI models create groups or clusters of products that are exactly the same or “similar” (based on some objectively defined similarity criteria) to solve different use cases for retailers and consumer brands.

    Accurate product matching offers several key benefits for brands and retailers:

    • Competitive Pricing: By identifying identical products across platforms, businesses can compare prices and adjust their strategies to remain competitive.
    • Market Intelligence: Product matching enables brands to track their products’ performance across various retailers, providing valuable insights into market trends and consumer preferences.
    • Assortment Planning: Retailers can analyze their product range against competitors, identifying gaps or opportunities in their offerings.

    Why Product Matching is Incredibly Hard

    But product matching stands out as one of the most demanding technical processes for commerce intelligence tools. Here’s why:

    Data Complexity

    Product information comes in various (multimodal) formats – text, images, and sometimes video. Each format presents its own set of challenges, from inconsistent naming conventions to varying image quality.

    Data Variance

    The considerable fluctuations in both data quality and quantity across diverse product categories, geographical regions, and websites introduce an additional layer of complexity to the product matching process.

    Industry Specific Nuances

    Industry specific nuances introduce unique challenges to product matching. Exact matching may make sense in certain verticals, such as matching part numbers in industrial equipment or identifying substitute products in pharmaceuticals. But for other industries, exactly matched products may not offer accurate comparisons.

    • In the Fashion and Apparel industry, style-to-style matching, accommodating variants and distinguishing between core sizes and non-core sizes and age groups become essential for accurate results.
    • In Home Improvement, the presence of unbranded products, private labels, and the preference for matching sets rather than individual items complicates the process.
    • On the other hand, for grocery, product matching becomes intricate due to the distinction between item pricing and unit pricing. Managing the diverse landscape of different pack sizes, quantities, and packaging adds further layers of complexity.

    Diverse Downstream Use Cases

    The diverse downstream business applications give rise to various flavors of product matching tailored to meet specific needs and objectives.

    In essence, while product matching is a critical component in eCommerce, its intricacies demand sophisticated solutions that address the above challenges.

    To solve these challenges, at DataWeave, we’ve developed an advanced product matching system using Siamese Networks, a type of machine learning model particularly suited for comparison tasks.

    Siamese Networks for Product Matching

    Our methodology involves the use of ensemble deep learning architectures. In such cases, multiple AI models are trained and used simultaneously to ensure highly accurate matches. These models tackle NLP (natural language processing) and Computer Vision challenges specific to eCommerce. This technology helps us efficiently narrow down millions of product candidates to just 5-15 highly relevant matches.

    The Tech Powering Siamese Networks

    The key to our approach is creating what we call “embeddings” – think of these as unique digital fingerprints for each product. These embeddings are designed to capture the essence of a product in a way that makes similar products easy to identify, even when they look slightly different or have different names.

    Our system learns to create these embeddings by looking at millions of product pairs. It learns to make the embeddings for similar products very close to each other while keeping the embeddings for different products far apart. This process, known as metric learning, allows our system to recognize product similarities without needing to put every product into a rigid category.

    This approach is particularly powerful for eCommerce, where we often need to match products across different websites that might use different names or images for the same item. By focusing on the key features that make each product unique, our system can accurately match products even in challenging situations.

    How Siamese Networks Work?

    Imagine having a pair of identical twins who are experts at spotting similarities and differences. That’s essentially what a Siamese network is – a pair of identical AI systems working together to compare things.

    How it works:

    • Twin AI systems: Two identical AI systems look at two different products.
    • Creating ‘fingerprints’ or ‘embedding’: Each system creates a unique ‘fingerprint’ of the product it’s looking at.
    • Comparison: These ‘fingerprints’ are then compared to see how similar the products are.

    Architecture

    The architecture of a Siamese network typically consists of three main components: the shared network, the similarity metric, and the contrastive loss function.

    • Shared Network: This is the ‘brain’ that creates the product ‘fingerprints’ or ‘embeddings.’ It is responsible for extracting meaningful feature representations from the input samples. This network is composed of layers of neural units that work together. Weight sharing between the twin networks ensures that the model learns to extract comparable features for similar inputs, providing a basis for comparison.
    • Similarity Metric: After the shared network processes the inputs, a similarity metric is employed. This decides how alike two ‘fingerprints’ or ‘embeddings’ are. The selection of a similarity metric depends on the specific task and characteristics of the input data. Frequently used similarity metrics include the Euclidean distance, cosine similarity, or correlation coefficient, each chosen based on its suitability for the given context and desired outcomes.
    • Loss Function: For training the Siamese network, a specialized loss function is used. This helps the system improve its comparison skills over time. It guides and trains the network to generate akin embeddings for similar inputs and disparate embeddings for dissimilar inputs.

      This is achieved by imposing penalties on the model when the distance or dissimilarity between similar pairs surpasses a designated threshold, or when the distance between dissimilar pairs falls below another predefined threshold. This training strategy ensures that the network becomes adept at discerning and encoding the desired level of similarity or dissimilarity in its learned embeddings.

    How DataWeave Uses Siamese Networks for Product Matching

    At DataWeave, we use Siamese Networks to match products across different retailer websites. Here’s how it works:

    Pre-processing (Image Preparation)

    • We collect product images from various websites.
    • We clean these images up to make them easier for our AI to understand.
    • We use techniques like cropping, flipping, and adjusting colors to help our AI recognize products even if the images are slightly different.

    Training The AI

    • We show our AI system millions of product images, teaching it to recognize similarities and differences.
    • We use a special learning method called “Triplet Loss” to help our AI understand which products are the same and which are different.
    • We’ve tested different AI structures to find the one that works best for product matching, including ResNet, EfficientNet, NFNet, and ViT. 

    Image Retrieval 

    • Once trained, our AI creates a unique “fingerprint” for each product image.
    • We store these fingerprints in a smart database.
    • When we need to find a match for a product, we:
      • Create a fingerprint for the new product.
      • Quickly search our database for the most similar fingerprints.
      • Return the top matching products.

    Matches are then assigned a high or a low similarity score and segregated into “Exact Matches” or “Similar Matches.” For example, check out the image of this white shoe on the left. It has a low similarity score with the pink shoe (below) and so these SKUs are categorized as a “Similar Match.” Meanwhile, the shoe on the right is categorized as an “Exact Match.”

    Similarly, in the following image of the dress for a young girl, the matched SKU has a high similarity score and so this pair is categorized as an “Exact Match.”

    Siamese Networks play a pivotal role in DataWeave’s Product Matching Engine. Amid the millions of images and product descriptions online, our Siamese Networks act as an equalizing force, efficiently narrowing down millions of candidates to a curated selection of 10-15 potential matches. 

    In addition, these networks also find application in several other contexts at DataWeave. They are used to train our system to understand text-only data from product titles and joint multimodal content from product descriptions.

    Leverage Our AI-Driven Product Matching To Get Insightful Data

    In summary, accurate and efficient product matching is no longer a luxury – it’s a necessity. DataWeave’s advanced product matching solution provides brands and retailers with the tools they need to navigate this complex landscape, turning the challenge of product matching into a competitive advantage.

    By leveraging cutting-edge technology and simplifying it for practical use, we empower businesses to make informed decisions, optimize their operations, and stay ahead in the ever-evolving eCommerce market. To learn more, reach out to us today!

  • Why Strategic Competitive Insights Are Key to Optimizing Your Product Assortment

    Why Strategic Competitive Insights Are Key to Optimizing Your Product Assortment

    For retailers, the breadth and relevance of their product assortment are critical for success. Amid a crowded market filled with countless products clamoring for consumer attention, retailers must find innovative ways to distinguish themselves. While pricing undeniably impacts purchasing decisions, the diversity and distinctiveness of a retailer’s product range can provide a crucial competitive advantage.

    Creating an attractive and profitable assortment that resonates with your target audience requires more than intuition; it demands deep insights into both your own and your competitors’ offerings. A well-curated assortment aligned with current trends can drive higher conversions and foster customer loyalty. However, achieving this perfect balance is a formidable challenge without the right insights.

    This is where a data-driven strategy becomes essential, enabling you to curate a product mix that captivates and converts.

    However, retailers often encounter significant challenges when attempting to strategically plan their assortments:

    • Limited Competitive Insights: Gaining a clear understanding of your competitors’ assortment strengths and weaknesses across various categories is challenging. Without this visibility, it’s difficult to know where you have an edge or where you might be falling behind.
    • Tracking Demand Patterns: Identifying top-sellers and monitoring shifts in consumer demand can be a struggle. Without the ability to easily detect trends or changes in demand, you risk missing opportunities to stock trending items.

    Attempting to navigate these challenges manually is not only arduous but also susceptible to substantial errors.

    How Assortment Analytics Solutions Help

    The ideal Assortment Analytics solution must offer a fact-based approach to:

    • Identify Strengths and Weaknesses: Understand how your assortment measures up against the competition.
    • Stay Trend-Responsive: Keep your product mix fresh and aligned with the latest consumer trends.
    • Boost Conversions: Create a relatively unique, customer-focused assortment that enhances conversions.

    Many retailers attempt to analyze competitor assortments using manual, in-house methods, which inevitably leads to significant blind spots:

    • Variations in product classifications and taxonomies across competitors make meaningful comparisons challenging.
    • Gathering complete and accurate data across a vast competitive landscape is difficult.
    • Inconsistent titles and descriptions hinder reliable product matching without AI assistance.
    • Capturing and comparing detailed product attributes efficiently is nearly impossible without advanced tools.

    To overcome these challenges, retailers need a scalable, accurate Assortment Analysis solution designed specifically for the complexities of modern retail needs.

    DataWeave’s Assortment Analytics Solution

    DataWeave addresses these challenges by providing retailers with a robust platform to gain actionable insights into their product assortments and the competitive landscape. Leveraging advanced analytics and AI-driven algorithms, Assortment Analytics empowers retailers to make informed assortment management decisions, optimize their product offerings, and stay competitive.

    Armed with our insights, retailers can lead with their strengths and stock unique and in-demand products in their assortment. Further, by recognizing the strengths in their product catalog, they can craft effective pricing strategies and optimize their logistics, creating a more competitive and appealing shopping experience for their customers. Here are a few capabilities of DataWeave’s solution:

    In-Depth Competitive Analysis Across Retailers

    The solution offers detailed competitive analysis, revealing insights into competitors’ assortments. It maps competitor products to a common taxonomy, making comparisons accurate and meaningful. Retailers can visualize assortments at granular levels like category, sub-category, and product type.

    The data for these insights is collected at configurable intervals, typically monthly or quarterly, and is consumed not only via dashboard summaries but also raw data files to enable in-depth analysis. Retailers have the flexibility to choose specific competitors, brands, products, and categories for tracking, allowing for a tailored and strategic approach to assortment optimization.

    Brand and Category Views to Assess Your Portfolio

    The solution provides a comprehensive evaluation of your product assortment through brand and category views. In brand views, your portfolio is assessed against competitors at the brand level, highlighting:

    • Newly Introduced Brands: Insights into recently introduced brands, revealing shifts in the brand landscape.
    • Absence or Limited Presence: Identification of brands lacking representation or with minimal presence compared to competitors, indicating areas for improvement.
    • Strong Presence and Exclusivity: Recognition of brands where you excel, including exclusive offerings, showcasing your competitive edge.

    Identifying Top-Selling Competitive Products To Boost Assortment Strategy

    Beyond just comparing assortment numbers, the DataWeave solution surfaces insights into which competitor products are actually performing well. It equips category and assortment managers with indicators that assess competitor products in terms of their popularity and shelf velocity.

    It analyzes metrics like pricing fluctuations, ratings, customer reviews, search rankings, and replenishment rates to pinpoint hot sellers you may want to stock. With these insights, merchandizing managers can pinpoint top-selling products among competitors, enabling informed decisions to enhance their assortment in comparison.

    Sophisticated Attribute Tagging and Analysis

    Using AI-powered attribute tagging, the solution simplifies granular product analysis within specific categories. An Apparel retailer, for instance, can filter the data to compare assortments based on attributes like material, pattern, color, etc.

    Retailers can select attributes relevant to their products and gain detailed insights. These custom filter attributes dynamically populate the panel, facilitating targeted data exploration. Category and merchandizing managers can delve into critical details swiftly, enabling strategic decision-making and comprehensive competitive analysis within their categories.

    You can capitalize on opportunities by stocking in-demand, on-trend items and address assortment gaps quickly. At the same time, you can double down on your strengths by enhancing your exclusive or top-performing product sets.

    In summary, DataWeave’s Assortment Analytics solution provides an invaluable competitive edge. The insights enable evidence-based decisions to attract more customers, encourage bigger baskets, and maximize the value of every assortment choice.

    To learn more, read our detailed product guide here or get on a exploratory call with one of our experts today!

  • 5 Must-Have Capabilities of Your Ideal Competitive Pricing Intelligence Solution

    5 Must-Have Capabilities of Your Ideal Competitive Pricing Intelligence Solution

    In the cutthroat world of retail, where razor-thin margins and fierce competition reign supreme, pricing becomes your secret weapon to driving sales. The magic bullet unlocks sales, attracts customers, and ultimately fuels your bottom line. But with ever-changing market trends and competitor tactics shifting constantly, effective pricing strategies become even more crucial.

    A recent Bain & Company study highlights this very point. 78% of respondents acknowledged that their pricing decisions could be improved, leaving significant revenue untapped. John Furner, President and CEO of Kroger, echoes this sentiment. In a press release announcing a new pricing strategy, he emphasized their commitment to “providing our customers with predictable, affordable prices on the products they need most.” This focus on transparent and consistent pricing reflects the growing importance of building trust with customers, where value goes beyond just the lowest price tag.

    The right pricing strategy can unlock a treasure trove of benefits for retailers, including attracting new customers, boosting sales, and ultimately increasing their bottom line.

    But here’s the challenge: keeping pace with market trends and competitor strategies requires constant vigilance. This is where an advanced, user-centric pricing intelligence tool comes into play. Retailers need a platform specifically designed to address their unique challenges. It should empower them to protect margins, create a seamless pricing process, and attract and retain price-sensitive customers. To help you navigate this landscape, we’ve identified the must-have capabilities of a pricing intelligence solution that will transform your pricing strategy and propel your business toward long-term success.

    1. Reliable and Accurate Data Collection

    Retailers need a competitive intelligence solution that goes beyond merely capturing information en masse from competitor sites. An ideal solution ensures that data is consistent, extensive, and highly accurate, with an added level of granularity. This is achieved through statistical process control methods for data quality assurance, enabling highly accurate data capture and processing.

    Such a platform should be capable of scraping data from various sources, including desktop sites, mobile sites, and mobile applications, as well as a variety of online platforms: aggregators, omnichannel retailers, delivery intermediaries, quick commerce platforms, D2C sites, and more. This versatility ensures that data is captured across any global region and in dozens of languages, making the system geography and language agnostic.

    DataWeave’s solution includes a fast and automated data source configuration system, enabling a swift setup of new web sources for data capture. This capability ensures that retailers can stay ahead of the curve as the market landscape and competitor strategies evolve.

    An effective competitive pricing intelligence solution allows retailers to move away from working with incomplete or inaccurate data and instead leverage a comprehensive information stream to create strategic pricing decisions and optimize their overall business strategy. At the end of the day, the insights you base your decisions on are only as good as the data you aggregate. Even with the world’s best analytics engine, it’s always a case of “garbage in, garbage out.”

    2. Hyperlocal Insights From Store-Level Data

    Monitoring pricing and availability across specific stores is crucial for retailers to gain critical insights into a vast network of locations, enabling them to make strategic decisions that enhance pricing strategies and supply chain effectiveness, thereby minimizing stockouts or pricing inefficiencies in key markets. A platform like DataWeave provides retailers with a comprehensive view of store-level data across ZIP codes, maintaining a hyperlocal competitive strategy. It offers detailed visibility into product availability, highlighting out-of-stock scenarios across different competitors. This capability is invaluable, allowing quick identification of price improvement opportunities and providing retailers with a bird’s eye view of where products can be priced higher than usual to gain margins.

    The system operates at configurable intervals—daily, weekly, or monthly—enabling retailers to keep a vigilant eye on pricing, product availability, and delivery timelines based on the selected fulfillment option. Unlike many other providers who offer limited insights from a sample of stores, this solution delivers exhaustive analytics from every storefront. This comprehensive approach grants retailers (and brands) a strategic edge, facilitating efficient inventory tracking, precise pricing adjustments, and rapid responses to fluctuating market dynamics.

    3. Sophisticated, AI-Powered Product Matching

    A solution that matches products accurately at scale is essential for a robust and reliable competitive pricing strategy. Advanced platforms use unified systems for both text and image recognition to accurately match similar SKUs across thousands of eCommerce stores and millions of products. Deep learning architecture is employed to develop unique AI that matches text and images, grouping similar products based on their features, ensuring accurate matches even for private label products.

    This AI identifies critical elements of products in images, such as focusing on the top half of a model wearing a shirt, the sleeve length, the color of the product, etc.. Deep learning models, trained on extensive datasets of images, enhance these images by removing irrelevant background details and improving the quality of the core product image. Innovative AI then extracts unique signatures from the photos, allowing for quick and efficient identification and grouping of products across billions of indexed items.

    No matter how powerful the AI, combining it with human expertise is key to achieving true data veracity—ensuring accuracy, freshness, and comprehensive coverage required for reliable product matching. A human-in-the-loop approach elevates the AI-powered product matching process by addressing key challenges. AI algorithms may initially identify product matches with 80-90% accuracy, but human validation corrects errors, pushing accuracy closer to 100%. Humans apply contextual judgment for subjective criteria like aesthetics and design, making nuanced decisions that quantitative rules might miss. Continuous learning through an iterative feedback loop allows AI models to quickly adapt to changing trends and preferences as human experts provide context and re-label incorrect predictions. By integrating AI’s automation and scale with human validation, judgment, and knowledge curation, pricing intelligence solutions can achieve the accuracy and coverage necessary for actionable competitive pricing insights.

    This approach results in retailers being able to match products and compare prices between identical products, similar products, and private label brands.

    4. Unit of Measure Normalization

    Effective product matching and grouping are crucial for maintaining competitive pricing, but this requires a tech stack that can normalize units of measure across various sites. For example, a 10.75oz can of chicken noodle soup priced at $3 may seem cheaper than a 12.90oz can priced at $3.20, but this isn’t always the case. Initially, the larger package might appear more expensive, but when prices are compared based on the same unit amount, it often offers better value. Therefore, it is essential for retailers to standardize units to accurately compare prices. Advanced technology goes beyond simply matching products; it ensures accurate comparisons by normalizing unit measurements, including weight, quantity, and volume—crucial factors for establishing a clear pricing picture across competitors.

    Imagine comparing soup prices regardless of whether they are advertised in ounces, milliliters, or liters. By normalizing unit measurements, retailers can develop tailored pricing strategies on a level playing field, eliminating the risk of being misled by seemingly lower prices that conceal smaller quantities. Unit normalization allows retailers to uncover hidden value propositions by accurately determining the cost per unit, enabling them to set competitive prices, highlight the true value of their products, and make data-driven decisions.

    5. Ease of Actionability

    The most valuable insights are ineffective if they cannot be easily accessed and acted upon. Imagine a solution that not only provides industry insights but also customizes alerts and dashboards to show exactly how your prices compare to competitors in your specific categories and product groupings. An ideal solution would offer all this in one centralized platform, giving retailers easy access to data through intuitive dashboards, seamless data export options, and flexible API integrations. This enables a smooth, effortless process for adopting and utilizing the platform.

    Ease of use and actionable insights should be at the core of such a solution. A SaaS-based web portal can provide businesses with access to insights through user-friendly dashboards, detailed reports, and impactful visualizations. Customized insights tailored for each persona within the organization facilitate swift actions on relevant competitive intelligence. Whether it’s day-to-day tactical recommendations or inputs for long-term strategies, the platform should ensure that all insights are easily consumable and actionable.

    Moreover, the data should be accessible using plug-and-play APIs, enabling businesses to integrate external data with their internal pricing or ERP systems and BI tools. This integration generates predictive intelligence, enhances decision-making, and drives more robust business outcomes.

    Choosing the Right Pricing Intelligence Solution Will Determine Your Success

    Retailers need to leave behind generic pricing intelligence tools. For true success, retailers need a solution built to tackle their specific challenges. With capabilities like comprehensive data collection capturing granular details across regions and languages, local insights into store-level data for informed decision-making, accurate price comparisons with unit normalization, and access to actionable insights, retailers gain a complete and holistic picture of the pricing landscape, setting them up for success. Additionally, AI-powered and human-aided product matching ensures accurate competitor analysis

    These are just some of the essential capabilities DataWeave offers to retailers. By prioritizing these, retailers can transform their pricing strategy into a profit-generating machine, keeping them ahead of the curve and exceeding customer expectations in a competitive market to help them stay at the forefront of their categories.

    To learn more, talk to us today!

  • 6 Common Pricing Intelligence Challenges Retailers Face (And How to Overcome Them)

    6 Common Pricing Intelligence Challenges Retailers Face (And How to Overcome Them)

    When your product pricing is sub-optimal, you leave money on the table. This is especially significant for eCommerce retailers who must contend with their consumers ‘shopping around’ for the best price before making a purchase. All eCommerce retailers experience some amount of cart abandonment. In fact, the average cart abandonment rate is estimated at 70.19%, and the reason is often that customers find a better price elsewhere, whether at other online stores or in traditional brick-and-mortar ones.

    If you want to win the business of price-sensitive shoppers, you need a robust pricing strategy to keep up with changing competitor pricing. That’s one reason (among others) that retailers rely heavily on pricing intelligence solutions. With the right pricing intelligence solution, retailers can stay on top of market shifts, manage profit margins, maintain price perception, and of course, price their products competitively.

    Unfortunately, adding a new pricing intelligence solution to a retailer’s tech stack is not without its challenges. But the good news is there are ways to overcome them.

    In this post, we’ve rounded up six challenges most commonly cited by retailers and proposed strategies to overcome them. So if you’re considering a pricing intelligence solution that can get you closer to your business goals, read on to learn more.

    1. Scalability Constraints

    As access to the internet has expanded globally, the ratio of brick-and-mortar sales compared with eCommerce continues to narrow. A natural consequence of this is that more shoppers than ever before now browse and buy across diverse web environments, including mobile apps.

    This means that retailers need to track pricing across not just websites and physical stores, but also across mobile apps — a sales channel that was largely sidelined before.

    Modern pricing intelligence solutions need to consolidate data from:

    • Online storefronts
    • Mobile apps


    … and also from delivery channels, which often have different assortments and pricing:

    • Standard home delivery
    • Expedited, same-day home delivery
    • Buy online, pickup in-store (BOPIS)
    • Subscription
    • Curbside pickup


    In this context, imagine having to track the pricing of millions of SKUs compared against dozens of competitors each day. When new channels and devices are added, many pricing intelligence solutions in the market are unable to handle such data complexity and scale. They’re not built to continually grow and expand to meet changing needs. Even worse, some retailers opt for homegrown DIY systems, which struggle to keep the datasets updated, accurate, and current—activities that require significant cost and human effort.

    How DataWeave Bridges This Gap:

    What you need is a platform that can track millions of SKUs across dozens of competitors and geographies. No matter where the data is coming from or how vast the demand for the product is, an ideal solution should be able to synthesize huge amounts of complex data and generate meaningful insights.

    Your competitors are continually changing their eCommerce setup, whether through subtle changes to their product attribute listings or broader changes to domains or apps. With DataWeave’s pricing intelligence solution designed to scale up as required, you never need to worry about the backend flexing to accommodate changes.

    2. Inability to Match Products Without Clear UPC/EAN Identifiers

    Another problem with many pricing intelligence solutions is their inability to match products if a UPC/EAN identifier is missing. Often, a competitor will list an identical product on their storefront and omit any clear identifiers. On Amazon, an ASIN might be listed or you might be able to bring in a DPCI from Target.com. However, without clear identifiers across eCommerce platforms, retailers struggle to aggregate every instance of the products, and as a result, are unable to achieve accurate pricing comparisons. They often face this challenge when they work with commoditized web scraping service providers who have very limited expertise or experience in refining the data into meaningful insights.

    How DataWeave Bridges This Gap:

    If you can’t match UPC/EAN codes, you need a solution that leverages artificial intelligence to match products based on other variables, such as product titles, descriptions, and images. AI, in combination with human expertise, can take on the task at a speed and accuracy that would be unfeasible for humans alone.

    Artificial Intelligence is constantly learning and improving. At DataWeave, we accelerate this process by introducing new scenarios and datasets for the product to continually learn from. At the outset, our AI product matching is roughly 80-90% accurate every time. To improve this number to over 95%, we introduce human validation and nuanced judgments. Over the years, this feedback loop has continued to refine its algorithms, resulting in near-perfect data accuracy for retailers.

    Our solution uses AI built on more than ten years of data to perform robust product matching for retailers at a massive scale. Using a unified platform with text and image recognition, DataWeave matches products from among hundreds of eCommerce websites and across millions of products.

    3. Poor and Inconsistent Data

    Retailers often complain that the data within their pricing intelligence solution isn’t accurate, is inconsistent, and may even be comprised of statistical smoothing and gap-plugging smokescreens. The root of this problem often lies in the inability of these tools to consistently track prices across diverse web environments. Poorly designed web scraping infrastructure fail when eCommerce websites change their underlying configuration and structure (which happens periodically). As a result, they don’t have enough data to see the market as a whole, and end up viewing synthetic or small sample-set data.

    How DataWeave Bridges This Gap:

    At DataWeave, transparency drives our approach to delivering insights. We only present real-world data in our data feeds and dashboards to customers. This is possible only due to the supreme confidence we have in our ability to consistently capture and present accurate data. We achieve this by using a combination of AI and sophisticated web scraping infrastructure developed and enhanced over a decade.

    In fact, we are the first in the industry to launch a Data Statistics Dashboard that helps our customers scrutinize match rates, track data freshness, highlight any gaps in the data, and manage product matches independently.

    4. Limited Integration Options with Internal Systems

    Too often, a retailer will select a pricing intelligence solution that promises exceptional insights but then fails to offer a manageable workflow for day-to-day use. This usually happens because it doesn’t integrate with the retailer’s existing tech stack.

    Without a convenient process that connects internal systems, your pricing intelligence solution is just another piece of technology that your team does not use to its full potential. You may require your competitor pricing data to flow into price optimization tools, price management tools, BI tools, ERP systems, or revenue management systems. Without this capability, you’ll see limited ROI and underwhelming results because all the insights in the world are of little use if you can’t consume them easily and put them into action.

    How DataWeave Bridges This Gap:

    At DataWeave, we understand the importance of being able to integrate external data with your internal tech stack. Our data can be accessed and extracted using plug-and-play APIs, enabling businesses to combine their external and internal data to generate predictive intelligence.

    We also have other data feeds ready to be integrated, including FTP and Amazon S3. Our integration experts can work with you to create custom integrations to existing internal pricing platforms. Our ultimate goal here is to seamlessly elevate your pricing intelligence strategy with minimal change management.

    5. Limited Custom Analysis Capabilities

    Every retailer is unique. There are various geographies, languages, markets, product categories, and pricing strategies that differentiate one retailer from the next. Many retailers find it challenging to derive actionable insights from their pricing intelligence solution because the analysis and customization capabilities are too limited.

    For example, some retailers might want to evaluate their competitiveness after applying coupons and promos to selling prices. Others may want to perform a one-time pricing analysis of just list prices across competitors. Some may want to view insights that help them take tactical decisions day-to-day, while others would like a historical view across multiple dimensions to help make strategic long-term pricing decisions.

    Without the ability to customize their views or the underlying data, retailers could feel restricted in their ability to drive meaningful impact with their pricing intelligence.

    How DataWeave Bridges This Gap:

    What you need are foundational dashboards, reports, and visualizations in a web portal that can be tailored to your business needs. Then, you need the expertise and guidance of a team of business analysts who can help you configure custom reports and dashboards.

    At DataWeave, we offer bespoke insights for each persona, enabling swift actions on relevant competitive intelligence. These include day-to-day tactical recommendations or inputs for long-term strategies. And because all DataWeave customers get access to our team of expert analysts, it’s simple and straightforward to configure unique reports and dashboards to suit your business.

    6. Sloppy Support

    No solution, at least not one that undertakes complex work, works optimally with a ‘set it and forget it’ approach. From time-to-time, you need human intervention to ensure your pricing intelligence is working in the way that it needs to for you. Unfortunately, one of the most common challenges retailers face with their pricing intelligence tool is a lack of support.

    Unavailable or patchy customer support is a significant challenge that can result in low confidence, delayed resolutions, and even abandoned pricing actions.

    How DataWeave Bridges This Gap:

    Dataweave’s global team of pricing experts are available around the clock for support and guidance. Not only do we have tech experts and business analysts that you can consult at any point, we also have an exceptional team of customer success professionals to help you overcome any technical and strategic issue you might face.

    As one customer puts it, with DataWeave you gain access to: “Excellent customer service, super collaborative staff, user-friendly interface.”

    Another verified user from the consumer goods industry had this to say:

    “Great platform and customer service! Our client service team is very helpful and always responds to ad-hoc requests in a very timely matter!”

    Read more reviews from real DataWeave users on G2: https://www.g2.com/products/dataweave/reviews

    Finding The Right Pricing Intelligence Solution

    As the competition heats up, retailers need to unlock every available opportunity to gain an edge and capture market share. At DataWeave, our AI-powered pricing intelligence software helps you uncover gaps quickly and build a pricing strategy that is as attractive as it is effective. Our ability to scale, match your products across the entire ecosystem with consistent accuracy, and slide right into your current operations to provide advanced analytics, makes us the preferred choice for many of the world’s leading retailers.

    Want to start benefiting from actionable product matching and pricing intelligence? Request a demo today.

  • Cinco de Mayo 2024 Pricing Insights: An Analysis of Discounts Amid Inflation

    Cinco de Mayo 2024 Pricing Insights: An Analysis of Discounts Amid Inflation

    Cinco de Mayo is a vibrant celebration of Mexican-American and Hispanic heritage, marked by lively parades, festive tacos, and refreshing tequila across North America. For the service industry, brands, and retailers, this day offers a golden opportunity to roll out enticing promotions on beloved Mexican foods and beverages, drawing in large crowds and boosting sales.

    Americans love to indulge in Mexican cuisine during Cinco de Mayo. Take avocados, for example: despite inflation, avocado sales soared to 52.3 million units this year, marking a 25% increase from last year, according to the Hass Avocado Board’s 2023 Holiday Report. Such festive events see a significant sales spike, largely driven by appealing discounts and special offers.

    So, what discounts did retailers roll out this Cinco de Mayo?

    At DataWeave, our cutting-edge data aggregation and analysis platform tracked and analyzed the prices and deals on Mexican food and alcohol products offered by leading retailers. Our in-depth analysis sheds light on their pricing competitiveness during Cinco de Mayo, revealing how pricing strategies differed across various subcategories and brands.

    We conducted a similar analysis in 2022, allowing us to compare the prices of identical products this year versus last year. This comparison helps us understand the impact of inflation over the past two years on the prices offered today.

    Our Methodology

    For our analysis, we monitored the average discounts offered by major US retailers on over 2,000 food and beverage products during Cinco de Mayo, as well as in the days leading up to the event. Many retailers kick off their Cinco de Mayo promotions a week before, so we included the entire week leading up to May 5th in our analysis.

    Key Details:

    • Number of SKUs: 2000+
    • Retailers Analyzed: Target, Amazon Fresh, Safeway, Walmart, Total Wines & More, Sam’s Club, Meijer, Kroger
    • Categories: Food, Alcohol
    • Analysis Period: April 28 – May 5

    To truly demonstrate the value of Cinco de Mayo for shoppers, we concentrated on price reductions and additional discounts during the event. By comparing these with regular day discounts, we were able to highlight the genuine savings and benefits that Cinco de Mayo promotions offer to budget-conscious consumers.

    Our Findings

    Safeway led the pack with the highest average additional discount of 4.91%, covering 38.6% of their food inventory for Cinco de Mayo. Total Wine & More followed closely, offering an average discount of 3.46% across 70.8% of its tequila, whiskey, mezcal, and other spirit products during the Cinco de Mayo week.

    In contrast, Target provided minimal additional discounts, averaging just 0.8% over a small fraction (11.6%) of its SKUs. Similarly, Kroger’s additional discounts were also 0.8%, but they were spread across over 60% of its tracked products. Walmart (1.4%) and Amazon Fresh (1.2%) offered relatively conservative discounts during the sale period.

    During Cinco de Mayo, various brands rolled out attractive discounts to entice shoppers. Among beverage brands, The American Plains vodka led the way with the highest average discount of 20.80%. Coffee brands also joined the festivities with significant discounts: Death Wish Coffee at 14.30%, Dunkin’ at 11.10%, and Starbucks at 5.70%. Notably, Dunkin’ and Death Wish Coffee introduced complimentary beverages such as whiskey barrel-aged coffee and spiked coffee products to celebrate the event.

    In the wine category, Erath stood out with a 10% additional discount. However, brands like Jose Cuervo and Franzia offered more modest discounts of 0.70% and 1.80%, respectively.

    Food brands associated with traditional Mexican ingredients or products, such as tortillas, salsas, and spices, provided higher discounts compared to mainstream snack brands. For instance, McCormick (25%), El Monterey (13.3%), and La Tortilla Factory (16.7%)—known for ready-to-eat frozen foods, seasonings, and condiments—delivered the highest discounts. Other notable discounts included Jose Ole (12.5%), a frozen food brand, and Yucatan (8.3%), known for its guacamole.

    Safeway’s private label brand, Signature Select, offered a 5.20% discount. Additionally, Safeway provided deep discounts on brands like Pace, Herdez, and Taco Bell, indicating an aggressive discounting strategy. In contrast, brands closely associated with Mexican or Tex-Mex cuisine, such as Old El Paso, Mission, Rosarita, and La Banderita, offered relatively modest discounts ranging from 0.5% to 3.3%.

    The discount patterns varied between alcohol and food categories, with food brands generally offering higher discounts. This trend may be attributed to pricing being regulated in the alcohol industry. These differing discount levels highlight how brands navigated the balance between driving sales and maintaining profit margins during Cinco de Mayo, particularly in the context of inflation affecting costs.

    Impact of Inflation on Cinco de Mayo Prices (2024 vs 2022)

    To gauge the impact of inflation on popular Cinco de Mayo products, we analyzed the average prices at Walmart and Target between 2022 and 2024. These two retailers were chosen due to their prominence in the retail sector and the robustness of our sample data.

    At Walmart, the Tex Mex category saw the highest average price increase, rising by 22.51%. Other notable subcategories with significant price hikes include Condiments (23.21%), Vegetables/Packaged Vegetables (21.22%), and Lasagne (14.10%). Categories like Dips & Spreads (13.77%), Pantry Staples (14.92%), and Salsa & Dips (8.23%) experienced relatively lower increases.

    At Target, the Snacks subcategory had the steepest average price rise at 27.94%, followed by Meal Essentials (16.07%) and Deli Pre-Pack (8.82%). Categories such as Dairy (0.51%), Frozen Meals/Sides (7.11%), and Adult Beverages (7.41%) saw smaller price increases.

    Brands associated with traditional Mexican or Tex-Mex cuisine faced higher price hikes. Examples include Old El Paso (24.59% at Walmart, 8.70% at Target), Tostitos (35.44% at Walmart, 11.41% at Target), Ortega (30.59% at Walmart, 19.69% at Target), and Rosarita (14.39% at Walmart).

    In contrast, private label or store brands generally experienced lower price increases compared to national brands. For instance, Good & Gather (Target’s private label) saw a 9.55% increase, while Market Pantry (Walmart’s private label) had a 17.27% rise. This trend is understandable as retailers have more control over their costs with private label brands.

    The data clearly indicates that both Walmart and Target have significantly raised prices across various categories and brands, reflecting the broader inflationary environment where the cost of goods and services has been steadily climbing.

    Interestingly, we observed higher price increases at Walmart compared to Target. Although Walmart is renowned for its consumer-friendly pricing strategies, it too had to elevate grocery prices post-2022 to combat inflationary pressures. As consumers become more cost-conscious and reduce spending on discretionary items, Walmart and other retailers are now cutting prices across categories to align with shifting consumer behaviors.

    Mastering Pricing Strategies During Sale Events

    Our pricing analysis for Cinco de Mayo reveals compelling insights into the dynamics of retailer landscapes in the US. It highlights the enduring relevance of private label brands, even amidst fluctuating demand, showing the emergence of local, national, and small players vying for market share.

    As retailers navigate inflationary pressures and evolving consumer behaviors, understanding these pricing dynamics becomes crucial for optimizing strategies and bolstering market competitiveness. This analysis offers actionable intelligence for retailers seeking to navigate the intricate terrain of sale event promotions while addressing shifting consumer preferences and economic challenges.

    Access to reliable and timely pricing data equips retailers and brands with the tools needed to make informed decisions and drive profitable growth in an increasingly competitive environment. To learn more and gain guidance, reach out to us to speak to a DataWeave expert today!

  • How Monitoring and Analyzing  End-User Prices can Help Retailers and Brands Gain a Competitive Edge

    How Monitoring and Analyzing  End-User Prices can Help Retailers and Brands Gain a Competitive Edge

    Retailers and brands are constantly engaged in a fierce battle over prices and discounts. Whether it’s major events like Amazon Prime Day, brand-led sales, or everyday price wars, they depend on pricing intelligence and digital shelf analytics to fine-tune their strategies. With a variety of offers such as sales, promotions, and bundles, determining the actual cost to the customer becomes a complex task. The price set by the brand, the retailer’s offer, and the final amount paid by the customer often vary significantly.

    In their analysis, retailers and brands frequently focus on the listed price or the final sale price, overlooking a critical factor: the “end-user price.” This includes all discounts, taxes, and shipping costs, providing a more accurate picture of what customers are truly willing to pay at checkout.

    Grasping this end-user price is vital for both retailers and brands. For retailers, it helps them stay competitive and refine their promotional strategies. For brands, it offers insights into competitive positioning, net revenue management, and shaping customer price perception.

    However, emphasizing the end-user price is challenging, as it involves comprehending all the intricate elements of pricing.

    How end-user pricing is calculated

    The list price, also known as the manufacturer’s recommended retail price (MSRP), is the initial price set by the brand. This may not always be displayed on marketplaces, especially in categories like grocery. The selling price, on the other hand, is the amount at which a retailer offers the product, often reduced from the list price. The end-user price is the actual amount the customer pays at checkout, which includes taxes, promotions, and other factors that affect the final cost.

    The process involves 3 key stages:

    Step 1: Identifying and categorizing promotional offers

    The first critical step in calculating end-user pricing is to identify and categorize the various promotional offers available for a given product that can reduce the final amount paid by the consumer. These promotions span a wide range of types:

    • Bank Offers: Involving discounts or cash back incentives when paying with specific bank credit or debit cards. For instance, a customer may receive 10% cashback on their purchase by using a specific bank’s card.
    • Bundled Deals: Combining multiple products or services at a discounted bundle price. A common example is a smartphone bundle including the phone itself, a protective case, and earphones at a reduced total cost.
    • Promo Codes/Coupons: Customers can enter promo codes or coupons during checkout to unlock special discounted prices or percentage-off offers, like 20% off a hotel booking, or even a special brand discount personalized for their needs (think loyalty offers and in-app promotions).
    • Shipping Offers: These include free shipping or reduced shipping fees for certain products or orders, such as free delivery on orders above a set amount.
    • TPRs (Temporary Price Reductions): TPRs play a significant role in the strategies of most retailers. Brands and retailers use them to encourage shoppers to purchase more of a product or to try a new product they wouldn’t usually buy. A TPR involves reducing the price of a product by more than 5% from its regular shelf price.

    By accurately identifying and classifying each type of promotion available, brands can then calculate the potential end-user pricing points.

    Step 2: Accounting for location and fulfilment nuances (delivery, in-store pickup) that impact final pricing

    Product pricing and promotional offers can vary based on the consumer’s location or ZIP code. Additionally, customers may opt for different fulfilment modes like delivery, shipping, or in-store pickup, which can further impact the final cost. Accurately calculating the end-user price necessitates considering these location-based pricing nuances as well as the chosen fulfilment method.

    In the example below, the selling price is $4.32 for one retailer (on the left in the image) after a discount for online purchase. In another case with Meijer, the item total shows $17.91, but the consumer ends up paying $15.74 after taxes and fees are applied (on the right in the image).

    Step 3: Applying each eligible promotion or offer to the selling price to determine potential end-user price points

    With the various promotional offers and discounts categorized in the previous steps, retailers and brands can now apply each eligible promotion to the product’s selling price. This involves deducting percentages for bank cashback, implementing bundled pricing, applying coupon code discounts, and incorporating shipping promotions.

    For retailers, this step allows them to calculate their true effective selling price to customers after all discounts and promotions. They can then compare this end-user price against competitors to ensure they remain competitively priced.

    For brands, by systematically layering every applicable offer onto the baseline selling price, they can accurately calculate the multiple potential end-user price points a customer may pay at checkout for their products across different retailers and regions.

    Why the end-user price matters

    Optimizing pricing strategies using the end-user price can benefit retailers and brands in several ways:

    • Price Competitiveness: By monitoring end-user pricing, retailers can adjust for discounts and promotional offers to attract customers, while brands can refine their pricing models to stay ahead in the market.
    • Customer Acquisition and Loyalty: Offers, promotions, and discounts directly impact the final price paid by customers, playing a crucial role in attracting new customers and retaining existing ones. For example, Walmart’s competitive pricing in groceries boosts customer loyalty and repeat purchases.
    • Consumer Perception: End-user pricing significantly shapes how consumers perceive both retailers and brands. Competitive pricing and promotional transparency enhance reputation and conversion rates. Amazon, for instance, is known for its competitive pricing and fast deliveries, which strengthen its consumer perception and satisfaction.
    • Sales Volumes: The final checkout price influences affordability and perceived value, directly affecting sales volumes. Both retailers and brands benefit from understanding this, as it guides consumer purchasing decisions and drives revenue streams.
    • Brand Perception: Consistent and transparent pricing enhances the perception of both the retailer and the brand. This not only strengthens the value proposition but also builds consumer trust and fosters long-term loyalty.

    While the listed and selling prices are readily available, calculating the true end-user price is quite complex. It involves meticulous tracking and application of various types of promotions, offers, location-based pricing nuances, and fulfillment costs – an uphill task without robust technological solutions.

    Track and Analyze end-user prices with DataWeave

    DataWeave’s end-user price tracking capability empowers retailers and brands with the insights and tools necessary to comprehend the complexities of pricing dynamics. For retailers, it offers the ability to monitor end-user pricing across various products and categories compared to competitors, ensuring competitiveness after all discounts and enabling optimization of promotional strategies. Brands benefit from informed pricing decisions, optimized strategies across retail channels, and a strengthened position within their industries.

    Our intuitive dashboard presents classified promotions and corresponding end-user prices across retailers, providing both retailers and brands with a transparent, comprehensive view of the end-user pricing landscape.

    Within the detailed product view of DataWeave’s dashboard, the Price and Promotions panel showcases diverse promotions available across different retailers for each product, along with the potential end-user price post-promotions.

    Harness the power of DataWeave’s sophisticated Pricing Intelligence and Digital Shelf Analytics to gain an accurate, real-time understanding of your end-user pricing dynamics. Make data-driven pricing decisions that resonate with customers and propel your brand toward sustained success.

    Find out how DataWeave can empower your eCommerce pricing strategy – get in touch with us today or write to us at contact@dataweave.com!

  • Augmenting AI-powered Product Matching with Human Expertise to Achieve Unparalleled Accuracy

    Augmenting AI-powered Product Matching with Human Expertise to Achieve Unparalleled Accuracy

    In today’s expansive omnichannel commerce landscape, pricing intelligence has become indispensable for retailers seeking to stay competitive and refine their pricing strategies. The sheer magnitude of eCommerce, spanning thousands of websites, billions of SKUs, and various form factors, adds layers of complexity. Consequently, ensuring the accuracy and reliability of competitive insights presents a formidable challenge for retailers aiming to leverage pricing data effectively.

    At the core of any robust pricing intelligence system lies product matching. This process enables retailers to recognize identical or similar products across competitors. Once these matches are identified, tracking prices is a relatively more straightforward task, facilitating ongoing analysis and informed decision-making.

    Accurate matching is crucial for meaningful price comparisons and tailoring product assortments. The challenge is matching products is often complicated, especially for non-local brands, niche categories, or items lacking consistent global identifiers. It becomes even trickier when trying to match very similar but not identical products. A comprehensive approach that compares and analyzes multiple attributes like product titles, descriptions, images and more is essential.

    Artificial intelligence algorithms are commonly used to automate product matching, leveraging machine learning techniques to analyze patterns in images and text data. While AI can adapt and improve over time, the question remains: Can it fully address the complexities of product matching on its own?

    The reality is that many retailers still struggle with incomplete, inaccurate, or outdated product data, despite these AI-powered product matching solutions. This can lead to suboptimal pricing decisions, missed opportunities, and reduced competitiveness.

    Challenges in an ‘AI-only’ Approach to Product Matching

    While AI plays a vital role in automated product matching solutions, there are complexities that AI alone cannot fully address:

    Subjectivity in Matching Criteria

    Some product categories have subjective or hard-to-quantify criteria for determining similarity. AI learns from historical data, so it may struggle with nuanced aspects like:

    Aesthetics, style, and design: In the Fashion and Jewellery vertical, for example, products are matched according to attributes like style, aesthetics, design – all of which have some subjectivity involved.

    Quantity/packaging variations: In the grocery sector, variations in product packaging and quantities can introduce complexities that require subjective decision-making. For example, apples may be sold in different packaging like a 0.5 kg bag or a pack of 4 individual apples. Determining if these different packaging options should be considered equivalent often involves making a qualitative judgment call, rather than a clear-cut objective decision.

    Matching product sets: For categories like home furnishings, the focus is often on matching coordinated sets rather than individual items. For example, in the bedroom category, matching may involve grouping together an entire set of complementary furniture like a bed frame, dresser, and wardrobe based on their cohesive design and style. This goes beyond simply making one-to-one product associations, requiring more nuanced judgments about aesthetic coordination.

    Contextual Factor

    Products can have regional preferences, cultural differences, or evolving trends that impact how they are matched. AI may miss important context like Local/regional product names or distinct brand names across countries.

    For instance, in the image we see Sprite (in the US) is branded Xubei in China. Continuous human curation is needed to help AI adapt to this context.

    High Accuracy & Coverage Expectations

    Retailers rely on AI powered and automated pricing adjustments based on product matching for insight. To ensure that pricing recommendations and updates are accurate, accurate product matching is crucial. For this, simply identifying similar top results is not enough – the process must comprehensively capture all relevant matches. While AI excels at finding the top groupings with around 80% accuracy, even small matching errors can have significant consequences.

    As AI matching improves, customer expectations may rise even higher. If AI achieves 90% accuracy, for instance, SLAs may demand over 95%. Reaching such a high level of accuracy is very challenging for AI alone, especially when faced with incomplete data, contextual nuances, evolving trends, and subjective matching criteria across products and categories.

    The solution is to combine the power of AI with human expertise. This is the key to achieving true data veracity – the accuracy, freshness, and comprehensive coverage required for precise and reliable product matching.

    Human-in-the-Loop Approach for Elevated Product Matching

    Human intelligence and quality testing can elevate the AI powered product matching process by addressing key challenges:

    • Matching Validation: AI algorithms may identify product matches with 80-90% accuracy initially. Having humans validate these AI-suggested matches allows for correcting errors and pushing the accuracy close to 100%. As humans flag issues, provide context, and re-label incorrect predictions, it allows the AI model to learn and enhance its reliability for complex, high-stakes decisions.
    • Applying Contextual Judgment: For subjective matching criteria like aesthetics, design, and categorizing product sets, human discernment is needed. Humans can make nuanced judgments beyond just quantitative rules, ensuring meaningful apples-to-apples product comparisons. Their contextual understanding augments AI’s capabilities.
    • Continuous Learning Via Feedback Loop: Product experts possess rich category knowledge across markets. Integrating this human insight through an iterative feedback loop helps AI models quickly learn and adapt to changing trends, preferences, and context. As humans explain their match assessments, the AI continuously enhances its precision over time.

    By combining AI’s automation and scale with human validation, judgment, and knowledge curation, pricing intelligence solutions can achieve the accuracy and coverage demanded for actionable competitive pricing insights.

    DataWeave’s Data Veracity Framework: A Scalable Workflow Combining AI and Human Expertise

    Given the vast number of products, retailers, and brands that exist today, any product matching solution must be highly scalable. At DataWeave, we bring you such a scalable workflow to address these complexities by integrating human expertise with AI-driven automation. The image below outlines our approach for combining AI with human intelligence in a seamless, scalable workflow for accurate product matching:

    Retailers and brands can benefit in several ways with this workflow, as listed below.

    Several Rounds of Data Verification Due to Hierarchical Validation Teams

    The workflow employs a hierarchical validation team of Leads and Executives to efficiently integrate human expertise without creating bottlenecks. Verification Leads play a pivotal role in managing the distribution of product matches identified by DataWeave’s AI model to the Verification Executives.

    The Executives then meticulously validate these AI-suggested matches, adding any missing product associations and removing inaccurate matches. After validation, the matched product groups are sent back to the Leads, who perform random sampling checks to ensure quality.

    Throughout this entire workflow, feedback and suggestions are continuously gathered from both the Executives and Leads. This curated input is then incorporated back into DataWeave’s AI model, allowing it to learn and improve its matching accuracy on an ongoing basis.

    This hierarchical structure ensures that human validation seamlessly scales alongside the AI’s matching capabilities. Leveraging the respective strengths of AI automation and human expertise in an iterative feedback loop prevents operational bottlenecks while steadily elevating overall accuracy.

    Confidence-based Distribution of Matched Articles for Validation

    The AI model assigns confidence scores, differentiating high-confidence (>95%) and low-confidence matches. For high-confidence groups, executives simply remove incorrect matches – a quicker process. Low-confidence matches require more human effort in adding/removing matches.

    As the AI model improves over time with feedback, the share of high-confidence matches increases, making validation more efficient and swift.

    Automated, Standardized Process with Iterative Feedback Loop

    The entire workflow is standardized and automated, with verification metrics seamlessly tracked. At each step, feedback captured from both leads and executives flows back into the AI, enhancing its matching accuracy and coverage iteratively.

    DataWeave’s closed-loop system of AI automation with hierarchical human validation allows product matching to achieve comprehensive accuracy at a vast scale.

    Unleash the Power Accurate and Comprehensive Product Matching

    In summary, combining AI and human expertise in product matching is crucial for retailers navigating the complexities of omnichannel retail. While AI algorithms excel in automation, they often struggle with subjective criteria and contextual nuances. DataWeave’s approach integrates AI-driven automation with human validation, delivering the industry’s most accurate product matching capabilities, enabling actionable competitive pricing insights.

    To learn more, reach out to us today!

  • How Gas Stations and Convenience Stores in the U.S. Can Adapt To Evolving Fuel Pricing Trends in 2024

    How Gas Stations and Convenience Stores in the U.S. Can Adapt To Evolving Fuel Pricing Trends in 2024

    As we move into the second quarter of 2024, the US energy landscape is poised for notable shifts that will impact gasoline and diesel prices. The shift towards renewable energy sources, evolving consumer preferences, and volatile global market forces are all converging to reshape the fuel retail industry.

    For fuel retailers, understanding these projections is crucial – changes in consumer demand and cost pressures can significantly affect their bottom line. In this article, we provide insights on the factors shaping the fuel pricing environment for the remainder of the year, covering a variety of fuel types.

    Gasoline Prices: A Downward Trend Ahead

    According to the January Short-Term Energy Outlook by the EIA, US retail gasoline prices are projected to decline in 2024. Similarly, the forecast also predicts reduced gasoline consumption in 2025. This is attributed to a significant increase in inventories, thanks to expanded refinery capacity. US operable refinery capacity has grown from 18.06 million barrels per day in January 2023 to 18.31 million barrels per day by December 2023.

    Meanwhile, the World Bank reports that global trade growth in 2024-25 is expected to be only half the average in the decade before the pandemic, leading to reduced consumption and demand.

    The increase in supply, coupled with this dip in demand and consumption expected in 2025, sets the stage for further price reductions. Such expansion not only enhances supply but also alleviates price pressures for consumers.

    Diesel Dynamics: Supply Up, Prices Down

    Similar supply-side dynamics are at play in the diesel market, with retail prices expected to fall in both 2024 and 2025. Despite a forecasted uptick in US diesel consumption in these two years, an increase in production capacity and easing inventory strains are likely to keep prices in check. This is particularly noteworthy, as diesel fuel plays a critical role in transportation and logistics, underpinning the movement of goods and services nationwide.

    Crude Oil and Crack Spreads: The Refining Equation

    Crude oil prices, a pivotal factor in the fuel price equation, are expected to mirror 2023 levels through 2024.

    The anticipated decrease in gasoline and diesel prices is largely attributed to narrowing crack spreads—the differential between wholesale fuel prices and crude oil. A lower crack spread signifies reduced refining costs, a welcome development for both refiners and consumers. This expectation is grounded in the increasing availability of refinery capacity and, consequently, fuel supply, even as demand shows signs of tapering off.

    Global Influences and Economic Implications

    The outlook is further buoyed by new refinery capacities coming online internationally, particularly in the Middle East. This global increase in refined product supplies is poised to ease price pressures for consumers not just domestically but across international markets. Interestingly, this forecast comes at a time when gasoline consumption is expected to remain flat or slightly decrease, a rare occurrence in the context of positive economic growth. This decoupling of fuel consumption from economic expansion highlights evolving consumer behaviors and efficiency gains across the automotive sector.

    Looking Ahead: Uncertainties and Transformations

    While the projections offer a glimpse into a future of potentially lower fuel prices, they are not without uncertainties. Factors such as crude oil price fluctuations, refinery shutdowns, and logistical challenges could sway outcomes.

    The projected decrease in US gasoline and diesel prices presents both opportunities and challenges.

    • For American consumers, lower fuel costs offer relief for household budgets and transportation expenses, potentially freeing up disposable income and stimulating broader economic activity.
    • However, these pricing trends pose a need for strategic planning and adaptation within the US energy sector. Companies must navigate shifting supply dynamics and the ongoing transition towards renewable energy sources – a pivotal chapter in the quest for sustainable and affordable solutions.
    • Energy firms will need to carefully analyze the implications, aligning their business models through refining capacity expansions, logistical optimizations, and a focus on renewable fuels.

    Staying Ahead of Competition with Fuel Price Tracking

    In this evolving landscape, closely tracking fuel prices and having access to up-to-date data is crucial for informed decision-making and staying competitive in the market for fuel retailers. While prices may go down in the long- to medium-term, ensuring short-term price competitiveness at a hyperlocal level is essential for gas stations and convenience stores navigating the changing tides.

    DataWeave’s real-time fuel pricing data, covering a wide range of fuel types from gasoline to diesel and updated as frequently as every 30 minutes, empowers retailers to quickly adapt to market changes and remain strategically aligned with evolving consumer preferences.

    By closely monitoring hyperlocal fuel price fluctuations across their coverage areas, retailers can quickly adapt their pricing strategies to remain competitive and align with shifting consumer behaviors.

    Further, DataWeave’s real-time fuel pricing intelligence can help retailers understand the relationship between crude oil prices, crack spreads (the differential between wholesale fuel prices and crude oil), and their own pricing strategies. Our solution offers real-time insights and analytics to help retailers navigate the evolving fuel pricing landscape.

    Visit our recently launched U.S. Fuel Price Interactive Dashboard which displays weekly fuel prices across 400+ unique ZIP codes, delivering insights into price changes by region, store, fuel type, and other dimensions.

    To learn more about DataWeave’s solutions or to discuss how we can support your fuel retail business, reach out to our team today!

  • Why Localized, Store-Specific Pricing and Availability Insights is Critical for Consumer Brands

    Why Localized, Store-Specific Pricing and Availability Insights is Critical for Consumer Brands

    Brands are becoming increasingly proficient in monitoring and refining their presence on online marketplaces, utilizing Digital Shelf Analytics to gather and analyze data on their online performance. These tools offer invaluable insights into enhancing visibility, adjusting pricing strategies, and improving content quality on eCommerce sites.

    Yet, as the retail landscape shifts towards a more integrated omnichannel approach, it’s crucial for brands, particularly those in CPG, to apply similar strategies to their offline channels. For brands that count physical stores among their primary sales channels, gaining localized insights is key to boosting in-store sales performance.

    Collecting shelf data from offline channels presents more challenges than online. Traditional methods, such as physical store visits, often fall short in reliability, timeliness, scale, and level of coverage.

    However, the world of eCommerce provides a solution. As part of the effort to facilitate options like buy-online-pickup-in-store (BOPIS) for shoppers, major retailers make store-specific product details available online. Consumers often go online and select their nearest store to make purchases digitally before choosing a fulfillment option like picking up at the store or direct delivery. Aggregating this store-level information offers brands critical insights into pricing and inventory across a vast network of stores, enabling them to make informed decisions that improve pricing strategies and supply chain efficiency, thus minimizing stockouts in crucial markets.

    Further, as consumers increasingly seek flexibility in how they receive their purchases—be it through in-store pickup, delivery, or shipping—brands need to adeptly monitor pricing and availability for these different fulfilment options. Such granular insight empowers brands to adapt swiftly and maintain a competitive edge in today’s dynamic retail environment.

    Why does monitoring pricing and availability data across stores matter to brands?

    • Hyperlocal Competitive Strategy: This allows brands to adjust their pricing strategies based on regional competition. By understanding the local market, brands can decide whether to position themselves as cost leaders or premium offerings. In particular, this is indispensable for Net Revenue Management (NRM) teams.
    • Targeted Marketing Initiatives: Understanding regional price and availability enables brands to customize their marketing efforts for specific markets. By aligning their strategies with local demand trends and inventory levels, brands can more effectively engage their target audiences.
    • Efficient Inventory Management: By keeping a close eye on store-level data, brands can better manage their stock, ensuring high-demand products are readily available while minimizing the risk of overstocking or running out of stock.
    • Minimum Advertised Price (MAP) Monitoring: While brands cannot directly control retail pricing, staying updated on pricing trends helps them adjust their MAP to reflect the competitive landscape, consumer expectations, cost considerations, and regional differences. A strategic approach to MAP management supports brand competitiveness and profitability in a fluctuating market.

    DataWeave’s Digital Shelf Analytics solutions equip brands with the necessary data and insights to do all of the above.

    DataWeave’s Digital Shelf Analytics is location-aware

    DataWeave’s Digital Shelf Analytics platform stands out with its sophisticated location-aware capabilities, enabling the aggregation and analysis of localized pricing, promotions, and availability data. Our platform defines locations using a range of identifiers, including latitudes and longitudes, ZIP codes, or specific stores, and can aggregate this data for particular states or regions.

    The strength of the platform lies in its robust data collection and processing framework, which operates seamlessly across thousands of stores and regions. This system is designed to operate at configurable intervals—daily, weekly, or monthly—allowing brands to keep a vigilant eye on product availability, pricing strategies, and delivery timelines based on the selected fulfillment option.

    Unlike many other providers, who may provide limited insights from a sample of stores, our solution delivers exhaustive analytics from every storefront. This comprehensive approach grants brands a strategic edge, facilitating efficient inventory tracking, precise pricing adjustments, and rapid responses to fluctuating market dynamics. It cultivates brand consistency and loyalty by enabling brands to adapt proactively to the changing landscape.

    Aggregated store-level digital shelf insights via DataWeave

    In the summarized view shown above, a brand can track how its various products are positioned across stores and retailers like Walmart, Amazon, Meijer, and others in the US.

    Using DataWeave, brands can easily see important metrics like availability levels, prices, and other metrics across these stores gaining immediate visibility without having to physically audit them. the brand can track the same metrics for products across competitor brands and inform its own pricing, stock, and assortment decisions.

    Store-level availability insights

    We provide a comprehensive view of product availability, highlighting the distribution of out-of-stock (OOS) scenarios across various retailers and pinpointing the availability status throughout a brand’s network of stores. This capability enables swift identification of widespread availability issues, offering a bird’s-eye view of where shortages are most pronounced. By simply hovering over a specific location, detailed information about stock status and pricing for individual stores becomes accessible.

    Such insights are crucial for brands to adapt their strategies, mitigate risks, and ensure they meet consumer needs despite the ever-changing retail ecosystem.

    Store-level pricing insights

    Retailers often adopt different pricing strategies to deal with margin pressure, local competition, and surplus stock. Grasping these pricing dynamics at a hyperlocal level enables brands to tailor their strategies effectively to maintain a competitive edge.

    Our platform offers an in-depth look at how prices vary among retailers, across different stores, and throughout various regions. This analysis reveals the nuanced pricing tactics employed by retailers on a regional scale.

    For example, brands might see that some retailers, like Kroger and Walmart in the chart below, maintain consistent pricing across their outlets, demonstrating a uniform pricing strategy. In contrast, others, such as Meijer and Shoprite, might adjust their prices to match local market conditions, indicating a more localized approach to pricing.

    With DataWeave, brands can dive deeper into the pricing landscape of a specific retailer, examining a price map that provides detailed information on pricing at the store level upon hovering over a given location.

    By presenting a historical analysis of average selling prices across different retailers, we equip brands with the insights needed to understand past pricing strategies and anticipate future trends, helping them to strategize more effectively in an ever-evolving market.

    Digital Shelf Analytics that work for both eCommerce and brick-and-mortar store data

    While established brands have made strides in gathering online pricing and availability data through Digital Shelf Analytics solutions, integrating comprehensive insights from both brick-and-mortar and eCommerce channels often remains a challenge.

    DataWeave stands out for its capacity to collect data across diverse digital platforms, including desktop sites, mobile sites, and mobile applications. This capability ensures that omnichannel brands can have a holistic view of their pricing, promotional, and inventory strategies across all locations and digital landscapes.

    Leveraging localized Digital Shelf Analytics to understand the intricacies of pricing and availability at the store level allows brands to fine-tune their approaches, swiftly adapt to local market shifts, and uphold a unified brand presence across the digital and offline spheres. This strategic agility places them in a favorable competitive position, enhancing customer satisfaction and trust, which are crucial for sustained success.

    Know more about DataWeave’s Digital Shelf Analytics here.

    Schedule a call with a specialist to see how it can work for your brand.

  • Easter Candy Pricing Trends 2024: Winning Strategies for Retailers and Brands Amid Cocoa Price Surge

    Easter Candy Pricing Trends 2024: Winning Strategies for Retailers and Brands Amid Cocoa Price Surge

    Easter egg hunts just got more challenging for families this year as the price of chocolate and other candies has soared. The root of this price surge lies in a cocoa deficit, attributed to diseases affecting crops and the adverse effects of climate change on West African farms, which supplies over 70% of the world’s cocoa. This has resulted in a tripling of cocoa prices over the last year, causing a “cocoa crunch,” and severely impacted confectioners and chocolate makers.

    Reuters recently reported that Iconic brands such as Hershey’s and Cadbury find themselves grappling with the need to adjust to escalating costs for raw materials. Given that Easter is one of the top three candy-purchasing occasions, these manufacturers are contemplating raising their prices to sustain their profit margins.

    Despite the challenges posed by the cocoa shortfall and persistent inflation, the National Confectioners Association anticipates that Easter candy sales in the U.S. will match or even exceed last year’s figures, which amounted to approximately $5.4 billion. This expectation is predicated more on price increases than on a rise in sales volume.

    At DataWeave, our ongoing analysis of pricing trends across various consumer categories among retailers has provided insight into the evolving landscape of chocolate and candy prices in 2023 and 2024.

    Our Analysis of Inflation in Candy and Chocolate Prices

    Our study encompassed a broad array of 3,300 products from leading U.S. retailers, Amazon, Target, Kroger, and Giant Eagle. As illustrated in the following chart, the trajectory of prices over the past 15 months was compared against the average prices in January 2023. Our tracking focused on two key price points: the selling price, which represents the final cost to consumers after applying any discounts or promotions, and the Manufacturer’s Suggested Retail Price (MSRP), as determined by the brands themselves.

    The findings from our analysis indicate that the average selling price, primarily influenced by retailer decisions, has experienced a steady increase throughout 2023, reaching a peak at 16.2% above January 2023’s figures by December. As of March 2024, coinciding with the Easter season, the selling prices are approximately 10% higher than they were at the beginning of the previous year.

    Simultaneously, the MSRP has seen a consistent uptick, driven by the climbing costs of cocoa. Brands have adjusted their suggested prices accordingly, with the current MSRP standing about 7% above its January 2023 level, after having peaked at a 7.6% increase by December 2023. This reflects the direct impact of rising cocoa costs on product pricing strategies.

    Chocolate Candies Are Hit The Hardest

    Across all candies, chocolate-based products have witnessed significantly sharper price increases than their non-chocolate counterparts. In the past 14 months, the selling prices of chocolate items have surged by 14.9%, a stark contrast to the modest 4% rise observed in non-chocolate candies.

    This price escalation was particularly pronounced during the Christmas shopping period, a response to heightened demand, before experiencing a temporary decline in February.

    The diminishing availability of cocoa, coupled with rising costs for packaging and transportation, has compelled brands and retailers alike to transfer these added expenses onto the consumer. This dynamic underpins the distinct pricing trends observed across the candy spectrum, with chocolate items bearing the brunt of these cost pressures.

    Discounts Offered By Retailers and Brands to Entice Easter Shoppers

    In our analysis, we delved deeper to identify the retailers and brands offering the most compelling prices for Easter-centric confections, including Chocolate Eggs, Chocolate Bunnies, and Easter-themed gift packs.

    Kroger emerged as the frontrunner among the retailers we monitored, offering an impressive 19% discount on Easter candies. Giant Eagle followed with a solid 14% average markdown. Meanwhile, Amazon and Target provided more modest promotional discounts at 12% and 10%, respectively.

    Kroger is making significant efforts to ensure consumers have access to attractively priced Easter treats. The retailer planned to keep its doors open throughout the Easter weekend, featuring baskets brimming with discounted items such as Russell Stover chocolate bunnies, Brach’s jelly beans, Reese’s eggs, and assorted bags of popular candies from Snickers, Twix, and Starburst, among others. Additionally, Kroger is enhancing its value proposition through gift card offers and exclusive Easter deals for its loyalty program members.

    On the brand front, Starburst by Mars Wrigley leads with the steepest discount of 25%. Cadbury, under Mondelez, is not far behind, offering 21% off its mini eggs and other Easter treats, marking an increase from last year’s 17% discount. Ferrero Rocher is making a strong pricing move with an average 20% markdown on its Easter selections, including the chocolate bunny and squirrel figures.

    The beloved Peeps marshmallow candies by Just Born are being offered at an 18% discount this year, slightly less than the 23% discount seen in 2023, likely reflecting the impact of rising sugar costs, given their sugar and corn composition.

    Other notable brands, including M&M’s and the premium Swiss chocolatier Lindt, have elevated their average Easter discounts to 17% this year, up from the previous year’s discounts of 12%, and 10% respectively, showcasing a competitive pricing strategy to delight consumers this Easter season.

    Coping With Inflation This Easter Season

    Retailers and brands aiming to remain profitable and competitive in the current challenging environment can adopt a few strategic approaches:

    • Creative Product Bundling: Design innovative combo packs that mix chocolate and non-chocolate items. Such bundles can cater to diverse consumer preferences and budget ranges while preserving profit margins.
    • Encouragement of Bulk Purchases: Offer enticing discounts on larger quantities to promote bulk buying. This strategy can help amplify sales volumes, compensating for increased costs per item and fostering economies of scale.
    • Strategic Competitive Pricing: Keeping a vigilant eye on competitors’ pricing strategies is vital. Aim to capture market share through well-thought-out discount strategies that balance competitiveness with margin preservation. Leveraging advanced pricing intelligence, such as that offered by DataWeave, can provide invaluable insights for making informed pricing decisions.
    • Product Size Adjustments: Consider revising the size or weight of products as a cost management measure, a strategy known as “shrinkflation.” It’s crucial to approach this transparently, ensuring clear communication on packaging to uphold consumer trust.

    Adopting these strategies—focusing on bundle offerings, incentivizing bulk purchases, optimizing pricing strategies based on competitive intelligence, and thoughtfully adjusting product sizes—will be pivotal for confectioners to navigate the challenges posed by the cocoa price surge.

    For more information, reach out to us to speak to a DataWeave expert today!


  • How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    As eCommerce increasingly becomes a prolific channel of sales for consumer brands, they find that maintaining a consistent and trustworthy brand image is a constant struggle. In an ecosystem filled with dozens of marketplaces and hundreds of third-party merchants, ensuring that customers see what aligns with a brand’s intended image is quite tricky. With many fakes and counterfeit products doing the rounds, brands may further struggle to get the right representation.

    One way brands can track and identify inconsistencies in their brand representation across marketplaces is to use Digital Shelf Analytics solutions like DataWeave’s – specifically the Content Audit module.

    This solution uses advanced AI models to identify image similarities and dissimilarities compared with the original brand image. Brands could then use their PIM platform or work with the retailer to replace inaccurate images.

    But here’s the catch – AI can’t always accurately predict all the differences. Relying solely on scores given by these models poses a challenge in tracking the subtle differences between images. Often, image pairs with seemingly high match scores fail to catch important distinctions. Fake or counterfeit products and variations that slip past the AI’s scrutiny can lead to significant inaccuracies. Ultimately, it puts the reliability of the insights that brands depend on for crucial decisions at risk, impacting both top and bottom lines.

    Dealing with this challenge means finding a balance between the number-based assessments of AI models and the human touch needed for accurate decision-making. However, giving auditors the ability to pinpoint variations precisely goes beyond simply sharing numerical values of the match scores with them. Visualizing model-generated scores is important as it provides human auditors with a tangible and intuitive understanding of the differences between two images. While numerical scores are comparable in the relative sense, they lack specificity. Visual interpretation empowers auditors to identify precisely where variations occur, aiding in efficient decision-making.

    How AI-Powered Image Scoring Works

    At DataWeave, our approach involves employing sophisticated computer vision models to conduct extensive image comparisons. Convolutional Neural Network (CNN) models such as Resnet-50 or YOLO, in conjunction with feature extraction models, analyze images quantitatively. This AI-powered image scoring process yields scores that indicate the level of similarity between images.

    However, interpreting these scores and understanding the specific areas of difference can be challenging for human auditors. While computer vision models excel at processing vast amounts of data quickly, translating their output into actionable insights can be a stumbling block. A numerical score may not immediately convey the nature or extent of the differences between images

    In the assessment of these images, all fall within the 70 to 80 range of scores (out of a maximum of 100). However, discerning the nature of differences—whether they are apparent or subtle—poses a challenge for the AI models and human auditors. For example, there are differences in the placement or type of images in the packaging, as well as packing text that are often in an extremely small font size. It is, of course, possible for human auditors to identify the differences in these images, but it’s a slow, error-prone, and tiring process, especially when auditors often have to check hundreds of image pairs each day.

    So how do we ensure that we identify differences in images accurately? The answer lies in the process of visual highlighting.

    How Visual Highlighting Works

    Visual highlighting is a method that enhances our ability to comprehend differences in images by combining sophisticated algorithms with human understanding. Instead of relying solely on numerical scores, this approach introduces a visual layer, resembling a heatmap, guiding human auditors to specific areas where discrepancies are present.

    Consider the scenario depicted in the images above: a computer vision model assigns a score of 70-85 for these images. While this score suggests relatively high similarity, it fails to uncover major differences between the images. Visual highlighting comes into play to overcome this limitation, precisely indicating regions where even subtle differences are seen.

    Visual highlighting entails overlaying compared images and emphasizing areas of difference, achieved through techniques like color coding, outlining, or shading specific regions. The significance of the difference in a particular area determines the intensity of the visual highlight.

    For instance, if there’s a change in the product’s color or a discrepancy in the packaging, these variations will be visually emphasized. This not only streamlines the auditing process but also enables human evaluators to make well-informed decisions quickly.

    Benefits of Visual Highlighting

    • Intuitive Understanding: Visual highlighting offers an intuitive method for interpreting and acting upon the outcomes of computer vision models. Instead of delving into numerical scores, auditors can concentrate on the highlighted areas, enhancing the efficiency and accuracy of the decision-making process.
    • Accelerated Auditing: By bringing attention to specific regions of concern, visual highlighting speeds up the auditing process. Human evaluators can swiftly identify and address discrepancies without the need for exhaustive image analysis.
    • Seamless Communication: Visual highlighting promotes clearer communication between automated systems and human auditors. Serving as a visual guide, it enhances collaboration, ensuring that the subtleties captured by computer vision models are effectively conveyed.

    The Way Forward

    As technology continues to evolve, the integration of visual highlighting methodologies is likely to become more sophisticated. Artificial intelligence and machine learning algorithms may play an even more prominent role in not only detecting differences but also in refining the visual highlighting process.

    The collaboration between human auditors and AI ensures a comprehensive approach to maintaining brand integrity in the ever-expanding digital marketplace. By visually highlighting differences in images, brands can safeguard their visual identity, foster consumer trust, and deliver a consistent and reliable online shopping experience. In the intricate dance between technology and human intuition, visual highlighting emerges as a powerful tool, paving the way for brands to uphold their image with precision and efficiency.

    To learn more, reach out to us today!


    (This article was co-authored by Apurva Naik)

  • How Enterprise AI is Transforming Business Outcomes for Customers Across Industries

    How Enterprise AI is Transforming Business Outcomes for Customers Across Industries

    Is AI just a flashy new trend that helps you create some amusing or stunning art and whip up amazing content in seconds (not this one) or is it a force for much greater good? Let’s dive into one of the most disruptive tech moments in our recent history in analyzing the use and transformative powers of AI, for not just end consumers but also businesses as customers. 

    “Enterprise AI can bring joy and meaning to its stakeholders. No more meaningless work, we are delivering value at the speed of need”, golden words from tech leader Vala Afshar, one of the staunchest endorsers of AI tech today as AI continues to drive top-line revenue growth for the Enterprise (B2B).

    How AI has evolved from basic tasks to complex processes

    AI or Artificial Intelligence which essentially means the use of a machine – typically computer – to perform tasks usually performed by humans such as processing data, making connections, and coming up with solutions has come a long way since its chess playing days.

    Since its application in various industries, AI has been changing the way we live and work. From enabling personalized experiences  to automating marketing and CRM to processing vast amounts of complex and scattered data, AI has been helping businesses perform increasingly complex tasks.

    With recent advances in Machine Learning and large Language Learning Models (LLM) AI is becoming increasingly sophisticated and is being commissioned in a growing range of applications, from healthcare and education to transportation and finance across the B2B/Enterprise industry.

    Enterprise AI vs Consumer AI

    Enterprise AI is an ecosystem of tools, processes and companies that leverages AI to provide solutions to businesses, as opposed to the end consumer.

    To draw a simple example, a generative prompt-based AI tool like ChatGPT or MidJourney is a consumer AI which helps consumers perform individual tasks. On the other hand, Salesforce provides various AI-led tools that help other businesses in their marketing and CRM processes.  A 2020 McKinsey study showed that nearly 70% of all businesses are taking AI seriously and increasibly looking to adopt it into their processes. 

    The potential of AI is virtually limitless across industries and everyday AI is being leveraged to drive efficiency, innovation and growth across sectors. It is being used to automate routine tasks, provide predictive analytics, analyze customer data, and improve supply chain operations.  In this article, we’ll dive into how Enterprise AI is transforming business outcomes for customers across industries through four real world examples. 

    Enterprise AI at play in these five industries

    1. Enterprise AI in Ecommerce

    For big ecommerce giants that play in a complex and multi-player ecosystem of today, being on top of their competition is the name of the game. However, it’s nearly impossible to keep a tab on the market manually and that’s where Enterprise AI technology in the domain of Price Intelligence and comparisons come in. Once a domain of manual and web scraping, Enterprise AI now equips both brands and ecommerce marketplaces with large, real-time and dynamic data to help them keep on top of competition and adjust their prices and other variables accordingly. 

    One of the biggest use cases for Enterprise AI in ecommerce is DataWeave, which provides SaaS-based Pricing Intelligence and Digital Shelf Analytics to its retail and ecommerce clients. Their AI-led matching and image recognition, can scrape millions of ecommerce data like price, inventory, image and others across the ecommerce landscape and enable the companies to make strategic pricing and competitive decisions. According to the company, these real-time pricing insights have helped their 50+ retail customers save millions in revenue by offering the right product at the most strategic price points.

    2. Enterprise AI in logistics

    AI has a profound impact on logistics, transport and global shipping commerce.  By predicting demand, identifying the most efficient routes, and improving warehouse management, AI logistics enterprises are helping their enterprise customers improve delivery rates, timelines and helping reduce carbon emissions.

    3. Enterprise AI in healthcare

    A case study in how AI has helped logistics comes from Far Eye, an AI-led delivery logistics platform, that helped one of its key clients BlueDart improve first attempt success rate by 22% and delivery success rate by 75%.

    AI can be used to improve patient outcomes by evaluating patient data, including processing complex medical report imagery and developing personalized treatment plans. One of the biggest players disrupting the field of AI in healthcare is IBM with its “Watson for Oncology”, a SaaS that delivers an advanced ability to analyze the meaning and context of structured and unstructured data in clinical notes and reports, easily assimilating key patient information written in plain English. By combining attributes from the patient’s file with clinical expertise from Memorial Sloan Kettering, external research, and data, Watson for Oncology identifies and ranks potential treatment plans and options.  It has been implemented by several hospitals around the world, including established ones like Apollo in India.

    4. Enterprise AI in marketing and CRM

    It would be a safe assumption to make in saying that marketing is one of the biggest beneficiaries from the evolution of AI. Marketing is at its core all about understanding the consumer and their problems, crafting a messaging and value to appeal to that consumer, and presenting the solutions  at the right time and at the right place. Various AI tools, systems and processes have been continuously enriching marketing. From tools to analyse large amounts of customer data, to generative AI content generators like ChatGPT and Grok, to AI-assisted CRM tools that have made personalization at scale a possibility, AI in marketing has been a boon.  

    One of the case studies in how a large company has been Uber Eats that used Salesforce’s Einstein’s AI that powers its service operations. With a shared view of merchants, end consumer data, and uber-fast response times in issue resolution, the company can keep 25 million restaurateurs and their customers happy.

    Sitting at the confluence of technology, innovation, strategy and foresight, the world is limitless in how AI can be leveraged to improve systems, provide solutions, and drive business growth. Enterprises, by harnessing the ever-evolving powers of AI, can improve business outcomes not only for their top line growth and revenue, but also in offering superior customer experiences and service.

    To learn more, reach out to us today or email us at contact@dataweave.com.

  • How DataWeave Enhances Transparency in Competitive Pricing Intelligence for Retailers

    How DataWeave Enhances Transparency in Competitive Pricing Intelligence for Retailers

    Retailers heavily depend on pricing intelligence solutions to consistently achieve and uphold their desired competitive pricing positions in the market. The effectiveness of these solutions, however, hinges on the quality of the underlying data, along with the coverage of product matches across websites.

    As a retailer, gaining complete confidence in your pricing intelligence system requires a focus on the trinity of data quality:

    • Accuracy: Accurate product matching ensures that the right set of competitor product(s) are correctly grouped together along with yours. It ensures that decisions taken by pricing managers to drive competitive pricing and the desired price image are based on reliable apples-to-apples product comparisons.
    • Freshness: Timely data is paramount in navigating the dynamic market landscape. Up-to-date SKU data from competitors enables retailers to promptly adjust pricing strategies in response to market shifts, competitor promotions, or changes in customer demand.
    • Product matching coverage: Comprehensive product matching coverage ensures that products are thoroughly matched with similar or identical competitor products. This involves accurately matching variations in size, weight, color, and other attributes. A higher coverage ensures that retailers seize all available opportunities for price improvement at any given time, directly impacting revenues and margins.

    However, the reality is that untimely data and incomplete product matches have been persistent challenges for pricing teams, compromising their pricing actions. Inaccurate or incomplete data can lead to suboptimal decisions, missed opportunities, and reduced competitiveness in the market.

    What’s worse than poor-quality data? Poor-quality data masquerading as accurate data.

    In many instances, retailers face a significant challenge in obtaining comprehensive visibility into crucial data quality parameters. If they suspect the data quality of their provider is not up to the mark, they are often compelled to manually request reports from their provider to investigate further. This lack of transparency not only hampers their pricing operations but also impedes the troubleshooting process and decision-making, slowing down crucial aspects of their business.

    We’ve heard about this problem from dozens of our retail customers for a while. Now, we’ve solved it.

    DataWeave’s Data Statistics and SKU Management Capability Enhances Data Transparency

    DataWeave’s Data Statistics Dashboard, offered as part of our Pricing Intelligence solution, enables pricing teams to gain unparalleled visibility into their product matches, SKU data freshness, and accuracy.

    It enables retailers to autonomously assess and manage SKU data quality and product matches independently—a crucial aspect of ensuring the best outcomes in the dynamic landscape of eCommerce.

    Beyond providing transparency and visibility into data quality and product matches, the dashboard facilitates proactive data quality management. Users can flag incorrect matches and address various data quality issues, ensuring a proactive approach to maintaining the highest standards.

    Retailers can benefit in several ways with this dashboard, as listed below.

    View Product Match Rates Across Websites

    The dashboard helps retailers track match rates to gauge their health. High product match rates signify that pricing teams can move forward in their pricing actions with confidence. Low match rates would be a cause for further investigation, to better understand the underlying challenges, perhaps within a specific category or competitor website.

    Our dashboard presents both summary statistics on matches and data crawls as well as detailed snapshots and trend charts, providing users with a holistic and detailed perspective of their product matches.

    Additionally, the dashboard provides category-wise snapshots of reference products and their matching counterparts across various retailers, allowing users to focus on areas with lower match rates, investigate underlying reasons, and develop strategies for speedy resolution.

    Track Data Freshness Easily

    The dashboard enables pricing teams to monitor the timeliness of pricing data and assess its recency. In the dynamic realm of eCommerce, having up-to-date data is essential for making impactful pricing decisions. The dashboard’s presentation of freshness rates ensures that pricing teams are armed with the latest product details and pricing information across competitors.

    Within the dashboard, users can readily observe the count of products updated with the most recent pricing data. This feature provides insights into any temporary data capture failures that may have led to a decrease in data freshness. Armed with this information, users can adapt their pricing decisions accordingly, taking into consideration these temporary gaps in fresh data. This proactive approach ensures that pricing strategies remain agile and responsive to fluctuations in data quality.

    Proactively Manage Product Matches

    The dashboard provides users with proactive control over managing product matches within their current bundles via the ‘Data Management’ panel. This functionality empowers users to verify, add, flag, or delete product matches, offering a hands-on approach to refining the matching process. Despite the deployment of robust matching algorithms that achieve industry-leading match rates, occasional instances may arise where specific matches are overlooked or misclassified. In such cases, users play a pivotal role in fine-tuning the matching process to ensure accuracy.

    The interface’s flexibility extends to accommodating product variants and enables users to manage product matches based on store location. Additionally, the platform facilitates bulk match uploads, streamlining the process for users to efficiently handle large volumes of matching data. This versatility ensures that users have the tools they need to navigate and customize the matching process according to the nuances of their specific product landscape.

    Gain Unparalleled Visibility into your Data Quality

    With DataWeave’s Pricing Intelligence, users gain the capability to delve deep into their product data, scrutinize match rates, assess data freshness, and independently manage their product matches. This approach is instrumental in fostering informed and effective decisions, optimizing inventory management, and securing a competitive edge in the dynamic world of online retail.

    To learn more, reach out to us today!

  • Capturing and Analyzing Retail Mobile App Data for Digital Shelf Analytics: Are Brands Missing Out?

    Capturing and Analyzing Retail Mobile App Data for Digital Shelf Analytics: Are Brands Missing Out?

    Consumer brands around the world increasingly recognize the vital role of tracking and optimizing their digital shelf KPIs, such as Content Quality, Share of Search, Availability, etc. These metrics play a crucial role in boosting eCommerce sales and securing a larger online market share. With the escalating requirements of brands, the sophistication of top Digital Shelf Analytics providers is also on the rise. Consequently, the adoption of digital shelf solutions has become an essential prerequisite for today’s leading brands.

    As brands and vendors continue to delve further and deeper into the world of Digital Shelf Analytics, a significant and often overlooked aspect is the analysis of digital shelf data on mobile apps. The ability of solution providers to effectively track and analyze this mobile-specific data is crucial.

    Why is this emphasis on mobile apps important?

    Today, the battle for consumer attention unfolds not only on desktop web platforms but also within the palm of our hands – on mobile devices. As highlighted in a recent Insider Intelligence report, customers will buy more on mobile, exceeding 4 in 10 retail eCommerce dollars for the first time.

    Moreover, thanks to the growth of delivery intermediaries like Instacart, DoorDash, Uber Eats, etc., shopping on mobile apps has received a tremendous organic boost. According to an eMarketer report, US grocery delivery intermediary sales are expected to reach $68.2 billion in 2025, from only $8.8 billion in 2019.

    In essence, mobile is increasingly gaining share as the form factor of choice for consumers, especially in CPG. In fact, one of our customers, a leading multinational CPG company, revealed to us that it sees up to 70% of its online sales come through mobile apps. That’s a staggering number!

    The surge in app usage reflects a fundamental change in consumer behavior, emphasizing the need for brands to adapt their digital shelf strategies accordingly.

    Why Brands Need To Look at Apps and Desktop Data Differently

    Conventionally, brands that leverage digital shelf analytics rely on data harnessed from desktop sites of online marketplaces. This is because capturing data reliably and accurately from mobile apps is inherently complex. Data aggregation systems designed to scrape data from web applications cannot easily be repurposed to capture data on mobile apps. It requires dedicated effort and exceptional tech prowess to pull off in a meaningful and consistent way.

    In reality, it is extremely important for brands to track and optimize their mobile digital shelf. Several digital shelf metrics vary significantly between desktop sites and mobile apps. These differences are natural outcomes of differences in user behavior between the two form factors.

    One of these metrics that has a huge impact on a brand’s performance on retail mobile apps is their search discoverability. Ecommerce teams are well aware of the adverse impact of the loss of even a few ranks on search results.

    Anyone can easily test this. Searching something as simple as “running shoes” on the Amazon website and doing the same on its mobile app shows at least a few differences in product listings among the top 20-25 ranks. There are other variances too, such as the number of sponsored listings at the top, as well as the products being sponsored. These variations often result in significant differences in a brand’s Share of Search between desktop and mobile.

    Share of Search is the share of a brand’s products among the top 20 ranked products in a category or subcategory, providing insight into a brand’s visibility on online marketplaces.

    Picture a scenario in which a brand heavily depends on desktop digital shelf data, confidently assuming it holds a robust Share of Search based on reports from its Digital Shelf Analytics partner. However, unbeknownst to the team, the Share of Search on mobile is notably lower, causing a detrimental effect on sales.

    To fully understand the scale of these differences, we decided to run a small experiment using our proprietary data analysis and aggregation platform. We restricted our analysis to just Amazon.com and Amazon’s mobile app. However, we did cover over 13,000 SKUs across several shopping categories to ensure the sample size is strong.

    Below, we provide details of our key findings.

    Share of Search on The Digital Shelf – App Versus Desktop

    Our analysis focused on three popular consumer categories – Electronics, CPG, and Health & Beauty.

    In the electronics category, brands like Apple, Motorola, and Samsung, known for their mobile phones, earbuds, headphones, and more, have a higher Share of Search on the Amazon mobile app compared to the desktop.

    Meanwhile, Laptop brands like Dell, Acer, and Lenovo, as well as other leading brands like Google have a higher Share of Search on the desktop site compared to the app. This is the scenario that brands need to be careful about. When their Share of Search on mobile apps is lower, they might miss the chance to take corrective measures since they lack the necessary data from their provider.

    In the CPG category, Ramen brand Samyang, with a lot of popularity on Tiktok and Instagram, shows a higher Share of Search on Amazon’s mobile app. Speciality brands like 365 By Whole Foods, pasta and Italian food brands La Moderna, Divinia, and Bauducco too have a significantly higher Share of Search on the app.

    Cheese and dessert brands like Happy Belly, Atlanta Cheesecake Company, among others, have a lower Share of Search on the mobile app. Ramen brand Sapporo is also more easily discovered on Amazon’s desktop site. Here, we see a difference of more than 5% in the Share of Search of some brands, which is likely to have a huge impact on the brand’s mobile eCommerce sales levels and overall performance.

    Lastly, in the Health & Beauty category, Shampoos and hair care brands like Olaplex, Dove, and Tresemme exhibited a higher Share of Search on the mobile app compared to the desktop.

    On the other hand, body care brands like Neutrogena and Hawaiian Tropic, as well as Beardcare brand Viking Revolution displayed a higher Share of Search on Amazon’s desktop site.

    Based on our data, it is clear that there are several examples of brands that do better in either one of Amazon’s desktop sites or mobile apps. In many cases, the difference is stark.

    So What Must Brands Do?

    Our findings emphasize the imperative for brands to move beyond a one-size-fits-all approach to digital shelf analytics. The striking variations in Share of Search between mobile apps and desktops conclusively demonstrate that relying solely on desktop data for digital shelf optimization is inadequate.

    If brands see that they’re falling behind on the mobile digital shelf, there are a few things they can do to help boost their performance:

    • If a brand’s Share of Search is lower on the mobile app, they can divert their retail spend to mobile in order to inorganically compensate for this difference. This way, any short-term impact due to lower discoverability is mitigated. This is also likely to result in optimized budget allocation and ROAS.
    • Brands also need to ensure their content is optimized for the mobile form factor, with images that are easy to view on smaller screens, and tailored product titles that are shorter than on desktops, highlighting the most important product attributes from the consumer’s perspective. Not only will this help brands gain more clicks from mobile shoppers, but this will also gradually lead to a boost in their organic Share of Search on mobile.
    • CPG brands, specifically, need to optimize their digital shelf for delivery intermediary apps (along with marketplaces). The grocery delivery ecosystem is booming with companies like DoorDash, Delivery Hero, Uber Eats, Swiggy, etc. leading the way. Using Digital Shelf Analytics to optimize performance on delivery apps is quite an involved process with a lot of bells and whistles to consider. Read our recently published whitepaper that specifically details how brands can successfully boost their visibility and conversions on delivery apps.

    But first, brands need to identify and work with a Digital Shelf Analytics partner that is able to capture and analyze mobile app data, enabling tailored optimization approaches for all eCommerce platforms.

    DataWeave leads the way here, providing the world’s most comprehensive and sophisticated digital shelf analytics solution, rising above all other providers to provide digital shelf insights for both web applications and mobile apps. Our data aggregation platform successfully navigates the intricacies of capturing public data accurately and reliably from mobile apps, thereby delivering a comprehensive cross-device view of digital shelf KPIs to our brand customers.

    So reach out to us today to find out more about our digital shelf solutions for mobile apps!

  • AI-powered Product Matching: The Key to Competitive Pricing Intelligence in eCommerce

    AI-powered Product Matching: The Key to Competitive Pricing Intelligence in eCommerce

    With thousands of products and hundreds of online retailers to choose from, the average modern-day shopper usually compares prices across several e-commerce sites effortlessly before often settling for the lowest priced option. As a result, retailers today are forced to execute millions of price changes per day in a never-ending race to be the lowest priced – without losing out on any potential margin.

    Identifying, classifying, and matching products is the first step to comparing prices across websites. However, there is no standardization in the way products are represented across e-commerce websites, causing this process to be fairly complex.

    Here’s an example:

    What’s needed is a pricing intelligence solution that first matches products across several websites swiftly and accurately, and then enables automated tracking of competitor pricing data on an ongoing basis.

    Pricing intelligence solutions already exist. What’s wrong with using them?

    There are several challenges with the incumbent solutions in the market – the biggest one being that they don’t work in a timely manner. In essence, it’s like deferring the process of finding actionable information that helps retailers acquire a competitive advantage, and instead doing it in hindsight. Like an autopsy of sorts.

    Here are the various solution types we have in the market today:

    • Internally developed systems – Solutions developed by retailers themselves often rely on heavy manual data aggregation and have poor product matching capabilities. Since these solutions have been developed by professionals not attuned to building data crunching machines, they pose significant operational challenges in the form of maintenance, updates, etc.
    • Web scraping solutions – These solutions have no data normalization or product matching capabilities, and lack the power to deliver relevant actionable insights. What’s more, it’s a struggle to scale them up to accommodate massive volumes of data during peak times such as promotional campaigns.
    • DIY solutions – These solutions require manual research and entry of data. It goes without saying that due to the level of human intervention and effort required, they’re expensive, difficult to scale, slow, and of questionable accuracy.

    As common as it is nowadays, AI has the answer

    DataWeave’s competitive pricing intelligence solution is designed to help retailers achieve precisely the competitive advantage they need by providing them with accurate, timely, and actionable pricing insights enabled by matching products at scale. We provide retailers with access to detailed pricing information on millions of products across competitors, as frequently as they need it.

    Our technology stack broadly consists of the following.

    1. Data Aggregation

    At DataWeave, we can aggregate data from diverse web sources across complex web environments – consistently and at a very high accuracy. Having been in the industry for close to a decade, we’re sitting on a lot of data that we can use to train our product matching platform.

    Our datasets include data points from tens of millions of products and have been collected from numerous geographies and verticals in retail. The datasets contain hierarchically arranged information based on retail taxonomy. At the root level, there’s information such as category and subcategory, and at the top level, we have product details such as title, description, and other <attribute, value> relationships. Our machine learning architectures and semi-automated training data building systems, augmented by the skills of a strong QA team, help us annotate the necessary information and create labeled datasets using proprietary tools.

    2. AI for Product Matching

    Product matching at DataWeave is done via a unified platform that uses both text and image recognition capabilities to accurately identify similar SKUs across thousands of e-commerce stores and millions of products. We use an ensemble deep learning architectures tailored to NLP and Computer Vision problems specific to us and heuristics pertinent to the Retail domain. Products are also classified based on their features, and a normalization layer is designed based on various text/image-based attributes.

    Our semantics layer, while technically an integral part of the product matching process, deserves particular mention due to its powerful capabilities.

    The text data processing consists of internal, deep pre-trained word embeddings. We use state-of-the-art, customized word representation techniques such as ELMO, BERT, and Transformer to capture deeply contextualized text with improved accuracy. A self-attention/intra-attention mechanism learns the correlation between the word in question and a previous part of the description.

    Image data processing starts with object detection to identify the region of interest of a given product (for example, the upper body of a fashion model displaying a shirt). We then leverage deep learning architectures such as VggNet, Inception-V3, and ResNet, which we have trained using millions of labeled images. Next, we apply multiple pre-processing techniques such as variable background removal, face removal, skin removal, and image quality enhancing and extract image signatures via deep learning and machine learning-based algorithms to uniquely identify products across billions of indexed products.

    Finally, we efficiently distribute billions of images across multiple stores for fast access, and to facilitate searches at a massive scale (in a matter of milliseconds, without the slightest compromise on accuracy) using our image matching engine.

    3. Human Intelligence in the Loop

    In scenarios where the confidence scores of the machine-driven matches are low, we have a team of Quality Assurance (QA) specialists who verify the output.

    This team does three things:

    • Find out why the confidence score is low
    • Confirm the right product matches
    • Figure out a way to encode this knowledge into a rule and feed it back to the algorithm

    In this way, we’ve built a self-improving feedback loop which, by its very nature, performs better over time. This system has accumulated knowledge over the 8 years of our operations, which is going to be hard for anyone to replicate. Essentially, this process enables us to match products at massive scale quickly and at very high levels of accuracy (usually over 95%).

    4. Actionable Insights Via Data Visualization

    Once the matching process is completed, the prices are aggregated at any frequency, enabling retailers to optimize their prices on an ongoing basis. Pricing insights are typically consumed via our SaaS-based web-portal, which consists of dashboards, reports, and visualizations.

    Alternatively, we can integrate with internal analytics platforms through APIs or generate and deliver spreadsheet reports on a regular basis, depending on the preferences of our customers.

    To summarize

    The benefits of our solution are many. Detailed price improvement opportunity-related insights generated in a timely manner empower retailers to significantly enhance their competitive positioning across categories, product types, and brands, as well as ability to influence their price perception among consumers. These insights, when leveraged at a higher granularity over the long term, can help maximize revenue through price optimization at a large scale.

    Our solution also helps drive process-based as well as operational optimizations for retailers. Such modifications help them better align themselves to effectively adopt a data-driven approach to pricing, in turn helping them achieve much smarter retail operations across the board.

    All of this wouldn’t be possible if the product matching process, inherent to this system, was unreliable, expensive, or time-consuming.

    If you would like to learn more about DataWeave’s proprietary product matching platform and the benefits it offers to eCommerce businesses and brands, talk to us now!

  • The Indian E-Commerce Showdown: Unveiling the Price War Between Flipkart’s Big Billion Days and Amazon’s Great Indian Festival

    The Indian E-Commerce Showdown: Unveiling the Price War Between Flipkart’s Big Billion Days and Amazon’s Great Indian Festival

    India’s homegrown eCommerce giant Flipkart, now backed by Walmart, reported a record 1.4 Billion customer visits during the early access phase and throughout the seven days of its premier shopping event, the Big Billion Days, launched on 8th October 2023. Competing with Flipkart, Amazon’s Great Indian Festival sale event started on October 8th as well and saw a whopping 95 Million customer visits to the website within the first 48 hours of the event.

    For consumers, the most pressing question was, “Who offered more attractive deals and lower prices during these sale events?”

    To answer this question, we leveraged our proprietary data aggregation and analysis platform and analyzed the prices and discounts on Amazon and Flipkart across key product categories..

    The details of our sample are mentioned below:

    • Number of SKUs Analyzed: 30,000+
    • Websites: Amazon.com and Flipkart.com
    • Categories: Apparel, Home & Furniture, Electronics, Health & Beauty
    • Dates: 7th Oct 2023 to 22nd Oct 2023

    Key Findings

    Based on our analysis, the Big Billion Days by Flipkart showcased relatively higher price reductions across categories compared to the Great Indian Festival sale by Amazon. The Apparel category on Flipkart saw the highest average discount at 50.6%. The Health & Beauty category had the lowest discount across Flipkart at 39.4% and Amazon at 33%.

    Overall, Flipkart offered higher discounts in each product category. It is clear that the retailer invested heavily in leveraging its supplier partnerships with key brands or sellers to enable them to offer higher discounts, thereby attracting more customers.

    Next, let’s take a closer look at each product category.

    Apparel

    While a majority of retailers expected demand for apparel and clothing to dip this festive season in India, eCommerce giants like Amazon and Flipkart are likely to recognize the strong consumer inclination towards apparel during this period.

    In the detailed assessment of Apparel sub-categories, Women’s Dresses, Women’s Tops, Men’s Shirts, Men’s Shoes, and Women’s Innerwear emerged as the segments showcasing the most substantial discounts during the sale events. While Flipkart offered higher average discounts across all sub-categories, Amazon offered competitive discounts as well.

    We observed significant differences in the average discounts across brands between Flipkart’s Big Billion Days and Amazon’s Great Indian Festival. Reinforcing the significant discounts on the Shoes subcategory, brands like Red Tape, Arrow, Adidas, Reebok, Nike, and more offered extensive discounts on both Flipkart and Amazon. Notably, Adidas and Reebok offered better deals on Amazon’s Great Indian Festival as compared to Flipkart.

    One8 by Virat Kohli had a significantly lower discount on Amazon compared to Flipkart, indicating an exclusive partnership.

    For brands, however, reducing prices is just one approach to entice shoppers. They must also guarantee their prominent presence and easy discoverability within Amazon and Flipkart search results. To gain insight into this, we monitored brands’ Share of Search across various frequently used search terms in addition to the discounts they provided. The Share of Search denotes the portion of a brand’s products within the top 20 search results for a specific search query.

    Our data indicates that Jockey and Speedo gained in Share of Search on Flipkart, but reduced discoverability on Amazon. Van Heusen fell behind in search results on Flipkart but showed a higher Share of Search on Amazon.

    Home & Furniture

    With demand for home and furniture products picking up in October, right before the festive season, Amazon and Flipkart offered significant discounts in this category.

    Discounts on both Amazon and Flipkart hovered around 50%. Across a few subcategories, Flipkart offered slightly lower discounts compared to Amazon. Only Luggage, Rugs, Sofas, and Entertainment Units saw lower markdowns on Flipkart during the Big Billion Days. 

    Dishwashers and Washer/ Dryers saw higher discounts on Amazon compared to Flipkart. The significant discounts on these products on Amazon possibly point to changing consumer preferences, as demand for these products is traditionally low in India, but seems to be growing.

    When it comes to Home & Furniture brands, Nasher Miles, Safari, Aristocrat, VIP, and American Tourister, luggage brands mostly, offered higher discounts on Flipkart, followed closely by Amazon.

    In terms of Share of Search, Skybags had high discoverability on both Flipkart and Amazon. The brand leveraged a strategy of offering big discounts this festive season as well as ensuring prominent placement in search results. Wildcraft lost out on its discoverability on Flipkart in contrast to its prominence on Amazon. Duroflex saw lower searchability on Amazon compared to Flipkart’s Big Billion Days.

    Consumer Electronics

    The Consumer Electronics and Appliances Manufacturers Association (CEAMA) expected an uptick in sales of consumer electronics products this festive season in India. With more consumers buying premium products using credit cards and EMIs, demand for expensive, high-end electronics was expected to increase.

    Again, average discounts in this category hovered around 50% on Flipkart and Amazon.

    Across electronics subcategories, Smartwatches, Earbuds, and Drones had the highest markdowns with Flipkart leading the pack during the Big Billion Days. Amazon offered relatively higher discounts at 44.9% on the TV subcategory, compared to Flipkart’s 40.6%.

    Speakers, Laptops, Smartphones, and Tablets also saw lower markdowns on Amazon compared to Flipkart. Amazon was the official partner for the launch of many high-level smartphones and products in September-October, contributing to the higher markdowns in the subcategory.

    Across brands, Lenovo’s discounts were the most differentiated between the two sites, with the brand offering higher discounts on Amazon (45.4%) compared to Flipkart (24.7%). Noise offered the highest discounts at 72.5% on Amazon and 52.8% on Flipkart. Brands like Boat and Zebronics, also saw lower discounts on Flipkart.

    Mi and JBL offered deeper discounts on Flipkart’s Big Billion Days. Apple meanwhile stands out with only 11.83% discounts on Amazon, but the brand offered impressive 31.4% discounts on Flipkart.

    Samsung dominated the Share of Search on Amazon at 15.7%, compared to only 2.6% on Flipkart. Apple and Lenovo also saw higher discoverability on Amazon. On Flipkart, JBL and Skullcandy stand out as brands with high search visibility.

    Health & Beauty

    The Health & Beauty category saw the lowest markdowns with only 39.4% discounts on Flipkart and 33% on Amazon.

    In the subcategories analyzed, Electric Toothbrushes had relatively high markdowns across both sites. Staple and lower priced subcategories like Toothpaste had the lowest markdowns across both sale events, with Amazon offering only 17.4% average discounts.

    Across brands, Beardo, a leading beard care brand, offered significantly higher discounts on Amazon compared to Flipkart. Most other well-known brands, including Nivea and Vaseline, saw higher discounts on Amazon compared to Flipkart. Only Tresmme and Dove were exceptions with higher discounts on Flipkart.

    In terms of Share of Search, once again, Beardo was the most discoverable brand in this category. Brands like Dove, Pond’s, Swiss Beauty, and Tresemme saw a lower Share of Search on Flipkart compared to Amazon.

    Navigating the Competitive Landscape: How To Thrive During Sale Events

    Amazon and Flipkart’s strategic pricing during the Big Billion Days and the Great Indian Festival Sale reflects a balance of profitability, inventory, and competition. Competitive pricing insights empower retailers to make informed decisions, optimize strategies, and thrive during high-stakes sale events with timely and relevant insights at a massive scale.

    To learn more about how you can leverage competitive pricing insights to stay ahead of the game during sale events, reach out to us today!

  • Black Friday Cyber Monday 2023: Unveiling Health & Beauty Pricing and Discount Trends

    Black Friday Cyber Monday 2023: Unveiling Health & Beauty Pricing and Discount Trends

    On Black Friday this year, Health & Beauty brands saw a significant increase with a 13% jump in foot traffic, according to a report by RetailNext. Despite caution from various sources, higher prices for everyday goods, and high interest rates, consumers chose to spend big this cyber week.

    So what kind of deals did top retailers and brands offer in the Health & Beauty category this BFCM? At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of Health & Beauty products across prominent retailers to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.

    Also check out our insights on discounts and pricing for Consumer Electronics, Apparel, and Home & Furniture categories this Black Friday and Cyber Monday.

    Our Methodology

    For this analysis, we tracked the average discounts among leading US retailers in the Health & Beauty category during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across leading retailers during the sale.

    • Sample size: 15,253 SKUs
    • Retailers tracked: Amazon, Walmart, Target, Sephora, Ulta Beauty
    • Subcategories reported on: Shampoo, Toothpaste, Conditioner, Sunscreen, Makeup, Electric Toothbrush, Beard Care, Moisturizer
    • Timeline of analysis: 24 to 27 November 2023

    Our Key Findings

    Average Discounts Across Retailers

    Amazon leads the pack with a huge margin, offering an average discount of 31.9%, covering 62% of its products analyzed. Target follows an 18.8% average discount across only 5% of its analyzed assortment. The other retailers aren’t even close.

    Ulta Beauty was the next in line, providing a 9.2% average discount followed by Walmart with a 6.8% average discount. Sephora, known for its premium beauty offerings, adopted a more conservative approach with a 3.5% average discount, targeting only 9% of its top products

    Across retailers, it is clear that Amazon led the charge by far this cyber week, with the other retailers choosing to markdown prices conservatively in the Health & Beauty category.

    Average Discounts: Subcategories

    Amazon offered high discounts on lower priced subcategories like Toothpaste (49.4%), Sunscreen (46.3%), Moisturizers (38.5%), and Conditioners (37.5%), highlighting its focus on products with high demand that consumers would look to stock up on. Ulta Beauty also focused its discounts on Toothpaste (15.6%), Moisturizers (14.9%), and Conditioners (12.6%), targeting skincare and grooming.

    Sephora, meanwhile, offered the most attractive deals on the Makeup subcategory at 5.3% across 12.67% of its analyzed assortment, banking on the demand generated due to the brand’s popularity in this subcategory.

    Target prioritized discounts on Toothpaste (22.5%), Shampoo (21.6%), and Moisturizers (18.9%). Walmart too offered significant discounts on Shampoo (21.6%) and Toothpaste (22.5%).

    Retailers prioritized staple subcategories like Toothpaste and Moisturizer with substantial discounts during this Black Friday Cyber Monday, ensuring a broad consumer appeal. In contrast, discretionary items like Makeup may be less motivated by discounts alone, and hence saw lower discounts during the sale.

    Average Discounts: Brands

    Brands offered the most attractive deals on Amazon, with OGX leading the pack at 58.4% average discount. Neutrogena and Colgate followed with an average discount of 50.4% and 44%. This mirror’s Amazon’s subcategory focus on shampoos, conditioners, and toothpastes.

    Other instances of brands offering attractive deals across retailers include Belif (27.9%) and Anastasia Beverly Hills (17.6%) on Sephora, Johnson’s (20%) and Philips Sonicare (18.8%) on Target, and Olay (12.2%) and Colgate (10.6%) on Walmart.

    Ulta Beauty hosted several attractive deals by specific brands, including Moon (30.7%), Joico (24%), and Clinique (22.3%).

    Share of Search For Health & Beauty Brands Across Subcategories

    Our Share of Search analysis illuminates the strategic moves made by brands to enhance their visibility, playing a crucial role in influencing consumer choices during Black Friday and Cyber Monday.

    Among some of the leading brands, Head & Shoulders and Oral-B increased their Share of Search by 2.3% and 1% respectively, reflecting a successful strategy to boost brand visibility during the Black Friday and Cyber Monday shopping events. On the other hand, L’Oreal Paris, Colgate, and Neutrogena faced marginal decreases in Share of Search.

    Overall, since the difference in Share of Search values did not change dramatically, the visibility levels of leading brands across key subcategories remained consistent during the Thanksgiving weekend.

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    For deeper insights on pricing and discounting trends across a diverse range of shopping categories during Black Friday and Cyber Monday, check out our blog!

    To learn more about our AI-powered Pricing Intelligence and Digital Shelf Analytics platform, contact us today!

  • Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Home & Furniture

    Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Home & Furniture

    Insider Intelligence‘s forecast of a 4.5% growth in US Holiday Sales this year has been validated by the sustained robust spending observed during Black Friday and Cyber Monday. Despite multiple challenges impacting consumer spending, such as escalating prices of everyday products and elevated interest rates, shoppers continued to spend significantly, aligning with these earlier predictions.

    However, in response to these projections, retailers strategically adjusted their approach. Our analysis indicates substantial discounts prevalent in the Consumer Electronics and Home & Furniture segments during Cyber Week. Prominent retailers specializing in Home & Furniture, such as Wayfair, Overstock, and Home Depot, notably led the charge in offering attractive discounts.

    At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of home & furniture products across prominent retailers to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.

    We’ve also recently published our analysis of the Consumer Electronics and Apparel categories this Black Friday and Cyber Monday.

    Our Methodology

    For this analysis, we tracked the discounts offered by leading US retailers in the Home & Furniture category during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across leading retailers during the sale.

    • Sample size: 44,716 SKUs
    • Retailers tracked: Amazon, Walmart, Target, Best Buy, Overstock, Wayfair, Home Depot
    • Subcategories reported on: Dishwasher, Washer/Dryer, Mattresses, Beds, Dining Tables, Entertainment Units, Rugs, Luggage, Bookcases, Cabinets, Sofas, Coffee Tables
    • Timeline of analysis: 24 to 27 November 2023

    Our Key Findings

    Discounts Across Retailers

    Wayfair led the pack with the highest average discount of 27.5%, covering an impressive 88% of its Home & Furniture inventory. This bold strategy positions Wayfair as a go-to destination for consumers seeking substantial savings on high-quality Home & Furniture items during Black Friday and Cyber Monday.

    Home Depot offered an average discount of 17.5%, covering a substantial 69% of the products analyzed, choosing to cash in on the Cyber Week madness. Overstock followed next with an average discount of 16.6%.

    Interestingly, Home & Furniture happens to be one of the few categories in which Amazon did not offer the highest discount among the analyzed retailers, choosing a moderate average discount of 13.8%.

    Best Buy also maintained a competitive stance in the category, providing an average discount of 12.8% across 58% of their assortment. Target adopted a conservative markdown strategy, offering a relatively low average discount of 6.5%.

    In summary, the Home & Furniture category exhibited a diverse range of discounting strategies among retailers, reflecting a balance between competitiveness and profit margins. Consumers could have chosen from a spectrum of discounts based on their preferences and budget considerations during Black Friday and Cyber Monday.

    Average Discounts: Subcategories

    Among subcategories, Amazon offered a moderate 8.3% average discount on 32.9% of its products in this Dishwasher category, while Best Buy took a more aggressive stance with a 14.7% average discount covering 55.9% of its products.

    Home Depot emerged as a standout player in the Washer/Dryer category, providing a substantial 21.3% discount on 78.4% of its analyzed inventory. Best Buy closely followed with a 15.1% average discount targeting 67.6% of its products.

    Wayfair grabbed attention with a generous 36.9% average discount on Mattresses, covering almost all (99%) of its analyzed products. In addition, Wafair led the discount war in Beds, Dining Tables, Cabinets, Sofas, Coffee Tables, and Entertainment Units. Overstock took an aggressive pricing stance on Rugs, offering a substantial 52.3% average discount, covering 100% of its Rugs inventory.

    Average Discounts: Brands

    Among brands, Signature Design by Ashley maintained a consistent presence with substantial discounts on both Best Buy (25.24%) and Overstock (16.19%). This could be indicative of the brand’s commitment to appealing to a diverse customer base through varied retail channels. Costway emerges as a standout brand offering exceptionally high discounts at both Target (61.6%) and Walmart (51.7%).

    Home Decorators Collection, Home Depot’s in-house brand, offered a significant 30.9% discount at Home Depot. High-margin private label brands like these afford retailers the opportunity to offer markdowns while retaining significant margins.

    Strategic positioning on specific platforms, as seen with Alwyn Home on Wayfair and Noble House at Home Depot, suggests brands tailor their approach to the strengths and customer demographics of each retailer. The data suggests a nuanced interplay between brand positioning, discount strategies, and the perceived value offered.

    Share of Search For Home & Furniture Brands

    The Share of Search data for the Home & Furniture category unveils intriguing insights into brand visibility and performance during the Black Friday and Cyber Monday events. In this competitive landscape, where consumer decisions are influenced not only by discounts but also by brand visibility, the dynamics of Share of Search become pivotal.

    Samsung strategically increased its Share of Search during the sale, showcasing a 1.2% improvement. This suggests a deliberate effort to reinforce brand visibility and capture the attention of potential buyers actively searching for Home & Furniture products, in this case, Washer/Dryers and Dishwashers.

    Bosch too experienced a notable surge in Share of Search by 1.1%. LG, meanwhile, maintained a consistent Share of Search, with a marginal decrease of 0.1%. American Tourister experienced a modest increase in Share of Search by 0.4%.

    Like in the other categories analyzed, the dynamics of Share of Search in the Home & Furniture category reflect brand strategies aimed at not only offering discounts but also ensuring heightened visibility during the critical Black Friday and Cyber Monday shopping events. Positive shifts indicate effective marketing efforts, while stable performers demonstrate a resilient brand presence in a competitive online marketplace.

    To explore how our insights can help retailers and brands boost their pricing strategies during sale events, reach out to us today!

    For more in-depth analyses and trends across various shopping categories, stay tuned to our blog.

  • Black Friday Cyber Monday 2023 Insights: A Report on Pricing and Discounts in Apparel

    Black Friday Cyber Monday 2023 Insights: A Report on Pricing and Discounts in Apparel

    As the highly anticipated shopping season approached, industry analysts, including Deloitte, had forewarned consumer spending caution owing to persistent inflationary pressures tightening budgets. Despite these concerns, the holiday spirit was buoyed by sensational deals that delighted bargain-hunting shoppers.

    According to the National Retail Federation (NRF), over 200 million consumers participated in both in-store and online shopping activities over the Thanksgiving weekend. This marked an almost 2% uptick from the previous year, surpassing the NRF’s initial estimates of 182 million and showcasing a robust start to the holiday shopping season.

    So what was all the hype about this Black Friday and Cyber Monday? How did top retailers react to reports of possibly decreased consumer spending? At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of products across prominent retailers and categories to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.

    In this article, we focus on the pricing and discounting strategies of Amazon, Walmart, and Target in the Apparel category.

    (Read Also: Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Consumer Electronics)

    Stay tuned to our blog for insights on other shopping categories like Home & Furniture, and Health & Beauty!

    Our Methodology

    For this analysis, we tracked the average discounts of apparel products among leading US retailers during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across during the sale.

    • Sample size: 17,981 SKUs
    • Retailers tracked: Amazon, Walmart, Target
    • Subcategories reported on: Women’s Tops, Men’s Swimwear, Men’s Innerwear, Women’s Innerwear, Women’s Athleisure, Women’s Dresses, Men’s Athleisure, Men’s Shirts, Women’s Shoes, Men’s Shoes, Women’s Swimwear
    • Timeline of analysis: 24 to 27 November 2023

    Our Key Findings

    Average Discounts Across Retailers

    Amazon offered the most attractive deals, showcasing an average discount of 19.5%, applying to a substantial 61% of their apparel inventory.

    Trailing closely behind was Target, offering an average discount of 14.8% across 52% of the products analyzed. Walmart, however, took a more conservative approach, providing an average discount of 8.5%, applicable to 29% of its products.

    The contrast in discounting strategies highlights the diverse tactics employed by retailers to entice Black Friday and Cyber Monday shoppers within the Apparel category. Amazon remains the forerunner, balancing competitive discounts with a significant coverage of discounted items.

    Target follows suit with a competitive stance, while Walmart opts for a more reserved markdown approach, given that the retailer tends to carry a large number of products in the affordable price ranges.

    Average Discounts: Subcategories

    Examining the Black Friday and Cyber Monday discount landscape within the Apparel category reveals intriguing patterns among major retailers. Amazon led the charge, boasting an impressive 24.9% average discount on Women’s Tops, covering a substantial 76.5% of its products. In the same subcategory, Target competed fiercely with a 25.1% average discount, covering 87.5% of its products. Walmart, taking a measured approach, presented a 14.6% average discount across 45.1% of its Women’s Tops inventory.

    Notably, Men’s Swimwear at Target has no discounts. Meanwhile, Amazon remained aggressive across various subcategories, particularly in Women’s Shoes and Women’s Tops, aiming to capture a significant market share through both competitive pricing and a broad coverage of discounted items.

    Average Discounts: Brands

    Across brands, Tommy Hilfiger and Jockey took the lead on Amazon with an enticing average discount of 28.3% and 24.6% respectively, appealing to savvy shoppers. Calvin Klein followed closely with a 17.3% discount, offering a balance of style and affordability.

    In Walmart, Crocs stood out with a 39.9% average discount, followed by Reebok (15.7%) and Hanes (14.9%) Xhilaration, Target’s in-house brand, stole the spotlight on the retailer platform with an impressive 50% average discount. Reebok (32.3%) and Levi’s (22.9%) maintained competitive discounts, appealing to diverse tastes.

    Our analysis sheds light on the dynamic landscape of apparel discounts, showcasing how brands adopt varying pricing strategies to position themselves competitively for Black Friday and Cyber Monday shoppers.

    Share of Search For Apparel Brands Across Subcategories

    The dynamics of Black Friday and Cyber Monday extend beyond price reductions, with brands strategically vying for increased visibility through Share of Search metrics. This metric signifies a brand’s prominence among the top 20 ranked products in a given subcategory, offering valuable insights into their online marketplace visibility.

    Among the standout performers in the Apparel category, Jockey experienced a significant surge in Share of Search, leaping from 1.70% before the event to an impressive 13.30% during the Black Friday and Cyber Monday sales. Speedo, in the Women’s Swimwear subcategory, demonstrated a substantial increase from 4.40% to 13.30%, solidifying its presence and gaining an 8.90% boost in Share of Search.

    Tommy Hilfiger and Adidas also exhibited notable gains in Share of Search, increasing by 5.30% and 5.60%, respectively. However, some brands experienced a slight dip, with Speedo in the Men’s Swimwear subcategory seeing a 2.50% dip in their search visibility, and Reebok in Men’s Shoes witnessing a 3.3% decrease.

    These fluctuations highlight the dynamic nature of brand strategies during Black Friday and Cyber Monday in the Apparel category, where gaining visibility also proves to be crucial alongside offering competitive discounts.

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    For a deeper dive into the world of competitive pricing intelligence and to explore how our solutions can benefit apparel retailers and brands, reach out to us today!

    Stay tuned to our blog for forthcoming analyses on pricing and discounting trends across a spectrum of shopping categories, as we continue to unravel the intricacies of consumer behavior and market dynamics.

  • Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Consumer Electronics

    Black Friday Cyber Monday 2023: Insights on Pricing and Discounts in Consumer Electronics

    As Black Friday and Cyber Monday unfolded across the globe, there was a noticeable subdued atmosphere compared to previous years. TD Cowen brokerage adjusted its forecast for US holiday spending, revising it down from an initial 4-5% growth to a more conservative estimate of 2-3%.

    Compounded by persistent inflation and elevated interest rates, many consumers find themselves financially strained, leading to the projection of the slowest growth in US holiday spending in five years.

    In this context, it would be relevant to investigate whether this restrained reaction from consumers had an influence on the extent of attractive deals and discounts provided by top retailers and brands during the sale event.

    At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices and deals of consumer electronics products across prominent retailers to uncover unique insights into their price competitiveness this BFCM, as well as understand how pricing strategies varied across diverse subcategories and brands.

    Keep an eye on our blog for insights on other shopping categories like Apparel, Home & Furniture, and Health & Beauty!

    Our Methodology

    For this analysis, we tracked the average discounts among leading US electronics retailers during the Thanksgiving weekend sale, including Black Friday and Cyber Monday. We noticed prices and discounts didn’t change significantly over the course of the weekend, and hence the average prices of products between the 24th and 27th of November are being reported. Our sample was chosen to encompass the top 500 ranked products in each product subcategory across leading retailers during the sale.

    • Sample size: 23,505 SKUs
    • Retailers tracked: Amazon, Walmart, Target, Best Buy
    • Subcategories reported on: Headphones, Laptops, Smartphones, Tablets, Speakers, TVs, Earbuds, Wireless Headphones, Drones, Smartwatches
    • Timeline of analysis: 24 to 27 November 2023

    Our Key Findings

    Average Discounts Across Retailers

    The observed Black Friday and Cyber Monday discount strategies reveal a distinct competitive landscape among major retailers. Amazon emerged as the frontrunner, offering the highest average discounts at 23.30%, spanning a significant 74% of their consumer electronics inventory. Best Buy closely followed, with an average discount of 19.40% across 76% of their products.

    On the other hand, Target and Walmart adopted a more conservative stance, providing lower average discounts at 14.8% and 12%, respectively, with Target discounting 51% of its products and Walmart discounting 41%. This variation in discounting strategies highlights the diverse approaches retailers take to attract and retain Black Friday and Cyber Monday shoppers, balancing competitiveness with profit margins.

    Average Discounts: Subcategories

    In the Headphones subcategory, Amazon stands out with a substantial 31.40% average discount, targeting 84.69% of SKUs, showcasing an aggressive discounting strategy. Best Buy follows closely, demonstrating competitive pricing with a 21.80% average discount on 67.03% of products.

    Meanwhile, in TVs, Best Buy offered a significant 17.9% average discount across 89% of its products, signaling a targeted effort to capture a broad market share in this subcategory.

    In the Laptop subcategory, Target was highly conservative, with only a 4.1% average discount covering 14.3% of its products, while Walmart positioned itself with a moderate 9.5% average discount, targeting 39.8% of its inventory.

    Among Smartphones, Amazon (14.7%) was third to Best Buy and Target, which offered average discounts of 20.5% and 18.1%, respectively. Walmart, with an average discount of only 9.9% in the subcategory opted for a relatively muted approach.

    Average Discounts: Brands

    The discount strategies across top electronics brands during Black Friday unveil distinct approaches. Samsung emerges as a focal point across Amazon, Best Buy, Walmart, and Target. The brand was most attractively priced on Best Buy, with an average discount of 25.3%, followed by Target (18.3%) and Amazon (17.9%).

    Apple’s discounts were quite consistent across Amazon (17.6%), Best Buy (16.1%), and Target (17.8%), with the exception of Walmart (8.1%). JBL, interestingly, opted to discount very heavily on Best Buy, at an average of 38.8%, resulting in several attractive deals for shoppers on the website. Sony, too, offered impressive discounts at over 23% on Amazon and Best Buy, followed by 16% on Walmart. On Amazon, Amazon Renewed (13.9%) was among the most aggressively discounted products, highlighting an effort to further appeal to cost-conscious consumers.

    Overall, our analysis throws light on the nuanced strategies employed by leading brands on Amazon, Best Buy, Walmart, and Target, reflecting a delicate interplay between brand positioning, pricing competitiveness, and customer appeal.

    Share of Search For Consumer Electronics Brands Across Subcategories

    The Share of Search data reflects intriguing shifts in brand strategies during the Black Friday and Cyber Monday events. During sale events, brands looking to entice shoppers don’t rely only on price but also on search visibility to help drive awareness and conversion. Share of Search is defined as the share of a brand’s products among the top 20 ranked products in a subcategory, thereby providing insight into a brand’s visibility on online marketplaces.

    Some of the brands that improved their Share of Search the most include LG, Skullcandy, Asus, JBL, and Samsung. On the other hand, prominent brands like Sony and Apple actually lost ground on this metric by 0.4% and 2% respectively.

    At DataWeave, our commitment to empowering retailers and brands with actionable competitive and digital shelf insights remains unwavering. Our AI-powered platform provides a comprehensive view of market dynamics for our customers, enabling informed decision-making. As a partner in your journey, we offer tailored solutions to enhance your competitive edge, drive sales, and elevate your brand presence. To find out more about our solution, reach out to us today!

    To learn more about pricing and discounting trends during Black Friday and Cyber Monday across various other shopping categories, stay tuned to our blog!

  • Which Amazon Sale Offered Better Deals: Prime Day in July or Big Deal Days in October?

    Which Amazon Sale Offered Better Deals: Prime Day in July or Big Deal Days in October?

    Amazon reported a record-breaking Prime Day this July, marking it as the biggest sales event in the company’s history. So when the eCommerce giant announced the Prime Big Deal Days this fall, we were curious to find out how big a deal it really is.

    The Prime Big Deal Days, similar in magnitude to the Summer Prime Day, promised to present substantial savings across a diverse range of categories, including electronics, toys, home, fashion, beauty, and Amazon products.

    However, for a shopper, an important question is: Does the Prime Big Deal Days in October offer lower prices than Amazon’s mega Prime Day event in July?

    To answer this question, we turned our data aggregation and analysis platform to focus on these two sale events and analyzed which event offered better deals across key categories and brands.

    TL;DR: Surprisingly, the Prime Big Deal Days in October offered, on average, 2.02% higher discounts than its counterpart event in July.

    Read on for details on how we went about our analysis and how discounts vary across categories, sub-categories, and brands.

    Our Methodology

    We tracked the prices and discounts of a large sample of products during both Prime Day events. The following are some relevant details about our sample:

    • Number of products analyzed: 1500+
    • Categories: Apparel, Consumer Electronics, Home & Furniture, Health & Beauty
    • Prime Day Sale Analysis: 11-12 July 2023
    • Prime Big Deal Days Analysis: 10-11 Oct 2023
    • Website: Amazon.com

    Our analysis focused on the differences in the prices and discount levels of products between the two sale events.

    Our Key Findings

    The average discount during the Prime Big Deal Days in October was 29.44%, which was 2.02% higher than the average discount during the Prime Day sale in July (27.42%). Interestingly, the October event offered better deals across each product category analyzed, albeit at slightly varying levels.

    By offering deeper discounts in October, Amazon may have aimed to encourage early holiday shopping, thereby capturing a larger share of the consumer wallet before competitors intensify their promotional activities closer to the festive season.

    As other retailers and online marketplaces gear up for their own holiday promotional events, Amazon’s decision to provide heightened discounts in October could serve as a preemptive move to secure customer loyalty and drive sales momentum before the onset of the peak shopping period.

    Additionally, Amazon’s strategic push to amplify the visibility of its diverse product offerings, including exclusive launches and partnerships during the October event might have contributed to the higher discounts.

    Next, let’s take a closer look at each product category.

    Apparel

    During October’s Prime Big Deal Days, the Apparel category experienced a notable uptick, boasting a 2.29% increase in discounts compared to the earlier Prime Day event in July.

    In the detailed assessment of Apparel sub-categories, Men’s and Women’s Swimwear, alongside Men’s Shoes, Innerwear, and Athleisure, emerged as the segments showcasing the most substantial average discounts during October. Fall also brought about more affordable prices for Women’s Innerwear and Men’s Shirts. However, Women’s Athleisure, Dresses, and Tops displayed diminished average discounts during this Prime Big Deal Days event.

    Delving into brand-specific analyses revealed intriguing trends. Athleisure brands such as Ibkul, Esprlia, and Ryka notably escalated their discounts in October after minimal markdowns during the Summer Prime Day sale.

    Steve Madden, witnessing heightened discounts in October, hinted at a growing demand for boots and footwear in the Autumn and Winter seasons. For instance, the Steve Madden Men’s Fenta Fashion Sneaker was priced at $46 during the Summer Prime Day, and only at $35 during the Prime Big Deal Days in October.

    Conversely, brands like PGA Tour, Land’s End, Roxy, and Anrabess offered more substantial discounts during the Summer compared to the October event.

    Consumer Electronics

    The Consumer Electronics segment during October’s Prime Big Deal Days showcased an average price decrease of 1.98% compared to the Prime Day event in July.

    Nearly all scrutinized subcategories experienced heightened discounts during the Fall Prime Big Deal Days in October. Tablets, Speakers, Drones, and Smartwatches notably presented higher discounts of 4.06%, 3.51%, 2.99%, and 2.69%, respectively, in October. However, more enticing deals were found on Earbuds and TVs during July’s event.

    Examining consumer electronics brands, Google stood out by offering the most compelling deals in October, boasting an average discount of 23.35%, marking an 8.94% increase from the Summer Prime Days’ 14.41%. Psier, Sony, and OnePlus also featured significantly reduced prices during the Fall. For example, the OnePlus 10 Pro | 8GB+128GB was $500 during the sale in July and only $440 during the Prime Big Deal Days in October.

    Conversely, prominent brands such as Bose, Sennheiser, Samsung, LG, and Asus opted to offer heavier discounts in July. Notably, the Samsung All-in-One Soundbar w/Dolby 5.1 was priced at $218 in October but only $168 in July.

    Home & Furniture

    During October’s Prime Big Deal Days, the Home & Furniture category experienced a notable 1.59% increase in average discounts compared to the Prime Day event held in July.

    Notably, Entertainment Units, Rugs, and Coffee Tables emerged as standout sub-categories that were more attractively priced in October, exhibiting price differences of 7.73%, 5.33%, and 4.80%, respectively.

    Interestingly, among the scrutinized sub-categories, only Luggage showed a lower price during the Prime Day sale in July compared to the October event. This shift likely reflects evolving consumer demand as the holiday season approaches, with items like rugs and entertainment units becoming increasingly sought-after categories for purchase.

    If you’re keen to explore how these trends vary across brands within this category, reach out to us for more insights.

    Health & Beauty

    During October’s Prime Big Deal Days, the Health & Beauty category showcased products at an average of 1.99% lower prices compared to the Prime Day event held in July.

    Our analysis of Health & Beauty reveals that a majority of the subcategories presented higher discounts during the October Big Deal Days event. Essential items such as Toothpaste, Sunscreen, and Electric Toothbrushes notably stood out as significantly more affordable during the Fall event, reflecting not only consistent demand but also a seasonal emphasis on these products. For instance, the Oral B iO Series 3 Limited Edition Electric Toothbrush, priced at $140 during the summer Prime Days, was further discounted to $120 in the fall event.

    Interestingly, Beard Care emerged as an exception, displaying higher discounts during the Prime Day sale in Summer compared to Fall’s Prime Big Deal Days.

    Examining brands within the category, Babyganics, Thinkbaby, and Vaseline showcased substantial increases in average additional discounts during October’s Prime Big Deal Days.

    Conversely, prominent brands like Maybelline, Neutrogena, and Cetaphil offered lower discounts during the fall event.

    Competitive Insights to Drive Optimized Sale Event Pricing

    At DataWeave, we understand the pivotal role of competitive pricing insights in empowering retailers and brands to gain a competitive edge, especially during significant events like Prime Day. Our commitment lies in providing retailers with precise and extensive competitor price tracking on a large scale. This empowers them to devise impactful pricing strategies and consistently uphold a competitive stance in the market. To learn more about how this can be done, talk to us today!

  • Why Unit of Measure Normalization is Critical For Accurate and Actionable Competitive Pricing Intelligence

    Why Unit of Measure Normalization is Critical For Accurate and Actionable Competitive Pricing Intelligence

    Competitive pricing intelligence is pivotal for retailers seeking to analyze their product pricing in relation to competitors. This practice is essential for ensuring that their product range maintains a competitive edge, meeting both customer expectations and market demands consistently.

    Product matching serves as a foundational element within any competitive pricing intelligence solution. Products are frequently presented in varying formats across different websites, featuring distinct titles, images, and descriptions. Undertaking this process at a significant scale is highly intricate due to numerous factors. One such complication arises from the fact that products are often displayed with differing units of measurement on various websites.

    The Challenge of Varying Units

    In certain product categories, retailers often offer the same item in varying volumes, quantities, or weights. For instance, a clothing item might be available as a single piece or in packs of 2 or 3, while grocery brands commonly sell eggs in counts of 6, 12, or 24.

    Consider this example: a quick glance might suggest that an 850g pack of Kellogg’s Corn Flakes priced at $5 is a better deal than a 980g pack of Nestle Cornflakes priced at $5.2. However, this assumption can be deceptive. In reality, the latter offers better value for your money, a fact that only becomes evident through price comparisons after standardizing the units.

    This issue is particularly relevant due to the prevalence of “shrinkflation,” where brands adjust packaging sizes or quantities to offset inflation while keeping prices seemingly low. When quantities, pack sizes, weight, etc. reduce instead of prices increasing, it’s important that this change is considered while analyzing competitive pricing.

    Normalizing Units of Measure

    In order to effectively compare prices among different competitors, retailers must standardize the diverse units of measurement they encounter. This standardization (or normalization) is crucial because price comparisons should extend beyond individual product SKUs to accommodate variations in package sizes and quantities. It’s essential to normalize units, ranging from “each” (ea) for individual items to “dozen” (dz) for sets, and from “pounds” (lb), “kilograms” (kg), “liters” (ltr), to “gallons” (gal) for various product types.

    For example, a predetermined base unit of measure, such as 100 grams for a specific product like cornflakes, serves as the reference point. The unit-normalized price for any cornflake product would then be the price per 100 grams. In the example provided, this reveals that Kellogg’s is priced at $0.59 per 100 grams, while Nestle is priced at $0.53 per 100 grams.

    Various Categories of Unit Normalization

    1. Weight Normalization

    Retailers frequently feature products with weight measurements expressed in grams (g), kilograms (kg), pounds (lbs), or ounces (oz).

    2. Quantity or Pack Size Normalization

    Products are also often featured with varying pick sizes or quantities in each SKU.

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    3. Volume or Capacity Normalization

    Products can also vary in volumes or capacities with units like liters (L) or fluid ounces (fl oz).

    DataWeave’s Unit Normalized Pricing Intelligence Solution

    DataWeave’s highly sophisticated product matching engine can match the same or similar products and normalize their units of measurement, leading to highly accurate and actionable competitive pricing insights. It standardizes different units of measurement, like weight, quantity, and volume, ensuring fair comparisons across similar and exact matched products.

    Retailers have the flexibility to view pricing insights either with retailer units or normalized units. This capability empowers retailers and analysts to perform accurate, in-depth analyses of pricing information at a product level.

    In some scenarios, analyzing unit normalized pricing reflects pricing trends and competitiveness more accurately than retail price alone. This is particularly true for categories like CPG, where products are sold in diverse units of measure. For instance, in the example shown here, we can view a comparison of price position trends for the category of Fruits and Vegetables based on both retail price and unit price.

    The difference is striking: the original retail price based analysis shows a stagnation in price position, whereas unit normalized pricing analysis reflects a more dynamic pricing scenario.

    With DataWeave, retailers can specify which units to compare, ensuring that comparisons are made accurately. For example, a retailer can specify that unit price comparisons apply only to 8, 12, or 16-ounce packs, as well as 1 or 3-pound packs, but not to 10 and 25-pound bags. This precision ensures that products are matched correctly, and prices are represented for appropriately normalized units, leading to more accurate pricing insights.

    To learn more about this capability, write to us at contact@dataweave.com or visit our website today!

  • From Data to Dollars: How Digital Shelf Analytics Drives Tangible Business Impact and ROI for Brands

    From Data to Dollars: How Digital Shelf Analytics Drives Tangible Business Impact and ROI for Brands

    For consumer brands, the digital marketplace presents an unparalleled landscape of opportunities for engaging with consumers and expanding their market presence. Within this dynamic environment, Digital Shelf Analytics has emerged as a crucial pillar in a brand’s eCommerce strategy. This technology provides valuable insights into a brand’s organic and paid visibility on marketplaces, content quality, pricing strategies, promotional efforts, and product availability. These insights help brands gain a comprehensive understanding of their competitive positioning and overall market performance.

    Nevertheless, many brands often grapple with the question of whether this understanding translates into tangible actions that drive real business impact and return on investment (ROI). This uncertainty stems from a lack of clarity about the direct correlation between digital shelf insights and key metrics such as enhanced sales conversions.

    Nonetheless, there is compelling evidence that when these insights are effectively harnessed and strategic actions are taken, brands can realize significant, measurable benefits.

    So, the question arises: does Digital Shelf Analytics genuinely deliver on its promises?

    At DataWeave, we’ve partnered with numerous brands to fuel their eCommerce growth through the application of digital shelf analytics. In this article, we will delve into these insights, uncovering the concrete and quantifiable results that brands can achieve through their investments in digital shelf analytics.

    Digital Shelf KPIs and Their Impact

    Digital Shelf Analytics is a robust system that analyzes specific key performance indicators (KPIs) about the digital shelf, furnishing brands with precise recommendations to not only bolster these KPIs but also to monitor the enhancements over time. The following is a brief explanation of digital shelf KPis and their expected impact areas:

    Product Availability: Ensuring Shoppers Never Hear “Out of Stock” Again

    Timely insights on the availability of products ensures brands reduce replenishment times at scale, which can significantly impact sales, creating an unbreakable link between product availability and revenue. With Digital Shelf Analytics, procurement and replenishment teams can set up notifications to promptly identify low or out-of-stock items and take swift action. This can also be done for specific ZIP codes or individual stores. In addition, availability plays a crucial role in a brand’s Share of Search and search rankings, as online marketplaces often ensure only in-stock products are shown among the top ranks.

    Share of Search: Dominating the Digital Aisles

    If a product isn’t visible, does it even exist? In fact, 70% of consumers never go beyond the first page of search results on major online marketplaces. Therefore, as a brand, the visibility of your products for relevant search keywords and their appearance on the first page can heavily determine your awareness metrics. This is where the concept of Share of Search comes into play. Think of it as securing prime shelf space in a physical store. Digital shelf insights and benchmarking with category leaders for Share of Search help ensure your products command relevant attention on the digital shelf.

    Content Quality: Crafting the Perfect Product Story

    Creating engaging product descriptions and visuals is akin to giving your products a megaphone in a crowded marketplace. By enhancing content quality, including product names, titles, descriptions, and images, brands can climb the search result rankings, leading to increased visibility and subsequently, more sales.

    Ratings and Reviews: The Power of Social Proof

    Public opinion holds immense sway. Research indicates that a single positive review can trigger a 10% surge in sales, while a multitude of favorable reviews can propel your product to a 44% higher trajectory. The correlation between ratings and sales is not surprising—each step up the rating ladder can translate to substantial revenue growth.

    While it’s reasonable to anticipate a connection between these KPIs and downstream impact metrics such as impressions, clicks, and conversions, we were driven to explore this correlation through the lens of real-world data. To do so, we meticulously monitored the digital shelf KPIs for one of our clients and analyzed the improvements in these metrics.

    It’s essential to acknowledge that not all observed impact areas can be solely attributed to enhancements in digital shelf KPIs. Still, it’s evident that a robust correlation exists. The following section presents an in-depth case study, shedding light on the results of this analysis.

    A Success Story: Real-World Impact of Digital Shelf Analytics

    Let’s dive into the journey of one of our clients – a prominent CPG brand specializing in the sale of baked goods and desserts. Through their experience, we will illustrate the transformative impact of our DataWeave Digital Shelf Analytics product suite.

    Over a period of one year, from August 2022 to July 2023, the brand leveraged several key modules of Digital Shelf Analytics for Amazon, including Share of Search, Share of Category, Availability, Ratings and Reviews, and Content Audit. Each of these digital shelf KPIs played a vital role in shaping the brand’s performance across various stages of the buyer’s journey.

    The buyer’s journey is typically delineated into three key stages:

    • Awareness: At this stage, shoppers peruse multiple product options presented on search and category listing pages, gaining an initial understanding of the available choices.
    • Consideration: Here, shoppers narrow down their selections and evaluate a handful of products, moving closer to a purchase decision.
    • Conversion: In this final stage, shoppers make their ultimate product choice and proceed to complete the purchase.

    Let’s now examine the data to understand how digital shelf KPIs helped drive tangible ROI on Amazon for the brand across the stages of the buyer journey.

    Stage 1: Raising Awareness

    Enhancing Share of Search and Share of Category can help brands boost product visibility and raise brand awareness. The following chart demonstrates the steady, incremental improvements in our client’s Share of Search and Share of Category (in the top 20 ranks of each listing page) throughout the analyzed period. These enhancements were achieved through various measures, including product sponsorship, content enhancement, price optimization, promotional initiatives, and more.

    This amplified Share of Search and Share of Category directly translates into improved product discoverability, as evident from the surge in impressions depicted in the chart below.

    Stage 2: All Things Considered

    In the consideration stage, shoppers make their product selections by clicking on items that meet their criteria, which may include factors like average rating, number of ratings, price, product title, and images. For brands, this underscores the importance of crafting meticulously detailed product content and accumulating a substantial number of ratings.

    The subsequent chart illustrates the year-long trend in both average ratings and the number of ratings, both of which have displayed steady improvement over time.

    The enhancements in the number of ratings and the average rating have a direct and positive impact on product consideration. This, in turn, has led to a noticeable year-over-year increase in page views, as indicated in the chart below.

    These improvements are likely to have also been influenced by the overall enhancement of content quality, which is detailed separately in the section below.

    Stage 3: Driving Decisions

    As buyers progress to the next stage, they reach the pivotal point of making a purchase decision. This decision is influenced by multiple factors, including product availability, content quality, and the quality of reviews, reflecting customer sentiment.

    Our client effectively harnessed our Availability insights, significantly reducing the likelihood of potential out-of-stock scenarios and enhancing replenishment rates, as highlighted in the chart below. The same chart also indicates improvements in content quality, measured by the degree to which the content on Amazon aligns with the brand’s ideal content standards.

    Below, you’ll find the year-over-year growth in conversion rates for the brand on Amazon. This metric stands as the ultimate measure of business impact, directly translating into increased revenue for brands.

    As the data uncovers, growth in key digital shelf KPIs cumulatively had a strong correlation with impressions, page views, and conversion rates.

    It is also important to note that the effect of each KPI cannot be viewed in isolation, since they are often interdependent. For example, improvement in content and availability could boost Share of Search. Accurate content could also influence more positive customer feedback. Brands need to consider optimizing digital shelf KPIs holistically to create sustained business impact.

    Impact on eCommerce Sales

    After the implementation of digital shelf analytics, the results spoke for themselves. Sales consistently outperformed the previous year’s records month after month. As shown in the chart below, the diligent application of DataWeave’s recommendations paved the way for an impressive 8.5% year-over-year increase in sales, leaving an indelible mark on the brand’s eCommerce success.

    From boosting product visibility to catapulting conversion rates, Digital Shelf Analytics serves as the key to unlocking unparalleled online success.

    While the success story detailed above does not establish a direct causation between Digital Shelf Analytics and sales revenue, there is undoubtedly a strong correlation. It’s evident that digital shelf KPIs play a pivotal role in optimizing a brand’s eCommerce performance across all stages of the buyer journey. Hence, for brands, it is vital that they collaborate with the right partner and harness digital shelf insights to fine-tune their eCommerce strategies and tactics.

    That said, the eCommerce landscape is in a constant state of flux, and there is still much to learn about how each digital shelf KPI influences brand performance in the online realm. With more data and an increasing number of brands embracing Digital Shelf Analytics, it’s only a matter of time before a direct causation is firmly established.

    Reach out to us today to know more about how your brand can leverage Digital Shelf Analytics to drive higher sales and market share in eCommerce.

  • Revolutionizing Fuel Pricing: How Fuel Retailers and Convenience Stores Can Gain a Winning Edge with DataWeave

    Revolutionizing Fuel Pricing: How Fuel Retailers and Convenience Stores Can Gain a Winning Edge with DataWeave

    Consider this scenario: A retailer establishes its fuel prices using pricing data that’s a few days old, only to subsequently discover that a nearby competitor is offering substantially lower prices. The result? Lost customers, decreased foot traffic, and diminished sales. This serves as a stark reality that retailers must confront and address today.

    In the fiercely competitive realm of retail, where every decision holds weight, maintaining a competitive edge is paramount. The fuel category, frequently underestimated, has the potential to significantly impact a retailer’s revenue stream. This challenge is not unique; retailers worldwide, particularly in North America, grapple with a common hurdle: mastering the intricate art of real-time fuel pricing.

    The Quest For Reliable, Real-Time Fuel Pricing Data

    For retailers, traditional methods for procuring and analyzing fuel price data have proven to be both expensive and error-prone, often relying on manual data collection or third-party data providers. These outdated approaches yield frustrating delays, inaccuracies, and missed opportunities. When it comes to obtaining timely fuel pricing intelligence, the majority of fuel retailers grapple with three central challenges:

    • Low Accuracy: Ensuring that fuel pricing information remains up-to-date, dependable, and actionable, even when sourced from complex web-based platforms.
    • Less Coverage: Acquiring comprehensive data that encompasses all of North America, spanning across retailers, convenience stores, fuel stations, and beyond.
    • High Cost: Effectively managing the substantial costs associated with acquiring and processing this vital information.

    DataWeave’s Fuel Pricing Intelligence Solution

    Comprehensive, accurate, and real-time fuel pricing intelligence can play a huge role in the profitability of retailers throughout North America. DataWeave takes the forefront in delivering this transformative Data-as-a-Service (DaaS) solution to some of the most prominent retailers in the region, including the top 20 fuel retail behemoths.

    With a rich and extensive history spanning over a decade in the realm of competitive intelligence, DataWeave boasts an impressive track record of empowering well-informed decision-making in retail. We leverage state-of-the-art technology to bring an unparalleled level of accuracy, timeliness, and coverage to fuel pricing intelligence.

    The following are some compelling advantages offered by our solution:

    Accurate and Real-Time First Party Data

    We deliver retailers an unparalleled advantage through real-time, first-party fuel price data. Our data originates directly from the retailer’s own channels, encompassing websites and mobile apps, rendering it the industry’s foremost and most reliable source.

    Imagine having access to fuel pricing information that updates as frequently as every 30 minutes. This rapid update cadence guarantees that you, as a retailer, constantly possess the latest pricing insights at your fingertips, empowering you to respond swiftly to market fluctuations and competitor manoeuvres. Our comprehensive data spans a wide spectrum of fuel types, including:

    • Gasoline: Be it regular, mid-grade, super, premium, ethanol-free, ethanol blends, methanol blends, or reformulated gasoline, we have got you covered.
    • Diesel: Our data encompasses biodiesel, biodiesel off-road, biodiesel blends, biodiesel ultra-low sulfur (ULS), diesel ultra-low sulfur (ULS), diesel off-road, standard diesel, and premium diesel.

    Armed with our real-time, first-party data, you can make pricing decisions with unwavering confidence, secure in the knowledge that you possess access to the most current, authoritative, and extensive fuel pricing intelligence in North America.

    The data points we capture directly from relevant web sources include: gas station postal code, store name and code, location, city, state, ZIP code, fuel type, competitor name, regular price, member price (if available), time and date of data capture, and more.

    Click here if you wish to access a sample report of our fuel pricing data.

    Unrivaled Geographical Coverage

    Our extensive coverage of fuel data spans over 30,000 ZIP codes and encompasses the top 100 retailers across the western, mid-western, and eastern regions of the United States.

    Retailers benefit from the flexibility to configure and tailor the solution to their precise needs, whether it involves adding more locations or selectively acquiring specific segments of the data. This far-reaching coverage guarantees that retailers, whether situated in bustling urban centers or remote areas, can readily access the essential data required to maintain their competitive edge.

    Moreover, if you currently source your fuel pricing data from alternative providers, our solution seamlessly integrates, amplifies, and complements your existing array of data sources, ensuring a harmonious and unified approach to data acquisition.

    Optimization of Dynamic Pricing Strategies

    In the world of retail, the importance of timing cannot be overstated. Even a mere difference of a few cents can translate into millions of dollars in revenue impact. With DataWeave, retailers gain the capability to make data-driven decisions that provide them with a competitive edge around the clock, every single day.

    Our platform empowers you to unearth margin gaps by pinpointing opportunities to raise prices while maintaining your competitive pricing position. It also identifies instances where you may be substantially overpriced, prompting necessary price adjustments to ensure competitiveness within the market. All these valuable insights are available at a hyperlocal level, facilitating pricing efficiency and optimization across your various regions of coverage. Equipped with this real-time data, you can swiftly adapt to ever-changing market conditions.

    Furthermore, our comprehensive competitive data seamlessly integrates into your existing pricing systems through APIs, facilitating quick and informed pricing actions based on robust data.

    Reliable and Customer-First Tech Platform

    Our platform boasts a remarkable level of sophistication when it comes to data aggregation, normalization, visualization, and integration capabilities. It stands as a massively scalable system with the capacity to aggregate billions of data points daily, spanning thousands of web sources. This includes the intricate handling of sources like mobile apps and websites known for frequently altering their site structures, among others.

    What truly sets us apart is our proficiency in addressing these challenges through a blend of human expertise and large-scale machine learning. Additionally, our commitment to delivering unmatched service extends to round-the-clock, 24/7 support. This comprehensive approach makes our fuel pricing intelligence solution not only effective but also cost-efficient in meeting your fuel data requirements.

    We also provide a variety of options for you to consume our data, which includes receiving our reports via email, SFTP, S3 buckets, data lakes like Snowflake, and APIs.

    Enhance your Fuel Pricing Strategies with DataWeave

    In the ever-competitive world of retail, staying ahead is not just a goal; it’s a necessity. The fuel pricing landscape, often overlooked, holds immense power to impact a retailer’s profitability. DataWeave’s real-time, comprehensive, and accurate fuel pricing intelligence solution is the key to securing this advantage. Retailers and convenience stores now have a powerful platform at their disposal, offering unparalleled precision, comprehensive coverage, and the agility needed to navigate this landscape.

    Join the ranks of industry leaders who have already harnessed the potential of DataWeave. Reach out to us today to redefine your approach to fuel pricing and propel your business to new heights!

  • Backpacks to Binders: Examining Back-to-School Price Hikes in 2023

    Backpacks to Binders: Examining Back-to-School Price Hikes in 2023

    This year’s back-to-school shopping season has presented a considerable challenge for inflation-weary parents in the US. Despite chatter about alleviating inflation rates, the reality of rising prices tells a different story.

    As families hunt for school supplies, apparel, and other essential items for the academic year, the financial strain remains palpable. Experts note that elevated prices coupled with extensive shopping lists have compelled many parents to be more discerning about their purchases, expenditure thresholds, and preferred shopping venues. Essentially, shoppers are looking for more value for their money with every purchase. According to the National Retail Federation’s 2023 projection, this back-to-school season is poised to be the most financially demanding one to date. The forecast anticipates total spending exceeding $135 billion, marking an increase of over $24 billion compared to the previous year.

    At DataWeave, we continually monitor and analyze pricing activity among retailers across popular shopping categories. Our recent study delved into the pricing trends in the back-to-school category, which includes backpacks, fundamental school supplies, binders, planners, writing instruments, and more. The aim was to understand how the costs of back-to-school essentials have shifted in 2023 in comparison to 2022.

    Pricing of Back-to-School Products in 2023

    Our analysis, spanning 1200 products across major retailers such as Amazon, Walmart, Kroger, and Target reveals an average price surge of 9.8% in 2023 compared to the previous year.

    This upward pricing trend can be attributed to retailers’ strategic efforts to guarantee product availability and uphold quality during a period of heightened demand. As the back-to-school season sparks a surge in shopping activity, retailers like Kroger, Amazon, and Walmart are likely adjusting prices strategically to align with the expenses incurred in securing adequate supplies, managing logistics, and meeting operational demands.

    Average Price Increase 2022-23 By Retailer, Back-To-School Category

    Kroger led the way with a 12.1% price hike, the most significant among the scrutinized retailers. It was followed by Amazon with an average increase of 10.5% and Target with 7.8%. Walmart remains the outlier, with the smallest price increases for back-to-school products in 2023.

    Pricing across Categories and Subcategories

    Among the various categories examined, backpacks have experienced the most pronounced escalation, with prices soaring by a substantial 25%. Within the top 10 highest priced backpacks we looked at, the most substantial price hikes were observed for brands like The North Face (44%) and Fjallraven (33%).

    Average Price Increase 2022-23 By Category Across Retailers, Back-To-School

    The Office Organization category also witnessed a significant price surge of 16.8%, attributed to subcategories like File Folders and Desk Accessories, which saw respective price hikes of 31.3% and 25.2%.

    Categories like Memo Boards & Supplies (14.3%), Binders (12.5%), and Themebooks & Portfolios (12.4%) have likewise encountered notable price hikes. On the other end of the spectrum, Planners and Journals saw a modest rise of 4.4%, while Mailing and Shipping Supplies and Office Machine Accessories experienced comparatively lower price increases at 7% each.

    Interestingly, while items like Journals and Writing Instruments maintain popularity year-round, Backpacks and Memo Boards are particularly sought after during the back-to-school season, contributing to more substantial price hikes in these categories.

    On the other hand, consumers are consistently on the lookout for cost savings and deals from retailers, especially as they deal with inflationary pressures. In response, Kroger, Target, and Walmart have introduced back-to-school savings initiatives. For instance, Kroger is offering more than 250 items for less than $3 and some items for just $1, encompassing essentials such as paper, pencils, and glue sticks. Lower price increases across categories like journals and writing essentials could be attributed to these initiatives.

    Brands with the Highest Price Increases across Categories

    Across various back-to-school categories, some brands stand out with significant price increases. For instance, in the Office Organization category, Ubrands leads the pack with a substantial 38.30% surge, followed by Pendaflex at 30.80%. Meanwhile the Backpacks category sees Champion and Adidas recording significant price jumps of 29.6% and 23.6%, respectively.

    Brands with highest price increases across Back to School categories 2022-23

    Ubrands and Pentel from Basic School and Office Supplies Category also record high price increases at 22.70%, followed by Carolinapd from the Themebooks & Portfolios Category at 21.08%. 3M in Mailing in Shipping Supplies shows the lowest price increase at 6.80%.

    Interestingly, the ever popular Writing Instruments category showcases BIC at the forefront, exhibiting the most notable price escalation of 13.2%. Expo trails closely at 11.6%, while Uniball demonstrates an 11.4% increase. Even Sharpie, a beloved writing brand, displays a modest price uptick of 9.3%.

    The average price increments seen across brands mirror the overarching trend of increased costs throughout back-to-school categories.

    Navigating the Competitive Pricing Landscape During the Back-To -School Season

    Given the challenging pricing landscape during the back-to-school season, retailers would be wise to provide lower-cost alternatives alongside popular brand names. This allows parents to easily make substitutions while adhering to a school supplies list.

    With our competitive pricing intelligence solution, retailers can confidently analyze and monitor their prices relative to competition, ensuring they maintain a leadership position in pricing within their desired set of products, while posturing for margins with other products.

    To learn more about how we can help, reach out to us today!

  • DataWeave Launches PricingPulse: Empowering Retail Leaders With Comprehensive and Strategic Pricing Insights

    DataWeave Launches PricingPulse: Empowering Retail Leaders With Comprehensive and Strategic Pricing Insights

    In the evolving retail landscape, success often hinges on a singular focal point: pricing. A recent Statista survey revealed that 70% of US online users prioritize competitive pricing in their digital shopping choices. In this cutthroat arena, where surpassing rivals is paramount, a deep comprehension of pricing nuances is no longer just an edge, but a necessity.

    Retailers are increasingly adopting pricing intelligence solutions that meticulously dissect competitor pricing data in comparison to their own, down to the SKU level. This analysis empowers their pricing teams with the insights they need to price their products competitively on a day-to-day basis.

    However, in a landscape where a staggering 50 million price changes occur daily, reliance on a reactive pricing intelligence solution, though effective in many ways, often falls short. To develop a strategic and predictive pricing engine, retailers also need the ability to track historical pricing relative to market conditions, competitor actions, seasonality, promptness of competitor pricing actions, and more. This would be particularly useful for senior retail pricing and business unit leaders as they look to gain a strategic perspective on their competitive pricing health. However, even today’s leading providers of retail pricing intelligence solutions lack in this area. This results in a relatively myopic view of competitive pricing even in large retail organizations.

    Introducing DataWeave’s PricingPulse

    DataWeave’s PricingPulse helps retail leaders better understand their competitive pricing strategies in comparison to relevant market dynamics over time. The capability bridges the gap between day-to-day competitive pricing operations and long-term strategic pricing analysis and actions, enabling senior retail pricing leaders to untangle the complexities of their pricing strategies. Delivered as a dashboard, the view offers an elevated vantage point for industry-wide pricing dynamics, empowering retailers with the foresight needed to navigate market shifts, predict vulnerabilities, and capitalize on new opportunities.

    PricingPulse is provided to all DataWeave retail customers as an add-on to our Pricing Intelligence solution.

    The insights offered by PricingPulse enable retailers to answer pivotal questions about competitor pricing behaviors, price leadership across categories, timing of price changes, and the effectiveness of capitalizing on price improvement opportunities. Some of the questions that PricingPulse offers answers to include:

    • How frequently are my competitors changing prices and for which products?
    • How does my price leadership vary across key product categories?
    • Which day of the week or month do my competitors change their pricing most and least frequently?
    • How well do I seize on price improvement opportunities over time?

    Strategic Pricing Views Via PricingPulse

    In the following section, we share a few views available to retail leaders via our PricingPulse dashboard. For a complete list of insights available on the dashboard, request a demo today.

    Competitive Price Leadership Across Retailers and Categories

    This view provides retailers with an overview of the price leaders across various product categories and how it changes with time. More often than not, retailers would aim to gain price leadership in certain categories, while maintaining healthy margins in others.

    Retailers can also gauge their consistency and effectiveness in maintaining a competitive edge for key categories over time. They can fortify areas of strength and identify opportunity areas to optimize their pricing.

    In addition, the dashboard tracks a retailer’s price index across categories, a measure that determines its price competitiveness.

    The price index is determined by dividing the retailer’s price by the lowest price offered by any of its competitors. A ratio lesser than 1 indicates that the retailer is the lowest priced in the market. This measure is also presented for competitors, providing insights into competitors that are most attractively priced in the market. A timeline trend of this metric helps track how price leadership among retailers changes over time.

    Price Change Trends

    This view provides a summary of the level of price changes by a retailer and its competitors over a period of time, which includes the average magnitude of price changes as well as the proportion of the retailer’s assortment that underwent these price changes.

    In addition, the number of price changes each month are provided for each retailer. This is further broken down into the total number of price changes during each day of the week.

    These insights help retailers determine which competitors are most and least active in their pricing activities, how aggressive the pricing actions are, and if there are any specific price change patterns followed in terms of the days of the week or month.

    Price Improvement Opportunities and Actions

    The dashboard actively reports on price improvement opportunities, which could include either a price increase opportunity or a price decrease opportunity, for a retailer and its competitors across categories over time. A price increase opportunity occurs when a product is significantly under priced (by more than 2%) and a price decrease opportunity occurs when a product is significantly overpriced (by more than 2%).

    Further, the retailer gains insight into how many price improvement opportunities were actually acted on within 15 days of the opportunity presenting itself. This “action rate” helps retailers quantify how well they seize on price improvement opportunities, which eventually result in higher sales and margins. The dashboard also reports on the average number of days it took for a retailer to act on a price improvement opportunity, thereby quantifying the responsiveness and agility of pricing teams.

    This is especially useful for pricing leaders to “audit” or evaluate the performance of their pricing teams. When similar insights are viewed for a set of competitors as well, retailers can better understand the level of sophistication of their competitors’ pricing operations.

    Ready to Elevate Your Pricing Game?

    The launch of DataWeave’s PricingPulse marks a significant advancement in the realm of pricing solutions for retail leaders. As the retail landscape undergoes continuous transformation, the significance of precise pricing strategies cannot be overstated. PricingPulse is the first and only pricing view in the industry to bridge the gap between tactical pricing decisions and comprehensive strategic analysis.

    In a world where agility and foresight are crucial, PricingPulse equips retail leaders with the ability to predict competitor actions, optimize pricing strategies, and stay ahead of the competition.

    If you are a senior pricing leader or retail business unit head, reach out to us today to either sign up or learn more!

  • Amazon India’s Pricing and Discounts on Prime Day 2023: A Deep Dive Analysis Across Leading Categories and Brands

    Amazon India’s Pricing and Discounts on Prime Day 2023: A Deep Dive Analysis Across Leading Categories and Brands

    Amazon’s India Prime Day 2023 shattered previous records with a peak of 22,190 orders received in a minute. An important aspect of Amazon’s India Prime Day was the benefits it offers to Prime Members. Thousands of sellers, brands, and bank partners collaborated to help Prime members save a staggering sum of over Rs. 300 Crores. The 2 day (July 15-16) event even witnessed strong growth in Prime membership, with 14% more members shopping than last year’s Prime Day event. 45,000+ new products were launched by over 400+ top Indian and global brands.

    However, our analysis reveals that Amazon was able to make a huge splash despite adopting a relatively modest discounting strategy for the event.

    Pricing and Discounts on Prime Day 2023

    While Prime Day is Amazon’s showstopper, bringing huge benefits to partner brands and sellers, it’s interesting to also see how Flipkart responded to such a massive sale by its biggest competitor. Therefore, we leveraged our proprietary data aggregation and analysis platform to analyze the prices and discounts of Amazon and Flipkart across key product categories – Apparel, Home & Furniture, Consumer Electronics, and Health & Beauty – during Prime Day.

    Since products on Amazon and other eCommerce websites are often sold at discounts even on normal days not linked to a sale event, we delved into the real value that Prime Day offers to shoppers by focusing on price reductions or additional discounts during the sale compared to the week before. As a result, our approach highlights the genuine benefits of the event for shoppers who count on lower prices during the sale.

    Research Methodology

    For our analysis, we tracked the prices of a large number of products across Amazon and Flipkart during Prime Day as well as the week prior to the event. The details of our sample are mentioned below:

    • Number of SKUs: 85,000+
    • Retailers: Amazon, Flipkart
    • Categories: Apparel, Home & Furniture, Consumer Electronics, Health & Beauty
    • Pre-event Analysis:10-14 July 2023
    • Prime Day Analysis: 15-16 July 2023

    Our Findings

    Based on our analysis, Prime Day showcased relatively higher price reductions in the Health and Beauty category, offering an average additional discount of 5.3%. In comparison, the Apparel category had lower discounts at 4.90%, followed by the Home & Furniture category at 2.50% during the sale event.

    Average price reduction on Amazon on Prime Day across categories.

    The Consumer Electronics category, known for attractive prices during sale events, featured only 0.9% price reductions. This is due to the fact that the category was already being sold at a very high average discount of around 44.8% the week prior to Prime Day.

    Below, we delve deeper into our analysis of each category to better understand how price reductions were distributed across key subcategories on Amazon. We also report on the degree to which Flipkart responded to Amazon’s pricing actions during the event.

    Apparel

    As Amazon grappled with heightened costs and reduced profit margins in apparel (like most other retailers), its average discount before Prime Day was already at 36.5%. Then, on Prime Day, Amazon’s apparel deals were tempered at around 4.9% average price reduction across 43.7% of its assortment.

    Flipkart, on the other hand, offered only a modest additional discount of 1.8% across 17.7% of its Apparel assortment. It’s clear that while Flipkart took steps to compete against Amazon in this category, it was done to a lower extent on fewer products than Amazon.

    Apparel average price reduction across retailers on Prime Day.

    Across all the apparel subcategories we analyzed, Men’s Shoes (11.6%), Women’s Shoes (9.5%), and Men’s Shirts (8.7%) were among the ones with the highest price reductions. On the other hand, Men’s and Women’s Swimwear (2.3%), Women’s Innerwear (2.9%), and Women’s Athleisure (3.3%) had conservative markdowns.

    Apparel average price reduction across subcategories on Amazon.

    Pricing choices within different subcategories likely stemmed from a range of factors, such as inventory quantities, trends in demand, and the aim to harmonize competitive deals with the maintenance of viable profit margins. These decisions reflect Amazon’s attempt to cater to a consumer base that is particularly conscious of pricing.

    Across all apparel subcategories, leading brands that offered the highest markdowns were Sweet Dreams (65.5%), Ketch (55.1%), Clarks (44.9%), and Kibo (38.4%). Meanwhile, Reebok and Adidas offered significant additional discounts at 26.3% and 24.9%, respectively, as well.

    Apparel average price reduction across leading brands on Amazon.

    For brands, however, reducing prices is just one approach to entice shoppers. They must also guarantee their prominent presence and easy discoverability within Amazon’s search results. This significantly amplifies their potential to generate higher clicks and conversions. In our analysis, we monitored brands’ Share of Search across various frequently used search terms in addition to the discounts they provided. The Share of Search denotes the portion of a brand’s products within the top 20 search results for a specific search query.

    Our data indicates that certain brands gained ground in their discoverability during Prime Day, while others fell behind. Van Heusen in Women’s Athleisure (30%), Campus in Men’s Shoes (50%), and Rovar’s (30%) in Women’s Swimwear among others, improved their Share of Search by significant levels during Prime Day.

    Apparel share of search on Amazon on Prime Day.

    On the other hand, brands like Sparkx in Men’s Shoes, Xyxx in Men’s Innerwear, WomanLikeU in Women’s Swimwear, and Adidas in Women’s Shoes lost around 40%-80% in their Share of Search during the event. This is likely to have impacted their sales volumes adversely.

    Home & Furniture

    The Home & Furniture industry faced challenges of reduced demand and overstocked inventory over the past year. Therefore, even before Prime Day, discounts offered in this category on Amazon averaged a staggering 45.3%. Consequently, on Amazon Prime Day, additional discounts averaged only 2.5% on Amazon, offered across 33.3% of its assortment. Flipkart opted, in effect, not to compete with Amazon in this category, offering a negligible additional discount of 0.8% across 14.70% of its assortment.

    Home & furniture average price reduction across retailers on Prime Day.

    Of all the Home & Furniture subcategories we analyzed, Luggage (5.1%), Beds (3.9%), and Coffee Tables (3.1%) had high price reductions, while Rugs (0.6%), Bookcases (1.5%), and Washer/Dryers (1.2%) had lower markdowns. This highlights the difference in consumer preferences across geographies, with rugs being more discretionary in India but staple in the US.

    Home & furniture average price reduction across subcategories on Amazon.

    The Home & Furniture category is not known for its brand loyalty among shoppers. Therefore, brands often rely on attractive pricing to gain shopper interest. This Prime Day, brands that offered the highest markdowns in this category include It Luggage (40%), Couch Culture (25.8%), Story@Home (23.3%), and Verage (21.2%).

    Home & furniture average price reduction across leading brands on Amazon.

    In terms of Share of Search, Wudparadise in Entertainment Units gained the highest (50%). Solimo (an Amazon Brand) in Beds (40%), Sofas (30%), and Coffee Tables (10%) gained significant ground in its respective categories too. In contrast, About Space in Bookcases (-60%), Anika in Entertainment Units (-40%), and Sleepyhead in Mattresses (-40%) lost out on their discoverability in their respective categories during the event.

    Home & furniture share of search on Amazon on Prime Day.

    To gain a competitive edge during sale events like Prime Day, brands need to monitor their Share of Search closely, especially in categories like Home & Furniture with low brand loyalty.

    Consumer Electronics

    This Prime Day, five smartphones got sold every second with 70% of the demand coming from Tier 2 & 3 cities in India, largely comprising of foldable smartphones and newly launched smartphones (OnePlus Nord 3 5G, Samsung Galaxy M34 5G, Motorola Razr 40 Series, Realme Narzo 60 Series and iQOO Neo 7 Pro 5G). Multiple new products were launched this Prime Day, by brands such as OnePlus, iQOO, Realme Narzo, Samsung, Motorola, boAt, Sony, and more in India.

    Consumer electronics average price reduction across retailers on Prime Day.

    Despite the high demand and new product launches, Amazon’s price reductions in the Consumer Electronics category averaged only 0.9% across 27% of its assortment. Similar to what we observed in the Home & Furniture category, this can be attributed to the prevailing high average discount of 44.8% the week prior to Prime Day. Essentially, in Consumer Electronics, shoppers needn’t always wait till sale events like Prime Day to view the most attractive deals. Several are offered even during the days leading up to the sale.

    Across subcategories, Earbuds (2.4%), Wireless Headphones (1.6%), and TVs (1.3%) received the highest price reductions due to their popularity and high sales volumes during sales events. On the other hand, Smartwatches (0.6%), Drones (0.4%), and Smartphones (0.3%) had lower markdowns.

    Consumer electronics average price reduction across subcategories on Amazon.

    In terms of price reductions across brands, Da Capo (52.6%), Muzen (33.3%), JLab (23.6%), and Earboss (21.5%) offered the most attractive deals in the Consumer Electronics category. Notably, Amazon Basics also offered modestly attractive deals (12.2%), highlighting Amazon’s strategy of promoting in-house brands.

    Consumer electronics average price reduction across leading brands on Amazon.

    The Consumer Electronics category has a loyal shopper base, but generic search keywords like earbuds, headphones, and tablets remain essential for attracting high-intent shoppers and increasing brand awareness. So when it comes to Share of Search, Noise in Smartwatches, Samsung in Smartphones and Tablets, and HP in Laptops, all made strong strides in building their discoverability on Amazon during Prime Day.

    Consumer electronics share of search on Amazon on Prime Day.

    Xiaomi in Laptops, Ekko in Earbuds, OnePlus in Smartphones and Apple in Tablets, lost out to other brands during the sale.

    Health & Beauty

    Health & Beauty emerged as the top-performing category in terms of additional discounts during Prime Day in India. Our data shows that Amazon offered an average additional discount of 5.3% on almost half of its products (46.8%) in this category. Competing head to head with Amazon in this category, Flipkart offered 5.5% additional discounts across 35.8% of its assortment.

    Health & beauty average price reduction across retailers on Prime Day.

    Within all the subcategories we analyzed, Sunscreen (7.5%), Make-Up (7.2%), Shampoo (6.6%), and Moisturiser (6.4%) saw the highest price reductions on Amazon. Conversely, staple items like Toothpaste (3.%) and Beardcare (3.6%) had lower markdowns.

    Health & beauty average price reduction across subcategories on Amazon.

    During the sale event, brands like Sadhev (43.4%), Clear (41.1%), Teenilicious (40.4%), and Coal Clean Beauty (38.4%), offered the most attractive deals.

    Health & beauty average price reduction across leading brands on Amazon.

    In terms of significant gains in Share of Search for brands, L’Oreal Paris in Shampoo and Conditioner led the pack along with Oracura in Electric toothbrushes and The Formularx in Moisturiser. Perfora in Toothpastes and Ustraa in Beardcare also gained more than 10% in their Share of Search during the sale event.

    Health & beauty share of search on Amazon on Prime Day.

    Other popular brands like Tresemme in Conditioners, and Swiss Beauty in Make-Up surprisingly had reduced visibility among the top search results for relevant subcategories.

    Navigating the Competitive Landscape: How To Thrive During Sale Events

    Amazon’s strategic pricing during Prime Day reflects a balance of profitability, inventory, and competition. Competitive pricing insights empower retailers to make informed decisions, optimize strategies, and thrive during high-stakes events. Prime Day serves as a crucial opportunity to drive sales, attract new customers, and boost loyalty. Therefore, monitoring competitor prices accurately, at scale, is essential for impactful pricing strategies.

    For more insights on staying ahead during sale events, reach out to us today!

    If you’d like to learn about Amazon’s pricing and discounts during Prime Day 2023 in the US, check out our analysis here.

  • Amazon US Prime Day 2023: Insights on Pricing and Discounts Across Popular Categories and Brands

    Amazon US Prime Day 2023: Insights on Pricing and Discounts Across Popular Categories and Brands

    Amazon’s Prime Day this year proved to be a record-breaking success, becoming the largest Prime Day event in the company’s history. Over the two-day extravaganza, shoppers in the US spent a staggering $12.7 billion, a 6.1% increase from the previous year. Amid inflationary pressures and supply chain disruptions, Amazon adopted a bold discounting strategy, offering steeper discounts compared to Prime Day 2022.

    An interesting aspect of Amazon’s approach is their loyalty based offerings. In the weeks leading to Prime Day on July 11-12, members of the loyalty program were given access to “invite-only deals” where shoppers could request invites to specific products that they were looking to purchase on deals. Overall, Amazon’s pricing and discount strategies during Prime Day were carefully designed to create a buzz among shoppers, generate increased sales, and maintain a competitive advantage in the market.

    While Prime Day is Amazon’s showstopper, it’s interesting to also see how other leading retailers respond to such a massive sale by their biggest competitor. Do they also lower their prices during the event, or are they happy to take a backseat? To answer these questions, we leveraged our proprietary data aggregation and analysis platform to analyze the prices and discounts of Amazon and its leading competitors across key product categories – Apparel, Home & Furniture, Consumer Electronics, and Health & Beauty – during Prime Day.

    Since products on Amazon and other eCommerce websites are often sold at discounts even on normal days not linked to a sale event, we delved into the real value that Prime Day offers to shoppers by focusing on price reductions or additional discounts during the sale compared to the week before. As a result, our approach highlights the genuine benefits of the event for shoppers who count on lower prices during the sale.

    Research & Methodology

    For our analysis, we tracked the prices of a large number of products across several leading retailers during Prime Day as well as the week prior to the event. The details of our sample are mentioned below:

    • Number of SKUs: 110,000+
    • Websites: Amazon, Walmart, Target, Overstock, The Home Depot, Wayfair, Ulta Beauty, Sephora
    • Categories: Apparel, Home & Furniture, Electronics, Health & Beauty
    • Pre-event Analysis: 4-10 July 2023
    • Prime Day Analysis: 11-12 July 2023

    Our Key Findings

    Our data reveals that Amazon’s price reductions were most aggressive in the Consumer Electronics category, with an average price reduction of 10.4% on Prime Day, due to the category’s popularity and high demand.

    The Health & Beauty (6.7%), Apparel (5.9%), and Home & Furniture (4.8%) categories offered relatively modest deals during the sale event.

    The Health & Beauty (6.7%), Apparel (5.9%), and Home & Furniture (4.8%) categories offered relatively modest deals during the sale event.

    Below, we delve deeper into our analysis of each category to better understand how price reductions were distributed across key subcategories on Amazon as well as the discounting strategies of Amazon’s leading competitors.

    Apparel

    As Amazon grappled with surplus inventory, heightened storage costs, and reduced profit margins in apparel (like most other retailers), its average discount before Prime Day was already as high as 13.3%. Then, on Prime Day, Amazon’s apparel deals were tempered at around 5.9% across an impressive 33.1% of its assortment, while Target and Walmart chose not to compete in a meaningful way.

    Unlike Prime Day 2022, when Target competed with Amazon with high discounts, the retailer offered only 0.8% additional discount across 4.4% of its assortment in this category. Walmart, too, reduced its prices by only 1.4% on 8.5% of its assortment during Prime Day.

    Check out our latest analysis on fashion pricing trends across 2022-23 to better understand the pricing dynamics in this category in greater detail.

    Across all the apparel subcategories we analyzed, Women’s Athleisure (8.7%), Men’s Swimwear (8%), and Women’s Tops (7.6%) were among the ones with the highest price reductions. On the other hand, Men’s Athleisure (2.5%), Women’s Shoes (3.5%), and Men’s Innerwear (4.1%) had conservative markdowns.

    Pricing decisions across the various subcategories are likely to have been influenced by several factors like inventory levels, demand patterns, and the need to balance competitive offers with maintaining reasonable profit margins, as Amazon tried to cater to a more price-sensitive consumer.

    Across all apparel subcategories, leading brands that offered the highest markdowns were Tommy Hilfiger (11.5%), Amazon Essentials (9.4%), Adidas (8.6%), and Calvin Klein (8.6%).

    For brands, however, lowering prices is only one lever to attract and convert shoppers. They also need to ensure they’re highly visible and discoverable on Amazon’s search listings. This exponentially improves their chances of driving more clicks and conversions. In our analysis, we tracked the Share of Search of brands across several popular search keywords. Share of Search for a brand is defined as the proportion of the brand’s products in the top 20 search results for a search query.

    Our data indicates that several brands gained impressive ground in their discoverability during Prime Day, while others fell behind. Gildan in Men’s Innerwear, Adidas in Men’s and Women’s Shoes, Anrabess in Women’s Athleisure, and Lululemon in Men’s Athleisure, among others, improved their Share of Search by significant levels during Prime Day.

    On the other hand, brands like Hanes in Men’s and Women’s Innerwear, Kanu Surf in Men’s Swimwear, Cupshe in Women’s Swimwear, and others lost around 10% in their Share of Search during the event. This is likely to have impacted their sales volumes adversely.

    Home & Furniture

    The Home & Furniture industry has been challenged with reduced demand due to inflationary pressures over the past year or so. Leading retailers in the category overestimated the demand, leading to overstocking of inventory. As a result, Home & Furniture is one of the few categories that saw Amazon’s competitors participate at a significant level on Prime Day in order to ensure they don’t fall behind on liquidating their stock.

    Amazon’s additional discounts averaged 4.8% across 30.2% of its assortment. Wayfair and Overstock too reduced their prices by 4.8% and 4.3% on around 44% of their respective assortments. Wayfair’s move is likely a part of their strategy to attract new customers and expand their market share, in response to a decline in their consumer base. Last year, Wayfair experienced a loss of 5 million out of its 1.3 billion consumers due to weakening demand.

    Target and Walmart did offer additional discounts, but they were not at a competitive level. The Home Depot effectively opted not to compete at all during the sale event. Overall, the pricing actions of these retailers are in stark contrast to the highly conservative pricing strategies observed on Prime Day last year.

    Our recent pricing analysis of the Home & Furniture category revealed more interesting insights and pricing dynamics over the past year.

    Across all the subcategories we analyzed, Bookcases (8.2%), Rugs (7.8%), Mattresses (6.5%), and Luggage (6.2%) were among the ones with high price reductions.

    Meanwhile, Sofas (2.4%), Washer / Dryers (2.4%), and Entertainment Units (2.7%) had lower markdowns. These are large and substantial purchases, making retailers more cautious about deeply discounting them while still ensuring profitability.

    The brands that stepped up and offered the highest markdowns in this category include Zinus (20.2%), Comfee (10.8%), Sauder (9.9%), and Best Choice Products (8.7%).

    In terms of Share of Search, Rockland in Luggage gained the highest (21%), followed by Farberware in Dishwasher, Olee Sleep in Mattresses, and Homeguave in Mattresses gained significant ground in their respective categories as shown in the image below.

    Brands like Best Choice Products in Coffee Tables, Molblly in Mattresses, and Black+Decker in Washer/Dryers and Dishwashers lost a good portion of their Share of Search during the event. Due to high competition for visibility during sale events, brands that fail to keep an eye on their Share of Search stand to take a hit in their sales, especially in categories like Home & Furniture that tend to have low brand loyalty.

    Consumer Electronics

    2023 was the year of consumer electronics on Amazon Prime Day. Amazon’s price reduction during the sale averaged 10.4% across 54.5% of its assortment in the category. Target and Walmart, on the other hand, offered significantly lower additional discounts of 1.9% and 2.7% on 10.4% and 19.1% of their assortment, respectively.

    The consumer electronics category often witnesses aggressive price reductions during Prime Day and other sale events due to its popularity and high demand. In addition, since retailer margins are usually low in this category, shoppers often have to wait for sale events like Prime Day (when brands markdown their wholesale rates) to have several attractive deals to choose from.

    Across all the subcategories we analyzed, Smartwatches (15.4%), Wireless Headphones (15.4%), Earbuds (14.9%), Headphones (12.5%), and Tablets (12.0%), were among the ones with the highest price reductions. All of these subcategories are quite popular that tend to sell in large volumes during sale events.

    Meanwhile, Laptops (2.1%), TVs (3.1%), and Smartphones (7.6%) had lower markdowns. A lower markdown on smartphones may reflect steady demand throughout the year, reducing the urgency to offer significant discounts during the short Prime Day window.

    Amazon (22%), Tozo (12.5%), Lenovo (10.8%), JBL (8.3%), and Apple (5%) offered the highest price reductions in Consumer Electronics as a whole. Clearly, Amazon didn’t hold back on offering attractive deals on its own private label products in this category.

    Consumer Electronics as a category tends to have a brand loyal shopper base. However, Share of Search generic search keywords are still very important for keywords like earbuds, headphones, and tablets that result in relatively lower priced products. HP in Laptops, Samsung in Tablets and TVs, and Oneplus in Smartphones all made strong strides in building their discoverability on Amazon during Prime Day. Beyond just driving more sales, this also has the intended effect of boosting brand awareness among high-intent shoppers.

    Sony in Headphones, Asus in Laptops, and Insignia in TVs lost out to other brands in terms of their discoverability during the sale. Sony and Asus, especially would be hurting as they are prominent brands in their respective categories.

    Health & Beauty

    The Health & Beauty category is a favorite among consumers during Prime Day, as it encompasses a wide range of products like skincare, cosmetics, and grooming items. As shoppers often tend to stock up during the sale, brands and retailers are willing to offer competitive discounts and gain an edge over their competitors.

    Our data reveals that the average additional discount on Amazon was 6.7%, offered on a little over a third of its assortment. Walmart reduced its prices sizably as well, by an average of 3.1% on 13.4% of its assortment.

    Interestingly, Sephora and Ulta Beauty, leading retailers in the Health & Beauty category did not compete on price at all this Prime Day. It is likely they are confident their loyal customer base will not be influenced by Amazon’s Prime Day deals and be driven away merely by lower prices. In addition, keeping their prices steady during Prime Day might have been a strategic choice to protect their brand reputation and premium positioning.

    Relatively premium subcategories like Electric Toothbrushes (10%), Moisturizer (8.3%), Beardcare (7.3%), and Make Up (6.7%) saw the highest price reductions on Amazon.

    In contrast, staple items like Toothpaste (3.7%), Shampoos (5.4%), and Conditioners (5.7%) had lower markdowns.

    Among the leading brands in this category, Oral-B (10.3%), Philips Sonicare (8.7%), Neutrogena (8.4%), and Colgate (5.6%) offered the most attractive deals during the sale event.

    In terms of significant gains in Share of Search for brands, Oral-B in Electric Toothbrushes led the pack again. Neutrogena in Sunscreens and Somall in Toothpastes also gained more than 10% in their Share of Search during the sale event, followed by Tresemme in Shampoos and Airspun in Make-Up products.

    Other popular brands like Crest in Toothpastes, e.l.f in Make-Up, Philips Sonicare in Electric Toothbrushes, and Sheamoisture in Beradcare surprisingly had reduced visibility among the top search results for relevant subcategories.

    Staying Ahead of the Curve During Sale Events

    This Prime Day, Amazon leveraged its scale to offer aggressive discounts across key product categories, while several competing retailers chose to sit back and let the sale play out. Others chose a selective discounting strategy that focused their modest price reductions on a small set of items.

    At DataWeave, we understand the pivotal role competitive pricing insights play in empowering retailers and brands to gain a competitive edge, especially during crucial events like Prime Day. For retailers, the ability to track competitor prices accurately, at scale, in a timely manner is essential to plotting and acting on impactful pricing strategies and staying ahead of the curve.

    To learn more about how this can be done, reach out to us today!

  • Navigating the Turbulent Home and Furniture eCommerce Market in 2023 with the Power of Competitive Intelligence

    Navigating the Turbulent Home and Furniture eCommerce Market in 2023 with the Power of Competitive Intelligence

    The home and furniture retail industry is going through a turbulent time. As inflation reared its head mid-2022, leading retailers in the category have been grappling with the higher costs associated with producing and distributing their products, as well as reduced shopper demand. The rising costs of raw materials, transportation, and labor have had a direct impact on the pricing dynamics within the industry. For example, reports indicate container rates soared to nearly 10 times pre-pandemic levels towards the end of 2021.

    Furthermore, shoppers’ spending power has been constrained, while higher interest rates have suppressed demand. Retailers have had to adapt their assortment and pricing strategies to cater to a wider range of shopper preferences driven by changing lifestyles and a growing emphasis on sustainability. Post-pandemic, demand has been primarily driven by affluent shoppers.

    Towards the end of 2021, due to supply delays and disruptions, retailers heavily stocked up on available products. However, when demand subsequently decreased in 2022, they were left with a significant amount of unsold stock that was purchased at high rates. This put them in a difficult situation, as they had an excess of products but were unable to sell them even at reduced prices without impacting their profit margins. Additionally, staying competitive in a rapidly changing market environment was equally important.

    Given this context, it is crucial for home and furniture retailers to adopt a data-driven approach that utilizes competitive and market insights to consistently maintain or increase their online sell-through rates. DataWeave’s Commerce Intelligence solution offers exactly that, empowering retailers across various industry segments to stay updated on evolving consumer trends and competitor actions.

    To gain a better understanding of the pricing strategies employed by leading home and furniture retailers throughout the past year, we leveraged our proprietary data aggregation and analysis platform to track and analyze the pricing of a wide range of products across multiple retailers and subcategories within the industry.

    Our Research Methodology

    • Number of SKUs: 400,000+
    • Key retailers tracked: Amazon, Wayfair, Home Depot, Overstock, Target, Walmart
    • Key categories reported: Home and Office, Bed and Bath, Bathroom, Bedroom, Decorative, Dining Room, Kitchen, Garden & Patio, Hardware
    • Timeline of analysis: April 2022 to April 2023

    Our Findings

    Interestingly, our analysis indicates that average prices in the home and furniture category rose by around 5% between March 2022 and April 2023. However, there have been seasonal fluctuations in the prices over the course of the year.

    Among the various subcategories, the most substantial price surge was observed in home office equipment, with an uptick of 9.3% in January 2023 when compared to March 2022. The surge in demand for home office furniture, fueled by the widespread adoption of work from home arrangements, played a pivotal role in depleting inventories and consequently driving up prices. Additionally, the shift towards collaborative workspaces and the gradual expansion of office environments have contributed to the sustained demand for office furniture.

    Avg. price changes MoM across home and furniture subcategories from April 2022-23.

    While prices for several subcategories rose significantly, others experienced subdued growth, such as bed and bath. The subcategory experienced the lowest price increment, registering a modest 2.8% increase annually. This can be attributed to the impact of a subdued housing market and a decrease in first-time buyers, which may partly be due to the global recession and inflationary pressures.

    Moreover, retailers overestimated the demand for home furniture during the holiday season, leading to an overstocking of inventory. Consequently, prices experienced a dip from October to December 2022. In fact, this was a common trend across all home and furniture subcategories. As retailers emerged from the holiday season, prices rose to their highest level in January 2023, and have stayed relatively stable since.

    Some of these trends vary among retailers as each faces different challenges and responds in distinct ways.

    Wayfair, for example, shows a significant dip in pricing after October 2022, with prices stabilizing in 2023. This could be in response to the retailer’s shrinking consumer count, losing 5 million of its 1.3 billion consumers in 2022 due to declining demand.

    Avg. price change MoM within the home and furniture sector across retailers from April 2022-23.

    In fact, online furniture retailers like Wayfair and Overstock reported declines in annual revenue in 2022, as the furniture sector continued to normalize from the high spending seen during COVID-era lockdowns. Wayfair reported that its 2022 net revenue was $12.2 billion, down almost 11% from the year prior. The company also laid off 10% of its workforce in August 2022. Overstock’s reported annual net revenue in 2022 was $1.9 billion, a 30% decrease year-over-year.

    Interestingly, both companies took contrasting approaches in response to this situation. Wayfair opted for aggressive cost-cutting measures, including layoffs and a reduced marketing budget. On the other hand, Overstock focused on attracting new customers through influencer marketing and improving their app, aiming to expand their customer base. With a strategy geared towards younger buyers, Overstock allocated a larger marketing budget than ever before. Our data supports the fact that Overstock did not rely on price reductions to entice shoppers.

    Target has consistently maintained lower price increases compared to Walmart, defying the common perception of Walmart being more conservative in its pricing. Notably, Amazon also stood out minimal price increases throughout the year, being surpassed only by Wayfair since November 2022.

    As price sensitive shoppers increasingly compare prices before making a purchase decision, retailers need to ensure they are priced competitively in the market on a consistent basis to liquidate stock and gain market share without compromising significantly on margins.

    A Sophisticated and Versatile Product Matching Solution is Essential to Achieving Price Leadership

    Product matching plays a vital role in monitoring competitive prices and analyzing price leadership. Within the home and furniture category, there is often a multitude of representations for the same product across various online platforms. Furthermore, eCommerce websites offer a wide array of options, including variations in size, color, material, and similar products. Without an accurate and comprehensive method of matching these products, it becomes impossible to track and compare prices effectively, especially on a large scale. Thus, a versatile product matching engine tailored to the unique requirements of the home and furniture sector becomes essential.

    DataWeave offers an industry-leading product matching platform that harnesses advanced AI models specifically trained to identify and leverage multiple product attributes extracted from titles, descriptions, and images to accurately match products across websites. Additionally, our platform intelligently matches similar products based on a diverse range of extracted attributes. This empowers our retail partners to gain competitive pricing intelligence not only on exact product matches but also on similar and substitute products, as well as their respective variants.

    With our competitive pricing intelligence solution, retailers in the home and furniture industry can confidently analyze and track prices, ensuring they stay at the forefront of price leadership in their market.

    To learn more, reach out to us today!

  • Fashion eCommerce 2023: Leveraging Pricing Intelligence to Stay Competitive Despite Reduced Demand

    Fashion eCommerce 2023: Leveraging Pricing Intelligence to Stay Competitive Despite Reduced Demand

    The fashion industry is currently undergoing a period of stabilization after facing significant disruptions in recent years. Fashion retailers find themselves navigating not only changing consumer preferences but also the challenges brought about by inflation and supply chain issues that are remnants of the COVID-19 era.

    The effects of inflation have raised concerns regarding overabundance, rise of sustainable and pre-used fashion and declining sales, creating a mismatch between supply and demand within the market. As consumers scale back on spending due to rising prices, fashion retailers are left grappling with surplus inventory, heightened storage costs, and reduced profit margins.

    Consequently, these market dynamics have significantly impacted the pricing strategies employed by fashion retailers, resulting in dynamic shifts in pricing and competitiveness across different time periods, subcategories, and individual retailers.

    Counteracting this impact requires fashion retailers to adopt a data-driven approach that leverages competitive and market insights. They must adopt agile and versatile pricing strategies that enable advanced pricing and assortment management. By understanding their market position and the competitive landscape, retailers can effectively react to reduced demand and inflationary pressures without compromising heavily on their top line and profitability.

    At DataWeave, we harnessed the power of our proprietary data aggregation and analysis platform to track and analyze the prices of prominent fashion retailers to uncover unique insights into their price competitiveness over the past year, as well as understand how pricing strategies varied across diverse subcategories.

    Our Methodology

    For this analysis, we tracked the average price changes among leading US fashion retailers over 12 months to understand how their pricing across several fashion subcategories altered in response to supply chain inefficiencies, inflationary pressures, seasonal effects, and changing consumer preferences.

    • Sample: 88,000+ SKUs matched across 5 leading retailers
    • Retailers tracked: Amazon, Walmart, Target, Macy’s, Zappos
    • Key subcategories reported on: Boots, Bottoms, Coats, Denims, Flats, Heels, Jackets, Kids
    • Timeline of analysis: April 2022 to April 2023

    Our Analysis

    While prices have generally been rising in several industry segments, such as groceries, due to inflation, the fashion sector has experienced relatively stable prices, with even a few periods of price drops. In fact, average prices in April 2023 are 1.2% lower than those in April 2022. The main reason for this trend is that consumers have become cautious about discretionary spending on fashion in order to prioritize other necessities, resulting in lower demand and overstocking by retailers.

    In the first quarter of 2022, clothing accounted for only 3.9% of total expenditure by US consumers, down from 4.3% in 2019 before the pandemic. Additionally, in March 2023, 60% of fashion retailers in the US still had surplus goods, accounting for almost 20% of their entire stock. As demand decreased, fashion retailers started offering freebies with purchases, bundling products, giving away unwanted items, and notably, slashing prices.

    Subcategory level analysis of Average Price Change Month-on-Month between April 2022 – April 2023

    Our analysis at a subcategory level reveals that in winter 2022, seasonal demand led to the largest price increases of 6-11% in coats, boots, and jackets. However, these prices quickly declined afterward. In 2023, stabilization of raw material costs and a continuing decline in demand for non-essential apparel and fashion accessories are factors contributing to a significant drop in prices.

    Some of these trends vary among retailers as each faces different challenges and responds in distinct ways. Our data indicates that some retailers have chosen to increase their prices from Q3 2022 due to mounting pressure on profit margins, while others have further lowered prices due to increasing inventory levels.

    Average Price Change Month-on-Month Across Amazon, Macy’s, Walmart, Target, and Zappos between April 2022 – April 2023

    _____________

    Capability Spotlight

    Matching products across competitor websites is an essential part of tracking competitive prices and analyzing price leadership. In fashion, matching exact products is no mean feat. Websites often host a slew of variants in terms of size, color, material, etc. without any form of standardization in the way the products are represented. So fashion retailers often struggle with simply unusable pricing insights resulting from inaccurate and incomplete product matching. 

    DataWeave’s industry-leading product matching algorithm recognizes and leverages dozens of product attributes extracted from product titles, descriptions, and images to match products at very high levels of accuracy and coverage. What’s more, our platform can also match similar products based on a large variety of parameters, so our customers can benefit from a comprehensive competitive perspective.

    _____________

    For example, in August, Target reported a 90% plunge in profits during the second quarter of 2022, as shoppers concerned about inflation reduced spending on nonessential items. The company stated that its price cuts did not have the desired impact, resulting in a 1.5% increase in inventory compared to three months prior. As a result, we can see that Target’s average fashion prices spiked in August 2022 and have remained steady since then. Walmart also faced similar challenges and increased its prices in October 2022.

    However, during the same period in August 2022, Macy’s announced increased discounts to clear out excess inventory in preparation for the holiday shopping season. In the same announcement, Macy’s highlighted how the rising cost of groceries, which had experienced a double-digit increase, was impacting consumers’ budgets, changing their behaviors, and increasing the need for discounts. Our data reflects this, showing a significant drop in prices from October 2022 to January 2023.

    However, in January 2023, Macy’s successfully managed its inventory levels, reducing them from $6.4 billion in October 2022 to $4.3 billion in January 2023. As a result, average prices at Macy’s have started to rise.

    _____________

    For today’s fashion retailers, achieving a balance between expansion goals and profitability is crucial. It requires a meticulous examination of competitive and market insights on a regular basis to mitigate competitive pressures and navigate through these challenging times successfully.

    DataWeave’s platform offers retailers the insights they need to gain a competitive advantage. With access to accurate, timely, and actionable pricing and assortment insights, retailers can make informed decisions and stay ahead of the competition. To learn more, reach out to us today!

  • Impact of Inflation on Grocery: Pricing Insights on Leading US Retailers

    Impact of Inflation on Grocery: Pricing Insights on Leading US Retailers

    Inflation, like an invisible force, silently shapes the dynamics of economies, gradually eroding the purchasing power of consumers and leaving its imprint on various industries. High costs, hiring lags, and stagnating earnings pose severe challenges to businesses. One industry segment that intimately feels the impact of inflation is grocery, where price increases can be extremely concerning for the average consumer.

    Over the last 12-plus months, the US has experienced a notable rise in inflation, stirring up concerns and influencing the way we shop for everyday essentials. Rising costs of raw materials, transportation, and labor have all played a role in driving up prices. Additionally, disruptions in global supply chains and fluctuations in currency exchange rates have further exacerbated the situation, creating a complex web of interdependencies.

    To understand the magnitude of this phenomenon across leading e-retailers, we delved into an in-depth analysis of four major retail giants: Walmart, Amazon, Target, and Kroger.

    Each of these retailers possesses a unique business model and competitive strategy, as well as faces unique challenges. This leads to distinct approaches to managing inflationary pressures. Walmart for instance, expects operating income growth to outpace sales growth in 2023. Given the persistence of high prices and the potential for further macro pressures, the retailer is taking a cautious outlook. In 2022, Amazon’s eCommerce business swung to a net loss of $2.7 billion, compared to a profit of $33.4 billion the previous year.

    Amid these challenging circumstances, understanding the grocery pricing trends and strategies becomes imperative for retailers, both online and in stores to adapt and thrive in the current economic landscape. By examining their pricing trends, we can gain valuable insights into how these companies navigate the turbulent waters of the grocery industry against the backdrop of inflation.

    Our Research Methodology

    The data collected for our analysis encompassed a diverse range of products, from pantry staples like flour and rice to perishable goods like dairy and produce – a basket of around 600 SKUs matched across Amazon, Kroger, Target and Walmart, between January 2022 to February 2023.

    Further, we separately focused on the prices of a smaller subset of 30+ high-volume daily staples that are likely to yield higher sales and margins for these retailers.

    Average Selling Price of a Broad Set of Grocery Items

    Our analysis reveals that Walmart consistently offers the lowest prices, with an average of 8% below its closest competitor, Target, despite an annual price increase of about 5%. Walmart seems to prioritize a “stability and predictability” strategy over margin optimization. The retailer’s 8% growth last quarter indicates that this strategy is bearing fruit. However, it’s important to note that this approach may have its drawbacks as Walmart’s margins come under pressure.

    Average selling price trend across a basket of 500+ SKUs across Target, Walmart, Kroger, Amazon in the grocery category from Jan ’22 to Feb ’23.

    In order to weather inflationary pressures, Walmart may adopt a cautious approach to growth while also focusing on securing margins. Reports suggest that the retailer has been pushing back against consumer packaged goods (CPG) manufacturers following a series of price hikes to counter inflationary cost pressures in early 2023. One of the reasons behind Walmart’s growth and increased sales can be attributed to ‘non-traditional’ higher-income households now seeking deals and discounts at Walmart as their spending power declines.

    Interestingly, Amazon emerges as the highest-priced retailer, followed by Kroger, which increased its prices by 10% throughout the year. Consumer perception commonly associates Amazon with the lowest prices, but the data tells a different story. In fact, Amazon has been charging 12% to 18% higher prices than Walmart for groceries and is still maintaining its success.

    While the company’s online sales declined by 4%, it saw a significant 9% increase in revenue from third-party seller services, such as warehousing, packaging, and delivery, in 2022. Amazon’s strong logistics and same-day delivery services give it a competitive advantage over other retailers, contributing to its revenue growth and margins. Interestingly, this presents an opportunity for Walmart and other retailers to increase prices while maintaining their strong competitive price positions.

    Kroger, on the other hand, seems to be aiming for a premium price perception, consistently raising prices almost every month. Kroger’s pricing strategy appears to be closer to Amazon’s.

    Average Selling Price for High-Volume Daily Staples

    Pricing strategies often change for different categories of products. To better understand this, we focused our analysis further on a small subset of 30+ high-volume staples across retailers. These include baked goods, popular beverages, canned food, frozen meals, dairy, cereals, detergents, and other similar items.

    Average selling price trend of 30+ high-volume daily staples across Target, Walmart, Kroger, Amazon in the grocery category from Jan ’22 to Feb ’23.

    Walmart, possibly overestimating the impact of inflation, has continued to keep its prices the lowest, potentially aiming to increase margins through volume.

    The level of price disparity across retailers is expectedly lower here, with Amazon and Kroger closely tracking Walmart’s average prices.

    Target’s pricing strategy stands out as it consistently emerges as the highest-priced retailer for daily staples, despite being one of the lower-priced retailers for a broader basket of grocery items. This suggests that Target’s underlying technology may not be as optimized to address market dynamics compared to other leading retailers. In our opinion, Target may want to strengthen its efforts to track pricing more intensely for this sub-category.

    A Data-fuelled Approach is the Need of the Hour

    In the challenging economic landscape, retailers and grocery stores are under pressure to maintain their revenues and margins. Adopting a comprehensive and dynamic pricing strategy is crucial. Understanding which product categories are experiencing price increases among competitors can help retailers make informed decisions on pricing at both the category and product level.

    Retailers should consider their balancing margin performance with consumers’ willingness to pay, rather than implementing broad price increases that may harm customer trust. Price increases can be challenging for both customers and merchants. Retailers who employ a data-driven and insight-based approach are more likely to succeed.

    Keep an eye on the DataWeave blog for analysis on pricing, discounting, stock availability, discoverability, and more, across retailers and brands from other industry segments as well.

    For immediate insights, subscribe to our interactive grocery price tracking dashboard. Better still, reach out to us to speak to a DataWeave expert today!

  • Competitive Pricing and Availability Trends of South Africa’s Leading Retailers and Brands in 2023

    Competitive Pricing and Availability Trends of South Africa’s Leading Retailers and Brands in 2023

    South Africa’s eCommerce market is primed for robust growth in 2023 and beyond, despite the short-term impact of COVID19 over the last few years. According to Statista, the country’s eCommerce market size is $7.2 billion in 2023, with an expected CAGR of 12.5% till 2027. South Africa’s user penetration is already as high as 49.4% and is only set to grow. In essence, there is a massive opportunity for retailers to capitalize on. 

    For retailers, capturing the lowest priced spot among competitors is often the most certain way to attract and convert online shoppers. Ensuring their products are priced competitively on a consistent basis is key to gaining and maintaining market share in this booming market. This requires South Africa’s online retailers to track and compare the pricing of their products relative to their competitors on an ongoing basis. 

    For brands, ensuring their pricing and discounting strategies are aligned to market trends are key to gaining market share. In addition, on the backdrop of supply chain challenges globally in the recent past, brands need to ensure high stock availability rates to capitalize on rising consumer demand. In addition, as brands conceptualize and implement a cohesive eCommerce strategy, price parity across all online channels becomes increasingly important. 

    In this report, we leveraged DataWeave’s proprietary data aggregation and analysis platform to focus our analysis on leading South African retailers and brands and their eCommerce performance across several key dimensions.  

    Our Research Methodology

    Retailers tracked: Takealot, ShopRite, Pick n Pay, Leroy Merlin  
    Number of SKUs: 40,000+
    Number of categories: 190+
    A few key categories highlighted: Bath, Food, Home, Spirits
    Timeline of analysis: Oct 2022 to March 2023

    Key metrics reported:

    • Price Increase Opportunity: When a retailer can increase the price of its product by a certain amount while continuing to stay the lowest priced among competitors. This directly helps boost margins.
    • Price Decrease Opportunity: When a retailer must decrease the price of its product by a certain amount to gain the lowest price position. This helps gain more sales. 
    • Action Rate: The share of price improvement opportunities (either price increase or price decrease) actually acted on by the retailer within 15 days of the opportunity presenting itself. For example, an action rate of 25% would mean that for every 100 price improvement opportunities identified, the retailer acted on 25 of them within 15 days. 

    Competitive Pricing Actions of Retailers

    Retailers often have dramatically varying approaches to responding to their competitors’ pricing actions. Also, it is not sufficient merely to react but also to react fast. The following chart displays the average action rates for price increase and decrease opportunities, and also breaks down the reported pricing actions into ones acted on within 5 days, 6-10 days, or 11-15 days of the opportunity presenting itself.  

    Takealot clearly has a strong competitive pricing engine, using which the retailer is able to act on more than half (51%) of price increase opportunities (thereby gaining margin), and over a third (36%) of price decrease opportunities. However,  it can do better in acting on many of them faster. Only 52% of the opportunities are acted on within 5 days, while the rest take up to 15 days.

    Average Action Rate and Velocity of Pricing Actions of Retailers

    ShopRite and Pick n Pay each have a healthy action rate of 23% for price increase opportunities, but only 9% and 5% respectively on price decrease opportunities. This displays a relatively inconsistent effort in gaining price leadership. ShopRite follows a similar distribution of pricing action velocity as Takealot, albeit for fewer price improvement actions. In comparison to ShopRite, Pick n Pay (which has a similar level of pricing action levels) acts much faster, with 64% of price improvement opportunities acted on within 5 days. 

    Leroy Merlin acted on only 9% and 6% of its price increase and decrease opportunities, respectively. However, in terms of its velocity of pricing actions, it is far ahead than the rest, with 90% of its pricing actions done within 5 days. This, however, would matter little without strong action rates to begin with. So, Leroy Merlin has a massive opportunity to boost its competitive pricing tracking and operations. 

    Average Action Rate of Retailers, Oct 2022 to Mar 2023 | Price Leadership of Retailers, Nov 2023 to March 2023

    Based on the chart above, over the last 6 months, it is clear that Takealot has maintained its price leadership throughout the period, driven by consistently aggressive action rates on price improvement opportunities. 

    Certain nuanced trends do emerge when month-on-month variances are observed on both action rates and price leadership rates across the analyzed retailers. For example, Takealot’s actions rates have been marginally declining, which has resulted in price leadership rates also declining from 14% in November 2022 to 10% in March 2023.

    Also, when ShopRite started getting more active in its action rates early in 2023, we observed an uptick in its price leadership positions. Leroy Merlin’s low action rates, coupled with low magnitudes of price changes has resulted in an average of only 2% of its products being the lowest priced relative to its competitors. 

    Pricing and Availability of Leading Brands

    In the following section, we report on the average discounts, price parity levels, and the stock availability of the top 5 brands (in terms of number of SKUs) in each of four key product categories: Bath, Food, Home, and Spirits. 

    Bath

    Nivea and Protex have on average been offering high discounts in the last 6 months, peaking around the holiday season in December 2022 and January 2023. 

    What’s more, these two brands have the highest share of their own products carrying discounts (10% and 9% respectively). Sanex has been conservative in both the average discounts offered as well as its share of discounted products. 

    Though Nivea has been offering attractive discounts, its stock availability has been low on average. However, it has been improving in the past few months, starting as low as 70% in October 2022 to reaching around 90% in March 2023. Dettol, too, has faced issues with its availability while showing some improvement in the recent past. All of the other leading brands in this category display healthy stock availability levels of above 90%.

    Food

    Kellogg’s and Rhodes have been offering the highest discounts in the food category, especially around the holiday season. They also lead in the share of discounted products (14% and 7% respectively).  

    Typical to the Food category, most other leading brands have offered lesser discounts on a smaller share of their products. 

    In addition to offering attractive pricing, Kellogg’s seems to also have its supply chain operations in control, with almost a consistent stock availability of 100%. Most other brands struggled towards the end of 2022 and have been improving ever since. Nestle, in particular, was challenged with an availability of only 70% in October 2022, but has not improved it to 90% in March 2023. 

    Home

    The Home category displays the highest variance in discount ranges among its leading brands compared to the other categories highlighted in this report. Brands like LocknLock and Legend have been offering discounts in the range of 25% to 35% on a share of more than a third of each of their products.

    On the other hand, ADDIS and Prestige have been offering discounts in the range of 0% to 5%, on a very small range of their products (<5%). 

    In categories like Home, maintaining a consistent brand perception among consumers is essential to boost brand value and loyalty. If there is a large disparity in the prices of the products of a brand across multiple eCommerce websites, then it negatively affects how consumers perceive the brand.  

    Here, the average price disparity (or variance) for LocknLock is only 10%, compared to almost 30% or more for other brands like Eetrite, Prestige, and ADDIS. Essentially, the brands discounting the most are also the ones with the lowest pricing disparity, which indicates a well thought-out, data-driven approach to eCommerce pricing and discounting – one that values both sales conversion as well as brand reputation. 

    Prestige and Eetrite have been struggling with their supply chain operations, with the availability of Prestige dipping to as low as 55% in December 2022. Legend’s stock availability was strong towards the end of 2022 but has been steadily declining in 2023 so far. LocknLock and ADDIS display healthy stock availability levels of 95% and above.

    Spirits

    The average discounts of almost all leading Spirits brands peaked during Christmas, with Tanqueray, Jameson, and Glenlivet offering up to 30% discounts in this period. 

    Since then, the discounts of most brands have been oscillating within the 5% to 20% range. The percentage of discounted products for all leading brands are around 20%, with the exception of Smirnoff. 

    Similar to the Home category, brand perception is vital to the Spirits category as well. However, here we see almost a consistent level of pricing disparity among leading brands, varying between 18% and 25%. Brands looking to build a “premium” perception among consumers, such as Glenlivet and Tanqueray, might do well to ensure better pricing parity across their eCommerce channels. 

    A few Spirits brands saw a dip in stock availability in December 2022, likely due to increasing demand during the Christmas season. For example, Jameson’s availability dipped to below 80% in this period. It has since improved to reach an availability level of above 90%. Johnnie Walker, too, has shown a significant improvement in its stock availability, moving from 80% in October 2022 to 95%+ in March 2023. 

    For more details on the state of South Africa’s eCommerce landscape as well as similar insights for other retailers and brands in the region, talk to us today! 

  • Decoding the 2022 Black Friday Record Sales: The Who, The What, and The How?

    Decoding the 2022 Black Friday Record Sales: The Who, The What, and The How?

    Contrary to popular speculation of lukewarm online sales owing to the weak economy, high inflation, and stretched wallets, Black Friday this year recorded a whopping $9 billion in e-commerce sales. Despite the lull in online shopping across many retailers in the months preceding Thanksgiving and weakened consumer sentiment, US online merchants saw a sizable boost in sales during and after Thanksgiving, albeit at a slower growth of 2.3%, as reported by Adobe Analytics.

    This article looks closely at the Black Friday data to understand which brands, retailers, and product categories were key players. Through DataWeave’s innovative Digital Shelf Analytics product, we deep dive into the availability, discount, and share-of-search data to deduce why some product categories and retailers fared better than others.

    Who: Retailers and Brands that had the Highest Presence

    Black Friday sales this year were driven by consumers grabbing the biggest and best deals to make the most of their already stretched wallets. Many shoppers opted for flexible payment schemes, and Buy Now Pay Later (BPNL) payments rose by 78% compared to the week before Thanksgiving. Surprisingly, Amazon, which was the most searched retailer during Black Friday last year, came only fourth this year, as reported by the Search Intelligence company, Captify.

    According to Captify, Walmart was the most searched retailer for Black Friday deals, followed by Target, Kohls, and Amazon in that order. Amazon, however, has reported its biggest Thanksgiving sale this year, with independent retailers selling through Amazon seeing a total sales of $1 billion. With the economic slowdown and thin wallets looming large, discount rates greatly influenced consumer spending. Mobile shopping accounted for 55% of digital sales, 8.5% more than the previous year. 

    As told by Adobe, Electronics were the significant sales driver, reporting 221% higher sales than in October this year, with smart home items and audio equipment playing an important role with 271% and 230% higher sales. Toys ( popular purchases were Fortnite, Roblox, Bluey, Funko Pop!, and Disney Encanto) and exercise equipment also registered a substantial growth of 285% and 218%, respectively. 

    Other top-selling items included gaming consoles (Xbox Series X and PlayStation 5 devices, games including FIFA 23, NBA 2k23, and Pokemon Scarlet & Violet), drones, Apple MacBooks, and Dyson products (airwrap and vacuum). Amazon’s most popular items were reported to be Apple Airpods, Nintendo Switches, Echo Dot smart speakers, and Fire TV sticks. 

    What: Top Selling Product Categories

    Electronics, closely followed by home appliances (robotic vacuum cleaners), toys, and exercise equipment, were popular product categories in demand during Black Friday this year. Several retailers, including Amazon, Walmart, Target, Kohls, BestBuy, and Home Depot, offered lucrative pre-Black Friday discounts to trigger early sales kick-off. 

    Amazon carried an early discount of 50% on its Echo smart speaker, Target offered 30% off on Dyson vacuum cleaners, Walmart offered 25-35% off on Apple ipads and watches, and Kohls offered 51% off on the iRobot Roomba. 

    Amazon’s top ten best-selling products ranged from Amazon devices like Echo Dot speakers, Fire TV sticks, and Echo Show to Apple AirPods, Nintendo Switches, New Balance sneakers, Champion Apparel, and Burt’s Bees Lotions. The popular product categories were home, fashion, toys, beauty & health, and Amazon devices. Consumers heavily supported small businesses, contributing to $1 billion in sales. Top sellers from small businesses included card and board games.

    Briefly correlating the discounts offered with the best-selling product categories, one can notice that the deals have largely influenced Black Friday sales this year. Popular categories are those that have had deep discounts, reflecting the consumer’s tendency to wait and grab the best deals.  

    How: Role of Digital Shelf Analytics – Key Performance Indicators 

    Digital Shelf Analytics
    DataWeave’s Analysis Methodology

    We have seen a summary of the Black Friday 2022 statistics – sales recorded, top-selling products, product categories, and retailers. Using DataWeave’s e-commerce analytics product, we track and study the variations in digital shelf KPIs across retailers before Thanksgiving and during Thanksgiving to understand how these influence sales. 

    Availability scores, discount rates, and share of search data are analyzed for top retailers in the US for key product categories. Data is tracked and analyzed across two time periods – before Thanksgiving (Nov 10 – Nov 21) and during Thanksgiving (Nov 21 – Nov 25).

    Methodology

    • Retailers tracked: Amazon, Best Buy, Sephora, Target, Ulta, Walmart
    • Product Categories tracked: Electronics, Home Improvement, Beauty, Furniture
    • Digital Shelf KPIs tracked: Availability, Discount rates, Share of Search
    • Location: USA
    Amazon Digital Shelf Analytics

    Amazon maintained good availability across all product categories – Beauty ranks the highest.

    Salient Insights

    • Amazon maintained good overall availability – an improvement of 3% over Prime day. 
    • Beauty had the highest availability of 95%, with none and Lotion & Brushes reporting 97% and 95% availability, respectively. Shampoo reported the lowest availability at 92%
    • Home Improvement had the least availability at 87%, with dishwashers (68%) and washers and dryers (78%) having the lowest availability. 
    • Unlike Furniture and Home Improvement, most categories maintained similar availability scores before and during Black Friday.
    • Furniture improved its availability during Black Friday by 4%, while Home Improvement reported a decrease in availability during Black Friday by 4%. 
    • Electronics, which was a major sales driver, had an availability of > 90% across all sub-categories except for Television, which had a low availability of 70%
    • Tables and chairs registered 99% availability under Furniture

    The above data indicates that Amazon ensured the high availability of utility products that consumers would buy even during a slow economy. Other retailers showed similar availability trends, with scores being similar prior to and during the event.  

    Black Friday Discounts with ecommerce analytics

    Discounts Drove Sales – Best Buy offered the Highest Discounts

    Highlights

    • Best Buy offered the highest early Black Friday discount of 30%, followed by an additional 9% discount around Black Friday. Walmart followed next with 21% early discounts and an extra 4.5% discount during the event. Amazon came next with 17% early discounts and a 5% discount during Black Friday. Discount rates seem to strongly correlate with online searches, with Walmart beating Amazon this year as the most searched retailer for Black Friday deals. 
    • Electronics was the most discounted category across Amazon, Best Buy, and Target, with an average discount of 21%. Walmart gave lower deals on electronics (12%). Electronics also had heavy early discounts of 12%, with most retailers giving an additional discount of 7-8% closer to Thanksgiving.
    • Best Buy offered early discounts of 10% and further upped their discounts by another 12% closer to Thanksgiving. Being the most discounted category, electronics was also a significant sales driver this Black Friday.
    • Amazon offered the highest discounts for Beauty products (18%), followed by Ulta at 10% and Walmart at 8%. Sephora and Target gave minimal discounts on beauty products (3%)
    • Best Buy gave the maximum discounts on Home Improvement products (16%), followed by Amazon at 14%. Walmart gave much lower discounts of 7% on Home Improvement products. 
    • Furniture is another category with 12-13% discounts at both Amazon and Walmart.
    • Best Buy’s strategy this year has been to offer heavily discounted early deals to boost their sales.
    Black friday 2022 Beauty Analytics
    Icons: Flaticons.com
    Black friday 2022 Electronics analytics
    Icons: Flaticons.com
    Home improvements black friday 2022
    Icons: Flaticons.com
    home furniture black friday 2022
    Icons: Flaticons.com

    Highlights

    • Airpods and headphones were the most discounted item under Electronics, with Amazon and Target offering a whopping 27-29% discount. This clearly resulted in heavy sales of AirPods this Thanksgiving.
    • Best Buy and Target had good discounts on all electronic items, while Amazon gave heavier discounts on AirPods, headphones, and smartwatches.
    • Walmart did not offer hefty discounts on laptops and headphones, instead focused on Smartwatches, smartphones, and television.
    • In Home improvement, Best Buy offered the biggest discounts for refrigerators, washers and dryers, and dishwashers, while Amazon focussed more on Tools.
    • Walmart did not offer many discounts in this category.
    • Amazon topped the discount charts for maximum combined discounts for makeup and hair brushes on the day of the event. 
    • All retailers offered better discounts for utility products like tables, chairs, and cots (~15-17%), while dressers and couches carried lower discounts (~6-10%).
    Discount brackets - Black Friday 2022

    Highlights

    • Different companies had different discount strategies based on price buckets.
    • Amazon gave heavier discounts in the lower price buckets (< 200$) and lower discounts for products priced higher than 200$. 
    • Best Buy offered the heaviest early discounts of >25% on products priced under 20$ but provided a few additional discounts during the event. For products priced higher than 20$, Best Buy uniformly offered substantial early discounts as well as further discounts during the event.
    • On the other hand, Target focussed on mid and high-priced items, offering heavy early discounts of 16-18% on products priced higher than 100$ and early discounts of ~7% for middle and lower-priced items. For middle-priced products (40-100$), it offered heavier discounts of 10-12% during the event. 
    • Walmart focussed on mid-priced products, offering the highest discounts (both early (~12%) and additional discounts (5%)). It offered the least discounts (~8-9%) on products priced higher than 200$.
    Share of Search - Digital Shelf Analytics - Dataweave

    Share of Search – Amazon is the only retailer with sponsored searches; Apple AirPods rule the roost.

    Salient Insights

    • Amazon is the only retailer with sponsored searches, with HP, Lenovo laptops, and Apple AirPods occupying the highest share. This correlates with AirPods being one of the most sold products.
    • HP laptops had the highest share on Amazon pre-Event but gave the spot to Lenovo during Thanksgiving.
    • Tracphone and Motorola smartphones, Insignia Televisions, and JBL headphones had a good SoS on Amazon.
    • On Best Buy, HP and Dell laptops featured most in searches, with HP ruling the roost during the event. Lenovo had a small presence.
    • Samsung smartwatches, televisions, and Apple AirPods have a big chunk of the search at Best Buy.
    • On Target, pop sockets, smartphones, Apple smartwatches, headphones, and AirPods have the most prominent presence. Apple was the most featured brand in this segment.
    share of search beauty - black friday 2022
    Note: The share of search percentage reported here is the average score across all subcategories (makeup, lotion, shampoo, hair dryers and hair brushes) of Beauty.

    Salient Insights

    • Amazon, Target, Sephora, and Ulta sold beauty products, with Amazon being the only retailer with sponsored products.
    • Ogx, bs-mall, conair, hywestger were popular brands on Amazon, with interest-based ads occupying a substantial part of the search results, especially in Lotions (~40-50%)
    • Tresemme, Scotch Brite, Revlon and Cerave were popular brands in Target
    • Dyson products (brushes and hair dryers) are featured at Ulta’s top of the search, followed by Pureology shampoos.
    • Sephora’s own collection of brushes featured prominently on their website both before and during the event, followed by Dyson and T3 brushes and hair dryers.
    share of search -Digital Shelf Analytics- home improvements
    Note: The share of search percentage reported here is the average score across all subcategories (refrigerator, washers/dryers, dishwashers, tools and coolers) of Home Improvements.

    Salient Insights

    • In Amazon, Frigidaire and RCA had the highest SoS amongst Refrigerators, and LG occupied the highest share among washers and dryers, Coleman in Coolers, Dewalt in Tools, and  Comfee in Dishwashers, both before and during Black Friday.
    • In contrast, on Best Buy, Samsung had the highest share of SoS amongst Refrigerators, package deals were most prominent in washers and dryers, LG among dishwashers, ifixit in Tools, and Corsair in Coolers.
    share of search furniture on amazon
    Note: The share of search percentage reported here is the average score across all subcategories (bed, chair, couch, dresser, and table) of Furniture.

    Salient Insights

    • Interest-based ads occupied the highest SoS on Amazon for Beds.
    • Urban shop and Amazon basics were popular in Chairs, Lifestyle in Couches, WLive in Dressers both before and during the event.
    • Vasagle was more popular during the event than Furrion in Tables, though the reverse was true prior to the event.

    Summary & Key Takeaways

    Black Friday this year was a pleasant surprise to Brands and Retailers, reporting much larger sales than predicted. After experiencing a slump in sales in the months leading up to Thanksgiving, e-commerce vendors have a reason to be optimistic about their holiday season sales forecasts.

    • A record-breaking $9.2 Billion in online sales was reported by Adobe Analytics, a growth of 2.3 % compared to the previous year.
    • Mobile shopping accounted for 55% of digital sales, a rise of 8.5% compared to last year.
    • Retailers wooed customers through deep discounts (~30%) prior to Thanksgiving and around Black Friday. Heavily discounted items like Apple AirPods were the most popular.
    • Thanks to inflation and stretched wallets, consumers were willing to spend but waited to grab the best and biggest deals. Utility products had better sales.
    • With tough competition between retailers on who offers the best discounts, Amazon slid down to the fourth position, and Walmart was the most searched retailer.

    DataWeave, through its Digital Shelf Analytics and Commerce Intelligence solutions, gleans useful insights from e-commerce data and breaks down trends during global shopping events like Prime Day, Black Friday, and Cyber Monday. If you are a brand or a retailer who would like to know more about our products and solutions, contact us at contact@dataweave.com.

  • It’s not easy being a Bakery Brand: Insights from Digital Shelf

    It’s not easy being a Bakery Brand: Insights from Digital Shelf

    By 2028, Fortune Business Insights projects that the global bakery products market will reach USD 590 billion. The CAGR (Compounded annual growth rate) for 2021-28 is estimated at 5.12%. Products in this segment include bread, buns, cookies, tortillas, salted snacks, English muffins, bagels, confectionery food, hot dogs, cakes, popcorn, and so on.

    Due to disruptions in the global supply chain caused by lockdowns and border closures, the pandemic has had a negative impact on the demand for bakery products and snacks worldwide. However, the market is not only changing, but consumer demand is increasing. Post-pandemic, health, food, and safety have gained renewed attention.

    People across the world are making healthier choices with a focus on wellness. 

    A growing number of people are interested in plant-based foods and beverages, reducing sugar consumption, and understanding the link between lifestyle and health, including obesity and diabetes. As a result of these trends, food producers are reshaping their product strategies to meet new consumer demands.

    In this article, we take a look at the ways companies can leverage data to inform their e-commerce strategy.

    What’s driving up the demand for bakery products?

    More people are choosing easy-to-use bakery products and snacks over other foods due to urbanization, convenience, western diets, and women’s participation in the workforce. Additionally, innovations in baking systems, food technologies, ingredients, formulations, and product ideas are providing customers with a greater level of choice, flexibility, and freedom.

    How is e-commerce changing the game for bakery product companies?

    To optimize their supply chains, bakery food and snack companies must better understand e-commerce metrics given the wide variety of products available and eventually convert sales. There are several measures that companies need to pay attention to. 

    Stock availability metrics, discounts across locations, and share of search results – are all critical metrics companies need to track. In addition to providing manufacturers and retailers with an insight into the trends, DataWeave’s tools also allow them to make better business decisions and ultimately improve their bottom line. 

    Grocery Retailers and Bakery Brands tracked

    Methodology

    • Data Scrape period: February 2022 to September 2022
    • Country: Canada
    • Grocery Retailers tracked: Atlantic Superstore, Fortinos, InstaCart, Loblaws, Voila, Walmart Grocery, Zehrs.
    • Bakery brands: Betty Crocker, Dempsters, Hostess, No Name, Presidents Choice, Quaker, Vachon, Doritos.
    • Category tracked: Bread and Bakery, Chips, Crackers, Deserts, Snacks.

    Share of Search Analysis

    Which brands feature the most on e-commerce portals?

    When listing items on e-commerce platforms, share of search is crucial. The highest share of the top ten or top twenty items available on these platforms is correlated with how many times the item may be viewed. As a result, it would have a greater chance of being selected by the customer.

    By Retailer for Category “Desserts”

    Share of Search for Brands in each retailer
    • In Walmart Grocery, Vachon has the highest share of search at 41%, whereas Betty Crocker, Presidents Choice and No Name had the lowest share of search at 0%, in the Desserts Category.
    • In Loblaws, Presidents Choice had the highest share of search of 34%, whereas Dempsters had the lowest share of search of 2%  in the Desserts Category. 
    • The brand Presidents Choice consistently ranks high in the share of search results for Desserts across multiple retailers, including Atlantic Superstore, Fortinos, Instacart, Loblaws, and Zehrs – except at two retailers, Voila and Watlmart Grocery, where its share is zero.

    Trend of Share of Search for “Desserts”

    Share of Search analysis by Brands over Time in category “Desserts”
    • Share of search had dropped by around 4% for No Name, whereas for Vachon, it increased by 3% from Jan’-22 to Sep’ 22
    • By brands, Presidents Choice had the highest share of search at 42%, whereas Betty Crocker had the lowest share of search at 12% between Jan’ 22 and Sep’ 22 in the Desserts Category.

    Availability Analysis

    Which products are widely available across e-commerce portals?

    The availability of the product on the e-commerce portal is one of the key indicators of meeting customer demand. Brands can use insights from DataWeave to strategize how to restock their inventory and ease customer demand. Based on data analysis, brands can also determine which products to prioritize on which platforms.

    By Category

    Availability analysis by Category over Time
    • Availability of all five categories was around 52% in Feb’ 22, which steadily increased to 61% until Aug’ 22 and has reduced to reach 55% availability in Sep’ 22
    • Sliced Bread category has seen the most drop in availability by 12% between Jun’ 22 and Sep’ 22
    • The tortilla category has the most rise in availability. It has increased by 16% between Feb-22 and May-22. It also showed an overall rise in availability of 5% from Feb-22 to Sep-22

    By Location

    Availability analysis by Location and Category
    • Across categories, Snacks & Candy had better availability at 73% than Bread & Bakery, with 56% availability.
    • By Location, New Brunswick had more than 65% availability across all three categories; the closest Location is Nova Scotia, with 63% availability.
    • Alberta had the highest availability of 100% in the Snacks & Candy category and the lowest availability of 21% overall in all three categories (weighted aggregate)

    Discounts Analysis

    Several discount-based insights can be studied on e-commerce platforms. From location-based trends, retailer-based trends, and manufacturer-based insights. These insights can help companies make the most of the revenue opportunity while creating an attractive value proposition for the retail consumer.

    By Category

    Discount analysis by Category
    • Discounts of all three categories were around 24% in Feb’ 22, which steadily reduced to reach 15% in Sep’ 22
    • Snacks, cookies & chips category has contributed the most to the drop in discounting, which dropped by 17% between Apr’ 22 and Sep’ 22
    • The Tortilla Category does not have any discount in the month of Jul’ 22

    By Retailer

    • Discount on Bread & Bakery category in Walmart Grocery was around 9% in Feb’ 22. It steadily increased to 13% by Jun’ 22 and thereon reduced to reach 11% availability in Sep’ 22.

    By Location

    • Across Retailers, Nova Scotia had the highest availability of discounts at 22%, whereas New Brunswick had the lowest with discounts at 9% in Bread & Bakery category.
    Discount analysis by Retailers and Locations – Alberta, Ontario, New Brunswick, Nova Scotia
    Note: Analysis does not cover all locations

    Bakery and snack product manufacturers on e-commerce platforms have access to a rich trove of insights they can leverage to benchmark their strategies. They can better understand customer demands, align their supply chain and critically understand the trends impacting their bottom line. Engaging with a third-party platform like DataWeave’s Digital Shelf Analytics  can help brands unlock tremendous value. 

  • Insights from the Digital Shelf of Indian FMCG Brands

    Insights from the Digital Shelf of Indian FMCG Brands

    Analyzing Search, Promotions and Availability for Chocolates, Biscuits, and Malt Drinks across BigBasket, Blinkit, Dmart, Swiggy etc.

    Imagine you log into an e-commerce portal with a list of food items you need for the month. You know what you want, and scroll through the platform to see if there are any discounts. You check competitor products to see if you can get better deals. And within no time, fill your cart with products that you need and proceed to check out. 

    It all happens quickly. It’s an online shopping experience that we are familiar with. But what exactly is happening in the background?

    Brands are tracking and ensuring the highest keyword ranking, optimal availability and competitive discounts to grab your order. And to enable this, Brands rely on Digital Shelf Analytics.

    The FMCG marketplace

    Source: NielsenIQ’s FMCG Snapshot for Q2 2022

    Here, we take a closer look at some of the key factors that Fast Moving Consumer Goods (FMCG) Brands on e-commerce platforms need to pay attention to – to ensure their products appear on the top of search items, to better understand competitor discounts and to monitor the availability of their products across regions.

    FMCG has experienced rapid growth in the last two years, largely attributed to digitalization, changing consumer habits, and increased spending post-pandemic.  Macro factors, including government impetus, inflationary pressures, and consumption recovery, indicate a double digital growth for FMCG brands in the country. According to NielsenIQ’s FMCG Snapshot for Q2 2022, the FMCG industry has grown by 10.9% in the quarter ending June 2022, compared to 6% in the previous quarter.  In the second half of 2022, consumers are expected to spend even more during the festive season. With these shifts underway, the growth opportunities in this sector can only be exploited by companies that can sense trends early – and take appropriate action.

    In addition to providing manufacturers and retailers with actionable insights into e-commerce trends, DataWeave’s tools also allow them to make informed business decisions and ultimately improve their bottom line. Data-driven insights on e-commerce products can help brands optimize their supply chain to maximize sales. A company can determine the key areas that need attention based on an analysis of the availability of products on specific e-commerce channels, associated discounts, as well as zip-code level demand and supply statistics.

    Here are a few sample insights and trend analyses for some popular FMCG brands in the Biscuits, Chocolate and Malt drinks categories spotted by DataWeave.  

    Analytics Methodology: An overview of the data set analyzed

    • Data Scrape period: January 2022 to August 2022
    • Grocery Retailers tracked: Amazon Fresh, BigBasket, Dmart, Jiomart, Swiggy, Milkbasket
    • FMCG brands: Britannia, ITC, Mondelez, Nestle, Parle, Complan, Boost, Amul, Hershey’s
    • Category tracked: Biscuits, Chocolate, Malt drinks


    Availability Analysis

    What is the availability of Biscuits, Chocolate and Malt Drinks on e-commerce portals?

    The availability of a product on an e-commerce marketplace is a key indicator of whether the product meets consumer demands.  DataWeave’s availability analytics can be leveraged by FMCG brands to strategize their inventory and stock planning.  Brands can also make data-driven decisions on product visibility, i.e. identify which products to prioritize on which platforms. 

    • Biscuits had a better availability at 63% when compared to chocolates at 56% across all retailers
    • Dmart and Swiggy had more than 85% availability across all three categories, with Bigbasket coming next at 67% availability 
    • Flipkart Grocery and Blinkit had the lowest availability at 46% and 50%, respectively.
     Figure 1: Availability Scores for Biscuits, Chocolates and Malt Drinks across Retailers

    Which manufacturers have the highest availability of products on e-commerce platforms?

    A study of the availability of products across different manufacturers can reveal brands that have successfully tapped the market opportunity. Here’s a look at brands that have steadily improved their availability on e-commerce platforms.

    Figure 2: Availability Trends for Biscuits across Manufacturers
    • In the biscuits category, all five manufacturers marked approximately 50% availability in Jan 2022. Availability steadily grew to 68% in June 2022, then declined to 63% in Aug 2022.
    • Unibic experienced the largest drop in availability, dropping 15% between May 22 and Aug 22.
    • Mondelez saw the largest rise in availability, an increase of  23% between Mar-22 and Aug-22.
    Figure 3: Availability Trends for Malt Drinks across Manufacturers
    • Except for Boost and Nestle, availability for all seven manufacturers of malt drinks was consistently above 50%. The average availability across all manufacturers rose gradually from 55% in Jan to 63% in Jul-22, followed by a small decline to 57% in August.
    • From 30% availability in January 22 to a mere 7% in August 22, Boost has seen the greatest drop in availability.
    • The availability of Amul has risen the most over the past year, rising from 51% in January 22 to 78% in August 22, hitting 80% in July 22.

    Chocolate: Which manufacturers have the highest availability of products on the e-commerce platforms?

    Figure 4: Availability Trends for Chocolates across Manufacturers
    • Chocolate availability across all manufacturers averaged 47% in Jan-22, reaching a peak of 64% in May-22 and dropping to 51% in Aug-22.
    • From 46% in Jan-22 to 74% in May, Mondelez saw the biggest increase in availability, followed by a decline to 68% in August.
    • Ferrero experienced one of the sharpest drops in availability. Although the brand’s availability steadily grew from 77% in Jan 22 to 94% in Jul 22., it registered a steep drop to 49% in August. 

    The drop in availability hurts the Brand’s eCommerce in two ways. Not only does the Brand lose sales directly. But poor availability also impacts the keyword search ranking, which further hurts the sales.

    Check out DataWeave’s Digital Shelf Analytics Product for insights on how Availability tracking can help reduce stock-outs and boost sales. Click here to know more.

    Discount Analysis

    Location-based, retailer-based, and manufacturer-based discount trends can be analyzed. These studies can help companies plan attractive and appropriate promotional and discount strategies to enhance their revenue opportunities. 

    Which manufacturer has been offering the most discounts?

    A study of discounts offered across manufacturers for chocolates, malt drinks and biscuits indicates that some brands have increased their discounts while others have reduced their discount rates. These decisions could be triggered by demand, availability, and production cycle. Parle, for example, has steadily reduced its discount rates. 

    Figure 5: Discount Rate Trends across Manufacturers

    Average discount rates across manufacturers were around 9% in Jan 2022 and rose steadily to reach 14% in Jul 2022. A small decline is observed post-July, with a 12% discount rate registered in Aug 2022.

    • In the biscuit category, Unibic offered the biggest discount of 28%, followed by ITC at 20%.     
    • In the Chocolate category, Hershey’s offered the largest discount of 14%, followed by ITC at 12%.
    • In the Malt Drink Category, Amul offered the largest discount of 16%, followed by Boost at about 10%. 

    Check out DataWeave’s Digital Shelf Analytics Product for insights to respond to Competitor’s pricing and promotions. Click here to know more.

    Share of Search Analysis

    Which brands feature within the top 5 on the first page of the search?

    A product that appears within the top 5 items on the first page of a search, has a higher probability of being purchased. Below is a study of the share of the search for biscuits across manufacturers and retailers. 

    Figure 6: Share of Search for Biscuits across Manufacturers and Retailers
    • Britannia dominates the top ten share of search across different online retail platforms.
    • Mondelez has the highest share of search at 62% in Amazon Fresh, whereas Parle-G has the lowest share of search at 7%.
    • In Bigbasket, Britannia has the highest search share of 62%, whereas Parle-G has the lowest search share of 7%.

    Check out DataWeave’s Digital Shelf Analytics to track the Share of Keyword and Navigation Search. Click here to know more.

    Conclusion

    FMCG is a rapidly evolving industry sector with a high potential for growth in the coming years. FMCG brands must compete with one another to fully tap this market opportunity on several factors to ensure that their products are visible, available, and attractive to consumers. Digital crawling and big data technologies have enabled manufacturers and retailers to collect publicly available e-commerce data for useful, actionable insights and trend analysis. To stay competitive, it is crucial for manufacturers and retailers to engage with analytics and data experts to seamlessly integrate e-commerce analytics into their short- and long-term business strategies. Whether it’s building keywords to increase the share of search, knowing the right discounts to attract customers in a particular city or increasing the availability of products on specific e-commerce platforms, companies need to invest in the right data intelligence!

    DataWeave for FMCG Brands

    DataWeave has been working with global CPG/FMCG brands, helping them drive their growth on eCommerce platforms by enabling them to monitor their key metrics, diagnose improvement areas, recommend action, and measure interventions’ impact. DataWeave’s KPIs enable Brands to fill in the blind spots in their funnel data and allows them to respond to competitors on a near-real-time basis.

    If you want to know to learn how your brand can leverage DataWeave’s data insights and improve sales, then click here to sign up for a demo

  • The Rapid Rise of Alcohol eCommerce in the UK

    The Rapid Rise of Alcohol eCommerce in the UK

    Alcohol eCommerce has been rapidly growing over the years, and like a lot of other industries, the pandemic accelerated its growth. Convenience, safety & home delivery became important criteria for customers in the post pandemic era and so the sale of alcohol via eCommerce went up. Kantar reported that UK booze sales were up £261m & online and convenience stores were the biggest winners. The latest IWSR Drinks Market Analysis Report 2022 reported on another interesting trend – when ordering alcohol online, consumers prefer using websites v/s apps in most parts of the world except China and Brazil. In the UK the largest chunk of online alcohol purchases happens on a retailer website instead of an app. 

    Platform used for last online alcohol purchase. Source

    To get a better understanding of this, we tracked 2 grocery retailers and 3 grocery Q-Commerce apps in the UK to get insights into Alcohol sales, pricing, trends & more! 

    Methodology

    • Data Scrape time period: Feb 2022 – June 2022
    • Grocery Retailers tracked: Tesco & Ocado
    • Grocery Apps tracked: Gorillas, Weezy & Getir
    • Category tracked: Alcohol

    Which retailer was the Price Leader in the alcohol category? 

    Before the pandemic Tesco was the only Big 4 retailer to increase their alcohol market share & Waitrose was the biggest loser, with its share of booze sales falling from 5.4% to 4.7%. Maintaining Price Leadership is a critical element and plays a big role in increasing sales & market share because consumers will buy the most competitively priced product. We wanted to track and see which retailer was the Price Leader in the alcohol category – i.e., had the most number of lower-priced items in the alcohol category. We also wanted to see if & how Tesco’s position had changed post pandemic. 

    Price Leadership
    • Tesco enjoyed price leadership in the Alcohol category from Feb – June 2022 with 38.9% products priced the lowest. This, followed by Ocado at 33.8%. Gorillas had price leadership for the least amount of products in the alcohol category at 5.6%. Tesco was the clear winner! 
    • Tesco’s Price Leadership kept declining through the months though – at the beginning of the year in Feb, Tesco had 44% products priced the lowest but by June, that number fell to a little over 36%. Ocado showed a reverse trend – in Feb they had price leadership on 32% items and by June that number rose to 35.3%.
    • One player Tesco could’ve potentially lost price leadership to was Getir. In Feb, Getir had price leadership on only 8.2% products but that increased gradually over the months to land on 14.5% in June. 

    Which retailers focused on Discounts to perk up alcohol sales? 

    Discounts are a great way to draw in inflation-hit shoppers. Loyalty card discounts, reward vouchers, and other promotional strategies retailers offer help make their products more competitive & attractive to customers. To stay competitive, retailers need to be aware of the discounts their competition is offering. They also need to understand the risk of deep discounting and the impact on margins. We wanted an insight into alcohol related discounts in the UK so we dug into our data. Here’s what we saw. 

    Average discounts across months by retailers
    • A host of European and UK based startups like Jiffy, Dija, Weezy, Zapp, Getir & Gorillas launched with the promise of delivering groceries the fastest & cheapest. 
    • Our data showed that Gorillas offered discounts in line with the competition, however, Getir likely went the deep discounting route. 
    • Getir offered the highest discounts across all months. And in the month of April they offered almost 9% more discount than Ocado – the retailer with the 2nd highest discounts. 
      Like we discussed above, Getir gained price Leadership from Feb to June. Deep discounting could have potentially played a role. 
    • Gorillas on the other hand had the lowest, almost non-existent discounts.

    Let’s look at Price Index trends across 5 months 

    We tracked the Price Index (PI) across these 5 retailers to measure how alcohol prices changed over a 5 month period from Feb – June 2022. 

    Note: Retailers selling at the 100% mark were selling at an optimal price & did not undercut the market. The pricing sweet spot is 95% – 105%. Anything lower would compromise margins, and higher would mean the retailer was not competitive. 

    Price Index across months by retailers
    • Weezy had a Price Index that was the most optimal, sitting in the 100% – 102% range.
    • Gorillas had the lowest Price Index, between 89% – 91%.
    • Getir had a low price index in Feb (96.1%) but slowly kept increasing to cross 110% in April, May & June.
    • What was interesting to see was the competition between the 2 retail giants Ocado & Tesco. Ocado had a lower price index at the start of the year at 105.1%, while Tesco was at 109.8%. In the subsequent months, Ocado kept increasing prices to be competitive with Tesco and Tesco decreased prices to likely match Ocado’s pricing. By June BOTH Tesco & Ocado had the exactly the same price index – 108.7%

    Which retailers were the quickest to make price changes?

    Competitive pricing is critical to eCommerce success. Competitive pricing involves tracking your competitor’s pricing & strategically tweaking your own prices without hurting margins. We tracked the month-wise average Price change from Feb – June across all 5 retailers to see which retailer was making price changes and at what frequency. 

    Average price change across months by retailers
    • Most retailers did not make massive prices changes, they were ballpark competitive with each other from a pricing standpoint. 
    • However, Gorillas made significant changes in the month of March when they dropped prices by 3.8% and in May when they increased prices by 5.5%!
    • In May, the same month Gorillas made a big price hike, Weezy dropped their prices significantly by 10% widening the gap between the 2 retailers. 

    Which retailers avoided lost sales by maintaining stock availability?

    Having a near real time view on stock availability is crucial to driving sales. Customers can buy products only when they’re available! So, we went ahead, looked into our data to see how each of these retailers managed stock availability from Feb to June.

    Average availability across months by retailers
    • Our data showed varying availability levels across retailers with Ocado having the highest availability across all 5 months. They had a robust stock at the beginning of the year at 100% but kept dwindling through the months to land at 95.8% by June. 
    • Tesco had a sharp drop in availability in May & June – from 97% at the beginning of the year to the 92-93% range.
    • Gorillas had the lowest availability across months between 90 & 94%.
    • Weezy consistently maintained availability at 95% across all 5 months.

    Conclusion

    For the most part, the UK market has a positive outlook towards buying alcohol online thanks to changes to shopper behavior arising from the pandemic. As per the IWSR Drinks Market Analysis Report 2022 in website-led markets, such as the UK, breadth of product range is important to customers along with price. These 2 play a key factor in purchase decisions. By contrast, consumers in app-driven markets have different preferences. While price matters, it is less important than convenience and speed. 

    As an alcohol retailer, if you need help tracking your competitor prices, discounts and product assortment, reach out to the team at DataWeave to learn how we can help!

  • 5 Ways to Manage and Improve Stock Availability

    5 Ways to Manage and Improve Stock Availability

    Stock availability is the degree to which a brand or retailer has inventory of all their listed items to meet customer demand. Product availability becomes even more critical when they have to respond to unforeseen changes in demand and supply. To maintain the ideal stock availability levels for all items, they need robust inventory management tools to ensure real-time updates on current stock and accurate insights into upcoming demand.

    However, managing stock availability is not a clear-cut science. Retailers must balance the change in demand and keep stock availability in check. 

    Why Stock Availability Matters

    One of the challenges of running a retail business is to optimize inventory and associated costs. Maintaining stock availability in stores is critical for offline retail businesses. And when selling online, making sure products are available across different retailers and marketplaces can have a huge impact on sales and conversions. 

    1. Understocking: It’s when a brand’s product fails to meet consumer demand. If this happens often enough, customers may not return to the brand’s website or app because of the initial experience. Understocking is not a brand’s fault entirely since they might not always be able to anticipate a change in demand. However, it’s about a their ability to adapt to a quick change in the market trends through historical analysis and accurate forecasting. 
    2. Overstocking: It’s when a company orders too much inventory. Holding too much stock will lead to higher storage costs, shrinkage, and obsolescence losses. Another loss occurs if the brand can’t quickly sell the items — diminishing the value of the products. 

    We gathered data to see the impact of a short-term stockout on Amazon for one of our customers. Read more about what we uncovered & how deep the damage was, here.

    7 Ways to improve stock availability 

    1. Collect Accurate Data

    Availability across Brands and Categories

    When multiple items are moving through a supply chain, companies can easily run into inventory inaccuracies. Discrepancies between the values of your system and the actual inventory of products can lead to understocking or overstocking. The best way to avoid discrepancies in inventory is to invest in an inventory management tool that gives you real-time updates on your stock. This is applicable for offline retail businesses. 

    2. Managing eCommerce inventory

    Availability at Individual Product Level
    Availability at Individual Product level by regions

    Effective eCommerce inventory management is as important as making sure products are available in stores. Keeping track of your inventory levels and ensuring that you’re always well-stocked can avoid lost sales and keep your company running smoothly. Brands must ensure their stock is available across all the online platforms they sell. Access to real-time inventory data can help to keep a close eye on stock status across all marketplaces & retailers the product is available. Retailers also need to keep track of market trends to ensure they have the right inventory assortment to match customers’ demands. 

    3. Understand Consumer Demand

    The only way to accurately predict future demand is to rely on historical data about your customer purchase trends. What do your customers purchase during holiday seasons? What are the upcoming trends in your category? Having data-backed answers to such questions will help brands and retailers properly stock up their inventory.

    4. Adequate forecasting 

    Anticipating demand will help determine which products should be stocked during which seasons. Tracking past sales and metrics such as economic conditions, seasonality, peak buying months, and promotions will help brands predict demand. Analyzing such statistics will also help you get insights into the target market.

    Availability across regions

    5. Improve supplier relationships

    It’s important to rely on a supply chain that delivers your shipment promptly. In fact, you should foster close relationships with your suppliers to trim costs and improve stock availability. You should be able to share key details such as future demands, so suppliers can ensure timely delivery. 

    Availability Analysis
    Availability Analysis across Retailers and Categories

    Consequences of Inefficient Inventory Management

    What are the effects of overstocking?

    Tied-up cash: Money spent on overstocking is tied-up money that your company could have put to better use. You can use it to pay off debts, wages, and rent. Inventory often has a limited shelf life due to material degradation, changing consumer trends, spoilage, and obsolescence.

    Product expiration: If your brand offers time-sensitive goods or perishable items, overstocking can lead to product obsolescence and expiry. eCommerce platforms that also sell time-sensitive goods or grocery delivery apps are forced to sell products at below-margin prices to free up resources, leading to losses. 

    What are the effects of understocking?

    Poor customer experience: Poor product availability will lead to low customer satisfaction & dropping customer loyalty. 

    Missed sales: Customers could gravitate towards the competition to make their current purchase if a product is unavailable at your online store. The more freequent the stockouts, the more lost sales. 

    Conclusion

    To avoid the knock-on effects of overstocking and understocking, companies need a real-time view of their inventory, both online & offline. At DataWeave, we help companies decrease their latency period between stock replenishment and efficiently plan their supply chain. If you need help tracking your eCommerce product availability, reach out to the experts at DataWeave to know how we can help!

  • 5 Ways DataWeave Helps Brands Drive Growth With Amazon Ads

    5 Ways DataWeave Helps Brands Drive Growth With Amazon Ads

    Consumers are discovering and trialing new eCommerce marketplaces, brands and products at a faster rate than ever before, given the vast amount of choices encountered browsing for products online. A recent analysis shows how events like Amazon Prime Day, Black Friday, and Cyber Monday are especially fruitful for new-to-brand customer advertising, encouraging B2C marketers to increase their digital advertising spend to fuel product discovery, sales and market share for their brands.

    Amazon advertisers grow market share and brand loyalty with ecommerce intelligence
    DataWeave joins Amazon Advertising partner network

    The majority of eCommerce consumers are discovering products via relevant keywords attributable to their needs, with most clicks happening on page one results for the first few products listed. Simplifying the digital shopping experience is critical for brands to be in the consideration set for the majority of consumers who won’t venture past page one results. 

    An internal analysis conducted shows getting a product to page one on retailer websites can improve sales by as much as 50 percent, but figuring out the right levers to pull to get there organically—without paid advertising—is a real challenge, especially given fast-changing algorithms. While more than half of all retail related online browsing sessions are “organic”, sometimes brands need to boost their product visibility by investing in sponsored (paid) opportunities to improve a product’s rank.

    Data analytics can equip brands with intelligence to help them decide when, where, and how to make digital advertising investments profitably, while simultaneously acting on insights that help drive organic growth. Considering a majority of U.S. consumers begin their product discovery on marketplaces like Amazon, it makes sense for brands to prioritize digital advertising opportunities with Amazon.

    Maximize Return on Ad Spend (ROAS) with Amazon Ads

    Brands use Amazon Ads to drive brand awareness, acquire new customers, drive sales and gain market share, with the goal of furthering their marketing return on investment. Top performing advertisers average 40 percent greater year-on-year (YoY) sales growth, 50 percent greater YoY growth in customer product page viewership on Amazon, and 30 percent higher returns on ad spend (ROAS) with Amazon Ads, according to a recent analysis. Sponsored Products, Sponsored Brands, Amazon DSP and Sponsored Display are among the types of Amazon Ads options cited that produce maximum return.

    Ensuring your product listings appear at the top of page one results on Amazon for the most relevant discovery keywords is therefore the most important determinant for maximizing ROAS. DataWeave has become a vetted partner and measurement provider in the Amazon Advertising Partner Network, with the goal of supporting brands to optimize digital advertising campaigns by providing visibility to Digital Shelf Analytics (DSA) key performance indicators (KPIs), like Share of Search, Pricing and Product Availability, Content Audits, Ratings and Reviews, and Sales Performance and Market Share.

    Below is a summary of how our Digital Shelf solutions, in partnership with Amazon Ads, can improve the performance of your Amazon Ads campaigns

    1. Keyword Recommendations Improve Share of Search

    With the DataWeave Share of Search solution, brands can monitor their placement of both organic and paid discovery keywords relative to their competition. Once your keywords are determined, you are also provided a weighted Share of Search score that helps measure how well each keyword performs relative to product discoverability. Below is an example of insights you’d gain.

    Share of Keyword Search

    Brands can provide their own list of keywords to monitor, or through our Amazon Ads collaborative solution, learn which keywords are the “best” for them to measure in the realm of Amazon. Performance results are based on data that shows which keywords consumers are actually using when browsing online alongside other keywords brands request to measure. Users are able to see exactly which keywords are most popular, competitive (and even unexpected), and relevant at an Amazon Standard Identification Number (ASIN) level of granularity. 

    We can also estimate the degree of relevance and estimated traffic for the recommended keywords. Brands can then use these insights to adjust campaign strategies based on these parameters, which can boost product discoverability and rank visibility. A brand could assume people find its products by brand name, yet traffic insights may reveal a majority of people look for a generic product type before they end up buying that particular brand. 

    2. Content Audits Increase Discovery Relevancy Scores

    Strong product content is critical to succeeding on Amazon. Thorough, accurate, and descriptive content leads to better click through rates (CTR), conversion rates, more positive reviews, and fewer returns, which results in increased discoverability. DataWeave’s Content Audit solution reviews existing copy and images on a per-attribute basis to highlight any gaps essential to improving visibility, as seen in the example below.

    Content Analysis

    To further growth, it is equally as important that your product content aligns with your advertising strategy. With Amazon Ads partner add-on, our solution can also audit your content to measure how effectively you are incorporating Amazon Ads keywords into your product content to enhance discovery relevancy.

    3. Discover More Opportunities with Pricing and Product Availability Insights

    Quality content and keyword updates will only get you so far if your products are not consistently available and priced competitively. With DataWeave’s Pricing and Promotions and Product Availability modules, advertisers can monitor their selling prices and availability trends alongside their competitors to uncover more opportunities to incorporate into advertising campaigns, as seen in the Pricing and Promotions dashboard example below.

    Promotion Analysis

    Additionally, product targeting recommendations can be utilized to target a competitor’s ASIN that may be overpriced or that is having issues staying in stock. Alternatively, broaden your strategy to target specific brands, complementary products, or category listing pages.

    You can also create alerts on your own products to monitor when items are low on inventory or out of stock to ensure key products are consistently available when customers are shopping.

    4. Leverage Ratings and Reviews to Increase Conversion

    Product ratings and reviews are also a critical component to running a successful Amazon Ads campaign. A large number of reviews and a positive star rating will provide customers with the confidence to purchase, resulting in higher conversion rates. Conversely, negative feedback can have a detrimental impact, resulting in lost sales and wasted ad spend. DataWeave’s Ratings and Reviews module can help you monitor your reviews and extract attribute-level insights on your products. This information can then be utilized to further optimize your advertising strategy.

    If you see consistent feedback in your reviews on aspects of a product not meeting customer expectations, address them in your product content to prevent potential misplaced expectations. Alternatively, if customer reviews are raving about certain product features, ensure these are promoted and relevant keywords are populated throughout your descriptions and feature bullets. Below is an example of insights seen within the DSA Ratings & Reviews dashboard.

    Ratings and Reviews

    5. Correlate Digital Shelf KPIs to Sales Performance and Market Share

    The newest DSA module, Sales Performance and Market Share, provides SKU, sub-category, and brand-level sales and market share estimates on Amazon for brands and their competitors, via customer defined taxonomies, to easily benchmark performance results.

    This data can also be correlated with other Digital Shelf KPIs, like Content Audit and Product Availability, giving brands an easy way to check the effect of attribute changes and how they impact sales and market share. Similarly, brands can see how search rank, both organic and sponsored, affects sales and market share estimates.

    Understanding the correlation between your advertising campaigns and your Digital Shelf brand visibility will help you identify which areas to prioritize to drive sales and win more market share.

    Digital Shelf Insights Help Brands Win with Amazon Ads

    The need for access to flexible, actionable eCommerce insights is growing exponentially as a way to help brands drive growth, increase their Share of Voice, and to gain a competitive edge. As a result, more global brands are seeking Digital Shelf Analytics for access to near real-time marketplace changes and to develop data-driven growth strategies that leverage pricing, merchandising, and competitive insights at scale.

    By monitoring, measuring and analyzing key performance indicators (KPIs) like Sales Performance and Market Share, Share of Search, Content Audits, Product Availability, Pricing and Promotions and Ratings and Reviews alongside competitors, brands will know what actions to take to boost brand visibility, customer satisfaction, and online sales. 

    DataWeave’s acceptance into the Amazon Advertising Partner Network enables Amazon advertisers to effectively build their Amazon growth strategies and determine systems that enable faster and smarter advertising and marketing decision-making to optimize product discoverability and overall results.

    Connect with us now to learn how we can scale with your brand’s analytical needs, or for access to more details regarding our Amazon Ads Partnership or Digital Shelf solutions.

    UPDATED: Read the full press release here

  • Prime Day India 2022 – highlights from the 2 day annual shopping festival!

    Prime Day India 2022 – highlights from the 2 day annual shopping festival!

    Amazon India’s much-awaited annual two-day shopping event, Prime Day, kicked off with a bang on July 23rd & 24th this year & was one of the most successful Prime Day events yet! Amazon reported that more than 32,000 sellers saw their highest ever sales day during the event. Interestingly 70% of these sellers who received orders during Prime Day were based in Tier 2 cities in India, further validating how the post-pandemic eCommerce boom has spread across the country. Also, Indian exporters saw 50% business growth on Amazon on Prime Day as customers across markets like North America, Europe, Australia, and Japan continued to purchase Made In India products.
    It was a great 2 days for Indian sellers, but what about customers who were waiting in anticipation for the great deals typically offered on Prime Day? We dug into our data to take a look at the deals, discounts, and brands that shone bright on Prime Day in India.

    Methodology

    • In addition to Amazon IN, we also tracked Flipkart on 23 & 24th July 2022, on Prime Day.
    • Categories tracked – Electronics, Grocery, Fashion & Beauty.
    • We looked at Additional Discounts offered on Prime Day: Additional Discount is the extra discount on an item during Prime Day when compared to the Pre-Prime Day price. 
    • We also looked at Post Prime Day Discounts, which were the discounts offered after the 2-day event ended.

    Amazon v/s Flipkart – who offered better discounts?

    Prime Day discounts are legendary. And across the globe, during Prime Day retailers try and compete to see if they can offer better deals than Amazon. Forbes even published an article on the 36 Prime Day competitor sales that were way more enticing than what Amazon had to offer. In India, we wanted to see if Amazon’s homegrown rival Flipkart might give it a tough fight, so we tracked the volume of discounts across categories on both retailers. 

    Discounts on Amazon & Flipkart across categories
    Discounts on Amazon & Flipkart across categories
    • Out of the 4 categories we tracked, in spite of Prime Day, Amazon offered discounts higher than Flipkart in only 2 categories – Electronics & Beauty. 
    • … while Flipkart offered higher discounts than Amazon in the Grocery & Fashion category. For groceries, Flipkart offered a 3.2% additional discount v/s 2.2% on Amazon. However, in the Fashion category, the difference was marginal – 8.1% on Amazon v/s 8.6% on Flipkart
    • Post-event, both Amazon & Flipkart went back to the original pre-event prices. This made it clear that Flipkart was tracking and making price changes based on their closest competitor. It’s what smart eCommerce businesses do to stay ahead in the race. 
    • Interestingly, post-event, in the fashion category, not only did Amazon revert to the original pre-event price, they even increased prices by close to 2%.

    Let’s take a look at discounts across 4 categories & the Brands that WON in each category.

    From Electronics to Fashion, Beauty & Groceries, let’s deep dive into the data to see which products were highly discounted within each category and brands that sprinted ahead to win the race on Amazon on Prime Day 2022.

    ELECTRONICS

    Tech publication Gadgets360 reported on the biggest Smartphone deals right from Brands like Samsung, Redmi, Oppo, and more. There were some fab deals on earphones too with Boat taking the lead. We wanted to take a look at electronics on Amazon and see which products had the heaviest discounts & if discounts were more lucrative than Prime Day 2021

    Discounts on Electronics on Prime Day
    Discounts on Electronics on Prime Day
    • Amazon India released highlights from Prime Day and reported that Smartphones & Electronics were among the categories that saw the most success in terms of units sold.
    • From the 6 product categories we tracked within electronics, we saw the highest additional discounts on Smartwatches (13.4%), followed by Bluetooth headphones (10.5%)
    • TV, Smartphones, cameras, and laptops had an additional discount of between 3 – 5.5%

    ELECTRONICS Brands that had the highest Share of Search on Amazon during Prime Day

    Research shows that on Amazon, the first 3 products garner 64% of business generated. This is why it is critical for brands to appear in the top few listings when consumers are searching for products. Being on top helps shoppers find your brand with ease & increases the chances of a sale. 

    On Prime Day 2022, Amazon India reported that the top-selling consumer electronics brands were HP, Lenovo, Asus, and Boat to name a few. Our assumption is, these brands must’ve had a high Share of Search (SoS), which played a massive role in increasing sales, so we looked into our data to see which brands had the highest SoS against specific keywords related to electronics. 

    Brand Visibility against the Keywords
    Brand Visibility against the Keywords
    • Our data aligned with what Amazon reported. HP had high sales, perhaps because they occupied the premium #1 spot in the laptop category with a 44% SoS! Simply put, this means of the 100 laptops that appeared on a page, against a search for the keyword laptop, 44 products were listed by HP! Consumers always gravitate towards buying products they can find with ease. 
    • Lenovo had a 32% SoS for Laptops. Asus at 14% 
    • The top selling smartphone brands reported by Amazon included OnePlus, Redmi, Samsung, Realme & iQOO – our data showed that 3 out of these 5 brands were in the top 5 listings on Prime Day! Redmi had a whopping 30% SoS against the keyword smartphone, Samsung at 15%, and iQOO at 5% – clear validation that a high SoS can positively impact sales.

    BEAUTY & GROOMING

    Now let’s look at discounts in the beauty & grooming category. 

    Discounts on Beauty Products on Prime Day
    Discounts on Beauty Products on Prime Day
    • The highest additional discounts were given on shampoos (9.3%), followed by Lipsticks (6.6%)
    • Shaving kits for men were at an additional discount of 3.4%. Hair gel at 4.9% & Face Masks at 4.3%

    BEAUTY Brands that had the highest Share of Search on Amazon during Prime Day

    Brand Visibility against the Keywords
    Brand Visibility against the Keywords

    In the beauty category, Amazon India reported that top-selling brands included Head & Shoulders, Dove, Biotique, L’Oreal, Sugar Cosmetics, and Mamaearth to name a few. Once again, we looked into our data to see the sort of brand visibility & SoS each of these brands had.

    • All the top-selling brand’s Amazon reported on we noticed appeared in the top 5 search results. 
    • Head & Shoulders & Dove were the top 2 listings against the keyword Shampoo at 26% & 16% SoS respectively. Biotique came in at #5 with a 7% SoS
    • Bombay Shaving Company, Gillette, and Axe were the top grooming brands for men in the Shaving Kit category. 
    • Lakme made a clean sweep with a 19% SoS against the keyword lipstick, which speaks volumes, considering the aggressive competition from D2C beauty brands in India today.

    GROCERY

    According to the New eCommerce in India report by consulting firm Redseer, grocery has been a major contributor to the growth of ecommerce in India & Amazon Fresh used Prime Day to grab a larger piece of that pie! As part of the Prime Day sale, Amazon Fresh also pushed discounts on groceries, as well as fruits and vegetables. We tracked products that fell into the “snack” category, and here’s what we saw.

    Discounts on Snacks on Prime Day
    Discounts on Snacks on Prime Day
    • Given changing lifestyles & healthy food fads, it was no surprise that we saw the highest additional discounts were given on Healthy Snacks (3.2%) & Diet Food (2.7%)
    • Chocolates and chips saw much lower additional discounts at 1.2% each.
    • Drinks were additionally discounted by 2.5% during Prime Day.

    SNACK Brands that had the highest Share of Search on Amazon during Prime Day

    Brand Visibility against the Keywords
    Brand Visibility against the Keywords
    • Cadbury had a 69% share of search against the keyword Chocolate, leaving some of its key competitors way behind. Amul had a 20% SoS, while Hershey’s was at just 4%. 
    • According to an article in the Economic times, YogaBar tripled sales in FY22, which is why we were not surprised to see the brand at #1 when users were searching for “Healthy Snacks” during Prime Day. YogaBar products typically enjoy high visibility year-round, which clearly helped with brand awareness on Amazon & sales.

    FASHION

    Amazon reported that Men’s t-shirts and polos, denims, Kurtis, tops, and dresses for women, designer wear, and clothing for kids were some of the most-loved fashion categories on Prime Day. We looked into our data to see the trends that emerged.

    Discounts on Fashion on Prime Day
    Discounts on Fashion on Prime Day
    • From the categories we tracked, women’s handbags had the highest additional discount (11.8%), followed by watches (9.1%)
    • Sneakers & jeans had additional discounts in the ballpark of 7% and sunglasses at 4.4%

    FASHION Brands that had the highest Share of Search on Amazon during Prime Day

    Brand Visibility against the Keywords
    Brand Visibility against the Keywords
    • Some of the usual suspects made it to the top 5, but what really stood out for us were brands that popped up against the keyword Jeans. While Levi’s came in at #2 with an 11% SoS, 2 Private Label Amazon brands featured in the top 5! Symbol at 27% SoS and Inkast Denim at 9%
    • Against the keyword Handbag, Lavie had a massive lead at 38% v/s the #2 brand – Caprese, at 13%
    • Boat found a #2 spot against the keyword watches, racing way ahead of the age-old popular brand Fastrack at #5 with a 4% SoS.

    Conclusion

    Amazon Prime Day 2022 in India came to a successful close as shoppers across India discovered the joy of the 2 day celebration with the best deals, savings, new launches, and more. Prime members from 95% of pin codes in India made purchases, there were 1000’s of deals and 500+ new product launches from brand partners & sellers. Nearly 18% more sellers grossed sales over INR 1 crore, and close to 38% more sellers grossed sales of over 1 lakh vs Prime Day 2021. Local neighborhood shops that sell on Amazon witnessed 4x sales growth. And start-ups and brands under the Amazon Launchpad program witnessed a growth of 3x. All in all, a successful event for everyone involved! 

  • Prime Day Germany 2022 – highlights from the 2 day annual shopping festival!

    Prime Day Germany 2022 – highlights from the 2 day annual shopping festival!

    In 2022, Amazon sold 300 million products during Prime Day – selling roughly 100,000 items per minute. Since Amazon started Prime Day in 2015 to celebrate its 20th birthday, the shopping festival has grown into a holiday and rivals Black Friday and Cyber Monday in the U.S. and Singles’ Day in China. 

    According to RetailDetail, the leading B2B retail community in Benelux, Amazon is planning a 2nd Prime Day shopping festival in the autumn, just a few months after its annual Prime Day event. The retailer has asked its sales partners to prepare for a promotional event in the autumn where they have until the beginning of September to propose attractive discounts, with at least 20% discounts. This year’s second Prime Day may occur in October, with or without the same name. 

    But before that, let’s examine what happened in Germany this year on Prime Day 2022.

    Methodology

    • We tracked Amazon.de both before & on 12 & 13th July 2022, on Prime Day.
    • Categories Tracked – Electronics, Wine & Spirits, Grocery, Furniture, Fashion, and Beauty. 
    • We looked at Additional Discounts offered on Prime Day: Additional Discount is the extra discount on an item during Prime Day when compared to the Pre-Prime day price.
    • We also looked at Post Prime Day Discounts, which were the discounts offered after the 2-day event ended.

    What kind of Discounts did Amazon.de offer?

    Amazon Prime Day will be significant, especially for customers hoping to get discounts amid soaring inflation. Both Amazon as well as other sources reported that electrical and electronic items were the most popular purchases, followed by general retail products. Electrical and electronics saw the value of transactions soar 90% on the first day. Mobile phones and accessories were the most popular, with transaction values almost doubling to 96% on day one.

    Discounts across Categories on Amazon.de
    Discounts across Categories on Amazon.de
    • Based on trends from past events, Amazon likely knew electronic items were going to be best sellers. Keeping this in mind, they made sure to offer high discounts in the electronics category. They offered a 6.5% additional discount on electronics on Prime Day. And once the sale ended, they continued to discount electronics by 1.3%.
    • The Fashion category also had a fair bit of discounts and came in at a close second at 5.9%
    • Looks like Amazon discounted everyday use items minimally. Groceries had an additional discount of just 1.8% on Prime Day, and wine and spirits had 2% extra discount.  
    Discounts on Electronics Category on Amazon.de
    Discounts on Electronics Category on Amazon.de
    • Within Electronics, in the four categories we tracked, we saw the highest additional discounts were offered on Bluetooth earphones (10.6%) and Smartwatches (9%)
    Discounts on Fashion Category on Amazon.de
    Discounts on Fashion Category on Amazon.de
    • Jeans and Sunglasses had the highest discounts at 8.6% & 7.6% respectively.
    • Sneakers & Watches too had additional discounts of 6.6% on Prime Day.
    • Post the Prime Day event, Amazon retained an average of 1.5% discount across all products in the fashion category instead of pricing them at the original price. 
    • However, in the case of women’s T-Shirts, they increased the price by 1.7% from the pre-event price.

    Discounts across Price Tiers

    Retailers must consider several factors when making strategic discounting decisions, including customer buying behavior, the type of discount offered & the volume of discount offered. The best discounting approach will vary depending on the product and other factors like the original selling price of the product.

    Now let’s compare the discounting strategy Amazon used in the Electronics v/s Fashion category on Prime Day.

    Discounts across Price Ranges
    Discounts across Price Ranges
    • Interestingly, in both the Electronics and Fashion categories, Amazon increased prices for the lowest-end products between the €0-10 range by 3.6% and 13.2%, respectively, during the sale instead of discounting them! Maybe this was a strategy to drive consumers to higher-value products with greater discounts? 
    • Another similarity in strategy was that most of the mid-priced items had maximum discounts. In electronics & fashion both, the maximum discounts were given to products between the € 30-100 range. 
    • Here’s a difference that stood out – for Electronics in the higher price range between €100 – 500, the volume of discounts dropped a bit which meant Amazon gave moderate discounts on high-end electronics. But the trend flipped for Fashion as luxury fashion items were made to look more attractive with higher discounts.

    Monitoring stock availability during key sales days is critical

    Brands need to have the right stock availability, especially during sale events, because more customers shop online during sales. What’s worse, non-availability of products may drive customers to competitors that are stocking the same product.  Out-of-stock situations lead to missed opportunities & lost sales! Let’s take a look at our data and see how Amazon planned product availability across categories on Prime Day. 

    Availability Analysis across Categories on Prime Day
    Availability Analysis across Categories on Prime Day
    • Amazon was betting big on 2 categories – Electronics & Home. This meant they needed to keep a keen eye on availability in these categories, especially since they forecasted the highest sales to be generated here.
      … it was no surprise that the Furniture category had almost 100% availability during Prime Day! Electronics too had a high availability at 94% during the event.
    • Generally, our data showed that availability across multiple categories we tracked seemed robust and above 80% in more cases. Only Beauty & Grocery had 79% availability.

    Conclusion

    Prime Day sales reached an estimated 12 billion U.S. dollars worldwide, 9.8% higher than last year, making it the most successful shopping event in Amazon’s history. If you’re a brand selling on Amazon or a retailer trying to compete with Amazon, reach out to us at DataWeave to know how we can help!

  • Prime Day UK 2022 – highlights from the 2 day annual shopping festival!

    Prime Day UK 2022 – highlights from the 2 day annual shopping festival!

    Prime Day launched in 2015 as a celebration of the 20th anniversary of Amazon’s founding & has quickly become the biggest shopping event of the year for Amazon. Prime Day is a great opportunity for customers to snag fantastic deals on products they might not otherwise consider buying. Last year, Amazon Prime Day was a tremendous success, with Prime members spending billions of dollars on discounted items. In 2022 alone, global sales during the event reached a new record high of $12 Bn.

    18 countries participated in Prime Day this year, including the US. We did a deep dive into what happened in the UK – the discounts Amazon offered and categories with the highest discounts as well as checked to see if other retailers tweaked their pricing strategy to compete with Amazon on Prime Day.

    Methodology

    • In addition to Amazon UK, we tracked some key retailers on 12 & 13th July 2022, on Prime Day.
      Retailers tracked – eBay UK, OnBuy, Selfridges, ASOS.com, Net-A-Porter 
    • Categories tracked – Electronics, Wine & Spirits, Grocery, Furniture, Fashion, Beauty. 
    • We looked at Additional Discounts offered on Prime Day: Additional Discount is the extra discount on an item during Prime Day when compared to the Pre-Prime day price. 
    • We also looked at Post Prime Day Discounts, which were the discounts offered after the 2-day event ended.

    Did other retailers compete with Amazon on Prime Day?

    Traditionally, as Amazon’s Prime Day sale approaches, other retailers adjust their prices by offering summer deals or getting creative with offers. However, we did not see aggressive strategies from other retailers this year. In the US, Walmart always has a sale during Amazon’s Prime Day. The Wall Street Journal reported that Walmart announced there wouldn’t be an annual promotional event on Prime Day 2022 this year.

    Another report published by Forrester stated that major retailers scaled back their promotions, and overall offers from other retailers were less than impressive. We took a look at the data we gathered in the UK to see if this trend aligned. 

    Discounts offered on Prime Day on Amazon v/s other retailers
    Discounts offered on Prime Day on Amazon v/s other retailers
    • Our data showed that most retailers we tracked offered negligible discounts (in the range of 0.1 – 1.5%) and did not really try and compete or match the discounts Amazon was offering. 
    • However, ASOS was the one retailer that competed heavily with Amazon in the Fashion & Beauty category. While Amazon offered an average additional discount of 7.7% in the Fashion category, ASOS offered 13.2%. And in the beauty category, Amazon offered 6.7%, while ASOS offered 15.2%.
    • When we looked at post-prime day discounts, we saw that as soon as Prime Day ended, ASOS went back to the original price and stopped offering a discount which clearly shows they were keeping an active eye on out their competitors pricing. In fact, ASOS was offering up to 80% off almost everything on the site until Prime Day.

    Which were the popular categories that offered the most discounts?

    During Prime Day, shoppers saw tons of deals on essential gadgets. Tech deals were a massive hit and saw big discounts on everything from TVs, laptops, smartwatches, phones, and tablets. We look at the data we collected to see if we saw a similar trend. 

    Discounts on Amazon UK across categories
    Discounts on Amazon UK across categories
    • Amazon offered discounts across categories and reported that some of the best-selling categories were Consumer Electronics & Home. 
    • Our data too showed that the highest additional discounts were offered in electronics – Bluetooth Earphones at 18.4%, followed by Smartwatches at 14.9% and Laptops as well as Cameras, both at 12%.
    • Low discounts were offered on Alcohol, with Beer at 0.9% and Wine at 1.3%, respectively.
    • Relatively attractive discounts were seen in the Fashion & Beauty category – Sunglasses (9.1%), Shampoo (9.7%), & Watches (9.4%)
    Discounts on Amazon UK in the Electronics category
    Discounts on Amazon UK in the Electronics category

    Electronics being the hot favorite – we wanted to deep dive into the data and get more insights on Amazon’s pricing & discounting strategy here. Discounts can entice customers to buy more, encourage customer loyalty, or clear out old inventory. However, businesses must be careful since too much discounting can eat into profits. They also have to be mindful of which products should be discounted and by how much. 

    • Our data showed that the highest discounts (between 13 – 18%) were given on electronics priced between the £ 20-100 price range.
    • Electronics priced higher, between the £ 100 – 500 pound price range, were discounted less than 10%
    • However, high-value premium electronics over £ 500 were discounted slightly above 10%

    How did Amazon manage stock availability during Prime Day?

    Keeping track of inventory is especially important during big sales like Prime Day when thousands of customers are actively looking for deals.  There’s nothing worse than them finding the item they wanted is out of stock (OOS). OOS leads to lost sales, a situation that must be avoided at all costs. Read about how a small short term stock out on Amazon led to long term negative impacts for one of our customers. And let’s also look at the data and see what product availability looked like on Prime Day.

    • Overall, Amazon maintained robust availability across categories, and re-stocking was constant both before, during & after the event. 
    • Furniture, Fashion & Electronics had the highest availability. No surprise there since Amazon estimated that Home/ Furniture would be one of the best-selling categories.
    • Grocery saw average availability – perhaps cause some of these products are perishables, so it’s best to be mindful about overstocking.

    Which Brands Won on Prime Day?

    If there is one thing to remember about improving your product visibility on Amazon, it’s that it all boils down to the usage of the right keywords. Using relevant keywords makes your product appear higher up in search when customers are running searches on Amazon for those products. And the higher up a product appears in search, the higher the chances of a sale! 

    Let’s take a look at some popular categories and which brands had the highest Share of Search (SoS) during Prime Day.

    • Corona, San Miguel, and Becks were the top 3 brands optimized for the keyword Beer. However, what’s really important to note is both Corona & Becks had 20% SoS that was completely organic. San Miguel had a 20% SoS too, but it was sponsored ads that gave them this artificial boost. 
    • While a whole bunch of other brands had a 10% SoS most of them achieved this via Sponsored Ads. Youngever was the only brand that achieved this completely organically. They must have made sure they optimized key KPIs like content, ratings & reviews & product availability to achieve this result.
    • There were deep discounts on a wide range of Lenovo laptops. For example, the Lenovo IdeaPad duet Chromebook and Lenovo IdeaPad Flex 3 Chromebook were available at £100 off. Our data, too saw Lenovo & Asus fight for the top spot.
    • Asus sponsored 28% of products before Prime Day, hoping to capitalize on the pre-sale frenzy. During the event, they sponsored only 13% of products, bringing down their total SoS from 31% before the event to 13% during the event. 
    • Lenovo followed the opposite strategy; they sponsored just 6% of products before the event and during the event sponsored a whopping 25% which made them “almost” dominate the Laptop category during Prime Day.
    • Then there was Microsoft, with the highest SoS at 38%, of which all of it was organic!
    • The Smartphone SoS battle was clearly between Samsung & Xiaomi. Samsung was a consistent #1 at all 3 time periods (Before, During & After Prime Day) with the highest total SoS. Xiaomi came in at a close second. 
    • Samsung had an exciting strategy – they went heavy on sponsorships before and after the event. Their sponsored SoS was 31% & 39% respectively. And SoS of 13% during the event. 
    • Xiaomi’s strategy was just the opposite. Their sponsored SoS was 16% before the event. And 17% after the event, which was moderate compared to their Sponsored SoS during the event at 25%, which was much higher than Samsung’s 13%
    • Critical to note, Xiaomi’s organic search visibility before, during, and after the event was 0%. It definitely should be a concern area for any brand.
    Share of search
    Share of search
    • Both before & after the event, Cadbury had the highest visibility for the keyword Chocolate. During the event, they were not in the top 5 brands.
    • During Prime Day, Nestle won the top spot and had a 29% SoS. However, before the event, they were at #3 and after at #2. Artificially boosting visibility might’ve had something to do with this.

    Conclusion

    Prime Day sales reached an estimated 12 billion U.S. dollars worldwide, 9.8% higher than last year, making it the most successful shopping event in Amazon’s history. If you’re a brand selling on Amazon or a retailer trying to compete with Amazon, reach out to us at DataWeave to know how we can help!

  • UK Grocery Pricing Wars in 2022! A quick look at Pricing Data we gathered from 5 Grocery retailers in the UK

    UK Grocery Pricing Wars in 2022! A quick look at Pricing Data we gathered from 5 Grocery retailers in the UK

    Grocery sales in the UK are dominated by the “big four” – Tesco, Asda, Sainsbury’s, and Morrisons. A Statista report on these Grocery Giants as of May 2022 indicates that Tesco, Sainsbury’s, and Asda own approximately 27%, 15%, and 13% market share of grocery stores in the UK. Whereas Ocado and Symbols & Independent have the lowest market share, 1.8% each.

    However, the grocery delivery market is seeing a major shift because of new-age Quick Commerce companies that have swooped into the already crowded grocery space offering super-speedy home delivery! These new entrants added to the already competitive Grocery market & price wars intensified. Customers today rely on ultra-fast delivery services for their grocery requirements. For example, Berlin-based Gorillas charges £1.80 to deliver anything from a £7 pizza to a 30p apple — with no minimum order value. 

    Investors funded over £5B in grocery delivery apps such as Getir, Gorillas, Zapp, Fancy, Dija, Weezy, Jiffy, and Beelivery, in the UK. These rapid grocery delivery apps offer shorter delivery times, as low as 10 minutes, along with deep discounts to attract customers. For example, Gorillas, Weezy, and Getir all claim a 10-minute delivery time and offer promotional codes for the first couple of orders. Customers also get discounts for inviting friends and family.  

    To get more insight into the Grocery space in the UK, we tracked 5 Grocery retailers & Q-Commerce companies to try and understand trends wrt pricing in this competitive environment. Let’s take a look at what our data found & which retailer won the competitive pricing tug of war. 

    Methodology

    • Data Scrape time period: January 2022 – June 2022
    • Grocery Retailers tracked: Tesco & Ocado
    • Grocery Apps tracked: Gorillas, Weezy & Getir
    • Categories tracked: Alcohol, Drinks & Beverages, Fresh & Frozen, Grocery, Health & Wellness, Home Care, Packed Food & Snacks, and Smoke shop.

    Grocery Giants v/s Grocery Delivery apps – who was the Price Leader?

    Price leadership by category
    Price leadership by category
    Price leadership across months by Retailer
    Price leadership across months by Retailer

    We wanted to track and see which retailer was the Price Leader – i.e., had the most number of lower-priced items in a particular category. Our data clearly showed that the Grocery Giants Ocado & Tesco won hands down! Interestingly, Ocado launched a new Ad Campaign earlier in Jan this year about bringing value to the table for customers with quality products at affordable prices – seems like they’re taking this new promise very seriously! 

    • Tesco and Ocado were price leaders in maximum categories when compared to Gorillas, Weezy, and Getir. 
    • Between Tesco & Ocado, Ocado enjoyed price leadership across all these categories for 4 out of the 6 months we tracked pricing for. Tesco occupied the top slot for just the balance 2 months. 
    • Tesco was the price leader in the Alcohol category, with close to 40% of products priced the lowest compared to other retailers. They were also price leaders in the Smoke Shop category.
    • Ocado won price leadership for the remaining 6 categories, with a marginal gap between both retailers. 

    Watching Price Index Trends as inflation soars!

    Price index across monthsby Retailer
    Price index across months by Retailer

    The Guardian reports that Grocery inflation has hit a 13-year high in the UK, and food price rises could hit 15% by this summer – the highest level in more than 20 years. Meats, cereals, dairy, fruit & vegetables are likely to be the worst affected. Keeping this in mind, we tracked the Price Index (PI) across these 5 retailers to measure how prices changed over a 6 month period from Jan – June 2022. 

    Note: Retailers selling at the 100% mark were selling at an optimal price & did not undercut the market. The pricing sweet spot is 95% – 105%. Anything lower would compromise margins, and higher would mean the retailer was not competitive. 

    • Getir & Ocado had a Price Index that was the most optimal, sitting in the 95% – 105% range.
    • Gorillas had the lowest Price Index, between 88% – 90%.
    • Weezy has the highest Price Index – they were selling at a minimum 30% – 40% premium over other retailers! Perhaps it’s their quick delivery service that justified these super high prices? Unlike other apps with a lower delivery fee but longer delivery times, Weezy offers a 15-minute delivery service & customers seem to be willing to pay for convenience! Wheezy also has a delivery fee of £2.95, which is at least £1 more than other platforms.
      Supermarkets like Ocado are now playing catch up to compete with Q-Commerce and quick delivery services. Ocado has launched a new “Zoom” service promising delivery in 60 minutes, and Amazon is now delivering “same day” groceries (but both have a minimum spend of £15)

    Which Retailers were the quickest to make price changes?

    Average price change across months by Retailer
    Average price change across months by Retailer

    Competitive pricing is critical to winning the eCommerce race. Competitive pricing involves tracking your competitor’s pricing & strategically tweaking your own prices without hurting margins. We tracked the month-wise average Price change from Jan – June across all 5 retailers to see which retailer was making price changes and at what frequency. 

    • The main observation was – across all 6 months, all retailers were likely tracking each other’s prices and making minor price changes accordingly – the need of the hour in this hyper-competitive environment. 
    • Gorillas made significant changes to prices between Jan & Feb. And Getir in the May/ June time period. 

    Discounts & Promos in a turbulent UK Grocery Market

    Average discount across months by Retailer

    Although customer acquisition starts with building awareness, discounts are a proven way to attract customers quickly. When approached with the right strategy, promotional discounts can promote long-term customer loyalty, drive customer acquisition, and improve customer lifetime value. However, deep discounting can risk margins and create more problems than benefits. We wanted an insight into discounting trends in the Grocery space, so we looked at our data. Here’s what we saw:

    • Getir offered by far the highest discounts compared to Ocado & Gorillas. In fact, in most cases, they offered discounts close to 2-3% higher than the retailer with the 2nd highest discounts! 
    • Our data showed that Gorillas offered the lowest discounts. As reported in The Sun & other sources, newer Q-Commerce players like Gorillas have been showering users with discount codes, and that is why this data surprised us! 

    We went & looked back at the Price Index earlier in this blog, we noticed that Gorillas had a low price index overall, with most products priced at a 90%, way below other retailers. Perhaps this already lower price is why they’re offered very few discounts?

    Conclusion

    The UK grocery delivery market saw a huge rise in new retailers who are currently fighting for better discounts, competitive prices, and quick delivery. Although Tesco and Ocado were the price leaders in our findings, new players like Gorillas, Weezy, and Getir are attracting customers with quicker delivery times and low delivery costs. 

  • The Role of eCommerce in Sustainable Fashion

    The Role of eCommerce in Sustainable Fashion

    Today, environmental damage is rapidly occurring on a global scale. And there are many reasons and causes for this. Global warming is one, deforestation, over population are some others. The list is long. In a small way, the retail & clothing industry contributes to environmental damage too. The good news is that sustainable fashion addresses this issue. Sustainable clothing is designed using sustainable fabrics like organic cotton, hemp, and Pima cotton that have less of a negative impact on the planet. 

    sustainable clothing and its benefits
    Sustainable clothing and its benefits

    In this blog, we will discuss the rise of sustainable clothing and its benefits. We will also discuss marketplaces for sustainable fashion.

    Benefits of Sustainable Fashion

    a. Reduces carbon footprint

    The fashion industry emits numerous greenhouse gases annually. Most clothes are made from fossil fuels and require significantly more energy in production. Sustainable brands often use natural or recycled fabrics that require less chemical treatment, water, and energy. Organic fabrics such as linen, hemp, and organic cotton are biodegradable and environmentally sound.

    b. Saves animal lives

    Leather isn’t a by-product of the meat industry, and it’s estimated that it alone is slaughtering and killing over 430 million animals annually. Sustainable fashion brands are increasingly embracing the use of cruelty-free alternatives. Various alternatives include polyester made with ocean trash, plant-based compostable sneakers, bags from recycled seatbelts, silk created from yeast, and bio-fabricated vegan wool. Another interesting leather alternative comes from pineapples, where the fabric is produced using the leaves of pineapples.

    c. Requires less water

    Water is used in the dyeing and finishing process for nearly all items in the fashion industry. It takes 2,700 liters of water to produce a single T-shirt. Cotton is highly dependent on water but is usually grown in hot and dry areas. Linen, hemp, Refibra, and recycled fibers are some other sustainable fabrics that require little to no water during production.

    d. Supports safer working conditions

    Endless working hours, unacceptable health & safety conditions, and minimum wages, are the reality for most garment workers in the fast fashion sector. A few informative documentaries like “The True Cost” or “Fashion Factories Undercover” document the social injustices of the fast fashion industry. Eco-ethical brands advocate for sustainable fashion, health care, humane working conditions, and fair wages for their workers. 

    e. Healthy for people and the environment

    Fast fashion products often undergo an intense chemical process where 8,000 types of chemicals are used to bleach, dye, and wet process garments. Those chemicals often lead to diseases or fatal accidents for workers and inflict serious congenital disabilities on their children. These chemicals harm our health, as our skin absorbs anything we put on it.

    5 Sustainable & Ethical Online Marketplaces

    Here is a list of five earth-minded and socially responsible marketplaces that have sustainable and fair trade brands for the discerning and mindful shopper:

    1. thegreenlabels

    Netherlands-based webshop thegreenlabels is a sustainable fashion retailer that sells sneakers, womenswear, and accessories from various “green labels” brands. Founded in 2018, this is a marketplace where people can buy products from brands that care about a positive impact on the environment. All brands featured here guarantee fair working conditions and represent at least one of these 4 values – “CLEAN PROCESS” environmentally friendly production, clothes that support “LOCAL” communities, “VEGAN” brands to assure no animals were harmed and “WASTE REDUCTION”

    2. LVRSustainable

    LVRSustainable
    LVRSustainable

    Luisa Via Roma started as a family-owned boutique in the early 1900s. They have grown into a luxury e-retailer and created an LVRSustainable section for people trying to insert sustainability into their wardrobes. They have brands rated ‘Good’ or ‘Great.’ The site offers a wide range of products like bags, accessories, sports, shoes, lingerie, and much more for men, women, and kids. You can find organic, vegan, eco-friendly, ethical, and recycled & upcycled items here.

    3. Brothers We Stand

    Brothers We Stand
    Brothers We Stand

    Brothers We Stand is a retailer set up in solidarity with the people who make our clothes. This retailer conducts rigorous research to ensure that every product in their collection meets the following three standards: designed to please, ethical production, and created to last. It’s a great platform to shop for ethical and sustainable menswear. They also have their private clothing line along with other brands.

    4. Labell-D 

    Labell-D was launched with a clear mission to reduce the negative impact of fast fashion on the planet. This retailer wants to make Responsible Fashion the new norm. They intend to make sustainable clothing and fashion easy for both brands and consumers. Labell-D has a transparent accreditation process where they evaluate the brand’s carbon footprint and environmental impact. Their verification assessment includes animal welfare, emissions, materials, production processes, chemical usage, waste management, and traceability.

    5. Cerqular

    Cerqular wants to make sustainable shopping affordable and accessible for all. The retailer promises that every product and seller is verified as organic, recycled, sustainable, carbon-neutral, eco-friendly, vegan, or circular. They have a wide range of sellers and do not limit products only from luxury brands, so sustainable shopping is no longer expensive or inconvenient.

    Conclusion

    The fashion industry is a contributor to worldwide carbon emissions. Sustainable fashion is the new big thing giving rise to more and more sustainable brands and marketplaces. 

    To stand out and shine in the crowded eCommerce space is not easy. Having a robust Digital Shelf becomes critical for brands. A brand’s Digital Shelf is all of the ways their customers digitally interact with the brand, not only on marketplaces but on the brand’s DTC website & shoppable social media. This is why brands need to closely track & optimize their Digital Shelf KPIs like assortment, availability, pricing, ratings & reviews, product discoverability & product content to increase their online sales.

    Want to learn how DataWeave can help you win the Digital Shelf? Sign up for a demo with our team to know more.

  • U.S. Prime Day Deals 2022: Promotion Intelligence First Look

    U.S. Prime Day Deals 2022: Promotion Intelligence First Look

    As inflation hits another 40-year high at 9.1 percent, U.S. consumers geared up for their first sign of hope and relief in the form of anticipated discount buys – 2022 Amazon Prime Days, or so we thought. While Prime Days have grown to become a promotional period almost as important as Black Friday to digital shoppers, the combination of economic uncertainty, inflationary pressures, and supply chain challenges seemed to alter the discount strategy expected given activity seen during 2021 Prime Days.

    Our analyst team has been hard at work aiming to provide a ‘first look’ at 2022 Prime Day Promotional Insights, tracking discounts offered across 46,000+ SKUs within key categories like Electronics, Clothing, Health & Beauty and Home, on seven major retailer websites – Amazon, Target, Best Buy, Sephora, Ulta, Lowe’s and Home Depot. Our analysis compares prices seen during Amazon Prime Day 2022 on July 12th, to pre-Prime Day maximum value prices seen in the ten days leading up to Prime Days, to determine the average change in discounts offered during the promotional period. Below is a summary of our findings.

    Competitive Promotions Give Amazon a Run for their Money

    Amazon offered the greatest average discount enhancements for Electronics at 5.6 percent followed by Health & Beauty items at 5.1 percent, and Home products at 4.2 percent versus pre-Prime Day discounts seen across the categories considered within our analysis. The only category reviewed where average discounts were greater on a competitor’s website was on Target.com within the Clothing category. As seen below, Clothing on Target.com average discounts were 6.8 percent greater than pre-Prime Day offers, which was 2.6 percent higher than the average discounts offered for Clothing on Amazon.

    Target Capitalizes on Growth Opportunity in Clothing Category

    Diving deeper into the details of where Target won within the Clothing category, you can see a majority of their promotional activity took place within Women’s Accessories where discounts offered were 18.5 percent greater than those seen pre-Prime Day 2022, which was almost 15 percent greater than the discount enhancements seen on Amazon for Women’s Accessories. In fact, Women’s Shoes and Sneakers were the only two categories where the average discounts offered were greater on Amazon than on Target.com.

    Overall, the discounts offered on Target.com within the Clothing category were primarily concentrated within items priced $40 and lower, but what was most interesting is that within the $10 and under price bucket, Target offered average discounts of over 11 percent whereas Amazon increased prices for these items on average by over 9 percent.

    While most of the Clothing available on both Amazon and Target.com during Prime Days 2022 were offered without a price change, the greatest discount percentages tracked were within the range of 10-25 percent off on Amazon whereas Target chose to offer the bulk of their promotions at 25 percent off an up.

    Strategic Promotional Strategies Defined at the Electronics Subcategory Level

    When it comes to the Electronics category on Prime Day, the big question is always who will win the battle of the brands. Below shows the difference in average pricing and promotions discounts offered between products manufactured by Samsung versus Apple across each retailer platform, noting discounts were almost 3 percent greater on average for Apple versus Samsung products on Amazon, and Apple discounts were almost 5 percent greater on Amazon versus than those seen on Target.com.

    Amazon wasn’t going all in on Apple however, as we saw ‘Alexa’ devices (Amazon products) available on Best Buy and Target websites also, but the discounts were almost 4 percent greater on Amazon versus Target and over 7 percent greater than the discounts seen on BestBuy.com.

    While the average discounts offered within the Electronics category were greatest on Amazon (5.6 percent) versus Best Buy (3.9 percent) and Target (3.4 percent) as noted within the first chart of this blog and across brands and technologies considered above, the discounts offered on Amazon were strategically focused between 10-25 percent as seen below.

    Amazon’s Electronics promotions were also targeted at smaller price points, items priced between $20-500, whereas Best Buy and Target offered greater promotions for electronics priced $500 and up than Amazon.

    Below is a snapshot of price buckets tracked for Electronics available on BestBuy.com, highlighting where most of the promotional activity was targeted at products priced $50 and up during Prime Days 2022, with discounts ranging from 10 percent up to greater than 25 percent greater than pre-Prime day prices.

    The standout categories were TVs on Target.com with discounts averaging nearly 12 percent greater than those seen pre-Prime day, and smartphones on BestBuy.com with discounts averaging just over 11 percent greater than those seen pre-Prime Day. The category with the greatest average discount enhancements seen on Amazon during Prime Days 2022 was for Wireless Headphones with an average discount of 8.7 percent.

    Home is Where Amazon’s Heart Was on Prime Day

    Amazon dominated offers within the Home categories, especially for products within mid ($40-100) and higher price ranges (items priced $200-500), with the bulk of the discounts offered between 10-25 percent. There was little to no promotional activity seen across all price points on Lowe’s or Home Depot’s websites within the categories we tracked, and most other competitive offers on Home products were seen on BestBuy.com for products priced from $50-500. Even a subcategory like Tools offered deeper average discounts on Amazon (4.7 percent) than discounts seen on HomeDepot.com (1.1 percent) or Lowes.com (0 percent).

    For Large Appliances, Amazon was the only retailer to off any significant discount across each major subcategory with the greatest average discount being on Ovens at 6 percent, followed by Refrigerators at 4 percent. One caveat with this category, when we reviewed Large Appliance prices two weeks prior to Prime Days, we saw average price increases around 16.7 percent occurring on Amazon.

    During Prime Days 2022 however, Amazon also offered top average discounts for small appliances, except for on Instant Pots which appeared to have greater average discounts on Target.com (5.9 percent versus 4.2 percent on Amazon), and Vacuum Cleaners which appeared to have the best promotion of appliances small and large at 13.8 percent average discount on BestBuy.com. Another subcategory deeply discounted on BestBuy.com was weighted blankets, which averaged discounts around 18.5 percent versus Amazon’s average discount at only 6.2 percent.

    Health & Beauty Retailer Pricing Strategies Revealed

    Given the importance Health & Beauty Brands placed on Prime Day sales last year, we had anticipated to see more offers, especially within pure-play beauty retail channels, than we did for this booming category.

    Amazon drove most of the Health & Beauty offers seen averaging 5.1% discounts versus other retailers only offering less than 1% on average, but discounts were aimed at a targeted group of SKUs on Amazon, bringing the average discount lower overall. Most of the promotions offered on Amazon fell within mid-range price points ($20-50) and were discounted between 10-25 percent versus pre-Prime Day prices.

    Target.com offered the most comparable discounts to Amazon for Health & Beauty products on average, but their strategy primarily focused on items within the $20 and lower price range with discounts ranging primarily between 10-25 percent.

    More 2022 Prime Day Insights Coming Soon

    We know the significance visibility to critical pricing and promotional insights play in enabling retailers and brands to offer the right discounts to stay competitive, especially during promotional periods like Prime Days. While this blog is intended to provide a ‘sneak peek’ into 2022 Prime Day insights for the U.S. market, we will be providing more extensive, global coverage and will proactively share new insights with the marketplace as they become available throughout the month of July.

    Be sure to also check out our Press page for access to the latest media coverage on Prime Day insights and more. Don’t hesitate to reach out to our team if there is any particular category you are interested in seeing in more detail, or for access to more information on our Commerce Intelligence and Digital Shelf solutions.

  • The challenges in scaling a ‘House of Brands’

    The challenges in scaling a ‘House of Brands’

    Let’s start with the basics – what is a ‘House of Brands.’

    House of Brands is a portfolio management strategy that defines how a family of brands owned by one parent company, each independent of one another and each with its own audience, marketing, look & feel operate in harmony with each other. 

    Advantages of a House of Brands Strategy

    • The Profit Playbook: The playbook generated by the success of one brand can be leveraged to scale other brands.
    • Economies of Scale: Cost across Marketing, Supply chain, Advertising, and Operations gets shared across multiple brands helping optimize costs.
    • Market Coverage: Multiple products enable brands to cover multiple market niches and audiences while maintaining unique messaging for each niche. 
    • Future-Proofing: By hedging bets across multiple brands, it cushions the parent company against changes in customer preferences and trends. 

    … for these reasons and more, it’s no surprise that every digital-first consumer brand today aspires to leverage a portfolio strategy to become a House of Brands.

    More and more companies are slowly adopting this strategy

    • In the US the brands like P&G, Newell, and Unilever which found early success in the online space are quickly acquiring more brands and betting on the “House of Brands” strategy to scale.
    • In India, Unicorn D2C start-ups like MamaEarth, Good Glamm Group, Sugar Cosmetics, Rebel, Boat, and Lenskart to name a few, are already knee-deep into this strategy as their brand portfolio keeps growing.
    • And then there are brand roll-ups like Thrasio, Perch, HeyDay in the USA, Branded, Hero in the UK and Mensa, and GlobalBees in India which started as a House of Brands from the get-go.

    More Brands. More Data. More need for Monitoring!

    You cannot improve what you cannot measure! In order to scale these brands, the first thing needed is DATA. Data across all digital platforms – data on social media performance, customer engagement, eCommerce sales, product stock availability, pricing, reviews, and customer sentiment to name a few. This data will unlock huge value for brands and it gives them a sense of what’s working and what needs to be improved in order to increase sales & scale. 

    All brands need to track this information – but here’s a challenge unique to a House of Brands – it is the sheer volume & scale of data needed across multiple brands across multiple digital platforms! For example, a House of Brands with let’s say 10+ brands, each brand with 50 SKUs, selling on 10 eCommerce platforms is the equivalent of managing 10 retail shops with 500 SKUs! 

    Let’s look at some of the questions the analytics, marketing, and brand management teams at House Of Brands would ask. And the data they would need almost on a daily basis for every single brand. 

    • What is the search ranking for all of our SKUs across each and every single eCommerce store it is available on? How does this benchmark to the closest competitor? And are competitors using aggressive advertising strategies to outperform & overshadow our SKUs?
    • Are competitors offering discounts? Are those discounts higher than what we’re offering leading customers to purchase their products instead of ours?
    • Are my products & SKUs available and not out of stock across every single marketplace and online store?
    • Are positive ratings & reviews driving my customers to purchase my product? Or do our competitors have a better customer perception than my brand does?
    • Are Amazon and other marketplaces displaying my product content correctly so customers have enough information to make an informed purchase decision?

    … if the sheer scale across multiple brands was not a big enough challenge when this data needs to be tracked hyper-locally for each brand, it becomes anyone’s worst data nightmare!

    Need Data? Lots of it? No problem!

    To get ample data, across key KPIs brands need to invest in a Digital Shelf Solution. However, traditional Digital Shelf Solutions were built for brands that got a majority of their revenue from in-store sales and only a part of their revenue was being generated online. 

    That’s where DataWeave is different. DataWeave’s AI-Powered Digital Shelf Solutions was built with Digital Native brands in mind. 

    What KPIs do we help House of Brands track?

    • Keyword Search Ranking: Track & improve your search rankings for priority keywords. Boost product visibility and sales
    Keyword Analysis
    Keyword Analysis
    • Content: Optimize your brand’s product content to drive up conversions
    Content Quality Analysis
    Content Quality Analysis
    Availability Analysis
    Availability Analysis

    The following metrics are available to view in one single dashboard, across multiple online stores and multiple geographies making it so easy to get a consolidated view of the health of the entire portfolio of products! What’s more, we’ve created a dashboard with multiple views – brand-wise, function-wise & even hierarchy-wise. This means a brand manager can see all KPIs specific for only the brand they manage, while the marketing team can look at keyword search rankings across all brands and the leadership team can see a brand-level daily scorecard for a quick health check. And that’s not all! Our dashboard highlights insights that can be “actioned asap” to make it easier to understand what critical tweaks and changes can help improve sales. Lastly, as a House of Brands adds more Brands & SKUs to its portfolio, our solution has the full flexibility to add and delete SKUs on the go!

    If you are a House of Brand and wish to explore how some of the problems you face daily can be solved – please email: contact@dataweave.com.

    Brand Roll-Ups and House of Brands are always scouting for new brands to acquire. DataWeave has a unique product to help you track a category daily, highlighting brands that show exceptional KPIs across – Ranking, Reviews, Ratings, Bestseller ranks, Sales Estimates, etc. Read more about how VC’s & Brand Rolls up are using Data for faster Acquisitions. 

  • How short term Stockouts on Amazon can have a long-term impact on your eCommerce business

    How short term Stockouts on Amazon can have a long-term impact on your eCommerce business

    It’s common knowledge that upward of 70% of Amazon customers never scroll past the first page of search results. And that the first 3 products garner 64% of business generated. This is why it is critical for brands and businesses to make sure they rank well on Amazon. A good search ranking helps customers find your product with ease. And findability is fundamental! Having a better ranking is also a driver of the “flywheel effect” at online retailers. According to this effect, products that sell more tend to rank better in search results, and products that rank well in search results tend to sell more.

    Negative impact on Sales Ranking due to Stockouts

    If you want to stay on top of search rankings on Amazon, one of the things you need to keep an eye out for is your product stock availability. It’s not the ideal customer experience to have to click on a product listing only to find out it’s currently out of stock. This is why Amazon will not rank products at the top if they’re not available & customers cannot buy them immediately. Not only does this lead to a lost sale for a brand, to make things worse, but customers also end up buying a competitor’s product instead. 

    We were tracking product availability on Amazon for one of our customers in the CPG space. We tracked availability for products across varying ranks & looked at how going out of stock impacted their search rankings.

    Product Availability
    Product Availability

    Impact on products with a Search Rank between 1 to 10:

    • Our data showed that when products that ranked between 1-10 went out of stock for just 1 day, their rank fell by over 28%. After 3+ days of being out of stock, their rank fell by 83% and after being out of stock for over 10 days their rank fell by close to 150%! 
    • This clearly illustrates that when the longer top-ranking products are out of stock, the greater the impact on search rank and product discoverability.

    Impact on products with a Search Rank between 10 to 20:

    • The impact of being out of stock on products that ranked lower, i.e. between 10 to 20 was much lower. After being stocked out for 1 day, the ranking fell 17% compared to the 28% dip for products in the top 10 ranks. 
    • Incremental change was minimal, too. After 3+ days of being OOS, search rank dropped by 22% and by 53% after 10+ days v/s close to 150% for higher-ranked products.

    Impact on products with a Search Rank between 20 to 30:

    • These products had the least impact but there was an impact nonetheless. 
    • After being OOS for 5+ days, search ranking fell by close to 8% and to close to 30% after 10 days of product unavailability.

    Avoiding Stockouts with better Inventory Management

    Customers can buy your product only when it is available. Failing to provide products at the right time will lead to losing sales to your competitors. If your products become unavailable, you’ll notice a drop in customers’ overall satisfaction and shopping experience too in addition to a lost sales rank on Amazon. In fact, your reputation and sales will take a beating long term in case of consistent product unavailability. Moreover, once the product is back in stock, the climb back to the top ranking is a slow and not-so-easy process. This is why brands need to maximize conversions by tracking product availability on a constant basis.

    Conclusion

    Stockout is a critical issue that has a significant impact on sales, brand image, and customer loyalty. Items ranking higher on eCommerce platforms take the biggest hit when they get out of stock. Brands can recapture their search share after restocking their inventory. 

    Want to maximize sales by reducing latency periods between stock replenishment? Reach out to our Digital Shelf Experts to know how! 

  • Feminine Hygiene Products Face Supply Chain Shortage and Price Increases

    Feminine Hygiene Products Face Supply Chain Shortage and Price Increases

    Last week the DataWeave analytics team identified the states most impacted by the baby formula shortage, only to see feminine hygiene products following similar trends with price increases occurring alongside a supply chain shortage. In this analysis, the team has identified over four hundred feminine hygiene products made available across eighteen retailer and delivery intermediary websites from August 2021 through June 2022, to see how product availability and price changes correlated.

    Within the feminine care products analyzed, both tampons and sanitary pads show to have under 58% availability as of June 2022. For sanitary pads, June 2022 shows the lowest level of product availability at around 58%, which has steadily declined each month from August 2021 where product availability started around 69%. Tampons however, reached their lowest level of availability in April 2022 at 45%, and appear to be slowly recovering each month, now reaching around 53% availability in June 2022.

    Product Availability for Feminine Care Products - June 2022
    Product Availability for Feminine Care Products – June 2022

    The Evolution of the Tampon Shortage by Retailer

    Looking at tampons in more detail and at a retail level, we can see how much and how often product availability fluctuated from August 2021 through June 2022 across Kroger, Meijer, Baker’s Plus, Target and Walmart websites. Baker’s Plus, for example, shows the lowest product availability, maintaining an average of around 39% from October 2021 through June 2022. Kroger appears to be a notable exception only facing stock availability issues in March and April 2022, achieving nearly 78% availability in June 2022, which is 16% greater than the other retailers analyzed.

    Product Availability for Tampons by Retailer - June 2022
    Product Availability for Tampons by Retailer – June 2022

    Feminine Care Product Price Changes Over Time

    When looking at Pricing Intelligence insights and average price changes occurring alongside declining product availability for tampons and sanitary pads combined, we see a very different story. Tampons have seen steep price hikes from December 2021 onward, increasing the most in June 2022, up 6% compared to prices seen in November 2021. This steep price increase could be attributed to consistently low availability for tampons that has been seen in recent months.

    To the contrary, sanitary pads have seen a price reduction of around 1.25% as of June 2022 compared to average prices seen in November 2021. While prices are lower in June 2022 for sanitary pads, the percentage by which they are lower is shrinking in recent months, potentially for the same reasons related to decreasing product availability.

    Price Change for Feminine Care Products - June 2022
    Price Change for Feminine Care Products – June 2022

    When looking at month-over-month average price changes for tampons only, we can clearly identify which months had the biggest price changes, noting price hikes that lead to the currently high prices seen in June 2022. In March and May 2022, over 10% of tampons offered had seen a price increase, and around 8% had seen significant price increases of more than 10%.

    Month-Over-Month Price Changes for Tampons - June 2022
    Month-Over-Month Price Changes for Tampons – June 2022

    eCommerce Intelligence Provides Early Visibility to Evolving Trends

    Price increases don’t seem to be stopping anytime soon given there was a 3.6% price hike seen on average in May 2022 versus April, with June seeing yet another .6% increase from May’s prices. That being said, as the market evolves and feminine hygiene products stabilize, our team will continue to provide visibility to critical pricing and product availability changes to enable our clients to stay ahead of the curve.

    From a baby formula shortage to a tampon shortage, what category will be next to follow the supply chain shortage trend? Follow our blog for access to the latest insights and be sure to reach out to our team if there is any particular category you are interested in tracking next, or for access to more information on our Commerce Intelligence and Digital Shelf solutions.

  • 7 Key Metrics that QSRs want (but may not get) from Food Delivery Apps

    7 Key Metrics that QSRs want (but may not get) from Food Delivery Apps

    The Quick Service Restaurant market is projected to be valued at $691 billion by 2022. As the QSR industry grows and the market becomes even more competitive, restaurant chains continuously seek ways to increase sales via food aggregators to market their business. To improve ROI and sales, having data and insights into key metrics could help QSRs to boost their success rate.

    QSRs would like to know how they stack up against their competition regarding discoverability on cluttered food aggregator apps. Restaurants want to know the gaps in their product assortment to understand what drives customers to their competitors. Getting insights into delivery time and competitors’ delivery fees will help QSR improve delivery ETAs and optimize fees. They can also set competitive pricing with insights into their competitors’ pricing. In addition, they can use data to optimize their ad spending on food apps and improve marketing ROI.

    In this blog, we will discuss the relationship between QSRs and food aggregators and how getting data about key metrics from these food delivery platforms can help QSRs scale their revenue. 

    Data: The Key Ingredient to increasing sales

    According to Statista, online food ordering revenue is expected to grow at a robust CAGR of 10.39% between 2021 and 2025. Food Aggregators apps like Uber Eats, DoorDash, and GrubHub offer convenient meal delivery options from various QSRs within a single app. Food aggregators provide a multitude of benefits for QSRs. They give access to a huge customer base, quick delivery, and an easy entry into quick commerce, helping QSRs increase visibility. Although QSRs rely on food aggregator platforms for hassle-free ordering, tracking, and delivery, they can’t always rely on them to share critical data that could help them optimize their operations & increase sales. 

    Online food ordering revenue
    Online food ordering revenue

    1. Data on Product Assortment

    QSRs need assortment insights to understand their competitor’s menu assortment. Assortment analytics plays a crucial role in ensuring that QSRs aren’t losing sales because their competitors are offering cuisines and dishes that they aren’t. Understanding gaps in menus helps QSRs to better plan their menu. However, food aggregator apps can’t share competitors’ assortment data with QSRs for a multitude of reasons, guidelines, and privacy laws. Thankfully, at DataWeave, our QSR intelligence solution can! We help restaurants improve their assortment by sharing insights into the dishes and cuisines their competitors’ have on display.

    Menu Assortment
    Menu Assortment

    2. Data on QSR Discoverability

    QSRs would love to know how to increase discoverability on food aggregators, as it will help them to appear ahead in search results and beat the competition. Improving visibility on these apps directly impacts sales and drives more orders for restaurants. Some aggregators offer discoverability information but give it on demand, usually after 20-30 days, making it irrelevant due to the enormous time gap. They also don’t provide information about the change in the discoverability of your competition. All these data points are so critical, and understandably so, Food Apps can’t share this level of information with restaurants. However, DataWeave’s QSR Intelligence solution can! It provides real-time discoverability insights into your restaurant and competitor’s visibility so that the data is actionable, and QSRs can use insights to improve visibility. 

    Read how DataWeave’s QSR Intelligence helped an American QSR Chain and how their ranking on search results page on Ube rEats, DoorDash & Grubhub impacted outlet discoverability & sales!

    3. Data on Pricing & Promotions

    Pricing a QSR’s menu is tricky. If you price too high, you’ll turn off new customers. If you price too low, you’ll cut margins & may even come off as low-qualify. Customer Price Perception is greatly influenced by the Price-Quality relationship. To add to this, restaurants are often up against stiff competition from restaurants with similar cuisine offerings so it’s critical that prices are competitive. Understanding competitor pricing doesn’t imply that you have to beat their prices. You can compensate for any price differences by offering higher quality cuisines, better customer service, and quicker delivery. Once again, food apps can’t share competitors’ pricing data with QSRs. But DataWeave’s QSR & Pricing Intelligence solution can! QSRs can use these insights to drive more revenue & margins by pricing their menu right.

    4. Data on Delivery Time

    QSRs must be able to deliver hot meals, in a timely manner to customers because customers want to quickly dig into the delicious food they ordered. Quicker deliveries within the ETA will also help earn the trust and loyalty of customers. However, food aggregators don’t share information on the delivery times with restaurants – not their own delivery time or their competitors. DataWeave can help QSRs to understand their peak hours and optimize their service to ensure quick ETAs. They can also get detailed insights into competitors’ delivery times to make sure they’re competitive. This is important because customers will often pick restaurants with quicker ETAs.


    Read how DataWeave’s QSR Intelligence helped an American QSR Chain understand the correlation between delivery time & sales volumes

    Delivery time trend by urbanity
    Delivery time trend by urbanity

    5. Data on Delivery Fee

    As a thumb rule, customers will always compare delivery fees across apps. They’re conscious of delivery dollars included in their bill and often choose a restaurant with lesser delivery fees. This makes it even more critical for restaurants to understand how they stack up against their competitors. Understanding competitors’ delivery fees could potentially help QSRs to optimize their rates. And once again, food aggregators can’t share information on competitors’ delivery fees with restaurants. However, DataWeave’s QSR Intelligence can provide all delivery-related insights – be it Delivery etas or fees. 

    Delivery fee trend by urbanity
    Delivery fee trend by urbanity

    6. Data on Ad Performance & ROI

    Getting ad analytics will help QSRs better manage their budgets & increase the ROI on their Ad spends. For example, wouldn’t it be great if QSRs were able to understand which ad formats or promotions led to the most sales? Or which carousal ads had the most visibility in key zip codes where your QSR is expected to do maximum business? Or even insights into a competitor’s ads and promotions on food apps. Knowing this information will help restaurants spend sensibly when buying media on Food Apps & get the most bang for their advertising buck. Food apps do provide standard ad analytics – a number of clicks, CTR, and so on, but for more complex, insightful & actionable insights, there’s DataWeave’s QSR Intelligence! 

    Read how DataWeave’s QSR Intelligence helped an American QSR Chain understand the ROI delivered on ad spends across Food Delivery apps.

    Insightful & actionable insights for QSR Chains
    Insightful & actionable insights for QSR Chains
    Insightful & actionable insights for QSR Chains
    Insightful & actionable insights for QSR Chains

    7. Data on Outlet Availability / Availability Audit

    To avoid lost sales, being available & “open for business” on Food Apps during peak lunch & dinner hours is critical. Also on weekends, when order volumes are usually high. Sometimes because of technical glitches, QSR outlets appear unavailable on Food Apps. A glitch like that can lead to lost business, and the longer the glitch stays undiscovered, the greater the impact on revenue. While Food Aggregators do their best to make sure all QSRs are up and running on their app, using DataWeave’s QSR Intelligence, restaurants can now do an outlet audit to make sure that’s the case. With just a mere 2.8% unavailability, we saw a 28% drop in the sales for one of our QSR customers! That’s how critical Availability insights are. 

    Conclusion

    Analyzing and optimizing sales, delivery, discoverability, availability & customer data is one of the fastest ways to help grow your QSRs revenue. However, the biggest challenge QSRs face is that it isn’t always easy to get this information. With DataWeave’s QSR Intelligence now some of that data is a little more accessible as we discussed in this blog. And additionally, here are the 7 Tricks we recommend QSRs to use to win on Food Apps. 

  • Baby Formula Shortage Continues Alongside National Price Increases – June 2022

    Baby Formula Shortage Continues Alongside National Price Increases – June 2022

    As the baby formula shortage continues, retailers and brands are working quickly to meet evolving consumer demand, considering supply chain driven headwinds, a baby formula recall, and inflationary-driven impacts. The DataWeave analytics team has actively tracked marketplace changes, alongside reports from the FDA, for the baby formula category at a state-level, and has shared the latest snapshot of product availability through June 7th, 2022, below.

    Average Baby Formula Product Availability by State - June 2022
    Average Baby Formula Product Availability by State – June 2022

    While the U.S. has reached an average of 84% baby formula availability the first week of June 2022, given recent news headlines related to the baby formula shortage, and tracking out of stock encounters by state, we see a continued decline in availability throughout the Midwest versus product availability levels seen in May 2022.

    Wisconsin, Michigan, Illinois, Indiana, Ohio, and Kentucky all show average availability for baby formula to be less than 50%, with Wisconsin being impacted the most at less than 18% average availability. While Texas shows an average availability improvement of 3.5% from the first two weeks of May 2022 to the first week of June 2022 as noted in the below chart, availability is also very low overall at less than 60%.

    Average Change in Baby Formula Product Availability by State: May-June 2022
    Average Change in Baby Formula Product Availability by State – May 2022 to June 2022

    Outside of the Midwest and Texas, the other states for consumers to be cautious in are California, Virginia, and South Carolina as their month-over-month average change in availability also declined 4%, 12.6% and 8.2% respectively. Below is a snapshot of where the baby formula availability average started as of May 1st through the 15th, 2022.

    Average Baby Formula Product Availability by State - May 2022
    Average Baby Formula Product Availability by State – May 2022

    Baby Formula Product Availability Changes – March 2021 through May 2022

    At an aggregated level overall, the availability for baby formula was relatively stable across all retailers considered within our analysis from March 2021 through September 2021, but has been on a steady decline ever since, starting at 81.7% availability in September and ending at 53.8% availability in May 2022 as noted in the below chart.

    Monthly Average Availability for Baby Formula Across Major Retailer Websites
    Monthly Average Availability for Baby Formula Across Major Retailer Websites

    Looking at baby formula availability at a retail level, we saw yet again not all availability challenges were alike, by month or retailer. Costco.com lead the other retailers within our analysis for greatest average availability from March 2021 through May 2022, but had one of the lowest availability percentages at 62.7% in May 2021, and dropped to the lowest availability of the group in May 2022 at 37.5%.

    Average Availability for Baby Formula Across Major Retailer Websites
    Average Availability for Baby Formula Across Major Retailer Websites

    Baby Formula Prices Increase as Availability Changes

    While unnecessary price gouging is prohibited, price increases are still happening at a slow and steady rate across all the accounts included within our Pricing Intelligence analysis given external market factors outside of baby formula recall related stockout scenarios.

    Kroger.com experienced the greatest average price increases overall, with the peak being in May 2022 at a 19% increase, 8% higher than other retailers on average, versus prices seen in March 2021 for the same baby formula products. The most significant price hike occurred on Kroger.com from December 2021 to January 2022. Other retailers like H-E-B, Target and Wegman’s have had minimal price changes from March 2021 through May 2022. 

    Average Price Inflation for Baby Formula, Indexed to March 2021
    Average Price Inflation for Baby Formula, Indexed to March 2021

    Address the Baby Formula Shortage With eCommerce Intelligence

    As the market continues to evolve and baby formula supply works its way to catching back up to demand, our team will continue providing critical pricing, merchandising, and competitive insights at scale, to enable retailers and brands to develop data-driven growth strategies that directly influence their eCommerce performance, accelerate revenue growth and drive profitability.

    Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis, or for more information on our Commerce Intelligence and Digital Shelf solutions, and let us know what other category insights you’d be interested in seeing this year.

  • eCommerce in South Africa: Data-Driven approach to getting ahead

    eCommerce in South Africa: Data-Driven approach to getting ahead

    What an exciting month we’ve had at DataWeave! Our team flew down to gorgeous Cape Town, South Africa to attend the 8th edition of #EcomAfrica! After months of Zoom calls and virtual events, it was a refreshing change to see our customers in person and meet some of the movers and shakers in eCommerce and some of the top South African brands. 

    Top eCommerce Companies in South Africa
    Top eCommerce Companies in South Africa

    My last visit to South Africa was before the pandemic. Things have changed since then, & the difference was stark! The eCommerce landscape had a paradigm shift during Covid-19 and grew exponentially. My customers spoke to me about the new opportunities, growth potential as well as challenges that came in because of this boom. For one, eCommerce in South Africa has become more competitive than ever – from online retail to grocery and food delivery to even alcohol delivery! All retail businesses seem to have jumped onto the eCommerce bandwagon.

    A recent Deloitte report found that over 70% of South Africans shop online at least once a month & 2 out of 3 respondents said they plan to increase their frequency of online shopping. 65% said they know what they want, search online & check all stores that stock the product to compare prices. Price is one of the key factors that influence consumer purchase decisions. Other critical factors include delivery fee, delivery time, promotions & discounts & product assortment to name a few. In order to stay ahead in this highly competitive arena, both retailers and brands need to make data-driven decisions about critical KPIs like pricing to stay ahead of the competition.

    Increased Online Shopping & Online Shopping Frequency
    Increased Online Shopping & Online Shopping Frequency

    We’ve been working with customers in South Africa for over 4 years now, even before the pandemic. So on Day 2 of the event – S.Krishnan Thyagarajan “Krish”, President & COO, Dataweave had a chance to share our learnings and experience from all these years and how user data is critical to getting ahead & winning the eCommerce race in South Africa.

    For the purpose of Krish’s keynote address, we tracked pricing insights for a finite set of categories across key South African retailers like Checkers, Pick n Pay, EveryShop, Incredible, Makro, Waltons, Shoprite & Dis-Chem to name a few over a period of 16 months from Dec 2020 to April 2022. We highlighted price increase and decrease opportunities and how each retailer reacted in order to stay competitive, increase sales and protect margins. 

    BATTLE of the eCommerce GIANTS!

    Key Highlights from the Keynote

    • Increasing prices where an opportunity exists helps retailers increase their margins exponentially. Pick n Pay had the highest action rate (73%) when it came to capitalizing on price increase opportunities v/s Dis-Chem at 11%. 
    • When it came to price decrease opportunities (in order to stay competitive with rival brands) Takealot was the most responsive retailer – they capitalized on 30% of the opportunities, followed by Pick n Pay at a close second (28%) and Shoprite & Dis-Chem at just 4%.
    • Most retailers took between 1 – 5 days maximum to make price changes which means responsiveness to the market among all retailers is high making it more important for online retailers to always be on their toes.  
    • The 2 categories where most retailers capitalized on Price Increase Opportunities were Sauces & Condiments and Crackers & Biscuits.

    Want to watch the Keynote video on Demand? Click here to register & watch.

    Price Increase & Decrease Opportunities
    Price Increase & Decrease Opportunities

    Bonus video content! 

    • Watch the Impact of price increase & decrease opportunities on Private Label brands! 
    • See how product stock availability impacts price changes over a 16-month period. 
    • Find out which brands are in the lead in the Skin Care, Pet, Baby, Laundry & Cleaning Aid categories 

    If you’re an online retailer in South Africa & need insights on staying competitive with the right pricing, product assortment, delivery time, delivery rates, and the other key influencers that affect customers’ choice of online retailers, sign up for a demo with our team at DataWeave to know how we can help!  

  • The Rise of South African eCommerce : The Growth, & the Future

    The Rise of South African eCommerce : The Growth, & the Future

    2020 onwards, the South African economy was crippled due to the pandemic and lockdowns. However, according to StatsSA, South Africa’s online retail market share grew to 2.8% in 2020, double that in 2018. After the pandemic, South Africa’s eCommerce industry grew by 66% in 2020 compared to the year before. This increase was primarily because of restrictions on traditional stores that led to a 30% reduction in in-store purchases. 
    According to a Deloitte study, over 70% of South Africans shop online at least once a month because of convenience. Household appliances, footwear, clothing, electronics, and health products are the most popular categories among South African online customers.

    Top Categories
    South African Ecommerce
    South African Ecommerce


    These eCommerce stores account for 15% of online revenue in South Africa

    1. Takealot.com: Revenue US$602 million 
    2. Superbalist.com: Revenue US$85 million 
    3. Woolworths.co.za: Revenye US$57 million

    In this blog, we will discuss emerging eCommerce trends in South Africa and their impact on the various retail segments. 

    Trends to watch in 2022

    Trends to watch
    Trends to watch

    1. Quick commerce

    Quick delivery, especially when it comes to groceries, medicines, and food has become a customer expectation now. Q-commerce, a trend that capitalizes on optimizing delivery time, has become common in food tech companies and is now gaining traction in grocery delivery too, especially after the pandemic. UberEats, Checkers, Pick ‘n Pay, and Jumia is some of the country’s biggest Q-commerce players.

    2. Omnichannel eCommerce

    Omnichannel experience has taken center stage for retailers in South Africa after the pandemic. According to Nielseniq’s study, 30% of South African consumers indicated they had shifted their shopping habits to online shopping from in-person grocery store visits between March 2021 and 2022. 

    3. Digital Payment Trends

    The digital payment ecosystem in South Africa has seen a massive growth trajectory after the pandemic. Customers seamlessly use digital payments across shopping, entertainment, groceries, food, health, and wellness – a trend we suspect is here to stay.

    4. Buy Now Pay Later

    Buy now pay later is an interest-free mode of payment that is popular worldwide for helping customers who cannot make high-value purchases. Consumers don’t have to pay any price upfront and pay off the amount in interest-free installments over a predefined period. The BNPL is forecasted to account for 13.6% of global eCommerce payments by 2024.

    5. Chatbots

    Quick response to customer queries and problems is instrumental in increasing conversion rate and sales. However, it can be difficult to respond to emails and instant chat 24/7 for small businesses. This is where automated chatbots are helping South African retailers answer customer questions promptly and correctly.

    The 4 Fastest-Growing Retail Segments

    4 Fastest-Growing Retail Segments
    4 Fastest-Growing Retail Segments

    1. Online Retail

    eCommerce & online retail grew 20% YOY after the pandemic. Retailers saw a huge increase in the adoption of online shopping by consumers. Traditional brick-and-mortar stores looked for omnichannel opportunities to keep up with online retailers. Mr. Price, a clothing retailer in South Africa, saw a surge in online sales by a massive 90% between April and June 2020. There is a similar success story where OneDayOnly, another South African online retailer, saw 40% growth during the same period.

    … but this growth surge brought in some challenges for retailers too. With more and more customers shopping online, competition increased. Price-sensitive customers would constantly compare prices across online retailers before making a purchase. It became critical for retailers to price their products right to beat the competition & win the sale, without hurting their margins! 

    2. On-Demand Grocery Delivery

    Groceries saw an increase of 54% from 2019 driven by the pandemic & lockdown restrictions.

    South African eCommerce companies offer a wide range of on-demand services, from taxi rides and grocery orders to liquor delivery. Retailers fulfill orders from stores to offer affordable rates and quick delivery across South Africa. It replicates the instant gratification of purchasing products from brick and mortar stores and the added benefits of the hyper convenience of shopping from a mobile or a computer. 

    Read quotes from our customers at Talabat, Glovo & Grab Food – we worked closely with them & helped them in their efforts to scale through this global Q-Commerce boom.

    3. Online Food Delivery

    According to Statista, revenue in the online food delivery segment in South Africa is projected to reach US$0.87bn in 2022. As competition heats up and more and more players enter the market, staying competitive is becoming increasingly challenging for food delivery businesses.

    Bolt Foods SA said they grew 50% month on month in mid-2021 and said they had to bet on making sure they were offering competitive prices in order to get ahead. Additionally, in their quest to have a stronger competitive advantage, Bolt Food says it is also offering customers a very low delivery fee, lower than Uber Eats & Mr. D since delivery costs are a major consideration for customers when using food delivery apps.

    The right price, product assortment, delivery fee, and delivery eta are critical to scaling a Food Delivery business. If you’re in the food-tech business, reach out and we can tell you how DataWeave’s Food Delivery Intelligence can help you scale quickly and profitably! 

    4. Social Commerce

    With approximately 41.19 million South African customers engaging in online activity, there is a huge shift in user behavior as customers get comfortable purchasing directly via social platforms instead of online retailers or physical stores. Social commerce uses networking websites such as Facebook, Instagram, and Twitter as vehicles to promote and sell products and services.

    What matters to South African online shoppers?

    Between June and November 2020, South African consumers mostly used online retailers monthly (42%), food delivery services weekly (36%), and online classifieds less than once a month (34%). 

    Here is a summary of things that matter to South African shoppers when they shop online:

    1. Easy product discovery and competitive pricing

    Most customers start their online shopping with a product in mind and look for discounts and sales across retailers. More than 67% of respondents of a survey have said that they go to a specific online store and search for the product they want. Almost the same share of consumers said they compare online stores to find offers for products they want. Price plays an important part in product selection. 

    In order to offer the most competitive pricing, retailers in South Africa need to keep a keen eye on competitor pricing. They need to identify gaps and opportunities to make price changes to not only offer the most attractive price to customers but also drive more revenue and margins by pricing products right.

    2. Reliable Delivery time

    81% of South African consumers say that unreliable delivery time is one of the reasons that affect their choice of an online store. Quick delivery time has become a differentiator in the eCommerce space, where ‘next day delivery or even ‘same-day delivery’ have become the norm. South African online shoppers want reliable delivery times that suit their busy schedules. 

    Read more here, about how DataWeave helped an America QSR understand the correlation between their delivery time & sales volumes! 

    3. Low delivery fee

    86% of South African customers believe that high delivery fees impact their online stores’ choices. The high delivery cost is a problem for low-income customers and customers who shop daily.
    If you want to track how your delivery fee compares to your competition and how it’s impacting your sales, our Food Delivery Intelligence solutions are for you!

    4. Customer Service

    Your company’s customer service should be responsive, smooth, omnichannel, and hassle-free. 78% of South African customers are frustrated with delays in customer support from online retailers. Slow response times and lack of communication in case of delays, delivery, and refunds hamper the customer experience drastically.

    Customer Service
    Customer Service

    Conclusion

    eCommerce in South Africa is growing at unprecedented rates. There has been a surge in the appetite of South Africans for online shopping and online retailers across the board are gearing up to meet this demand. 

    If you’re an online retailer in South Africa & need insights on staying competitive with the right pricing, product assortment, delivery time, delivery rates, and the other key influencers that affect customers’ choice of online retailers, sign up for a demo with our team at DataWeave to know how can help!  

  • The Future of eCommerce is Social: Demystifying the Social Commerce Revolution

    The Future of eCommerce is Social: Demystifying the Social Commerce Revolution

    Social commerce is the selling of goods and services within a social media platform. Brands use social platforms such as Instagram, Facebook, Snapchat, and Twitter to promote and sell products. These platforms have become an integral part of consumers’ everyday life because they continue to engage users with relatable content, making them scroll their feeds for hours. 

    The Social Commerce model capitalizes on this high user engagement & moves social media beyond its traditional role in the top-of-the-funnel marketing process by encouraging users to shop without leaving their preferred apps. According to the Social Media Investment Report, 91% of executives agree that social commerce is driving an increasing portion of their marketing revenue, and 85% report that social data will be a primary source of business intelligence.

    Let’s talk a little bit about why brands should consider selling via social media platforms:

    Social Commerce vs. eCommerce vs. QCommerce

    While they may fall under the same umbrella of online selling, social commerce, quick commerce, and eCommerce are three very different concepts

    • eCommerce refers to online shopping via a (retailer or brand) website or app. Customers can access these platforms via desktop or mobile devices. However, the sales funnel generally looks the same. These brands and retailers use top-of-the-funnel tactics like social media content, digital ads, and other marketing strategies to encourage customers to visit the online store. There are three main types of eCommerce businesses: Business-to-Business (Alibaba, Amazon Business, eWorldTrade), Business-to-Consumer (websites such as Amazon, Rakuten, and Zalando), and Consumer-to-Consumer (platforms such as eBay & Etsy).
    • Quick Commerce (or QCommerce) refers to eCommerce businesses that deliver goods within a couple of hours or even minutes. Although it’s sometimes used interchangeably with on-demand delivery or instant commerce, the idea of quick commerce has been around in the food industry for ages now. It has been recently ushered into the mainstream by evolving consumer preferences for quicker delivery of groceries and FMCG goods.
    • Social commerce brings the store to the customer rather than redirecting customers to an online store. It removes unnecessary steps and simplifies the buying process by letting the customer checkout directly through social media platforms, creating a frictionless buying journey for the customer. Additionally, social media platforms are mobile-friendly, a huge benefit for brands because increasingly more and more customers are accessing the internet through mobile devices.
    Social Commerce
    Social Commerce

    Rise of Social Commerce

    First used in 2005 by Yahoo!, ‘social commerce’ refers to collaborative shopping tools such as user ratings, shared pick lists, and user-generated content. Social media networks snowballed throughout the 2000s and 2010s, alongside a general increase in eCommerce, leading customers and merchants to quickly recognize the benefits of buying and selling through social media networks. Social media platforms have since evolved from merely a showcase tool for brands. They now serve as virtual storefronts and extensions of a company’s website or brick and mortar stores, capable of handling the buying experience.

    Top Social Commerce Platforms

    Social media platforms aim to keep visitors engaged on their platforms for as long as possible. Increased time in-app or on-site maximizes their opportunity to serve ads, a primary source of revenue generation. Social media platforms have millions of active users and they have a great power to help companies and individuals build their brands, interact with consumers, and support after-sales. Here are the top social commerce platforms:

    • Facebook

    Facebook introduced Facebook Shops to capitalize on the commercial opportunity by allowing vendors to advertise and sell directly through the platform. Facebook integrates social commerce with shopping, allowing users to purchase products smoothly. Facebook shops offer a smooth user experience where users can review products and get recommendations from trusted acquaintances. Customers can directly interact with the merchant’s customer service department post-purchase. 

    • Instagram

    60% of people discover new products on Instagram. Owned by Facebook, Instagram facilitates in-app shopping and handles the entire transactions within the app itself. Users scrolling on Instagram often wants to follow trends and replicate the looks of their role models or favorite influencers. By offering purchasing options in the app, Instagram benefits from the platform’s rich visual imagery and videos, allowing businesses to sell an idea rather than the traditional process of selling a product. 

    • TikTok

    Shopify partnered with TikTok to introduce shopping and drive sales through the younger and seemingly ever-expanding TikTok audience. With TikTok for Business Ads Manager, brands and merchants can create in-feed video-based content depending on their product offering. This partnership allows Shopify merchants to expand to the TikTok audience.

    • Snapchat

    Snapchat has recently launched Brand profiles, a feature that allows users to scroll through a merchant’s products and buy them in-app. This new experience is powered by Shopify too. Merchants can create Brand Profiles or Native Stores that allow users to purchase products from the app. 

    Pinterest users are there for Shopping Inspiration
    Pinterest users are there for Shopping Inspiration
    • Pinterest

    Pinterest is also an image-based platform where users create boards of their favorite wedding accessories, home decor, fashion trends, etc. Pinterest doesn’t specifically offer social commerce for the global audience. Rather, it allows business accounts to create ‘Product Pins’ that are displayed in the brand’s Pinterest shop. Only U.S. customers can purchase within the app. Users from other countries are redirected to the eCommerce site to complete the sale. We have added Pinterest to this list because 89% of Pinterest users are there for shopping inspiration.

    Pinterest is an image-based platform where users create boards
    Pinterest is an image-based platform where users create boards

    Why Should Brands Care About Social Commerce

    • To enhance social media presence and brand awareness

    If your target demographic is in the 18-to-34 age range, they’re already on social media and waiting to shop while they scroll. According to Sprout Social, over 68% of consumers have already purchased directly from social media and nearly all (98%) consumers plan to make at least one purchase through social or influencer commerce this year. You can enhance brand awareness by selling on social media platforms. Influencer marketing is an amazing way to build brand awareness since customers are now seeking authenticity from micro-influencers rather than big-name celebrities. 

    • To generate social proof

    90% of online shoppers say that they read online reviews before making an online purchase. Whether it’s an automated follow-up email or a message through the social media platform, ask for a review after your product has been delivered to the customer. You can also offer incentives like a contest to encourage previous customers to weigh in and share their experiences. These steps will allow you to collect social proof since it’s vital to build a positive reputation online. You can also ask customers to create small product review videos that you can share on your social feeds in creative ways. You can also post user-generated content, create a carousel of positive comments, or host a live video with happy customers.

    Social Proof
    Social Proof
    • To simplify the buying process for consumers

    Traditional eCommerce involves several steps. It starts with displaying ads on social media platforms and customers being redirected to the business website for completing the transaction. To complete the transaction, customers also have to create an account or manually fill in the credit card details and delivery address. On the other hand, social is only a three-step process — find, click and buy. 

    Counterfeit Products
    Counterfeit Products

    Conclusion

    While social commerce is proliferating, it also has a few setbacks like the rise of counterfeit products. Counterfeiting has expanded into social media and has become an under-reported but vital hub for counterfeiters. A counterfeit detection solution can help brands and merchants identify & remove fake and unauthorized products. Technologies like image recognition can help in counterfeit detection by capturing fake logos and discrepancies. Removing counterfeit products will help brands safeguard customer loyalty and prevent fake products from harming your bottom line. 

    Here’s how DataWeave helped Classic Accessories, a leading manufacturer of high-quality furnishings & accessories identify counterfeit products across multiple retail marketplace websites eliminating 22 hours of time spent per week conducting manual audits – read the case study here. 

    Are you a brand or a retailer worried about counterfeits? Sign up for a demo with our team to know how we can help you track, identify and eliminate fakes! 

  • How Inflation has hit the Retail Industry

    How Inflation has hit the Retail Industry

    Inflation has resurfaced after a decade of tranquil price increases. The persistent COVID-related supply chain disruptions have been a driving factor in increasing consumer costs since some commodities are harder to come by. While inflation is a normal economic phenomenon, the current 3.81% inflation rate has increased the cost of living for families across the globe.

    Global Inflation Rate
    Global Inflation Rate. Source: Statista

    Worldwide inflation is expected to remain near 5.0% in early 2022 before gradually easing in response to industrial and agricultural commodity price declines. Additionally, the global consumer price inflation peaked from 2.2% in 2020 to 3.8% in 2021 and will average 4.1% in 2022 before subsiding to 2.8% in 2023.

    In this blog, you’ll learn about the impact of inflation on the Retail Industry. 

    What is Inflation?

    Inflation is an economic term that describes an overall increase in the price of goods and services in an economy, and a by-product of inflation is the devaluation of the currency used within that economy. For example, a clothing retailer that used to pay $8 for a t-shirt two years ago will now have to pay $10 for that exact product. The t-shirt hasn’t changed at all. However, it has become 25% more expensive. Inflation and the devaluation of currency are part of the reasons why they’d now pay $2 more for that same T-shirt.

    Also Read: Top 7 strategies to sell effectively on Amazon

    Impact of inflation on Retail

    FMCG

    The Fast-moving consumer goods (FMCG) sector will continue to grow because there is growth in household goods spending despite the Russia-Ukrainian crisis, global interest rate, and rising fuel prices. In fact, the demand for consumer packaged goods rose sharply in countries heavily affected by the pandemic. However, the FMCG sector will see a rise in prices of commodities because crucial resources such as cooking oil, tea, cocoa, etc., become scarce. The persistent shock to the supply chain has forced various FMCG companies to increase their prices. For instance, Mondelez, a Fortune 500–listed snack and beverage company, announced a 6-7% price increase. 

    Inflation for Fashion & Pharma Industry
    Inflation for Fashion & Pharma Industry

    Fashion

    The global fashion industry posted a 20% decline in revenues in 2019–20. Inflation in fashion is caused by transportation bottlenecks, material shortages, rising shipping costs, and straining supply and demand. The global fashion industry will see complete recovery in 2022. COVID-caused supply and demand constraints have eased, but shoppers will have to reconcile to price jumps in everything from bags to shoes.

    Pharma

    Pharmaceuticals are recognized as an essential commodity and therefore have a massive impact on the household budget. Vizient has projected a 3.09% increase in the inflation rate in drug prices from July 1, 2022 – June 30, 2023. It shows how inflation has a direct impact on prescription drug costs. Notably, retail prices for some of the most widely used prescription drugs are expected to increase 2x as much as inflation. The demand for pharmaceutical drugs has been higher post-pandemic, ensuring that consumers’ total demand and spending in this vertical will remain unchanged. 

    Comparison of New, Used & Electric cars
    Comparison of New, Used & Electric cars
    Highest & Lowest Inflation in Beauty category. Source: nielseniq.com

    Automotive

    The rise of both new and used cars has been steeply increasing partly because of the shortage of semiconductors and the backlog from the closure of factories during COVID-19. According to the Bureau of Labor Statistics, there has been a 24.4% inflation in the used car purchase prices and an 8.8% increase in the new car purchase price. Rising oil prices across the globe and the historical oil crisis fuelled by the Ukraine-Russia war have strained many people’s budgets. However, the automobile market is seeing an uptake in demand for electrical vehicles (EVs). EVs represented 14% of car sales between January and June 2021. 

    Beauty

    COVID-19 brought new challenges to the cosmetics industry, chief among this being face-covering required by law. In light of social distancing and lockdowns across the globe, consumers were buying less makeup. The rising cost of labor, energy, and raw materials used in beauty products have resulted in a “once-in-two-decade” backdrop for price hikes. The cost of palm oil, a common material in beauty products, has soared 82% in two years due to Indonesian labor shortages. Nevertheless, consumers will spend more time outside the house. Beauty price per unit changes shot up 17% in-store and online in 2021.

    5 Things that will help retailers during inflationary times

    1. Observe Competition

    Retailers should follow their competitors closely—when they start to raise/lower prices, consider following suit. Using competitive data to gauge price changes will help in managing price parity. However, excessive discounts and lower prices to gain an advantage over your competitor could backfire in various ways. For example, low pricing may convey that your products aren’t as good as your competitors’, impacting your long-term brand image. Moreover, lowering prices to sell more doesn’t necessarily mean higher profits, especially during high inflation. To leverage this strategy effectively, retailers must first identify SKUs that have the highest impact on their pricing.

    2. Build a structured and targeted pricing strategy

    An effective pricing strategy that leverages differences in product, channels, and customers will help retailers to maintain long-term value for their business and customers. However, customers might react differently to a steep price increase. Broad price increases will demonstrate insensitivity and erode customer trust. Instead, retailers can thoughtfully tailor their inflationary price increases for each customer and product segment with a competitive pricing strategy. With a competitive and historical pricing strategy, brands can examine their customers’ end-to-end profitability and willingness to pay relative to a comparable peer set. 

    Price Competitiveness for the right items
    Price Competitiveness for the right items

    3. Rethink commercial positioning

    The pandemic and rise of inflation during 2020–2021 have profoundly impacted how consumers live and what they value. Understanding how your consumer’s needs have shifted and used a promotion strategy to manage today’s inflationary pressures is crucial. As new behaviors emerge post-pandemic, retailers must prepare for the potential top-line impact of demand shifts. Rethink commercial positioning and review marketing and packaging strategies, including the potential use of nonuniform and, in some cases, nonprice mechanisms.

    4. Ensure price competitiveness on the right items

    The Key-Value Item (KVIs) list should be reviewed again, considering changing shopper needs and habits during the pandemic, plus the supply and demand shock that the industry is currently experiencing. Price-sensitive and vulnerable shoppers are finding this inflationary period particularly tough, so brands might require an even deeper investment in KVI pricing. Reinvest base prices on essential products to drive volume for your best price-sensitive (PS) customers. Compete only where you need to be without overspending. Online channels should continue to reflect in-store prices and diverge during this time. Pricing Optimisation software enables best practices to simultaneously manage a high number of price increase requests.

    5. Revisit promotions to conserve costs and preserve stock availability

    Increasing the number of promoted products is a reflexive response to inflation, but it’s not the right response for building sustainable sales or longer-term loyalty. Inflationary times offer an excellent opportunity to reset promotional strategies to save money and margin. Retailers can increase sales and seize opportunities with a promotional pricing strategy. Increased promotional activity has a knock-on effect vs pricing position in high-low strategies and erodes overall value perception, creating a vicious circle of more promotions equals poorer value.

    Conclusion

    Today’s economic climate and associated pricing pressures are challenging for retailers and customers. Some companies have responded by announcing an increase in prices across product categories. Companies can manage pricing margins responsibly and profitably during inflation. Determining how and where new opportunities exist can help companies control inflation, drive growth, and remain profitable.

    Need help to arrive at the right pricing & discounting strategies to counter inflation? Sign up for a demo with our team to know how we can help!  

  • Share of Keyword Search Cinco de Mayo 2022

    Share of Keyword Search Cinco de Mayo 2022

    As inflation continues to hike costs for consumers and supply chains challenge them to maintain loyalty, there is still an active audience willing to pay the ultimate price for the convenience of food and alcohol delivery. That being said, we analyzed 8 popular Retail and Delivery Intermediary websites and 11 popular ‘Cinco de Mayo’ keywords to see which Brands are predicted to win the battle of Digital Shelf Share of Search this holiday.

    2022 Cinco de Mayo Share of Search Insights - Top Brands for 'Cinco de Mayo'
    2022 Cinco de Mayo Share of Search Insights – Top Brands for ‘Cinco de Mayo’

    Opportunities for Food & Bev on Cinco de Mayo

    While most of our analysis focused on popular Cinco de Mayo food and beverage products, none of these brands populated on either Target (pictured on left below) or Walmart (pictured on right below) page 1 search results for the term ‘Cinco de Mayo’. Keyword search results for this term are dominated primarily by décor brands as indicated below.

    Brands Achieving Top Share of Search for Food and Beverage Categories on Cinco de Mayo 2022
    Brands Achieving Top Share of Search for Food and Beverage Categories on Cinco de Mayo 2022

    Share of Keyword Search Results – Alcohol Category

    Three of the most popular alcohol types sought out during Cinco de Mayo are ‘Mexican Beer’, ‘Mezcal’, and ‘Tequila’. Below are the brands dominating Share of Keyword Search results on each of the major retail websites we researched.

    AmazonFresh, Meijer, Kroger, and Sam's Club Share of Search - Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022
    AmazonFresh, Meijer, Kroger, and Sam’s Club Share of Search – Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022

    We also reviewed the same keyword performance across popular delivery intermediaries to see how Share of Keyword Search altered for ‘Mexican Beer’, ‘Mezcal’, and ‘Tequila’. The results are below for TotalWine, Instacart, Drizly and GoPuff:

    TotalWine, Instacart, Drizly, and GoPuff of Search - Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022
    TotalWine, Instacart, Drizly, and GoPuff of Search – Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022

    The keyword ‘Agave’ is also a popular search term within the alcohol category during the time leading up to Cinco de Mayo. We reviewed keyword search performance at various zip codes to see how price points that populated on page 1 search results varied given the change in median income. Below are the results:

    Share of Search for Alcohol by Price Point and Zip Code on AmazonFresh
    Share of Search for Alcohol by Price Point and Zip Code on AmazonFresh

    Share of Keyword Search Results – Grocery Categories

    We also reviewed some of the most popular grocery items purchased during Cinco de Mayo by Keyword Share of Search results to see which brands are primed to win the Digital Shelf this year. Below are the results for Target.com and Walmart.com.

    Walmart and Target Share of Search - Food and Beverage Keywords on Cinco de Mayo 2022
    Walmart and Target Share of Search – Food and Beverage Keywords on Cinco de Mayo 2022

    Below are the results for the same popular grocery items and alcohol keywords related to Cinco de Mayo and the page 1 results seen for Brand Share of Search on Safeway.com.

    Safeway Share of Search - Food and Beverage Keywords on Cinco de Mayo 2022
    Safeway Share of Search – Food and Beverage Keywords on Cinco de Mayo 2022

    Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions and help drive profitable growth in an intensifying competitive environment. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis, and let us know what other holiday insights you’d be interested in seeing this year. Happy Cinco de Mayo!

  • What is Customer Price Perception and why it is important

    What is Customer Price Perception and why it is important

    Finding the right price often requires a trade-off between margin and price perception. Brands may want to defeat competitors’ prices on all their products, but that can often lead to losses because sales directly link to price perception. Instead of trying to stay competitive across the board on all products, brands must identify key value categories (KVCs) and key-value items (KPIs) whose prices buyers tend to remember and price those products competitively. In this scenario, they can make up for lowered prices on key products by fixing higher prices on other products. 

    Consumers’ perception of price fairness largely determines their experience with a brand. Brands selling online can often have a disconnect between their prices and what customers expect their prices to be. However, that does not mean spiraling downwards by getting trapped in discounting cycles and heavy promotions that can harm your bottom line. Instead, brands require real-time monitoring across thousands of stock-keeping units (SKUs) to identify key categories and items they need to price with care. In this blog, you’ll learn about price perception and the factors that influence it. 

    What is Price Perception?

    Price perception is the perceived worth of a product or service in the consumer’s mind. It is one of the leading variables in the consumer’s buying process. Buyers are unaware of the true cost of production for the products they buy. Instead, they make buying decisions based on an internal feeling about how much certain products are worth and which brand offers them the best value. To offer competitive prices and yet obtain a higher price for products, brands often pursue marketing strategies to improve the price perception of their brand and products.

    Price Perception
    Price Perception

    However, brands should not fall into the trap of assuming that price perception is a competitor’s price index. It’s not about offering the lowest price on certain SKUs. Not every brand strives to offer the lowest prices. Some brands take a slightly different approach to ensure the right value for their products. For example, take a look at Trader Joe’s, a grocery chain that has never claimed low costs. They’ve always taken a holistic approach to their pricing and customers to build a loyal following. And it worked well for them. Trader Joe’s can boast one of a high-value perception score, despite not having rock-bottom prices. 

    Marketplaces such as Walmart and Amazon may not have the best prices on every item. Still, customer perception is that they will have the lowest prices and will often shift the share of sales towards such platforms over businesses that offer the same or even lower prices. 

    Some things to consider:

    • What do your customers think of your brand?
    • What are the key factors that are driving your customers’ price perceptions?
    • Is your product mix properly aligned with your brand perception?
    • Are you communicating the most important and relevant information to your customers?
    • Is your message being received and understood?
    • Who do your customers see as your competitors, and why?

    Also Read: 11 Reasons why your eCommerce Business is fail 

    What is Price Positioning?

    Price positioning is pricing products or services within a certain price range. It indicates where certain services or products lie in relation to competitors’ pricing and in the mind of different customers. A brand’s price positioning has a huge impact on whether the products are seen as priced low or not. The following is a great way to understand the price-value matrix:

    Price Positioning
    Price Positioning

    Your brand’s position in this matrix will depend on your pricing objectives, competition, and customer loyalty. Price positioning helps the marketing and operating teams understand customers’ perceptions of your brand and convince customers to buy your products. Brands need a holistic approach toward setting prices for their products in order to drive conversions through intelligent pricing and competitive insights. 

    Factors that influence Price Perception

    Price-Quality Relationship

    Price is often an indicator of product quality. The general rule is that the higher-priced products are perceived to have better quality, implying that brands should consider a rational quality-price relationship in their pricing or promo strategy. For example, it might not be best practice to have similar prices for both good and low-quality products because customers will perceive low-quality products as overpriced and might not purchase from you.

    Price-Consciousness

    Customers aren’t price conscious about every product. Instead, they are only price conscious about certain products under the best price guarantee or BGP. For instance, if buyers find your BGP products more expensive than your competitors, the cheaper products in your assortment will still be perceived as expensive. 

    Value-Consciousness

    During markdown periods, ensure that you are not undermining the efforts to shape and maintain price perception by offering extreme or complex discounts. In an attempt to clear stocks, promotions simply confuse the shopping experience for customers and further deteriorate trust in your brand. Your promotional offers should keep price perception during the holiday season or clearance sales by offering a simplified promotional program. Start by understanding which price mechanics and SKUs work best for your target customer segment. You should also reduce over-communication on hero deals else buyers will assume that you incorrectly price products during new seasonal launches. 

    Prestige Sensitivity

    Gerald Zaltman, a Harvard professor, argues that 95% of all purchasing decisions are subconscious. Luxury brands are a great example of how psychology directly links to price perception. Customers buy premium or luxury products to demonstrate their social status. In this scenario, buyers don’t hesitate to buy expensive products from certain brands even if they are explicitly overpriced. Thus, brands selling premium products will have to ensure pricing is coherent with buyers’ expectations. 

    Every customer wants to know they’re getting the best value. They use the highest and lowest prices in a range to understand how expensive a product or brand is. So, by removing high price point lines with low volume, customers will see more minor price points around the store. Brands must merchandise entry price points to help customers identify the lowest prices and improve the perception of their product ranges. 

    Product Range
    Product Range

    How to adjust Price Perception

    Here are three ways for brands to improve price parity:

    • Marketing to influence Price Perception

    An efficient pricing management strategy will focus on competitiveness and establishing the right price perception among your customers. You can influence customers’ price perception by improving the look and feel of your online stores since simpler designs are often reflections of lower prices. Another great way to influence price perception is to offer loyalty and reward programs that also improve brand loyalty and reinforces the vision of an economy store irrespective of the prices of your products.

    • Competitive Analysis

    Brands can understand price differences after a competitive analysis. Customers often search for similar products across brands to find the best deals, and you will be able to understand customer opinion through competitor analysis.

    • Price Management Automation

    A price monitoring platform can help brands to stay on top of promotions and discounts offered by their competitors. A price intelligence software will help brands associate products by similarity criteria and compare the pricing of their products with those of competitors. It offers a detailed view of the market and ensures that brands take care of their bottom line.

    Conclusion

    When a consumer comes across a similar low-priced product or service from a different brand, they may see it as a good deal or might perceive it not worthy of their time or money. What consumers think about your brand’s price is just as important as the actual price of that product. A buyer may sense a company as “upscale” and assume that they have high prices, or they may see a brand as a discount retailer whose prices are too high for its reputation. At times, consumers might also see cheaper alternatives as inferior. It’s not easy for a brand to understand its customers’ perception of price vs. value it offers. Brands need a long-term, dynamic pricing strategy that matches the demands and trends of a global, competitive market. And in order to drive sustainable growth, they need to make smarter pricing and promotion decisions with insights into competitive pricing. 

    Learn how DataWeave can help make sense of your and your competitor’s pricing & promotional strategies and help your brand build the right Price Perception. Sign up for a demo with our team to know more.

  • Top 7 strategies to sell effectively on Amazon

    Top 7 strategies to sell effectively on Amazon

    According to MarketingCharts, 63% of online shoppers start their buying journey on Amazon. This shows that customers believe they will find the products they are looking for with competitive prices and excellent customer service on Amazon. Amazon is one of the most dominant eCommerce marketplaces with 197 million users and 112 million Amazon Prime members. Brands can sell on Amazon to capitalize on this vast customer base by showcasing and promoting their products properly. 

    In this article, we’re going to take a look at the top 7 strategies to sell effectively on Amazon:

    1. Boost Product Discoverability using Ads

    Amazon Advertising helps sellers, brands, and agencies to drive profitability by making sure product discoverability is high & shoppers are able to find their brand with ease. The ads on Amazon fuel product discovery and improve conversion rate. The advertising options on Amazon are designed to help brands increase exposure, generate incremental sales, boost organic rankings, and drive growth.

    Amazon has three PPC programs: sponsored product ads, sponsored brands ads, and sponsored display ads. Brands can increase visibility on Amazon with these three paid campaigns. You can sponsor products or your brand for related searches on Amazon. Businesses only pay for clicks received. 

    Sponsored products are for individual product listings that appear on shopping results pages and product detail pages. Sponsored brands are for showcasing brand portfolios such as logo, custom headline, and a selection of products on the shopping results page. The last is sponsored display, a self-service advertising solution for displaying ads on and off Amazon. 

    Promotions for Brand and SKU's
    Promotions for Brand and SKU’s

    2. Improve your Amazon SEO using effective Product Descriptions

    To effectively sell on Amazon, businesses first have to understand the A9 algorithm. Amazon uses A9 Algorithm to decide which products are ranked in search results, emphasizing sales conversions. This algorithm helps Amazon promote listings that are more likely to result in sales. 

    Keywords in product descriptions are one of the main driving factors that the Amazon A9 algorithm looks for in determining relevance to search queries and setting rankings on its results pages. Therefore, brands must integrate high volume and significantly relevant keywords as part of their listings. Crafting product descriptions with the right keywords will provide compelling reasons for buyers to purchase the product and for the A9 algorithm to better rank the brands. Brands can analyze and optimize their content to improve discoverability across Amazon. Accurate product descriptions help users make informed decisions and allow brands to deliver a consistent customer experience.

    Detailed Descriptions and Highlights
    Detailed Descriptions and Highlights

    3. Improve your Product Visuals

    Avoid using standard visuals when displaying your products in Amazon’s image gallery. Product images are the hook that encourages visitors to click on your products. However, Amazon has specific image requirements that you’ll need to adhere to while presenting products. When shopping on Amazon, potential buyers are looking for high-definition and clearly visible photos. Thus, you will need diversity in images if you want your product and photos to stand out.

    In addition to images, brands can make their product descriptions better through video content. Videos help your brand to stand out, build a more personal relationship with customers, and lead to increased sales. One study on eCommerce sellers found that using product videos increases sales for online stores by 144%.

    Product Images
    Product Images

    4. Switch to Intelligent Pricing & Win the Buy Box

    Intelligent and competitive pricing is the most essential lever for revenue growth. With advanced technology like AI and analytics, brands can get insights into competitive pricing and develop an intelligent pricing strategy to calculate real-time changes in pricing optimally. 

    Amazon wants to give the consumer the best value for their money and thus has a Buy Box option. The white box on the right side of the Amazon product detail page is called the Buy Box, and customers can directly add items for purchase to their cart. However, not all sellers are eligible to win the Buy Box. 

    Thanks to Amazon’s customer-obsessed approach and high competition, only businesses with excellent seller metrics have a chance to win a share of Buy Box. Amazon weighs low prices with high seller metrics. If your brand has near-perfect performance metrics, having higher prices can still get you a share of the Buy Box. In contrast, brands with mid-range metrics will probably need to focus on offering the most competitive price.

    But, why is the Buy Box important? According to BigCommerce, 82% of sales on Amazon go through the Buy Box, and the rate is even higher for mobile purchases. Getting insights into your competitor’s pricing with our Digital Shelf Solution will help you improve seller metrics and find the right pricing strategy for your products.

    5. Provide Plenty of Social Proof

    Testimonials can increase sales page conversions by 34%. Social proof has emerged to be of great importance in the eCommerce world, and it isn’t limited to recommendations from people customers know in the “real world.” A survey conducted by BrightLocal revealed that 31% of consumers reported that they read more online reviews in 2020 than ever due to Covid-19. 

    Product ratings and reviews on Amazon are at the center of the recommended products section, product listing page, and search results. Interestingly, customer feedback also has a huge impact on a brand’s ODR or Order Defect Rate. It is one of the most critical measurements tracked by Amazon. ODR is a measure of customers who have had a negative experience with you as a seller. Amazon uses it to assess a brand’s health as a seller. The ODR indicator is driven by customer feedback, so review management is the primary step for brands to avoid an Amazon ODR warning and improve their order defect. 

    6. Go Global

    The Amazon marketplace is available in countries and markets worldwide, allowing brands to explore new territories and sell their products globally. Each foreign territory has a unique Amazon site that resonates with its culture and audience, making it easy for global sellers to compete with other brands. If your eCommerce brand has the operation capacity to expand globally, Amazon offers state-of-the-art international logistic capabilities. 

    Brands can expand in European countries like France, Italy, Netherlands, Germany, Spain, etc., and Asia Pacific locations like India, Japan, and Australia. Amazon is also available in emerging eCommerce locations like the Middle East, Brazil, Turkey, and Singapore. 

    7. Build a Branded Store

    One of the best strategies to stand out on Amazon is to feature your products on a branded Store. Amazon has free tools that allow grants to build an online store where brands and sellers can showcase products and connect with customers. These stores look different from the typical Amazon listing layout and also have the option to create detailed pages with A+ content. 

    Build your Brand Page
    Build your Brand Page

    For instance, Netgear, a company that offers technology-related products has an excellent branded store on Amazon. The brand has embedded images and videos that address buyers’ needs and how users’ lives are affected by using their products. The most attractive feature about this store is that they have integrated the value offered by their products into new use cases because of the current pandemic. For example, they’ve used phrases like “Make Online Learning fast and fun” and “Work from office at office speed.” Additionally, the categories and search tab help buyers search for specific products easily.

    Creating branded stores allows you to build a beautiful brand experience for customers and offers a multi-page, immersive shopping experience. Brands can pick unique designs, integrate promotions, and use rich media to create a custom curation of handpicked products. 

    Conclusion

    Amazon has 9.7 million sellers worldwide, of which 1.9 million are actively selling on the marketplace. The competition on Amazon is fierce, and it’s continuously increasing. Despite a large number of active sellers on Amazon, only a tiny fraction generates a significant portion of its total sales. Fewer than one in ten active Amazon sellers generated over $100,000 in annual sales, and only one percent of them hit the $1 million sales mark. Use these strategies to develop a comprehensive understanding of the Amazon platform and how to sell effectively on the platform while maximizing your presence amid rising competition. 

  • 11 Reasons why your eCommerce Business is failing

    11 Reasons why your eCommerce Business is failing

    No matter where your eCommerce business sells, there are some fundamentals that brands have to get right to achieve sales targets. Brands need to find the right product/market fit, nail their lead acquisition strategy, and design a qualified sales funnel to turn prospects into leads and eventually returning customers. They will also have to analyze their customer’s buying journey and get insights into competitors’ strategies to understand what works for their business.

    If your eCommerce business is struggling, read this blog to learn about steps you can take to increase sales and keep your business afloat. 

    1. Lack of social proof

    Customers often check for reviews or testimonials before making a purchase. Our decisions are consciously or unconsciously influenced by the opinions, choices, and actions of people around us. Social proof helps brands build customer trust, adds credibility to their business, improves brand presence, and validates customers’ buying decisions. 92% of consumers are more likely to trust user-generated content (UGC) and non-paid recommendations than any other type of advertising. Additionally, brands should also find ways to combat negative reviews since bad reviews can sometimes be extremely damaging. 

    Understanding these reviews or the impact of your brand’s social proof is critical. At DataWeave, we help brands analyze online reviews to understand customer sentiment and adapt to feedback to enhance their experience with your brand. 

    2. Slow site speed

    Site speed of the home page and checkout page on your D2C website can be a roadblock. Slow sections on your site like My Accounts, checkout, and cart are often overlooked when it comes to tracking site speed. Brands should run their checkout process at least once a month to ensure it’s fast, smooth, and bug-free. You can optimize images, strip unused scripts, implement HTTP/2, etc., to improve site speed and performance. 

    3. Poor customer service

    69% of US consumers say customer service is very important when it comes to their loyalty to a brand. Guaranteeing a return customer is important to maintaining customer loyalty. While the focus is on the first purchase for new customers, your brand’s customer service will determine if first-time customers become repeat buyers. Loyal customers are known to spend 67% more on a brand product than new customers, even if they make up only 20% of your audience. 

    Types of customer service
    Types of customer service

    4. Failure to send traffic to popular products

    Be it your own D2C website, or when selling on a marketplace, you should be able to drive traffic to your best-selling products. One of the best ways for sending traffic to popular products on your website is to run paid ad campaigns and reach new audiences with influencer marketing on social media. Brands can also attract customers with organic media such as writing blogs and producing podcasts. 

    If you’re looking at driving traffic to key products on Amazon & other such marketplaces, sponsored ads are the way to go! Sponsored ads help your best-selling products more discoverable & helps shoppers find your brand with ease. 

    5. Inadequate pricing

    Finding the right pricing strategy for your eCommerce business is crucial for optimizing sales and increasing revenue. The first step is to perform a competitor and historical data analysis to get a general idea of the market and then develop a pricing strategy that is the right fit for your products. Brands also have to ensure that they have dynamic pricing that can adjust according to supply and demand. 

    Our Digital Shelf solution at DataWeave helps brands track pricing for products across different pack sizes & variants across multiple online retailers and marketplaces helping them stay competitive in the market. 

    Optimize the right pricing strategy
    Optimize the right pricing strategy

    6. Not targeting the right audience

    One of the biggest mistakes that eCommerce businesses can make is targeting the wrong audience. It’s crucial for brands to define that target audience and then tailor products and marketing toward them. To increase sales as an eCommerce business, brands have to understand their audience, their interests, and how to appeal to their interest. Start by creating ideal buyer personas that represent your ideal customers. Also, segmenting audiences and targeting various groups based on buyer personas for ad campaigns will lead to better sales and revenue. 

    Targeting the right audience
    Targeting the right audience

    7. Poor product descriptions

    One of the major and common mistakes by eCommerce brands is using irrelevant product descriptions that are not optimized for the product. Customers don’t add products to their cart if they have difficulty finding sufficient information relevant to the product. Brands should write attention-grabbing descriptions optimized for SEO that are informative for the users. Here are some tips to optimize content to drive more eCommerce sales.

    At DataWeave, our AI-Powered solution helps brands optimize content and visuals across product pages to improve discoverability. 

    8. Not having multiple revenue streams

    Due to COVID-19, many businesses have had to modify or temporarily shut down their daily operations. However, finding new revenue streams can be a great way for eCommerce businesses to make up for the lost income and keep the company afloat. The best solution is to diversify your product offerings by offering commonly purchased products in bundles. 

    9. Low-quality visuals

    Businesses fail to hit their sales targets because of low-quality visuals in product descriptions. High-quality and custom images can improve conversion rates from both marketplaces and image-based channels like social media. Social media users are attracted to exciting, high-quality content that conveys a desirable lifestyle. Brands should use high-resolution, attractive pictures of their products. Brands can also utilize UGC and influencers to help build up their content libraries.

    Low-quality visuals
    Low-quality visuals

    10. Wrong Assortment. Poor Availability

    When your target audience lands on your eCommerce store and cannot find what they’re looking for, it leads to a poor shopping experience, but more importantly a lost sale for your brand! While you cannot have endless inventory, it’s essential to optimize your assortment & product availability to decrease the chances of your customer walking away. Assortment & availability optimization begins with analyzing current and historical inventory trends. If done manually, assortment can be a time-consuming task. A healthy assortment can increase retail sales by creating a positive shopping experience for your customers and encouraging them to return to your store again.

    11. Bad eCommerce UX

    Offering a sub-standard user experience is a common reason why eCommerce businesses find it difficult to increase sales. According to a study, the conversions can fall by up to 7% for every one-second delay in page load time. Businesses can use a countdown clock on their landing page and exit pop-ups to improve conversations. Your landing page and product descriptions should provide information that helps your users make a better and more informed decision. 

    Conclusion

    If your eCommerce’s business sales are tanking, improving site speed, customer service, social proof, and product descriptions are some of the levers you can pull to remedy the situation. Brands should also work on improving online reviews & ratings, availability, assortment, visuals, and website UX to improve customer experience. These steps not only increase loyalty but also improve customer retention. 

    Need help tracking online pricing for your eCommerce business? Or decoding customer sentiment from reviews they’ve left for your products? Or do you need insights into your product assortment and availability? Sign up for a demo with our team to know how DataWeave can help!  

  • eCommerce Performance Analytics for CPG Private Label

    eCommerce Performance Analytics for CPG Private Label

    The combination of economic uncertainty, inflation, and perceived affordability has increased consumer’s willingness to buy and try more private label products, challenging National brands to differentiate their eCommerce strategies, especially those related to price positioning, in other ways.

    Our previously released report, Inflation Accelerates Private Label Share and Penetration, confirmed 8 out of 10 brands with the highest SKU count carried across all grocery retailer websites to be private label, signaling the strength of their digital Share of Voice. Given the growing shift in consumer preference toward private label brands, we are providing access to the latest trends seen from September 2021 through March 2022. Below you will find a summary of what the data revealed about the growing presence of private label brands on the Digital Shelf.

    Private Label Account and Category Penetration

    We analyzed private label penetration at an account level to understand which private label brands have the greatest presence on retailer digital shelves, and to see which retailers may be leaving product assortment opportunities on the table.

    Private Label Penetration Across Retail Grocer Websites

    As a retailer, it is important to understand how your private label penetration stacks up against the industry average at a category level, especially given the performance tracked for retailers included within our analysis and the vast number of SKUs they offer online (over 20,000).

    Private Label Penetration by Category Across Retail Grocer Websites

    The Private Label and National Brand Price Gap Widens

    Private label brands tried out of necessity mid-pandemic increased in popularity as grocery prices continued to rise, providing an opportunity for retailers to increase brand affinity and loyalty for their online shoppers. Retailers alike were able to keep affordability at the forefront of their strategies and maintain a price gap of 23% or more, despite inflationary pressures to increase prices.

    Private Label / National Brand Price Gap by Retailer

    Looking at the results at a category level, we can see that Meat is the only category found within our analysis where private label brands are priced higher than National brands at an average of 8% greater. The Alcohol & Beverages category tends to always see the greatest price gap between private label and National brands given the price variances by unit (ranging from under $10 to over $100), in this case averaging a 148% price gap.

    Private Label & National Brand Price Gap by Category

    Private Label Total Basket Value Comparison Across Retailers

    While SKU-level pricing is extremely important to product strategy, for a retailer, it is equally as important to be as mindful of the total basket value even more so now as consumers further their private label loyalty across various categories. A few SKU-level missteps in pricing decisions can exacerbate cart abandonment and negatively impact shopper loyalty in a world where prices can be compared instantly in the palm of your hand.

    Based on our analysis, Walmart and H-E-B private label products offered the lowest priced total basket of goods at $42.90 and $45.06 respectively, whereas AmazonFresh and Safeway offered the highest total at $73.19 and $69.52 respectively.

    Private Label Item Level Price Comparison by Retailer

    Inflation-driven Price Changes are on the Rise with Room to Grow

    Based on the 20,000+ SKUs analyzed, we saw a continual price increase every month since September 2021 when comparing future monthly prices to those we tracked in September. The greatest price increase happened in March 2022 at 12.5% on average, however, there are still 48% of SKUs that have yet to see a price increase even as inflationary pressures rise.

    When viewing the split between National and private label brand price increases in March 2022 versus September 2021, we saw National brands increased prices on average by 13% where private label brand prices only increased an average of 7%.

    Private Label & National Brand Price Change
    Private Label & National Brand Price Change (%)

    Price decreases are still occurring across all categories, despite inflation, but to varying degrees ranging from 5% for Deli items to 17% for Dairy & Eggs. Within the Dairy & Eggs and Pantry categories, private label brands reduced prices for an additional 10% of total SKUs compared to National brands.

    The greatest category of opportunity for price increases within private label were found within Beauty & Personal Care with 67% of private label products yet to see a price change since September 2021.

    Price Change (%) by Category and Brand Type

    Private Label Price Change Correlation to Product Availability

    The category with the greatest magnitude of price increase seen within private label brands occurred within Baby at 16.3% followed by Home at 14.3% on average. Private label products within Home and Baby categories were also showing the lowest availability rates, 75.9% and 79.5% respectively, indicating a high demand for these items even as prices increased.

    The private label categories with the smallest price increase on average were Dairy & Eggs at 2.4% and Other Foods and Pantry at 3.4% and 3.6%, respectively.

    Private Label Price Change Magnitude & Availability
    Private Label Price Change Magnitude & Availability

    While in many accounts both private label and National brands struggled with stock availability in March 2022, National brand availability is much lower (around 10% on average) than private label availability.

    H-E-B had the lowest overall product availability at 76% across both private label and National brands on average. Only Walmart had lower availability for Private Label at 75% compared to 93% for National brands, but they also had the greatest price gap between private label and National brands.

    Private Label & National Brand Product Stock Availability

    The Future of eCommerce Growth for Private Label

    Our greatest learning from this analysis is that it’s time for retailers to start thinking and planning more like the National brands they carry when it comes to positioning their private label brands for success. Successful retailers are taking this time to reset their private-label strategies and transfer short-term switching behavior into long-term customer loyalty.

    Retailers playing catch up have the opportunity to address some of the gaps highlighted throughout this analysis, for example, relative to pricing and assortment changes. Below are some of the highlighted opportunities:

    • Though inflation is driving price hikes, more than 50% of products analyzed have yet to see a price increase indicating an opportunity to protect margin
    • Narrowing the price gap between a store’s brand and National brands should not be the only focus as competitive private label brands are becoming a greater threat at a category and basket level
    • Modifying and expanding assortments as demand increases for private label can improve customer retention and loyalty, especially for cross-shopping consumers

    According to The Food Industry Association (FMI), only 20% of food retailers currently promote private brands on their homepages, and only 48% include detailed product descriptions indicating even more opportunities left on the table for retailers to optimize private label digital performance.

    Many leading retailers are leveraging real-time digital marketplace insights and eCommerce analytics solutions like ours to further their online brand presence and optimize sales performance. This report highlights only a small sample of the types of near real-time insights we provide our clients to effectively build competing strategies, make smarter pricing and merchandising decisions, and accomplish eCommerce growth goals. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.

    For access to a previously recorded webinar presented in partnership with the Private Label Manufacturers Association and conducted by DataWeave’s President and COO, Krish Thyagarajan, click here.

  • The Rise of On-Demand Grocery Delivery after the Pandemic

    The Rise of On-Demand Grocery Delivery after the Pandemic

    Before the pandemic, the grocery industry was set around brick-and-mortar stores, and there was a slow movement towards on-demand grocery. Online grocery delivery was still considered a peripheral channel. However, grocery shoppers started turning to on-demand platforms since the onset of COVID-19. According to Acosta’s report, since the pandemic, 45% of customers prefer online grocery shopping over physical stores. 

    COVID-19 drastically accelerated the online grocery delivery trend, increasing 10% and 15% of total grocery sales during the peak COVID-19 time. In the U.S., online grocery shopping reached nearly $90 billion in sales in 2020, increasing by more than $30 billion. 

    In this article, you’ll learn about the early pioneers of online grocery delivery in the U.S., the modern players, and the impact of COVID-19 on grocery trends.

    Early pioneers of online grocery delivery

    Early pioneers of online grocery delivery
    Early pioneers of online grocery delivery

    In the late 1990s, consumers had just started ordering products online. Online grocery shopping was an early area of focus. It offered lucrative rewards to high-spending consumers, increased convenience, and saved them time. Peapod, founded in 1989 by brothers Andrew and Thomas Parkinson, was the first online grocery delivery service. Back when they started, users had to install software from CD-ROMs and then place orders. Though it took years to become a well-known name in the industry, Peapod is still in business.

    Webvan and HomeGrocer.com were two other early pioneers of online grocery delivery that started in 1996 in California and 1997 in Washington respectively. Webvan had a successful launch in California, and they had aggressive expansion plans to operate in 26 major cities around the United States. However, the company filed for bankruptcy less than two years later. HomeGrocer.com quickly created the infrastructure needed to support the business, including a fleet of vans and a huge warehouse. They had impressive early growth, and sales reached over $1 million a day by mid-2000. They expanded into other markets, including California, Georgia, Oregon, Texas, and Illinois.

    Modern players of the on-demand grocery delivery

    Modern players of the on-demand grocery delivery
    Modern players of the on-demand grocery delivery

    Online Grocery Trends Post-Pandemic

    When COVID-19 first began to engulf the world, supermarkets and grocery delivery platforms like Amazon Fresh and Instamart became overwhelmed with huge demands. To handle the surge of online orders, stores had to make drastic changes to accommodate the switch to on-demand delivery requests. Popular grocery delivery brands had to introduce waitlists and online queues for new customers. According to a poll, 53% of shoppers would continue online grocery shopping because they had a good experience, indicating that the on-demand grocery trend will continue post-pandemic. 

    mckinsey grocery report
    Mckinsey Grocery Report

    As shoppers prefer more digital channels in their path to purchase, the on-demand grocery trend is becoming much more significant for both consumers and brands. According to a McKinsey and company survey, frozen fruits, health care items, fresh fruits and vegetables, packaged foods, household care items, beverages, and deli meats categories are likely to remain popular among U.S. consumers post-pandemic. Meanwhile, CoreSight Research found that fresh fruits and vegetables were the biggest bestsellers from 2020-to 2021 followed by fresh dairy, meat, eggs, frozen food, and bread and baked goods. 

    Why Grocery Shoppers are going digital

    Online ordering offers a more personalized experience to shoppers as they get recommendations for products that are often bought together. When paired with data analysis and AI-powered algorithms, grocery stores could work on targeted marketing and offer quick delivery services. 

    1. Flexibility

    On-demand grocery shopping offers customers a wide range of delivery options, including subscription services, buy online pick up in-store, click and collect, option-based pricing, and much more. This offers choice and accessibility to modern customers looking for speed and convenience.

    2. Convenience

    With the increasing focus on social distancing and safety, shoppers started to rely on delivery services rather than waiting in long queues and risking exposure. The focus and priority of grocery shoppers shifted from discounts and pricing to convenience, speed, and safety. Online grocery shopping order methods also differ by generation. 40% of millennials prefer to shop groceries on mobile, and 52% prefer computers. Similarly, 66% of Gen X prefer to shop on computers, and only 27% prefer to shop on smartphones. 

    Grocery Shoppers are going digital
    Grocery Shoppers are going digital

    3. Speed

    The fierce competition in the on-demand grocery delivery space has led to small delivery times. Startups like GoPuff (30 minutes), and Jiffy (15 minutes) are competing with the big boys like Walmart and Amazon Fresh to deliver groceries in under an hour. Quick delivery options like two-hour delivery and same-day delivery have made it easier for customers to shop for fresh produce. Customers can quickly order a few items for a specific recipe and get it delivered within a few hours. 

    4. Multiple payment methods

    At store checkouts, cash and card are the only two acceptable options. Customers prefer to have more options in today’s modern world. Online grocery shopping makes buying easier by offering multiple payment options like PayPal, credit/debit cards, and monthly payment plans that negate the delivery fees for each delivery.

    How to successfully run a Grocery Delivery Business?

    The increasing demand for speed and convenience puts pressure on the grocery industry that faces inventory issues like fresh produce and product availability. However, the benefit of online grocery delivery services is that it provides insight into the end-to-end view of the customer journey. Grocery delivery brands can use the data to design services and models that meet customer demand and minimize costs across the supply and distribution chain. 

    If you’re a Grocery Delivery company and want to track your delivery time, or product catalogue so you can boost sales with an in-demand product assortment, or you want to drive more revenue & margin by making sure your products are priced right v/s your competition, reach out to us at DataWeave! Sign up for a demo with our team to know how we can help you optimize your online sales.

  • Critical Features of an Effective Price Intelligence Tool For Retailers

    Critical Features of an Effective Price Intelligence Tool For Retailers

    In the age of a mature eCommerce and omni-channel retail ecosystem, pricing is the premier competitive battleground. It’s both the biggest offensive weapon to capture market share – and the biggest vulnerability if you stumble. In fact, a recent Statista survey revealed that 70% of US online users prioritize competitive pricing in their digital shopping choices. Yet most retailers still struggle with consistent, profitable pricing often replying on instincts rather than data-led intelligence.

    That’s where Pricing Intelligence (PI) comes in. PI is a fast-evolving discipline powering data-driven, continually optimized pricing strategies to help merchants make rapid, surgical adjustments that attract customers and protect margins. Most retailers are aware of Pricing Intelligence tools, but they miss out on getting one that serves their needs and proves its ROI consistently.

    Because of course, not all pricing intelligence solutions are created equal. Here’s top features retailers looking to invest in a Pricing Intelligence tool should look out for.

    1. Accurate Product Matching

    Of course, accurate pricing data is table stakes for any PI solution – The core premise of any pricing intelligence tool is enabling robust product tracking and price monitoring of your own catalog against the competition. 

    So, a PI tool must take care of matching each of your product across all other sources, so that you can make a straightforward comparison and take actions.

    But since the internet is not a one standard entity and even the same or similar products can have different titles, descriptions, specs and images, most retailers end up capturing incomplete or inaccurate data completely undermining their intelligence. A good Pricing Intelligence tool like DataWeave’s should be able to leverage Similarity Matching and AI-based image tracking to bring more products under product matches and present a more complete picture.



    2. Width of pricing types and factoring in real net effective prices

    Product accuracy must extend far beyond just basic “landed” or “street” pricing and cover more types of specialized pricing situations. A robust pricing intelligence tool should automatically detect and handle nuanced mechanics like:

    – Bundled/kit/packaged pricing 

    – Pricing regulated by manufacturer policies (MSRP, MAP, etc.)

    – Complex promotional structures (% off, BOGO, BXGX, etc.)

    – Inventory-level or stocking threshold-based pricing

    – Zonal/regional taxes, fees and price variations

    – Segment-based pricing for members, loyalty tiers, etc.

    – Pricing tiers or breaks based on volume/purchase quantities

    Properly capturing and classifying these additional pricing nuances by retail vertical is key. Otherwise you’ll have major blind spots and inaccuracies that leave you open to being undercut or overpriced compared to real-world market dynamics.

    3. Real-Time, Continuous Monitoring and High Data Update Frequency

    Data points like product prices and offers get stale fairly quickly. Ideally, we want to see real time data. Real time is not achievable at scale, or might even be an overkill in many cases.

    However, an effective PI tool must present up-to-date data to the extent possible. Based on requirement this can vary from a day to a few hours thus helping the business stay ahead of the price curve.

    4. Scalable Coverage and Contextual Enrichment For Full Product Information

    For many retailers, one of the biggest pricing intelligence challenges is scaling comprehensive, accurate monitoring across their full product catalog and relevant competitor ecosystem. This is especially true for those operating regionally or with multiple banners/brands. 

    You need robust data collection capabilities to ingest and process pricing data on everything from big box retailers and national sellers all the way down to small mom-and-pop shops that may only sell locally – but could still impact your pricing perception.

    A best-in-class PI solution should have the ability to dynamically monitor millions of products and tens of thousands of competitor sources globally, processing all those inputs in a normalized, unified way. Additionally, your PI solution needs to be flexible to adapt seasonal or special requirements – whether that involves tracking key value items more frequently, or getting updates on pricing changes during festive seasons.

    But beyond just raw data collection scale, leading PI solutions also enrich and add context around that pricing data to make it far more actionable through technologies like:

    – Machine learning models to extract intelligent insights 

    – Semantic processing to identify nuanced pricing mechanics

    – Competitive product knowledge graphs to map relationships

    – Location data appending for geographic/zonal context  

    This enrichment bridges the gap between simple “list prices” and real-world factors like localized promotions, inventory levels, demand elasticity and other variables that should be driving more nuanced, profitable pricing decisions.

    5. Pricing Opportunities

    A good PI tool should present data at different levels of granularity: category, sub-category, brand, and individual product. This helps the category/merchandizing team or the pricing analysts to surgically strike problem areas. For instance, when you are tracking 1000s or even 100s of products, it’s next to impossible to go over every product and take pricing decisions.

    Furthermore, with large, diverse product catalogs, it’s impossible for category managers to manually monitor pricing on every SKU. Your pricing intelligence tool must automatically analyze and highlight prioritized pricing opportunities where action is needed – enabling efficient pricing decisions at a glance.

    6. Historical Pricing

    “Prediction is very difficult, especially if it’s about the future.” But they also say, history can be a useful predictor of the future. Nowhere is it truer than in competitive price intelligence.

    An analysis of historical data almost always shows a trend that can be capitalized on for competitive pricing. A good PI tool stores and presents historical pricing data in a useful manner.

    7. “It’s not [just] about the money”

    Retail is a highly competitive and commoditized sector. So, price is an important factor for a consumer when making a decision to buy a product. Having said that, as a retailer, you don’t always want to compete on pricing.

    You may want to compete through better packaging, or giving the user more choice (variants/colours/sizes), or better SLAs. This is where a Price Intelligence tool needs to go beyond just pricing. It needs to capture and present all other relevant data points associated with a product.

    8. Uncluttered User Experience

    Any tool built for a user needs to be usable, intuitive, and uncluttered. More so for busy managers who need to take several decisions quickly day on day. A Price Intelligence tool is in essence a Data Product. A data product is built on top of a lot of data; however, a good data product is one “where data recedes to the background”.

    A data product is not one that delivers a lot of data, but one that delivers actionable data and insights based on data. Data presentation is also another important aspect. A good PI tool delivers the most important data points in formats and templates that a customer can easily consume.

    DataWeave provides Competitive Intelligence for retailers, brands, and manufacturers. It is built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches.

    DataWeave is powered by distributed data crawling and processing engines that enables serving millions of data points around products data refreshed on a daily basis. This data is presented through dashboards, notifications, and reports. PriceWeave brings the ability to use BigData in compelling ways to retailers.

    PriceWeave lets you track any number of products across any categories against your competitors. Still not convinced? Try us out. Just send us a request for a demo.

  • 9 Things to Build a Thriving Fashion eCommerce Brand

    9 Things to Build a Thriving Fashion eCommerce Brand

    According to the Statista Fashion eCommerce report 2021, the compound annual growth rate (CAGR) for online fashion is predicted to be 10.3% between 2018-2023. The widespread need for trendy fashion presents a challenge for fashion brands to succeed in a highly crowded and competitive space. With eCommerce shopping becoming more prevalent, fashion brands aren’t just competing for brick-and-mortar sales. Instead, they’re also competing for those late-night or impulse purchases from online customers.

    Looking to 2022 and beyond, this blog will highlight 9 things to build a thriving fashion eCommerce brand:

    1. Allow shopping on multiple channels

    Breakdown of Shopping journeys in Apparel
    Breakdown of Shopping journeys in Apparel

    Typically buyers from diverse age groups prefer different sales channels. Some prefer large retailers, and some choose web stores. If you know where your customers like to purchase your products, you can leverage the power of search engines and marketplaces to improve your sales. Multi-channel retailing helps fashion eCommerce brands to sell and promote products on a platform and device of the audience’s choice. 

    A brand should offer support and access to its products across all platforms, channels, and devices. It helps fashion brands to reach customers where they prefer to shop. If your customers prefer to shop on a computer or an app, your brand can offer a seamless customer experience. 

    2. Don’t sell on the Homepage

    Your online fashion store homepage is more about increasing credibility and trust among potential buyers. Your ideal home page shouldn’t display products or their prices. Instead, it would be best to integrate promotional and marketing strategies on the landing page to encourage visitors to explore your product categories and the rest of the website. You should have an intuitive interface that makes navigating the pages easier. You can also use the homepage to promote seasonal offers and new launches. Fashion brands can also display customer reviews, awards, brand achievements, and web security trust seals to increase the conversion rate.

    Don't sell on homepage
    Don’t sell on the homepage

    3. Product Descriptions with Unique Stories

    Product descriptions often get overlooked or underutilized even though they are important for eCommerce businesses. Your products won’t sell with spammy and same product descriptions. The modern product description is all about communicating a product’s worth and value with a story that captivates your buyer’s attention. Identify areas where your content & images don’t align with your product or represent it in the best light. Make sure to deliver an enhanced consistent brand experience across all online channels to improve your conversions.

    4. Focus on Review and Ratings

    Rating & Review of a fashion brand
    Rating & Review of a fashion brand

    Customer reviews have a huge influence on a buyer’s purchase decision, especially in the fashion industry. Encourage your consumers to leave reviews on your brand website. Reviews help fashion brands to build trust for their products and convert customers. Legitimate customer reviews help your shoppers to get crucial insights into what previous buyers liked or disliked about a particular product. 

    However, you should stay away from paid-for or false reviews usually encouraged by unscrupulous sellers as they are easy to spot and hurt your rankings. You must remember that receiving reviews also includes dealing with negative comments. They should be used to improve your upcoming product offerings. 

    5. Sell Looks

    Product can be combined with in the detail page
    The product can be combined with in the detail page

    Successful fashion brands don’t simply sell individual products. Instead, they sell complete looks that inspire shoppers to purchase the entire stylish look. As an online fashion brand, you’re not selling clothes; you’re selling an elegant collection of wearable art. When visitors reach your online store, you should appeal to their fantasies and sentiments through aesthetic look books that are both pleasing and congruent with your brand. Most successful online fashion shops are inspirational and visual. Look books help brands pair their previous season items or dead stock with new stock and increase sales. Brands can also share these look books on social media or in their monthly newsletters to increase reach. 

    6. Provide Promotions and Offers

    Fashion brands can take advantage of plenty of sales throughout the year, from New Year celebrations to Black Friday, Cyber Monday, and Christmas. Brands can leverage these high sales periods to sell looks and gift items to boost sales. Just make sure you’re measuring the effectiveness of your online promotions. Holiday and festive sales also offer an excellent opportunity to plan strategic discounts to get rid of old stock. Since trends in the fashion industry have been changing rapidly, you can use discounts to get rid of dead-stock or out-of-trend items each season. 

    7. Be active on social media

    Social media is a way to promote your brand, increase trust among your audience, and entertain your audience with exciting content. You can also engage the audience by providing gift coupons or giveaways. Brands can promote products while keeping their audience engaged with engaging content and promotional offers. 

    Social media is a great way to get influencer support, either organically or through a paid partnership. Brands have to focus on every element of social media marketing strategy, right from choosing a platform, creating Instagram/Facebook shops, jumping on trends/events, and tracking customer sentiment. 

    8. High-quality product photography

    Capture every detail of your product
    Capture every detail of your product

    Nothing is worse than ordering a piece of clothing online and not getting what you saw on the website. Not being able to accurately convey fashion products will hurt your bottom line. Fashion brands must use top-notch product photography that includes high-quality visuals, such as multiple angle views, 360-degree images of each product, accurate depictions of all color options, and the option to zoom in on product attributes.  

    High-quality product photography
    High-quality product photography

    A recent game-changer in the fashion industry has been including different sets of models to accurately feature clothes of various shapes, heights, and weights. Instead of displaying a dress in only one size, fashion brands can have multiple models wearing various sizes for the same article of clothing.  

    9. Stay up to date with new trends

    Fashion eCommerce brands have to be particularly careful of continuously updating their product offering with the latest fashion trends for each season. They can boost sales with an in-demand product assortment. Continuously updated fashion inventory signifies that the brand is up-to-date with the latest fashion trends in the market and has unique products to offer. You can always get creative with new styling, better looks, and personalized product recommendations. 

    Conclusion

    Fashion eCommerce is rapidly growing and transforming at a staggering rate as technologies continue to advance. Traditional fashion brands can now expand their reach from brick-and-mortar shops to digital and eCommerce platforms to reach shoppers across the globe. The new digital selling opportunities also come with considerable challenges – from staying up to date with ever-evolving trends to managing dead stock. 
    Are you a fashion brand that needs help monitoring your product content? Or measuring the effectiveness of your online promotions? Or decoding customer sentiment from reviews they’ve left for your products? Sign up for a demo with our team to know how DataWeave can help!

  • Fake Reviews: A Real Pain Point for Brands

    Fake Reviews: A Real Pain Point for Brands

    Online reviews have revolutionized how customers purchase products and services. In fact, eCommerce success for certain products hinges on the ratings and reviews. With this, have come the pitfalls of corruption in eCommerce.

    New brands trying to establish a presence and capture critical mass have been known to resort to soliciting fake and paid reviews to uplift their brand in search rankings. Similarly, these brands can also encourage fake negative reviews on competitor’s listings to bring down their value. Bots and paid manual reviews are usually employed to rake up the review count. Review sites like TrustPilot, Google Reviews, and marketplaces like Amazon are littered with fraudulent reviews. In fact, Guardian calculated that 3.6% of all reviews on TripAdvisor were fraudulent. According to a 2021 report by Statista, 46% of the 2.7 million online fake reviews that were removed were five-star reviews! 

    Fake online reviews are misleading since customers shopping both online and offline rely on reviews to make purchase decisions. Fake reviews also pose further problems because they deceive consumers into spending money on a product or with a company they may not have otherwise chosen. 

    Federal Trade Commission (FTC) made a recent announcement to send penalties to over 700 brands and retailers for fake endorsements and reviews. While this notice references influencer content and testimonials, it also applies to customer reviews. 

    In this blog, we will discuss the importance of reviews for brands and retailers, spotting fake reviews on Amazon, and steps that eCommerce companies can take to tackle fake reviews. 

    Importance of reviews for Brands and Retailers

    Customers do not make blind purchases. Consumers read reviews before buying products. Statistics show that irrespective of the industry, having a positive online presence is essential and has become an integral part of branding. It also indicates that customers have a high confidence level in fellow consumers’ opinions. Overall, positive online ratings & reviews can help skyrocket eCommerce sales.

    Customers are more likely to purchase if other customers, even strangers, agree that it was a great purchase. Reviews also make brands more visible. 

    Why are fake online reviews so resilient?

    A significant reason is that the ROI of getting fake reviews increases profitability & sales multifold. For example, an extra star on Yelp can increase a restaurant’s revenue by 5% to 9%. FTC has said that the expenditure on fake reviews can provide a 20x return. However, fake and incentivized reviews are a huge problem. Amazon, one of the largest eCommerce marketplaces, banned incentivized reviews in 2016. It took down suspicious reviews and has taken legal action against sellers who violate its policies. 

    Online Reviews
    Online Reviews

    How to Spot a Fake Review on Amazon

    Marketplaces, Google, and review sites like Yelp can get hundreds of thousands of reviews daily. In a survey by PCMag that interviewed 1,000 US shoppers who looked forward to shopping on Prime Day 2020, only 16% were very confident about detecting fake Amazon product reviews, and 24% were confident they could do it. The rest of the survey respondents were somewhat or not confident they could pick out the fakes on Amazon. Here are our best tips for spotting fake reviews on marketplaces like Amazon:

    • Duplicate Content: If you notice dozens of reviews with the same description and title as if they were copied and pasted multiple times, they’re most likely fake reviews. 
    • Multiple Reviews on the Same Day: Another identification of fake reviews is when there are dozens or multiple reviews on a single day. There can be a bunch of both positive and negative reviews for products.
    • Unverified or Anonymous Reviewers: You can see if the review is from a verified buyer on Amazon. Brands can also check if they have any record of the reviewer’s purchase to weed out fake reviews. 
    • Incorrect Language: Fake reviews can come from people outside your country. If you notice multiple reviews with similar incorrect words and common errors, there is a good chance those reviews are fake, and someone paid the reviewer to write them.

    What can eCommerce brands do to protect themselves against fake reviews?

    • Follow a zero-tolerance policy for fake reviews.

    The major step is to ensure that fake reviews are never posted on your site. Allowing fake reviews negatively affects your business and your bottom line. You can hire a third-party UGC moderator that uses data-driven, anti-fraud methods to evaluate reviews. It will be a much more successful and quicker step in protecting your brand’s reputation.

    • Don’t screen out negative reviews. 

    While receiving a negative review might be the worst nightmare, they’re necessary for a successful UGC program. Customers are more likely to purchase from a business that responds to all reviews, including negative reviews. Customers said that negative reviews have more detailed product information, while 32% of those customers think they’re less likely to be fake. Besides, brands that respond to negative reviews gain customers’ trust and loyalty.
    Here are some Tips on how to Respond to Negative reviews online. 

    • Be transparent about how you collect UGC.

    Brands can ensure that their customers trust user-generated content by being honest about how they collected it. Companies should never ask for paid or incentivized positive reviews. Instead, brands should empower their customers to leave honest feedback. If you’re offering free products, a chance to win something, or discount coupons in exchange for an unbiased review, then the review should specify how it was collected. For example, you can add indicators like “this reviewer received a coupon or a free product in exchange for honest feedback.

    • Maintain trust

    Having fake reviews causes a loss of trust, with many consumers believing that they have seen fake reviews for online and offline businesses. Removing fake reviews doesn’t only help with revenue and brand trust, but it also helps brands to maintain trust among their existing and future customers. 

    Conclusion

    Fake reviews are one of the biggest reputation killers and a huge problem for eCommerce platforms, brands, and customers. Brands must take the necessary steps to minimize the risk of fake reviews and expand businesses among authentic users. Although modern text generation tools are becoming more competent in writing realistic reviews, there are AI- and ML-backed tools that can accurately detect reviews written by other machines. 

    Need help tracking your online ratings & reviews? Or decoding customer sentiment from reviews they’ve left for your products? DataWeave offers a customizable and scaleable data solution to analyse ratings and reviews for online retailers and brands vis v vis their competitors.
    Sign up for a demo with our team to know how DataWeave can help.

  • How VCs and Brand Rollups are using Data for faster Acquisitions

    How VCs and Brand Rollups are using Data for faster Acquisitions

    When it comes to brands – the biggest story of 2021 was the astronomical growth of Brand Roll-ups. For the uninitiated, Brand Roll-ups are companies that acquire multiple digital consumer brands and then scale these brands 100x by leveraging their own operational expertise across eCommerce platforms, Supply Chain, Warehousing, Marketing, and so on.

    Thrasio is the poster boy for the Brand roll-ups and is valued at over 10 Bn USD.

    Brand rollups have raised over $12 billion in 2021 and the trend only seems to be accelerating in 2022. Not only Brand Roll ups, but VCs too have been pouring money into digital brands. In India, 77+ brands have raised more than 2B USD in 2021. In the US this number is estimated to be north of $10 billion.

    Cumulative capital raised by Amazon Aggregators
    Cumulative capital raised by Amazon Aggregators

    Scaling fast doesn’t come easy. It comes with its own set of challenges. So even with ample experience in running and scaling brands, Brand roll-ups are posed with unique challenges.

    Challenge of Scouting the right brand

    There are 1000s of online consumer brands and new ones are launching every day. Every Brand roll-up wants to be the first one to scout a brand – but this is not easy.

    The challenge here is to identify & pick the right brands without having access to any sales or financial data. Every Brand Rollup has a wishlist with regards to the number of SKUs, price points, reviews, and ratings as well – but don’t have tools in place to scout brands with these criteria in mind. And across multiple platforms and categories, the problem gets more complicated.

    This is an ongoing problem since a brand that was not selling well yesterday may start hitting higher sales numbers a week down the line – and that is why Brand scouting has to be a continuous process.

    One way these aggregators have solved this challenge is by offering mouth-watering referral fees for referring a brand. But this is not a sustainable long-term solution.

    Data Comes to the Rescue

    What Brand Roll-ups need is a continuous and automated data first Brand Scouting solution to enable them to scout the right brands.

    • What are all the brands in a category of interest?
    • Which of these brands is within the filters of Number of SKUs, Price Range, etc.?
    • Which brands have shown an exceptional rise in search rankings?
    • Which brands have shown the most increase in the number of ratings and reviews?
    • Which brands have the highest gain in the customer ratings?
    • What are the estimated sales and market share of the brands?

    DataWeave’s Brand Scouting solution solves exactly this.

    DataWeave’s Brand Scouting Solution

    DataWeave’s Brand Scouting Solution is a comprehensive solution to help Brand Rollups and VCs scout for the ideal brand that fits their acquisition profile. We leverage public data collected from multiple eCommerce platforms to get them the desired information on brands they’re looking for.

    For all the focused categories (Typically 30-40) – we collect data of all the SKUs (Typically 15,000-20,000) and aggregate that at a Brand level:

    • Ranking – Usually Brand Rollups are not interested in the Brands which are on the first page. But, they are interested in the brands which might be b/w 500 to 10,000 ranks but are showing an exceptional gain in ranking week on week.
    Brand Discoverability & Ranking on Amazon
    Brand Discoverability & Ranking on Amazon
    • Ratings – It’s important to look at brands that are showing high improvement in ratings or have consistently shown high ratings. The proportion of 5 stars vs. 1 star is an important metric here.
    • Number of Reviews and Ratings – We enable you to find brands that have both high ratings as well as a high number of reviews. This is a very good metric to find the brands in a category that are getting exceptional customer love.
    Brand Popularity Tracker
    Brand Popularity Tracker
    • Filters – We enable filtering in terms of – No. of SKUs, Price Range, Rating and Reviews and even can eliminate established brands so that you only see the brands which qualify your criteria. We also enable you to separately analyze brands that are buying sponsored ads in a category, so you have a clear distinction between organic and sponsored growth of these brands.
    • Trends – What is important is not just the static performance on the day of analysis – but a trend analysis over a period of time to find the brands which are growing exceptionally.
    Brand Score Trend, Average Rating trend & No of Reviews Trend
    Brand Score Trend, Average Rating trend & No of Reviews Trend

    … but, wait there’s more.

    We compliment Brand Scouting with three more solutions to provide the right context and further analysis needed to provide comprehensive insights into the category and platforms where you are scouting for brands:

    Category Analytics: When you are looking at a category and the brands in that category, it is often important to understand how dynamic that category is. We can help analyze:

    • If the category is crowded with more brands per product.
    • Does it have space for new brands?
    • What is the number of new brands entering that category?
    • What is the number of new SKUs entering that category?
    Category & Subcategory Evaluation
    Category & Subcategory Evaluation

    We can also help with benchmarking the category – to help understand how the brand that you are scouting is doing when compared to its category peers.

    Rank Group versus Price, Rating & No of Reviews
    Rank Group versus Price, Rating & No of Reviews

    Sales & Share: We can also provide a good directional estimate of the sales and market share of all the SKUs in the category wherein you are scouting for brands. These are estimates powered by our proprietary machine learning algorithms and can help you solidify your hypothesis around a blog or a category.

    Revenue by Price Points
    Revenue by Price Points

    Sentiment Analysis of Reviews: Customer reviews tell more about the qualitative aspects of the SKU and the brand itself. Our algorithms can help understand what features of a brand or a product do customers really care about. We can answer questions such as:

    • Which features are mentioned most commonly?
    • Which features are mentioned positively or negatively?
    • What adjective is used to describe that particular feature?
    Customer Sentiment Analysis
    Customer Sentiment Analysis

    The suite of Brand Scouting and complementary solutions is evolving rapidly as the space is evolving rapidly. We are supporting several VCs and Brand Roll-ups globally to scout for brands.

    The best aspect about DataWeave is our ability to scout brands across 2,000+ eCommerce platforms globally across geographies. We are super stoked to be playing an enabler in the Brand Rollup revolution.

    Beyond Brand Scouting – Digital Shelf Analytics

    The challenge for Brand roll-ups is not over by just scouting and acquiring a brand. The journey is just about starting – the next challenge that the Brand Rollup faces now is to scale up these brands.

    The challenge the Brand Rollup face is unique and very different from a single brand operator or even traditional CPG conglomerates.

    DataWeave’s flexible product philosophy enables Brand Roll-ups to diagnose and measure the performance of multiple brands across multiple platforms in one dashboard.

  • How Restaurants can use QSR Intelligence to Drive Sales

    How Restaurants can use QSR Intelligence to Drive Sales

    Quick service restaurants (QSR) are not only about delivering great food. They also have to overcome challenges like delivery, logistics, and affordable pricing, especially since covid-19 has staggered the entire industry. QSR intelligence helps restaurants get real-time insight into their performance across food delivery apps. With QSR intelligence, restaurants can identify the highest paying buyers across customer segments, demographics, and locations. Data-driven insights will help QSRs improve performance, decrease delivery time, optimize ad budget, and increase food quality – all with the goal to scale revenue and increase orders through food apps.

    The global fast food and quick service restaurant market are expected to grow at a CAGR of 5.1% from 2020 to 2027. The QSR industry is rapidly growing to encompass the changing needs of customers. 60% of U.S. consumers order delivery or takeout once a week and online ordering is growing 300% faster than in-house dining. With QSR intelligence, restaurants can get insights into metrics that will drive their profitability by helping them to fine-tune menus, enhance customer interaction, improve advertisements, and adjust inventory.

    Benefits of QSR Intelligence

    Continuous in-depth analysis of restaurant statistical data will help companies spot trends and devise strategies to improve sales via food apps. Here are a few benefits of QSR intelligence:

    a.    Improve estimates & minimize wait times

    QSR intelligence can help with accurate sales forecasting. With big data, restaurants can track their popular dishes or combos for various meal times to minimize wait times and increase delivery speed. It can also inform restaurants about upcoming trends, especially during holidays and festivals. Keeping an eye for trends will play a significant role in maximizing efficiency during food preparation and ensuring accurate food delivery ETAs.

    b.    Location-based promotions

    QSR intelligence allows restaurants to target customers based on their proximity to the restaurant. The food must be delivered at a particular time to the customers to enjoy the dish at the right temperature. QSRs can apply demographic intelligence to determine cancellation rates, delivery charges, and the proportion of demand and supply. These metrics will help QSRs to improve location-based promotions.

    c.    Increase ROI on deliveries

    To increase return on investment through food deliveries, QSRs can track metrics like location-based promotions, various payment options, ratings, etc. Tracking these metrics will help QSRs offer accurate ETAs, improve operational efficiency, and personalize services, which will increase revenue. Restaurants will also be able to understand where they can adjust their profit margins to increase revenue while maintaining a cumulative level of success.

    How to use QSR Intelligence

    a.    Assortment and availability

    The more restaurants can understand what and how their customers eat, the better they will be prepared to service those demands throughout the day. For example, QSRs can calibrate the menu, ingredients availability, and kitchen preparation time depending on their customers’ orders for lunch and dinner. This also helps optimize daily workflow, such as reorganizing staff to lower labor costs, optimizing the supply chain for ingredient delivery, and revamping the menu to offer better dishes. Another way to ensure your availability is to analyze your busiest hours and adjust the staff and delivery workforce accordingly. For example, if your customers tend to order more during breakfast, it’s worth considering opening your restaurant a bit earlier.

    QSR availability across 4 Food Delivery apps
    Availability across 4 QSR Food Delivery apps
    Availability trend during peak hours - Lunch & Dinner
    Availability trend during peak hours – Lunch & Dinner

    b.    Delivery time

    One of the most driving factors for the success of QSR is delivery time. Restaurants have to ensure the food is delivered as quickly as possible so customers can consume it at the right temperature. Data-driven insights can help restaurants track repeat addresses, find shortcuts or time-saving routes, and avoid unfamiliar or low delivery locations.

    QSRs have to analyze the entire delivery process from time taken to order on the app, how quickly kitchens can prepare orders, hand over to delivery partners, and get them to the customers. An essential part of QSRs is throughput, the speed at which they can process and deliver orders. During peak hours like lunch and dinner, faster service and quick ETAs ensure that customers do not choose other restaurants. If you have different menus for breakfast and other meals, ensure that your foodservice app can remove such menus when they are not available.

    Delivery Time Analysis
    Delivery Time Analysis
    Delivery Fee Analysis
    Delivery Fee Analysis

    c.    Pricing and Promotions

    QSRs have to understand customers’ price sensitivity while determining delivery costs and ensuring profitability for the business and delivery partners. Customers might look for free deliveries but not adding delivery charges might lead to loss. A deep dive into common transaction data across the locations will allow restaurants to understand the price sensitivity of all customer segments, helping them make intelligent pricing decisions.

    QSR intelligence can also help restaurants determine which delivery locations are most profitable. This helps to adjust the delivery radius, fee, and promotions. Restaurants can offer promo codes, coupons, referral codes, etc., to attract customers and encourage repeat purchases.

    d.    Discoverability

    Restaurants have to ensure that their dishes are on the first-page listing. With QSR intelligence on category analysis, keyword optimization, and competition analysis, restaurants can help their customers discover dishes. This also includes optimizing listings for pricing and rating and delivery fees and availability during peak times such as breakfast, lunch, and dinner.

    e.    Advertisement Optimizer

    QSRs can use data to optimize the advertisement budget and adequately improve return on investment. They can track the visibility of advertisement banners across locations and optimize them for different times of the day. Data analysis can also help restaurants understand which customer segments are more likely to convert to long-term loyalists. This data will help QSRs design personalized campaigns and align advertisement budgets while converting them to long-term customers, further improving the bottom line.

    Ad spends by identifying carousels with the highest visibility
    Ad spends by identifying carousels with the highest visibility
    Track QSRs performance across Carousels across multiple zip codes
    Track QSRs performance across Carousels across multiple zip codes

    f.     Growth & Expansion

    Upselling and cross-selling are two popular tactics that improve growth for quick-service restaurants. However, that requires a rich understanding of customers’ price sensitivity, preferences, and behavior. QSR intelligence can provide information about which upsell and cross-selling offers a customer segment is likely to value and which optimal channels for distributing the offer.

    Conclusion

    Quick service restaurants can track critical data points and use them to increase revenue and improve customer experience. Learning how to price, promote, and deliver food to customers during a pandemic can be challenging. QSR intelligence will help brands attract the right clientele, adjust inventory, reduce overall marketing costs, and increase order rates. This will also help increase customer loyalty across segments which can, in turn, increase the number of returning customers and profitability.

  • UK’s Biggest Sale Days: What we saw in 2021 and trends for 2022

    UK’s Biggest Sale Days: What we saw in 2021 and trends for 2022

    Customers love discounts, and promotions are the most effective tool to attract shoppers and increase sales during the holiday season and clearance sales. According to a survey, 76% of UK customers look for discounts before purchasing a product. Promotional discounts encourage customers to try new brands. And this is why brands often have a special coupon for first-time users. 

    According to Software Advice, discounting tops the pricing strategy for retailers across all industries. It is preferred by 97% of survey respondents over other promotional strategies. 

    Share of Respondents
    Share of Respondents

    Retail Trends in the UK for 2022

    The arrival of the Omicron variant in December 2021 slashed the shopping mood of UK customers and led to a 3.7% monthly drop in retail sales, but sales were still higher than February 2020 levels when Covid-19 first hit worldwide. Sales during the holiday season in 2021 took a hit due to a consistent decline in product availability and an increase in prices.  Inflation too started to rise in 2021 and is expected to increase by 7% by spring 2022. However, despite inflation, retail sales jumped back in January 2022. In fact, it is predicted that inflation will be a key driver of sales growth, with underlying demand across categories being uneven. Keeping that in mind, let’s look at sales growth across categories in 2021 and projected growth in 2022.

    Category Breakdown: Sales growth 2021/22
    Category Breakdown: Sales growth 2021/22

    Discounting Trends we saw in the UK in 2021

    Methodology

    • We tracked prices on the three biggest Sales Days in the UK
      – Amazon Prime Day, June 21st & 22nd 2021
      – Black Friday, Nov 26th, 2021
      – Cyber Monday, Nov 29th, 2021
    • Categories tracked: Beauty, Fashion, Electronics, Home Improvement, Furniture 
    • Websites tracked: Amazon UK, OnBuy, eBay UK, Etsy, Wayfair, Selfridges, John Lewis

    Prime Day, Black Friday, and Cyber Monday are three of the biggest sales days with comparable discounts. However, according to new research, in 54% of cases, it depends on the category of product you’re after that determines the volume of discount you get. For example, tech items such as smartphones, laptops, games consoles, smartwatches, and wireless speakers were cheaper on Black Friday but may not necessarily have been cheaper on the other sale days. 

    We wanted to see which sale period had the most number of products on discount during the three big sale events. We also wanted to see which of those three sales would’ve been the best for consumers to get a higher section of products at a discount. 

    How Big were the Discounts?

    Discount across 3 key Sale Days
    Discount across 3 key Sale Days

    32% of products went on discount during Black Friday, 35% on Cyber Monday, and only 6.6% on Prime Day. One factor contributing to the low Prime Day percentage is the fact that not all retailers participate in discounting wars during Prime Day since it’s an exclusive Amazon-only sale. Customers looking for the best deals would’ve gotten them during the holiday season with a combination of the Black Friday & Cyber Monday sales. 

    Another interesting thing to note is the percentage discount – on Prime Day, only 0.2% of products had a discount of over 50% of all the discounted products. While on Black Friday & Cyber Monday that number was 1.7% & 1.3% respectively. 

    In conclusion, more products were offered at a discount on Black Friday & Cyber Monday; and the total percentage discount on those products was also higher.

    Which Categories had the Maximum Discount?

    Discounts by category
    Discounts by category

    On Black Friday, an estimated 47% of consumers in the UK planned to shop for electronics, whereas 40% of customers planned to shop for clothing and footwear during Black Friday to Cyber Monday.  The top-selling categories across the 48 hours of Amazon UK’s Black Friday 2021 sale included Home, Toys, Beauty, Books, and Health & Personal Care.

    Our data shows that Categories with the highest discounts were Beauty and Electronics with the highest discount on all 3 sale events. These 2 categories had discounts on over 40% products on Black Friday & Cyber Monday while categories like Home Improvement were in the 30 – 35% range, Furniture in the 27 – 32% range and Fashion has the least products on discounts at a little over 15%

    In the fashion category in the UK, Amazon UK offered the highest percentage of items with a price decrease (31.6%), whereas eBay offered the most significant magnitude of price decrease (14.3%). 

    Which UK Retailers gave the most discounts?

    OnBuy is an emerging marketplace in the UK that offers impressive discounted prices and is taking on top UK marketplaces like Amazon. It’s ranked Britain’s fastest-growing eCommerce platform in 2020 and also the fastest grower by traffic. The low listing fees starting at 5% allow sellers to competitively price their products, making them more accessible to a greater number of buyers with huge discounts. The most prominent deals and discounts are highlighted on the landing page and featured across OnBuy’s social pages to grab the audience’s attention. 

    Discounts by Retailer
    Discounts by Retailer

    This was clearly reflective in the data we gathered from the 3 big sales in 2021. Most retailers in the UK, including Amazon offered at best 20% of their products, in the categories we tracked, at discount. The only outlier was OnBuy – OnBuy offered close to 90% of their products at discount! 

    OnBuy was able to offer a comparatively high number of discounted products than their competition because the magnitude of the discount was much much lower. The platform offered minimal discounts; out of the 90% of discounted products, 80% of those products had discounts that were less than 10%. As opposed to other retailers who had under 7% of their products on discounts of less than 10%.

    OnBuy’s discounting strategy built a perception that they were the biggest discounters, even when the discounts were not as deep.

    Black Friday v/s Cyber Monday – which one was better for holiday shoppers?

    Discount by category- Black Friday VS Cyber Monday
    Discount by category- Black Friday VS Cyber Monday

    Black Friday kicks off the holiday shopping season and is synonymous with some of the most significant sales after Thanksgiving. But until recently, Cyber Monday has become a great way for eCommerce retailers to capitalize on holiday discounts and expand their most beneficial sales events of the year.

    In 2021, retailers pulled in $8.9 billion in Black Friday online sales and a total sales of $10.7 billion on Cyber Monday. In the YOY review, Black Friday saw a decline of 1.3% from 2020’s record of $9.03 billion, and Cyber Monday saw a drop of 1.4%, only $100 million shy of $10.8 billion in 2020. 

    Across Beauty, Home Improvement, Electronics & Furniture categories, we saw that more products were on discount on Cyber Monday v/s Black Friday. However, the opposite was true for the Fashion Category. In the Fashion Category, we saw a marginally higher number of products on Discount during Black Friday than Cyber Monday.

    Discount percentages across categories
    Discount percentages across categories

    Across both sales, the Electronics category offered the highest discounts at over 40% of products discounted compared to other categories on both Black Friday & Cyber Monday. However, a very small fraction of the products had a discount of over 50%, indicating the lack of ‘BIG blockbuster deals’ in this category. At the same time, the Fashion category offered the least number of deals with less than 20% products on discount, but the highest magnitude of discount across the board! On Black Friday, 3.8% of products had discounts higher than 50%, and 2.6% of products on Cyber Monday. In most other categories, between 1 – 1.5% of products had over 50% discount. However, Fashion brands offered more than 50% discount on 2x the average number of products on both sale days.

    Why did the Fashion Category offer such high discounts? Brands are now capitalizing on customers’ need for instant gratification in the age of see-now, buy-now fashion trends by offering their products at high discounts. It also allows them to quickly eliminate overstock. However, this has given rise to fast fashion, a trend that focuses on rapidly producing low-quality clothes in huge volume. Fast fashion focuses on replicating trendy pieces like streetwear and fashion week designs, not four times a year but every week, if not daily. Fast fashion promotes brands to manufacture and sell low-quality merchandise that goes out of trend as soon as buyers wear it once. There is little to no time for quality control, and pieces are thrown away after a few wears. In the UK alone, 300,000 tonnes of used clothes are buried or burned in landfills each year. However, every element of fast fashion from rapid production, competitive pricing, to trend replication has a detrimental impact on the planet.

    Conclusion  

    The effects of COVID-19 can be seen far and wide in the UK retail industry, especially with a steep rise in inflation. Fortunately, even though retail sales in the UK declined during the 2021 holiday season due to the Omicron variant, they increased during Black Friday and Cyber Monday. Sales also jumped back in January 2022 and are further projected to grow by 5% in 2022. Additionally, brands can sustain the impact of disruptive factors throughout 2022 by ensuring their Digital Shelf is updated and flexible enough to react swiftly to both threats and opportunities in order to maximize the chances of success. 

    Reach out to the team at DataWeave if you’d like to make smarter pricing & discounting decisions with up-to-date competitive insights. 

  • Valentine’s Day eCommerce Insights

    Valentine’s Day eCommerce Insights

    Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions and help drive profitable growth in an intensifying competitive environment. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.         

  • What Historical Pricing Data can tell you & how to use it

    What Historical Pricing Data can tell you & how to use it

    For many brands, pricing strategy boils down to guesswork — shooting in the dark and hoping consumers are willing and happy to pay. However, the ‘throw it at the wall, and see what sticks’ pricing strategy leads to big pricing mistakes. Pinning down an optimal price for products requires a clear picture of ideal customers, understanding each customer segment’s behavior, a solid grasp of your product’s value, and an analysis of competitors. Pricing analytics can help brands track a wide range of pricing metrics with cutting-edge analytical tools and use insights to get ahead of their competition. This analysis uses historical data to understand how previous pricing and promotion activities affect brand, sales, and customer price perception. It often involves identifying opportunities and weaknesses in competitors’ pricing strategies and exploiting them to improve sales and revenue. 

    Pricing analytics helps brands understand how product pricing and promotions affect profitability and the steps they can take to optimize their pricing structures. Brands can leverage their pricing and consumer data to design appropriate pricing models for achieving their sales goals.

    Here is a brief overview of pricing analytics, its benefits, and ways to improve sales with historical pricing analytics.

    What is historical pricing data analytics?

    historical pricing data analytics
    Historical Pricing Data Analytics

    Pricing analytics uses historical pricing and demand data to understand how pricing activities have affected profitability and overall brand. It also helps to optimize a brands’ pricing strategy for maximum revenue. Manual tracking of pricing for brands with numerous product lines, multiple selling points, different customer tiers, and complex product bundles is a huge challenge. Brands from every sector and industry vertical, manufacturing and distribution to retail and eCommerce, can benefit from pricing analytics.

    There are three types of pricing analysis:

    Descriptive

    Descriptive pricing analytics involves analyzing historical data to evaluate how customers have perceived and reacted to pricing fluctuations in the past. It analyzes metrics such as month-on-month sales growth, average revenue per customer, year-on-year pricing changes, or changes to the number of registrations to a particular service over a specific period. 

    Predictive

    Although brands can’t accurately predict how pricing changes will reflect sales, they can use predictive pricing analytics to get insights into the best possible chance of doing so. Predictive pricing analyzes historical data with statistical algorithms and machine learning to predict the price and trends of products in the future. It also helps brands to optimize their prices with future goals.

    Prescriptive

    Prescriptive pricing analytics is the opposite of descriptive analytics. Unlike descriptive analytics that helps brands explore their historical data to understand customer response after an event, prescriptive analytics help brands design better and more informed strategies. With prescriptive analytics, brands can shape their growth strategies to achieve more sustainable results over the long term.

    Benefits of historical pricing data analytics

    Benefits of historical pricing data analytics
    Benefits of Historical Pricing Data Analytics

    Acquire insights into customers price perception

    While analyzing the metrics to understand pricing optimization, brands can also gather valuable insights into their customer’s price perception. Pricing analytics helps brands understand which customer segments are the most (and least) profitable and how each segment responds to specific pricing strategies. With historical pricing data analytics, brands can also intelligently link pricing and promotions by first determining customer price sensitivity then gauging the effectiveness of promotions. 

    Fully Optimized Pricing

    Historical pricing analytics means eliminating guesswork from deciding the optimal pricing for a given product. By analyzing historical pricing data, brands can discover how their past pricing and promotional decisions impact profitability. Based on this historical data, they can also test various pricing strategies like value-based and dynamic pricing. It also allows brands to learn which customer segments are most likely to respond positively to price change. These insights from pricing analytics will drive more effective (and profitable) pricing decisions.

    Recognize pricing tiers that work the best

    Many brands have gaps in their pricing strategy — underpriced or overpriced tiers, pricing leaks, markup errors, or neglected upsell opportunities. Tiered pricing models are prevalent in subscription-based brands where brands offer tiers to meet the needs of diverse customer segments. With historical pricing analytics, brands can improve their pricing tiers and get insight into the right number of tiers and optimal prices for each. Pricing analytics will comb a brand’s historical data to find tier pricing mistakes to improve sales and revenue.

    Planning Pricing Strategies and Promotions

    Promotional pricing decisions are critical for any brand, as pricing perception is directly linked to consumer demand and profits. Brands have to carefully plan promotions that include variables such as list prices, special offers, advertisements, and discounts while ensuring profit margins. With predictive analytics, brands can determine optimal discount levels, keep a close eye on the competition, and announce promotional offers when customers are likely to purchase. Historical pricing analysis also helps predict revenue and determine optimal locations and platforms for promotional ads.

    Discover profitable channels

    Not all sales channels bring equal revenue to your brand. Historical pricing analysis can help you determine the most effective quality, volume, and revenue channels. Brands must understand which marketing and sales channels bring quality leads that convert to paying customers. It also helps to determine which eCommerce channels are most profitable so you can optimize your budget and identify channels you should be investing in as a part of future customer acquisition strategies. 

    Metrics to track

    Metrics to track
    Metrics to Track

    Here are a few pricing analytics metrics that can help brands to understand customer behavior towards pricing:

    Willingness to Pay (WTP)

    WTP, also known as price sensitivity, is the maximum price your potential customers are willing to pay for your service or product. It is an essential part of pricing strategy since you have no other way of understanding whether your product can yield an augmented product value. Numerous factors are responsible for a customer’s willingness to pay, and it’s not static. Brands must track willingness to pay for all customer segments to ensure that the product is priced competitively and drives maximum profit while staying in line with current market conditions. 

    Feature Value Analysis

    Feature value analysis, also known as relative reference analysis, measures the most important features to customers in relation to other features of a product or service. Analyzing critical features to customer segments will help brands price products based on basic or premium components. It can also help to better bundle your services or products so you can drive the most revenue. 

    Average Revenue per User (ARPU)

    The average revenue per user is the revenue generated from the sum of active users divided by the total number of users in a monthly time frame. Delving deeper into ARPU can help brands compare numbers with rivals and check how all products or customer segments perform. 

    Lifetime Value (LTV)

    Lifetime Value offers a complete picture of a user’s journey and the average revenue that the user will generate throughout their engagement as a customer with your brand. It helps brands determine various economic decisions such as marketing budgets, profitability, forecasting, and resource allocation. 

    Customer Acquisition Cost (CAC)

    A successful and profitable brand needs to balance its customer acquisition cost or CAC. It is about spending the right amount of resources and time to drive new customers without jeopardizing their lifetime value and revenue. Correct calculation of CAC helps brands to quantify their sales funnel and determine the efficiency and profitability of their strategies.

    Conclusion

    Historical pricing analytics is a powerful tool, and it can make a huge difference to a brand’s potential by increasing sales and unlocking incredible profitability in a relatively short time. Historical analysis of pricing and promotions data will help brands get better marketing returns than relying on traditional pricing approaches. 

    Leveraging pricing analytics will prevent brands from blindly reacting to competitor price changes and support solutions for scaling up price transformation efforts. By using historical pricing data, brands can more effectively segment their customers for marketing and promotion strategies. Properly utilizing predictive analytics and past sales data can help cut costs and keep profit margins high by adjusting production and prices according to market trends.
    Need help tracking your competitor prices? Or want historic pricing insights for your own brand? Or need to track the efficacy of your online promotions?
    Sign up for a demo     with our team to know how DataWeave can help!

  • How to respond to Negative Online Reviews

    How to respond to Negative Online Reviews

    Most brand & marketing professionals fear negative feedback and reviews. Negative reviews and ratings can not only hurt your organic product visibility online, but they also impact real business outcomes and purchase decisions potential customers will make about your product. 

    … but getting negative reviews is not always a bad thing. These unflattering reviews help give consumers real insights into your product and help them understand their features, attributes, benefits, and downsides better as described by other customers to give them a more realistic picture. Shoppers trust user generated reviews more than content brands share with them, which is why it’s really important for brands to interject in these conversations, address negative reviews and nudge customers towards building trust in their products.

    Here are a few things to keep in mind when responding to negative reviews. 

    Be actionable and solution-oriented with your responses!

    Even the strongest brands can’t avoid negative reviews, but what sets one apart from the other is how they tackle these reviews. A prompt and solution-oriented response can actually help salvage a negative situation in a lot of cases. 

    build brand trust
    Build Brand Trust

    Let’s take a look at Clinique & the unique approach they took towards responding to negative reviews. Shown above is one of its bestselling products Moisture Surge™ 100H Auto-Replenishing Hydrator. This product got an average of 4.7 stars since its launch in early 2021. 99% of customers even said they would recommend this product. And, in comparison to the over 370 positive reviews, there were just 5 negative reviews! Instead of basking in the glory of the numerous positive reviews, Clinique chose to promptly reply instead & not dismiss negative reviews even if there were just a tiny number. This goes a long way for any brand. 

    Negative feedback
    Negative feedback

    Clinique not only addressed the customer’s concerns but also offered a ‘no questions asked’ refund and insisted that the customer take the conversation offline through a customer care agent. This action will help Clinique build long-term trust with not only the customers who had given them a low rating but the new ones too, who may stumble upon these negative reviews and see first-hand how customer-centric the brand is. 

    Respond promptly to keep things under control

    A quick response to a negative review is supercritical. Even if you’re unable to resolve the customer’s problem immediately, acknowledging the review promptly lets them know their concern is a priority. On the plus side, it may also help them calm down and hold them back from posting even nastier comments. Aim to respond within 24 – 48 hours from the time a negative review is posted. 

    The quicker a customer hears from you, the more sincere your words will feel to them.

    prompt response
    Prompt Response

    Here’s an example of how Chobani yoghurts tackled a negative review. You’ll notice, they responded almost immediately when a customer complained about the “RANCID” tasting yoghurt. Responding minutes after the review came in shows their seriousness towards dealing with the situation and that they value customer feedback. Apart from prompt response, they even offered to investigate and work towards a solution. 

    Look for a chance to take the discussion offline

    negative feedback system
    Negative Feedback System

    Take the conversation offline by giving a phone number or email where customers can connect with a real person or brand representative. The goal is to move the conversation from a public forum to a private channel where a personal touch can be added. It could be a customer care number, a DM on a social platform, or a direct call back to the customer to listen to the details of their complaint. Additionally, connecting offline helps resolve issues faster without letting the problem escalate. 

    Do NOT get defensive

    When it comes to responding to negative reviews, as a thumb rule pushing back or getting defensive is an absolute no-no. Being humble and accepting of negative feedback is important, and responding with grace, is even more important. 

    Let’s look at this example of a negative review left by an irate customer about the terrible IKEA customer service. Instead of getting defensive, IKEA politely acknowledges the feedback, apologies for the inconvenience, and offered a solution to help the customer sort out the issue with the order immediately! 

    customer feedback
    Customer Feedback

    A brand’s response to a negative review not only helps the individual who left the review in the first place but actually impacts other customers who will read it months down the line.

    Remember to follow-up

    Many times brands jump in promptly when a customer posts a negative review. They’re solution-oriented and some help resolve customer issues immediately too. The hard part’s done! However, where a lot of brands fall short, is when it comes to following up with customers once their concern has been addressed and they’re back to being brand advocates again. 

    Keeping that thought in mind, if it’s possible to get a customer who left a negative review to update or change it after their concern has been resolved could be a very impactful way to build brand trust. According to the Retail Consumer Report, 33% of customers turned around and posted a positive review, and 34% deleted the original negative review after having received a response from the brand or retailer in question. 

    Conclusion

    Even though brands have limited to almost no control over how customers perceive their products online, they can still participate and interact with customers to improve their online reputation. They can listen in on the online conversations and adapt to customer feedback promptly based on what shoppers are discussing via reviews. Also, don’t filter the types of reviews when responding to your customers, and aside from the positive and neutral reviews, treat your negative reviews with extra care. Resolve them responsibly to win a customer for life!

    If you need help tracking your online product reviews or analyzing the pulse of your customer sentiments to discover a wealth of insights, reach out to our Digital Shelf experts to learn more about our Review & Sentiment Analysis solution. 

  • Best Practices to avoid MAP Violations

    Best Practices to avoid MAP Violations

    Competition is a fundamental and healthy part of commerce that protects customers by keeping prices low and the quality of services (and choice of goods) high.

     Healthy competition drives prices down, but it can harm brands and their reputation without a pricing policy. The manufacturer or brand designs MAP or Minimum Advertised Pricing policies to stipulate retailers’ lowest price point to advertise the product. It is an agreement between distributors and manufacturers about the minimum price that retailers and resellers can advertise the product for sale. 

    Most legitimate brands have a MAP policy, especially brands that rely heavily on brand identity. It becomes critical that they maintain price parity across retailers. When a retailer violates MAP policies, brands can penalize them under the agreed-upon terms or terminate contracts. 

    In this blog, you will learn about MAP policy, its benefits, and tips on tackling MAP violations. 

    1. What is a MAP policy?

    MAP policy
    MAP Violations

    MAP stands for Minimum Advertised Price, and brands create MAP policies to ensure that retailers don’t advertise their products below the specified price. However, it only controls advertised prices, ensuring the retailers don’t display a lower price in online listings or advertisements. Since it doesn’t cover the checkout price, retailers can sell products at a lower price through promotional offers like discounts and cashback during checkout. 

    MAP policies ensure a price war between eCommerce platforms does not devalue products and that an even playing field is set among retailers that allow everyone to drive margins. Brands have a legal right to withdraw products if a retailer advertises products below the minimum advertised price. Brands can also restrict future sales or refuse to replenish products after the current stock has sold out if an eCommerce platform, reseller, or distributor violates MAP policies. 

    In the U.S., MAP policies fall under federal antitrust law since they restrict advertisement pricing rather than the last sales price. However, in the UK and the EU, violation of minimum advertised pricing is an infringement of current competition laws.

    2. Why Does Having a MAP Policy Matter?

    Having a MAP policy protects both brands and retailers while ensuring consumers get the best-priced items. Following are the benefits of having a MAP policy:

    a. Prevent margin erosion

    Although online retailers are willing to take a margin cut to attract traffic, selling products below MAP can significantly hurt a brand’s bottom line. Setting a minimum advertised price benefits both parties. It allows shoppers to purchase products at the best-valued price & also creates a balanced economy and prevents hyper-competition of products between retailers. However, manufacturers must set a realistic pricing policy that matches current market demand, ensuring eCommerce platforms implement MAP while taking care of the margins. 

    b. Retain brand identity

    pricing policy
    Brand Protection

    Price is one of the essential indicators consumers use to determine the authenticity and value of a product. Constant price fluctuations can negatively impact a brand’s reputation. Brands need to safeguard their pricing to create a consistent price perception. Price changes often make the buying decision complex since consumers no longer have a clear reference of prices. It also shifts purchasers’ attention from the brand and product features to its price. With price fluctuations, brands that were used to be differentiated for their features can be seen as commodities.

    Low prices & MAP violations on an online platform can even be a sign of counterfeit products or unauthorized sellers. However, customers might hold the brand responsible if they purchase counterfeit products from a retailer at lower prices. A negative product experience with a retailer will also reflect the brand’s reputation. An effective MAP policy that enforces consistent pricing will ensure that customers hunting for the best deals will stick with the most legitimate retailers.

    Read how DataWeave helped Classic Accessories, a leading manufacturer of high-quality accessories detect counterfeits and identify unauthorized sellers.

    c. Ensure price parity across retailers

    Comparing prices has become an essential and common milestone in every consumer’s purchasing journey. It’s imperative that a brand ensures price parity across platforms and stores because substantial pricing variations on different platforms can make customers suspicious of a brand. Consistent pricing across eCommerce platforms ensures brands maintain their identity. MAP policies also allow retailers to maintain profit margins while avoiding price wars.

    d. Combat revenue loss from illegitimate sales

    While most authorized sellers or distributors comply with pricing policies, unauthorized sellers or grey market sellers have no obligation to follow a brand’s MAP pricing infrastructure. Brands can reduce risk with an authorized seller badge on retailer websites. This will help customers to verify authorized retailers and resellers of your products & help safeguard your brand equity online. 

    3. Tips on Implementing MAP policy and Tackling violations

    Enforcing and tackling MAPs comes down to two things: monitoring the market for infringements and then acting on those violations. Here are a few tips for tackling MAP violations:

    price parity
    Implementation of MAP Policy & Tracking Violations

    a. Communicate actively with retailers

    To maintain a positive relationship with retailers and avoid confusion, brands should create proper communication strategies and channels to accompany the launch of the MAP policy. The policy should be easy to understand, but legal advisors are necessary to understand the jargon of the document. Brands can use checklists, videos, and well-briefed brand reps to communicate their policy clearly with retailers.

    b. Reward retailers for compliance

    Retailers who follow MAP guidelines can lose out to platforms that do not follow these pricing guidelines. Non-MAP following platforms undercut the price of products to drive sales and secure higher traffic. In such instances, brands can incentivize MAP following retailers to encourage them to comply with MAP guidelines while not affecting the competitive edge. It can be in the form of laxity of rules during promotion seasons like New Year, Christmas, and Black Friday sales. The laxity of rules for promotional seasons should be used as an exception to the general rule, and outlined in the guidelines.  

    c. Implement an AI-driven MAP monitoring

    When product distribution is spread across the globe through a network of resellers and retailers, keeping a close watch on all platforms for multiple products can become difficult. With the expansion of online marketplaces, manually tracking the pricing of numerous products on multiple platforms is time-consuming and unsustainable. An automated AI-driven monitoring platform can track the pricing of all products sold across hundreds of online platforms and identify violations around the clock. Such platforms can alert brands of violations, price inconsistencies, or suspicious activities in real-time. 

    d. Send cease and desist to MAP violators and unauthorized dealers

    Brands must enforce a MAP policy to ensure price parity among retailers and resellers. Brands must systematically monitor prices across retailers, social media, marketplaces, and price comparison websites. Whenever brands encounter a MAP violation, they should take action by sending a cease and desist letter to unauthorized sellers. For legitimate sellers, brands can notify them and outline the steps that will be taken if they don’t comply. Brands must be consistent in enforcing MAP policy violations, signaling retailers and unauthorized sellers that there will be repercussions for MAP violations. 

    Market Demand
    MAP Policy

    4. Conclusion

    The trend towards online shopping helps businesses to cut overheads, allowing their products to be sold at a significantly reduced price. Although price appears to be the most effective consumer attraction strategy, significantly lowering product prices can devalue products and hurt brand reputation in the long term. However, including and enforcing MAP policies helps brands to manage their reputation and allows retailers to manage their margins. 

    Want to see first-hand how DataWeave can help brands track MAP Violations, Counterfeit products, and identify unauthorized sellers? Sign up for a demo with our Digital Shelf experts to know more.

  • Quick Commerce in 2022: An Era of Hyperlocal Delivery

    Quick Commerce in 2022: An Era of Hyperlocal Delivery

    Busy lifestyles, urbanization, aging populations, and smaller households led to the preference for convenience and efficiency in eCommerce deliveries. However, the Covid-19 pandemic caused a massive shift in customer demand and buying decisions. The modern consumer journey moved from takeaway food to online shopping to quick or same-day deliveries. With evolving digital touchpoints, customers now favor fast deliveries and convenience. 

    According to a 2020 survey by KPMG in the UK, 43% of consumers chose next-day delivery, a 4% increase from last year. Interestingly, 17% of consumers abandoned a brand if they faced a longer delivery. Standard delivery time has shortened from 3 to 4 days and two-day shipping to next-day or same-day delivery. This increasing trend of quick delivery has led to the boom of quick commerce or Q-Commerce. Quick commerce or on-demand delivery refers to retailers that deliver goods in under an hour or as quickly as 10 minutes. The rise of Q-commerce is caused by changing consumer behavior and rising expectations since the pandemic. 

    In this blog, you’ll learn about quick commerce or Q-Commerce and its benefits. You’ll also read about factors to consider for quick commerce and tips to implement this business model. 

    1. What is Quick Commerce?

    on-demand delivery
    On-Demand Delivery

    Quick commerce or on-demand delivery is a set of sales and logistics processes that empowers eCommerce businesses, restaurants, grocery chains, and manufacturers to deliver products in less than 24-hours. A study shows that 41% of consumers are willing to pay for same-day delivery while 24% of customers will pay more to deliver their items within a one- or two-hour window.  

    Changing lifestyles and customer behavior directly impacted the rise of Q-Commerce. The takeaway food industry had used quick commerce for many years. But with Q-Commerce businesses consistently cutting delivery time, quick commerce for instant grocery delivery has become a new trend. For instance, India-based online grocery delivery firm Grofers rebranded to BlinkIt amid rising competition, promising 10-minute instant delivery. 

    2. How quick is Quick Commerce?

    The post-pandemic lifestyle & the rise in the number of small and single-person households has led to an increase in demand for products in small quantities that need to be delivered sooner than later. Sometimes in as little as 10 minutes! This trend is oriented towards specific products such as packed or fresh foods, Groceries, Food delivery, Gifts, Flowers, Medicines to name a few.

    quick delivery service
    Quick Commerce Categories

    Local shops that can reach more customers with less friction have swapped traditional brick-and-mortar warehouses to cater to an urban population. These online Q-Commerce stores can deliver goods from favorite stores and offer a vast choice of products that are available 24/7. However, it requires real-time inventory management, data-driven pricing management, innovative logistics technology, a fantastic rider community, and a proper assortment. 

    3. Factors to consider for Quick Commerce

    q commerce
    Competitive Assortment & Pricing

    a. Assortment

    With growing competition, getting product assortment right isn’t easy for quick commerce businesses, yet it’s critical to their success. To optimize assortment for quick commerce stores, they need to understand how demand differs between demographics and various stores. Since quick delivery involves packed and fresh products, it is even more essential to carry a unique assortment for each store. 

    Data analytics will help Q-Commerce businesses understand which products are repeatedly purchased in every store. It also helps identify high-demand gaps in your competitors’ platforms. Assortment analytics can help distinguish shifts in customer behavior across short- and long-term demands. The key to increasing sales is shaping inventory to match the overlap between market opportunity and consumer interest. With assortment analytics, they can determine the optimal mix of products for their daily inventory. 

    b. Pricing

    Pricing information is readily available on quick commerce businesses, allowing customers to compare prices before making purchase decisions. Before deciding on a product, shoppers actively track the best deals on platforms across various Q-Commerce delivery platforms. According to a survey, 31% of consumers rated price comparisons as the essential aspect of their shopping experience. Understanding price perception can help quick commerce companies to optimize their pricing strategy while remaining competitive. 

    A competitive pricing strategy does not imply that Q-Commerce businesses have to cut prices. Instead, it’s about adjusting prices relative to your competitors but not significantly impacting the bottom line. Competitive pricing provides real-time pricing updates, allowing quick commerce platforms to drive sales by nailing their pricing strategy. 

    c. Delivery Time

    delivery time
    Grocery Delivery Race In India

    Delivery time has become the game-changer in quick commerce, with platforms fighting over shorter delivery times. Unpredictable factors such as specific delivery windows, last-minute customer requests, and traffic congestion can wreak havoc in your planning. Optimizing your delivery time can improve operational efficiency through faster delivery, quick route planning, and driver monitoring. 

    Big eCommerce platforms like Amazon offer same-day or next-day delivery to prime members with no extra fee on minimum order criteria. The only demand of customers who do not worry about discounts or lower wholesale prices is quick delivery. The demand for quick delivery services has led to many global retailers offering same-day delivery to meet those expectations.

    d. Demand Forecasting

    Since quick commerce is a viable solution for certain products, businesses must determine what customers want and when they want it. Q-Commerce businesses can use historical data to predict future sales patterns with demand forecasting. It ensures that Q-Commerce businesses can limit wastages and their inventory can cater to a targeted market. Demand forecasting also helps to replenish stock based on real-time data. Furthermore, companies can identify bottlenecks and points of wastage in the supply chain with a demand-driven system in place.

    4. Benefits of Quick Commerce

    same day delivery
    Q-Commerce Benefits

    a. Competitive USP

    Q-Commerce businesses get new value propositions because customers that need immediate delivery are willing to try new brands and order from new stores. It also allows online Q-Commerce businesses to compete with global marketplaces and brick-and-mortar stores. 

    We at DataWeave have helped quick-service restaurants (QSRs) that are going the Q-Commerce route & selling via food aggregator apps to increase their revenue significantly. Our AI-Powered Food Analytic solutions have helped QSRs diagnose improvement areas, monitor key metrics, and drive 10-15% growth. Our data has helped them understand availability during peak times, monitor product visibility by region, track competitors, and choose suitable banners for promotion. Read more about that here.

    b. Increase margins

    A study from Deloitte suggests that 50% of online shoppers spend extra money to get convenient delivery of the products they need during the pandemic. These customers also paid extra for on-demand fulfillment and bought online pick-up in-store options. 

    Since the assortment of products in quick commerce is relatively small, Q-Commerce businesses can drive sales for their most profitable product lines. There is a potential for greater margins because wealthier demographics often require convenience. For instance, time-stranded professionals value convenience over discounts. 

    c. Customer experience is paramount

    With quick commerce, retailers can meet customer expectations and exceed them, fostering brand loyalty. Quick commerce addresses customer pain points such as running out of food before a small party or getting a birthday present for your friends. It can simply help people who cannot make it to the shop or stock up essentials.

    5. How to implement Quick Commerce

    quick delivery
    Implementation of Quick Commerce

    a. The need for local hubs

    To pack and deliver products in under an hour, businesses must be located close to the customers. Therefore, quick commerce relies on local warehouses that can serve customers in immediate proximity. Since the duration of two-wheelers is less likely to be impacted by heavy traffic or parking spaces, delivery services employ riders to deliver products.

    b. Ensure you have the right analytics in place

    Another essential part of running a quick commerce business is to have a web or phone application that can facilitate online ordering and offer accurate stock information to customers. Q-Commerce businesses also need a real-time inventory management tool that will provide insights into stock levels and allow for quick reordering and redistribution of products. This will also prevent deadstock and stockouts. 

    DataWeave’s Food Delivery Analytics product suite helps companies to increase order volumes, understand inventory, and optimize prices. It also provides access to discounts, offers, delivery charges, inventory, and final cart value across all your competitors. 

    c. It’s all about stock availability & assortment

    Q-Commerce in the Grocery Delivery space is excellent for specific product niches like packed or fresh foods and vegetables, drinks, gifts, cosmetics, and other CPG products that customers use every day.

    The stock assortment is as important in the Food Delivery space with restaurant chains like McDonald’s or Burger King that generate as much as 75% of their sales from online orders. These businesses have to make sure they’re carrying the most in-demand product assortment there is. 

    Conclusion

    same day delivery
    Same Day Delivery

    The rise of quick commerce represents the next big change in eCommerce, accompanied by a shift in consumer behavior towards online grocery shopping and food ordering. When positioned with proper assortment and pricing, instant delivery services can allow Q-Commerce businesses to capture the influx of consumers looking for speedy delivery. By tapping into big data from quick commerce markets, Q-Commerce businesses can gain insights into consumer demands. 

    If you’re a Q-Commerce business in the Food Delivery or Grocery Delivery space, reach out to our experts at DataWeave to learn how our solutions can help you understand the best Pricing Strategy, Delivery Time SLAs, Assortment Mix you need in order to successfully sell on Q-Commerce platforms. 

  • Beauty & Grooming Brands that are dominating on Amazon India

    Beauty & Grooming Brands that are dominating on Amazon India

    Growing awareness of personal hygiene and changing lifestyles has contributed to a significant development of India’s cosmetics, beauty, and personal care products. The Indian cosmetic industry reached a value of USD $26.1 bn in 2020. The major boom in sales is because of rising digitization, social media marketing, and the advent of eCommerce beauty platforms. However, the increase in demand and technological advancements has led to a competitive landscape for Indian and international brands competing for digital and physical channels. As of February 2019, 18.92% of respondents spent between 700 to 1700 rupees, and 43.9% spent up to 700 rupees monthly on cosmetics and personal care products in India.

    Personal care products in India
    Monthly spend on Personal Care Products in India

    Shattering stereotypes and gender norms, India is also seeing a revolution in the male grooming industry, which is expected to reach INR 319.82 bn by 2024. The D2C market is expanding beyond metropolitan cities, and at present both D2C brands and startups have launched over 177 new products for men. “We realized there is an opportunity to create India’s first experiential brand exclusive for men,” says Hitesh Dhingra, Co-founder, The Man Company. He adds ecommerce business has grown almost by 200 percent. In a similar vein, Shantanu Deshpande, founder, and CEO, Bombay Shaving Company, concurs and adds the pandemic boosted online sales. He says that it has become easier for the company to compete with big brands on marketplaces like Amazon and Flipkart.

    With the onset of the pandemic, it has become more and more important for these D2C brands to have a strong digital presence and an even stronger Digital Shelf when selling on platforms like Amazon, Flipkart, Nykaa, and the likes. On these marketplaces, brands need to track critical KPIs like product discoverability, stock status & availability, reviews and ratings, pricing & promotions to make sure they’re optimizing product performance across all online channels to amplify their eCommerce growth. 

    So which beauty and grooming brands and categories have a strong Digital Shelf and are dominating on Amazon? Let’s take a look. 

    Men's Grooming Brands and Categories Categories
    Men’s Grooming Brands and Categories

    Methodology

    • We tracked the first 250 products on Amazon against certain keyword searches specific to India’s Beauty & Grooming space. 
      – Keywords specific to women’s grooming: anti-aging Cream, Face Mask, Paraben-free Shampoo, Onion Hair Oil, Body Wash, Moisturizer
      – Keywords specific to male grooming: Beard Oil, Hair Wax for men, Shaving Cream, After Shave Lotion, Beard Trimmer
    • Share of Search (SoS) – The percentage of products that appeared on the search results page on Amazon belonging to a brand against a specific keyword or category. 
    • Data Scrape time period: From 14th Oct 2021 to 10th Nov 2021

    THE BEAUTY IS IN THE DATA

    On Amazon, brands use sponsored ads to increase visibility and drive more sales. When we looked at the product category with the most aggressive ad spends, products in the men’s grooming category came out on top and had the maximum number of sponsored products. 26% of beard trimmers were sponsored, followed by Beard Wax and Beard Oil at 25%. During the lockdown, more men started searching online for new products and watching instructional videos on how to groom their beards or how to get a salon-like shave at home. Demand for razors and trimmers is up by 50% compared to last year,” said Sidharth S Oberoi, founder and CEO, LetsShave. In contrast, we saw that only 11% of after-shave lotions and 15% shaving creams were discounted. 

    Sponsered Items per Product Category
    Percentage of Sponsered Items per Product Category

    For women, we saw a similar trend. 24% of products in the Paraben-free Shampoos and Onion Oil category was sponsored. In contrast, only 5% of anti-aging creams were sponsored. Additionally, 21% of products in the face mask category and 23% in body wash were sponsored. 

    Competition is fierce in these categories, making an artificial boost necessary for increasing discoverability. In fact, we saw that the competition was the fiercest in the face mask category, which had the highest “1st Page Change Rate.” It is an indicator of how much the results on the 1st page for a particular keyword change from time to time. This reflects higher competition and brands constantly updating their digital shelf KPIs to ensure their products appear on page 1. One of the biggest reasons why brands need to constantly gauge their online visibility is to track their sponsored & organic ranking compared to competitors.

    Driving sales using a smart Discounting Strategy

    Price can play a big role in the final purchase decision. So we looked at two things wrt price across all these beauty & grooming products.

    • Which product Category had the maximum number of products on discount? 
    • … & how large were these discounts? 
    Products on Discount
    Percentage of Products on Discount

    We saw that almost 55% of products in the body wash category & 46% of anti-aging creams were available at a discount. Beard Oil & Onion Hair oil had the least number of products discounted at 29% each.

    Magnitude of Discount
    Magnitude of Discount

    How high were these discounts? Let’s take a look.

    The highest discount was seen in the beard oil and moisturizer category, with an average discount of 17% across all products. The average discount trend across most product categories ranged between 14 to 17%, so we did see some consistency there.

    Digital channels provide transparent insights into pricing & promotions, which is why customers are constantly comparing prices across various brands before making a purchase. This is why it is crucial for brands to remain competitive by tracking & comparing promotional strategies with those of their rivals. 

    To Review or Not to Review?

    Consumers worldwide don’t make a purchase decision without reading online reviews. Online reviews and ratings have become a significant milestone in the modern consumer shopping journey, and eCommerce brands can leverage reviews as valuable sales tools. Given a choice between loyalty programs, discounts, reviews, and free shipping, online shoppers say reviews are the most important factor while making a purchase. Consumers trust user-generated content (UGC) more than product information and videos created by brands.  

    Number of Reviews per Product Category
    Number of Reviews per Product Category

    We looked at product reviews to check consumers of which categories are actively sharing their experience and found that three categories stood out — beard trimmers, moisturizers, and paraben-free shampoo. At the same time, beard oil was the product category with the least number of reviews. 

    Companies can build consumer trust by identifying and acting on negative feedback. But in order to do that, they first need to de-code and understand the collective sentiment behind these reviews. DataWeave’s AI-Powered solutions can help brands break down & analyze online reviews and give them a wealth of insights to enrich their market research as well as create a seamless customer experience.

    UNDERSTANDING THE COMPETITION ON AMAZON

    When selling on Amazon, brands need to make sure shoppers find their products with ease. Keyword searches are the top ways consumers discover and find products across eCommerce sites. We tracked search visibility for the following keywords to see which brands had the highest share of search and appeared on the 1st page on Amazon. 


    Be in any product category – moisturizers, shampoo, anti-aging cream, Mamaearth & WOW featured against most keywords, showing popularity among customers. WOW Skin Science raised $50 million in April 2021, and Mamaearth raised $50 million in July 2021. These two fresh-faced brands have built credibility among health- and environment-conscious users. They are big competitors when it comes to natural and toxin-free products. It’s their high product visibility in multiple categories that is likely leading to better discoverability, higher sales & increased valuation, and brand value. 

    Beauty and Grooming Brands
    Rankings of Top Brands in various cosmetic categories- (A)

    In the male grooming space, we observed that established brands like Nivea, Old Spice, and Park Avenue had a lower share of search than new D2C brands like Beardo, The Man Company, Bombay Shaving Company, and Ustraa. Here’s clear proof of concept that brands need to evolve and adapt their Digital Shelf to selling online if they want to beat the competition. 

    Beauty and Grooming Brands
    Rankings of Top Brands in various cosmetic categories– (B)

    Who were the Amazon Bestsellers?

    Products on Amazon that have the highest sales in their respective categories are called Amazon Bestsellers. The Amazon Bestsellers rank is based on product sales and sales history where the list undergoes an hourly update. The bestseller ranking or bestseller badge is available in the product information section on the product page. The rankings are determined by comparing sales and historical data with products in the same category or subcategory. 

    Brands can make it to Amazon’s bestseller list by optimizing their listings, encouraging reviews, and listing products in the relevance of categories. Although Amazon does not consider reviews for product ranking, they help users convince them to buy your product. 

    Here are the Brands we say that made it to #1 on the Amazon BestSeller List for the following product categories.

    Amazon Bestseller List
    Amazon Bestseller List

    Gillette made it to the top in the aftershave lotion and shaving cream category, while D2C brands Ustraa made its mark bearing number 1 on Amazon Bestseller list for hair wax for men and beard oil. 

    Amazon Bestseller List
    Amazon Bestseller List

    Products from Nivea and L’Oreal made it to #1 seller in 2 categories each. Interestingly, in the Paraben-Free shampoo category, when D2C brands like WoW, Mamaearth have a stronger value proposition, traditional brand L’Oreal had the best-selling product. 

    L’Oreal must’ve pulled various levers and built a robust Digital Shelf to get to the top – from optimizing their content, ensuring product availability, tracking ratings and reviews, and proper competitive pricing. 

    Conclusion

    An increase in new D2C brands in popular and trending categories has led to increasing competition. Unless a brand can position itself in front of the target audience and command their attention right away, another brand can step in and grab the sale. Do you know if your brand is prepped and ready to make an impact on marketplaces like Amazon? Or simply just wondering if your Digital Shelf is optimized with the right price, discounts, reviews, and keywords? Our team at DataWeave can help! Reach out to our Digital Shelf experts to learn more!

  • 2021 Cost-Push Inflationary Trends Ran Rampant, Impacting Holiday Discounts

    2021 Cost-Push Inflationary Trends Ran Rampant, Impacting Holiday Discounts

    Business has been anything but usual this holiday season, especially in the digital retail world. The holiday hustle and bustle historically seen in stores was once again occurring online, but not as anticipated given the current strength of consumer demand and the reemergence of COVID-19 limiting in-store traffic. While ‘Cyber Weekend’, Thanksgiving through Cyber Monday, continues to further its importance to retailers and brands, this year’s performance fell short of expectation due to product shortages and earlier promotions that pulled forward holiday demand.

    Holiday promotions were seen beginning as early as October in order to compete with 2020 Prime Day sales, but discounting, pricing and availability took an opposite direction from usual. This shift influenced our team to get a jump start on our 2021 digital holiday analysis to assess how drastic the changes were versus 2020 activity, and to understand how much of this change has been influenced by inflationary pressures and product scarcity.

    Scarcity Becomes a Reality

    Our initial analysis started by reviewing year-over-year product availability and pricing changes from January through September 2021, leading up to the holiday season, as detailed in our 2021 Cyber Weekend Preliminary Insights blog. We reviewed popular holiday categories like apparel, electronics, and toys, to have a broad sense of notable trends seen consistently throughout various, applicable marketplaces. What we found was a consistent decline in product availability over the last six months compared to last year, alongside an increase in prices.

    Although retailers significantly improved stock availability in November and early December 2021, even digital commerce giants like Amazon and Target were challenged to maintain consistent product availability on their website as seen below. While small in magnitude, there is also a declining trend occurring again closer toward the end of our analysis period, post Cyber Weekend, across all websites included in our analysis.

    Inventory Availability 2021 Holidays
    Source: Commerce Intelligence – Product Availability insights for Home & Garden, Jewelry & Watches, Clothing & Shoes, Bed N Bath, Lighting & Ceiling Fans categories

    Greater Discounts, Higher Prices?

    With inflation at a thirty-nine year high, retailers and manufacturers have realized they can command higher prices without impacting demand as consumers have shown their willingness to pay the price, especially when threatened by product scarcity. Our assessment is that while some products and categories have responded drastically, manufacturers’ suggested retail prices (MSRPs) have increased nearly seven percent on average from January to December 2021. MSRP adjustments are not taken lightly either, as this is an indication increased prices will be part of a longer-term shift in product strategy.

    2021 MoM Retail Inflation Tracker
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com & Target.com each month in 2021 comparing price increases from January 2021 base

    Our 2021 pre-Cyber Weekend analysis reviewed MSRP changes for select categories (Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion) on Amazon and Target.com, and found around forty-eight percent of products on Amazon and thirty-five percent of products on Target.com have increased their MSRPs year-over-year, but kept pre-holiday discount percentages the same.

    Looking more specifically as to what year-over-year changes occurred on Black Friday in 2021, we observed MSRPs increasing across the board for all categories at various magnitudes. This indicates why 2021 discounts appeared to be greater than or equivalent to 2020 for many categories, when in reality consumers paid a higher price than they would have in 2020 for the same items.

    2021 Black Friday MSRP Increases
    Source: Commerce Intelligence – MSRP Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Black Friday November 27th, 2021, versus average MSRP pricing for the same SKU count from November 20-26th 2021

    On Amazon.com, categories like health & beauty have already increase MSRPs by a much greater percentage and magnitude versus Target.com leading up to and during Black Friday 2021, while other categories like furniture have increased MSRPs evenly on average across both retail websites. The below chart cites a few specific examples of year-over-year SKU-level MSRP, promotional price, and discount changes within found within the electronics, furniture, fashion, and health & beauty categories.

    Black Friday 2021 vs. 2020 SKU-level Price Changes
    Source: Commerce Intelligence – MSRP Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Black Friday November 27th, 2021, versus average MSRP pricing for the same SKUs on Black Friday November 26th, 2020.

    Fewer, but Deeper Discounts

    From October through early November 2021, fewer products were discounted compared to this same period in 2020, and the few that were saw much deeper discounts apart from the home improvement category. The most extreme example we saw in discounts offered was within furniture where only three percent of SKUs were on discount in 2021 compared to twenty-six percent in 2020. Interestingly, the magnitude of discount was also higher pre-Cyber Weekend 2021 versus 2020, but this trend was not exclusive to furniture and was also seen within electronics, health & beauty, and home improvement.

    Pre-Black Friday 2021 and 2020 SKUs on Discount and Magnitude
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com & Target.com Pre-Black Friday average selling price during November 20-26th 2021 versus average selling price from November 13-19th 2021 compared to Pre-Black Friday average selling price during November 19-25th 2020 versus average selling price from November 12-18th, 2020.

    Within the furniture category, the subcategories offering the greatest number of SKUs with price decreases on Black Friday 2021 were rugs by a wide margin, followed by cabinets, bed and bath, and entertainment units, but the magnitude of discounts offered were all under twenty percent.

    2021 Black Friday Furniture Category Price Decreases
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    Accounting for this phenomenon could have been retailers’ attempts to clear inventory for SKUs which hadn’t sold even during the period of severe supply chain shortages. With more products selling at higher prices this year, retailers were also able to use fewer SKUs with greater discounts to attract buyer in hopes of filling their digital baskets with more full-priced goods, helping to protect margins heading in to Cyber Weekend. Scarcity threats also encouraged consumers to buy early, even when not on promotion, to ensure they would have gifts in time for the holidays.

    The same trends seen pre-Cyber Weekend 2021 were also seen on Black Friday with a year-over-year decrease in the percentage of SKUs offered on discount versus 2020, and steeper price reductions for the discounted products which can also be attributed to the increase in MSRPs.

    Black Friday 2021 and 2020 SKUs on Discount and Magnitude
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    2021 Black Friday Price Increases?

    We all know Black Friday is all about price reductions, discounts and deals and so it’s rare to see actual price increases, yet for Black Friday 2021, trends ran counter to this. We observed price increases across all categories for around thirteen to nineteen percent of SKUs, with an average price increase of around fifteen percent in 2021 versus an average of only two percent in 2020.

    SKUs with Price Increases Black Friday 2021 and 2020
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    At an account level, we noticed a few interesting differences happening on Black Friday 2021 versus 2020 regarding category price changes. On Target.com, almost ninety percent of the bed and bath SKUs analyzed had a price change on Black Friday in 2021 versus 2020 with eighty-two percent presenting a higher price year-over-year versus only around seven percent showing a decrease, where on Amazon nearly forty-four percent of bed and bath SKUs showed an increase in price and around thirty-eight percent showed a decrease. Except for the health and beauty category on Target.com, more than half of the SKUs in each category saw a price increase on Black Friday versus a price decrease.

    2021 YoY Price Changes on Black Friday
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs on Black Friday November 26th, 2020.

    The magnitude of year-over-year price changes seen on Black Friday 2021 was significant across all categories, but the magnitude of price increases found on Amazon.com within the health and beauty category outpaced the rest by far. We reviewed three hundred and sixty-five SKUs on Amazon.com within the health & beauty category and saw almost eighty-three percent of them had a price change with around thirty-one percent decreasing prices and around fifty-two percent increasing prices. This means that within the health & beauty category on Amazon.com, more than fifty percent of the SKUs tracked were sold at a one hundred and seventy-six percent higher price on average during Black Friday 2021 versus 2020.

    Magnitude of Black Friday 2021 Price Increases
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs on Black Friday November 26th, 2020.

    The subcategories offering the greatest number of SKUs with price increases on Black Friday 2021 were cameras, followed by men’s fragrances, laptops, and desktops & accessories, but the magnitude of discounts offered were all under ten percent.

    2021 Subcategories with Price Increases during Black Friday
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    The Aftermath Post-2021 Cyber Weekend

    Extending this analysis beyond the holiday weekend, we analyzed price change activity from December third through the ninth across the top US retailers (chart below) and found that price decreases have been very minimal, comparatively speaking. Though there was a spike in number of price decreases from December 8th to the 9th, the percentage of SKUs with price decreases was still very low (less than three percent). We anticipate this trend will continue into 2022.

    SKUs with Price Decrease Post Cyber Weekend 2021
    Source: Commerce Intelligence – Pricing insights for Home & Garden, Jewelry & Watches, Clothing & Shoes, Bed N Bath, Lighting & Ceiling Fans categories

    A Sign of Things to Come

    A confluence of inflationary trends, product shortages and consumer liquidity have driven many marketplace changes to occur simultaneously. Government programs in the form of stimulus checks, have put extra money in consumers’ hands, and so they’ve been more willing to spend. That, coupled with the shock in the supply chain, has motivated people to buy far ahead of the 2021 holiday season. Hence, retailers have needed to rely much less on across-the-board discounts. Promotions have been more strategic – we’ve seen deeper discounts over fewer products, likely used to draw consumers in to buy certain items, and once they’re there, customers are buying everything else at a non-discount level. When these factors once again normalize, we could see a return to the “race to the bottom” that has occurred since the financial crisis of 2008-2009, but for once, retailers may be able to maintain some pricing power as the 2021 holiday shopping season played out.

    Even though performance was not as anticipated and holiday sales did not grow as rapidly as they did in 2020, Cyber Monday was still the greatest online shopping day in 2021. Through it all, retailers managed to keep their digital shelves stocked and orders filled in time for the holidays for the most part, running the risk of housing aged inventory if goods didn’t arrive in time. Despite predictions for steep promotions in January 2022, with supply chains still challenged and inflationary pressures still full steam ahead, we don’t anticipate much in the way of enhanced discounts to continue beyond the holidays.

    Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions like how and when to address inflationary pressures, while also supporting many other day-to-day operations and help drive profitable growth in an intensifying competitive environment. Continue to follow us in the coming weeks for a detailed 2021 year-end review across more retailers and categories. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.         

  • Importance of Image Recognition in the Retail Industry

    Importance of Image Recognition in the Retail Industry

    When it comes to classifying and analyzing images, humans can easily recognize distinct features of objects and associate them with individual definitions. However, visual recognition is a highly complex task for machines because it involves identifying multiple objects and finding object relationships. Image recognition has been a long-standing research problem in the computer vision field. But, the recent development in AI has improved the process of object detection, image identification, and image classification. The image recognition market is assumed to rise globally to a market size of $42.2 billion by 2022. Various industries are adopting image recognition technology to improve augmented reality applications, optimize medical imagery, boost driverless car technology, predict consumer behavior, and much more. 

    Although image recognition is a relatively new aspect of analysis, it is also making its way into eCommerce. Image recognition is helping retailers to expand consumer reach, offer insights into trends, and improve customers’ online shopping experience for the eCommerce industry. The Global Image Recognition in Retail Market is estimated to be USD 1.8 Bn in 2021 and is expected to reach USD 4.5 Bn by 2026, growing at a CAGR of 20%.

    Image Recognition
    Global Image Recognition in Retail Market

    In this blog, you’ll learn about image recognition technology and its importance in the retail industry. 

    What is Image Recognition?

    Image recognition, a subcategory of computer vision, is a technology that can identify objects, entities, or attributes in digital images or videos. However, computer vision is a broader term, including methods for gathering, processing, and analyzing data from the real world. Image recognition can be performed at varying degrees of accuracy, depending on the type of information required.

    Image recognition can perform the following tasks:

    Object Detection, Semantic Segmentation & Instance Segmentation
    Object Detection, Semantic Segmentation & Instance Segmentation
    • Classification: It identifies the “class,” i.e., the category to which an image belongs. A picture can have only one class.
    • Tagging: It’s a classification task but involves a higher degree of accuracy. Tagging can recognize several concepts or objects within an image, and there can be more than one tag assigned to a particular image.
    • Detection and localization: This step helps locate object(s) in an image. Once the system locates the object in question, localization helps to place a bounding box around it. 
    • Segmentation: This is also a detection task but involves a higher degree of precision. Segmentation locates element(s) to the nearest pixel in an image. 
    • Instance segmentation: It helps differentiate multiple objects belonging to the same class. 

    Image Recognition in eCommerce and how it works

    Nowadays, increasing competition and customer expectations are forcing online retailers to constantly monitor market dynamics wrt their pricing, promotion & product assortment in order to stay competitive. To get these insights, retailers need to match and compare their products against their competitors to see where the gaps are. That’s where product matching comes in. 

    Product matching refers to finding the same or similar products against a target universe of products from across the web, across multiple competing retailers. Product matching uses AI-based image recognition to determine product attributes, find patterns, and detect text, product price, shipping information, and so on. 

    Here’s how DataWeave’s AI-powered analytics platform uses image recognition & aggregates insights & data for retailers from across the web to provide a comprehensive view of the online competitive environment.

    Image recognition use-cases in the retail industry

    a. Attribute tagging

    Attribute Tagging
    Attribute Tagging

    Getting shoppers to your eCommerce platform is one thing and getting them to complete a purchase is a steeper hill to climb. If your platform can’t provide search results that match with customers’ requirements, they’ll get lost, grow frustrated, and drop off. Attribute tagging with image recognition allows eCommerce stores to automatically generate attributes for all products so customers can quickly find products they are looking for. 

    Tags allow users to filter products based on the categories they want to explore. Product tags include everything the customer might specifically search for — color, type, size, brand, use, design, fabric, discount, etc. For example, a dress could have tags like red, evening, midi, summer, long-sleeve, silk, summer sale, etc. When a user looks for midi dresses or long-sleeve dresses, products with these tags will show up. 

    b. Search by image

    Visual Search
    Visual Search

    Visual Search allows users to look for similar products using a reference image from their camera roll or downloaded from the internet. The visual search feature also enables eCommerce businesses to implement image-based search into their software applications. It maximizes the searchable potential of their visual data. 

    Meanwhile, Gartner predicts a 30% increase in digital commerce revenue by 2021 for companies who start supporting visual and voice search on their websites and apps. The benefits of visual search include more personalized, easy product recommendations and enhanced product discovery.

    c. Fashion trend analysis

    similarity matching
    Similarity Matching

    Tapping into trending product categories is a goldmine for any eCommerce business. Having insights into trending categories and products means less competition on search engines, fewer ads, and intelligent pricing. All of which can boost any retailer’s margins. Image recognition technology provides information about colors, styling techniques, fabric textures, prints, and more to spark consumer demand. It works by scanning social media images to pinpoint trending attributes and predict fashion trends. For instance, while scanning images, technology understands that it’s seeing a photo of a color-blocked sweatshirt because it recognizes the product has a hooded neck, full sleeves, blocks of different colors, and even the type of fabric. This technology can analyze millions of images, helping retailers analyze the volume of color-blocked sweatshirts. 

    We do this seamlessly at DataWeave. Our similarity matching solution helps retailers gather insights into attributes for products similar to the ones they’re carrying on their site. Similarity matching helps retailers gain visibility into their entire competitive landscape to keep their e-commerce strategy responsive to price & product assortment shifts among consumers and rivals

    d. Augmented reality

    According to Statista, the AR market is valued at $9.5 billion, with around 810 million active mobile users. Since shoppers want the full sensory product experience before shopping online, augmented reality (AR) can help them understand what they’re buying and how the product will work for them. There are AR applications for trying makeup, clothing, accessories, and even eyeglasses. IKEA was one of the pioneers in using AR for eCommerce retail. In 2017, IKEA launched the Place app, allowing shoppers to see how thousands of items will look in their homes, with 98% accuracy. 

    Image recognition helps AR applications anchor virtual content with the real world. For instance, Sephora has a Virtual Artist that allows users to try different makeup looks and even take pictures of an outfit they’re planning to wear to match the shade. Users can even check out full-face looks and learn how to do their makeup with virtual tutorials. 

    e. Counterfeit Detection

    Counterfeit Detection
    Counterfeit Detection

    Another application of image recognition that has proven to be very successful is counterfeit product detection. It has become increasingly difficult for brands and retailers to find and eliminate fake items on eCommerce sites. U.S. Customs seized over 13,500 counterfeit goods worth $30 Million in November 2021, indicating how brands and online marketplaces have struggled in the past to find an effective solution. 

    Essentially, image recognition technology allows eCommerce sites to detect products with fake logos and designs attempting to sell as legitimate brands by capturing discrepancies in images and content. The system flags and delists the products and sellers when a fake is detected.

    Here’s how DataWeave helped Classic Accessories, a leading manufacturer of high-quality covers, furnishings, and accessories automate their counterfeit detection process using our super Image Recognition capabilities. 

    f. User-generated content analysis

    Visual content plays a vital role in eCommerce sites, especially when it comes to product photos and videos. Today, branded visual content isn’t as effective as it’s one-dimensional. As a matter of fact, 93% of marketers agree that customers trust user-generated content more than content produced by brands. However, user-generated content that features product images or videos is way more exciting, realistic, and creative. It gives customers an appealing view of products being used in real life. 

    The most common form of UGC, i.e., reviews and ratings, have been the key for eCommerce brands as they are quantitative and qualitative metrics about a product/service quality, worth, value, reliability, etc. With image recognition, retailers can access insights into strengths and gaps in all product offerings by understanding what consumers are saying about them. 

    Here’s how DataWeave can help retailers and brands analyze consumer reviews & help them adapt to customer needs.

    Conclusion

    Because of its massive influence, image recognition technology is becoming widely adopted by eCommerce companies. It benefits both retailers and customers. Image recognition based on deep learning can provide retailers with helpful capacities like customer analytics, counterfeit detection, personalized searches, and more. Retailers can also use the data gathered from image recognition eCommerce technology to design effective marketing campaigns and improve their ROI.

    With super sharp image recognition capabilities, DataWeave offers 90% accuracy in matching eCommerce products, allowing us to provide comprehensive and precise insights into pricing and assortments. Sign up for a demo with our team to know more.

  • 6 Promotional Strategies for the Holiday Season

    6 Promotional Strategies for the Holiday Season

    For eCommerce companies, holidays are the busiest season of the year. Whether creating brand awareness with your marketing campaigns or freshening up your landing pages or finding new ways to segment & understand your customers, the list of tasks seems endless. It’s the time of the year when most people look forward to shopping for friends and family. 

    The holiday shopping season begins with Black Friday and Cyber Monday and leads to the December holidays, including Christmas and New Year. Consequently, proper planning and marketing are essential for a successful holiday season. 

    In fact, holiday sales during November and December are forecasted to be between $843.4B – $859B, up 10.5% over 2020, according to the National Retail Federation (NRF). For online stores specifically, sales are predicted to increase between 11% – 15% to a total of between $218.3B and $226.2B driven by online purchases.

    This guide will share eight promotional strategies retailers can use during the holiday season. We will also discuss how data analytics can help retailers improve their promotional strategies. 

    Using data analytics to guide promotional strategies

    Promotional Strategies
    Promotional Strategies

    Data is the foundation of every successful marketing campaign. Data analysis helps companies understand which graphics worked well and campaigns that generated the most revenue. Gathering data and running analysis helps companies improve their next marketing campaign. Retailers can also get deeper insights into campaigns/channels with the highest conversion rate or average order value (AOV).

    With data analytics, retailers can prioritize campaigns and channels that resonate the most with their customers this holiday season. But, it would be best to try more than one promotional strategy to ensure you double down on what works without placing all of your eggs in one Christmas-themed basket. 

    Here are four ways that data analytics can help guide promotional strategies:

    a. Customized alerts for listing pages

    Data analysis helps retailers determine if certain products are out of stock on their rival’s website and adjust their own pricing accordingly. It allows retailers to grab market share for trending items. For example, if you get an out-of-stock alert for a particular product at competitors’ stores, you can invest more in advertising that product on your online store. In addition, customized alerts keep retailers informed about their inventory status, allowing them to plan promotions and ads. They can see which products are becoming commoditized due to intense competition and which ones offer better revenue opportunities. 

    Learn how DataWeave can help retailers track their competitor’s stock and inventory status.

    b. Maximize conversions by tracking product trends

    Assortment Analytics
    Assortment Analytics

    Customers are always looking for products that are currently trending. With assortment analytics, eCommerce companies can get insights into hot trends, allowing them to stock in-demand categories and products. Integrating assortment analytics with AI-powered image analytics can also provide insights into attributes that are popular among customers. By filling gaps in their current assortments, retailers can improve conversion rates and increase revenue. 

    Here’s a case study on how DataWeave helped Douglas, a luxury beauty retailer in Germany boost sales by building an in-demand product assortment 

    c. Monitor competitor promotions

    Promotional Insights
    Promotional Insights

    With increased competition and consumer demand for deals, it has become important for retailers to monitor their competitor’s promotions. Monitoring promotions helps retailers to optimize their ad spend accordingly. AI-powered image analysis tools can capture important information from competitors’ ad banners and deliver insights into metrics that are working to deliver sales. 

    Here’s how DataWeave can help retailers make their marketing magnetic with competitive promotional insights! 

    d. Optimize margins with a data-driven pricing strategy 

    Pricing Intelligence
    Pricing Intelligence

    It has become challenging to price products in recent years since digital tools enable price transparency across channels. Although this trend is excellent for consumers, it makes competition fierce for retailers. A data-driven pricing strategy incorporates a variety of factors, including industry needs, competitor analysis, consumer demand, production costs, and profit margins. 

    With data-driven competitive pricing, retailers can keep pace with the changing eCommerce environment with real-time pricing updates. It also helps them optimize margins and quickly respond to changes in prices on rival stores. 

    Promotional Strategies for the Holiday Season

    a. Virtual Webrooms

    When customers want to see a product in-person, they go to a store showroom. It helps them make a purchase decision. However, with the Internet, eCommerce companies can bring this tactic online. The only difference between showrooming and webrooming is that the former takes in-person, whereas the latter happens digitally. Webrooming grew in popularity during the COVID-19 pandemic. Instead of spending weekends browsing stores, consumers took to the Internet for most of their product research.

    A webroom allows customers to explore products from every angle, providing them with the complete in-person showroom experience online. Webrooming is a powerful holiday marketing strategy, especially regarding expensive purchases. Customers prefer to understand how the product will look. However, building a webroom is extensive and requires retailers to hire developers and professional photographers.

    Webrooms allow retailers to share their collections, schedule virtual appointments, share 3D product images, set up virtual fitting rooms for clothing products, and accept purchase orders. For example, in 2015, Tommy Hilfiger launched its first digital showroom in Amsterdam to improve sustainability and minimize its carbon footprint. Through remote wholesale selling and digital product creation, a digital showroom helped Tommy Hilfiger transform the buying journey and retail value chain.

    b. Loyalty-rewarding sales and perks

    Loyalty Rewarding
    Customer Loyalty

    Consider building customer loyalty during your holiday promotions. First, encourage your holiday shoppers to become loyal customers by offering bonus rewards, contests, or giveaways when they sign up for your loyalty program. You can encourage them to purchase right away by providing instant discount coupons or points for a reward to redeem on their next purchase. For maximum impact, you should run this promotion throughout the holiday season. 

    Second, you should attract your current loyalty program members with discount codes. Offer free shipping or provide a one-day-only discount code to ensure your customers choose you during their last-minute purchases. With these rewards, you’ll attract customers who are window shopping and simultaneously bring your loyal customers back throughout the holiday season.

    c. Charitable Tie-Ins

    AmazonSmile
    AmazonSmile

    Research shows that customers are four times more likely to purchase from brands with a strong sense of purpose. With the festive season being the time of giving, working with a charity and giving back to your community is a great way to reach out to customers. 

    After a tough one and half years because of the pandemic, people want to give back and help those in need this holiday season. You can partner with a non-profit and run campaigns that allow customers to give back. It’s also great for sharing your brand mission with your customers. For instance, Amazon allows customers to shop from AmazonSmile, which donates 0.5% of their eligible Charity List purchases to a selected charity, at no extra cost to the customers. 

    Consider partnering with an organization within your industry. For example, you can pair up with a non-profit that collects and gives clothes to the needy if you sell clothes. You can involve customers by asking them to exchange old dresses for coupons or cash discounts. 

    d. Omni-channel customer experience

    Omni-channel marketing provides customers with a seamless, consistent, and cohesive experience over multiple marketing channels. Omnichannel marketing aims to provide a meaningful and cohesive experience that inspires your customers to make a purchase. Unlike multichannel marketing, this strategy puts the customer at the center of marketing campaigns and elevates the cross-channel customer experience. 

    Omnichannel shoppers spend 10% more money and purchase 15% more items than the original shoppers. eCommerce companies can use historical data to analyze successful channels and create a more transparent marketing strategy for the holiday season. Omnichannel analytics will provide a holistic picture of customer data that will help retailers to better meet the customer’s requirements and predict inventory. 

    e. Buy now, pay later (BNPL)

    Buy Now Pay Later
    Buy Now Pay Later (BNPL)

    Technically, buy now, pay later isn’t a promotional idea since your customers will still be paying the full price. However, BNPL allows them to delay their payments and not pay in full right away at checkout. Buy now, pay later needs to be on every eCommerce company’s holiday promotions plans. Various retailers, including Walmart, offer affordable monthly payments at the pace of 3 to 24 months with Affirm. Target also has a similar scheme with Sezzle and Affirm. Whereas Sephora and Macy’s offer 4 interest-free payments with Klarna.

    BNPL is especially popular with millennials and Gen Z shoppers and will factor into their 2021 holiday shopping plans. Research showed 62% growth in the use of buy now, pay later service in consumers aged 18 to 24. Giving customers a means to manage their budgets during holidays while still taking home their purchases will attract more customers. While the customer doesn’t pay the full price right away for their purchase, businesses still get the total worth of the item. In the eCommerce industry—nearly 50% of BNPL users say they use it while shopping online, and among them, 45% use the service frequently.

    f. Buy One, Get One

    The last promotional idea is a classic buy one, get one offer. Everyone likes a good BOGO promotional offer. In fact, 66% of shoppers from a survey preferred BOGO over other promotions. It’s a win-win promotional strategy for retailers and customers. With this offer, people shop and stock up on gifts for their friends and family, while retailers make a more significant profit than 50% off sales. People prefer to get 100% off on a product over 50% on two items. 

    BOGO sales are best to move inventory by giving shoppers a deal they can’t pass up. If you have stocked up extra items during Black Friday, you can move those last-minute gifts as end-of-year BOGO sales, making room for new merchandise in January.

    Conclusion

    In this post, you saw that there’s more to holiday marketing than a few social media posts. eCommerce companies can use these holiday promotional ideas to offer Loyalty-rewarding sales and perks, buy now pay later service, and an omnichannel customer experience. Regardless of which strategies you’re using, remember that historical data analytics and early planning will play a significant role in increasing your sales and revenue. 

    Proper planning backed by insights into key metrics will help your team develop a one-of-a-kind holiday marketing strategy to drive your holiday sales upward. From sharing gratitude to offering personalized experiences, retailers have various options for promoting business this holiday season.

    Learn how DataWeave can help make sense of your and your competitor’s pricing, promotional, and assortment data this holiday season. Sign up for a demo with our team to know more.

  • Are Your Digital Shelves Prepared for Green Monday?

    Are Your Digital Shelves Prepared for Green Monday?

    Traditionally, retailers have staged multiple promotions between Black Friday and before Christmas Day to keep consumers excited about holiday shopping, so it’s easy to see why one more promotional day might fall into relative obscurity. As if ‘Early Start’ offers to Black Friday and extended ‘Cyber Weekend’ promotions weren’t enough to plan for, eBay added another day into the mix called ‘Green Monday’, much to the benefit of consumers, as it furthers the window of opportunity to secure a bargain during the holiday season. 

    Green Monday falls on the second Monday of December and has historically been one of the greatest sales days of the year for eBay, often attracting last-minute shoppers or those searching for last-minute deals. However, because of the 2021 Global Shipping Crisis, there is speculation that Green Monday may be the last chance this year to have items delivered in time for Christmas. For this reason, we believe it could turn into quite a fruitful event for participating retailers if it encourages procrastinating shoppers that traditionally spend closer to December 25th to buy earlier in the season.

    This isn’t the first year retailers outside of eBay have offered Green Monday promotions, however. Our team has been actively monitoring activity on this day from 2017 through present, to not only assess which retailers participate in the event, but also to understand how the discounts may change surrounding the event. The categories monitored include Apparel (Clothing, Shoes & Jewelry), Bed and Bath, and Home and Garden, and we’ve identified products offered on discount by comparing each applicable product’s price on Green Monday versus the most commonly seen price for the product offered throughout the month of December.

    Better Promotions Than Boxing Day

    Taking a closer look at 2020 Green Monday discounts within the categories and retailers analyzed, apart from Wayfair.com, we see all offered more SKUs on discount on Green Monday versus the days leading up to and out of the event. Kohls.com led the pack with around 93% of SKUs offered on discount, followed by Macys.com with 95%, and Wayfair.com with 83%. Overall, the number of SKUs on discount on Green Monday were greater than the SKUs offered on discount on Boxing Day, which is traditionally known as a great day to bargain shop.

    Source: DataWeave Commerce Intelligence – Promotional Insights tracking Apparel, Bed & Bath, and Home & Garden category product’s online price on Green Monday 2020 in the US versus regular prices for the same products in the month of December each year.

    What’s in Store for Green Monday 2021?

    The insights we’ve tracked over the last four years have not indicated any signs to an end for Green Monday any time soon. As we see it, for consumers it is an extremely convenient time to order holiday gifts, and for retailers it is a good time to build brand trust and loyalty by fulfilling last minute orders at a great value, in time for the holidays.

    Our prediction for the categories analyzed is to expect to see more retailers participate in Green Monday 2021 to a greater degree (more SKUs on sale and enhanced promotions). For retailers in this analysis, we would anticipate HomeDepot.com to enhance the number of offers to match 2020 competitive activity, and for Wayfair.com to look at increasing the number of offers on Green Monday versus the period leading into the event.

    If you are interested in learning more about the details behind this analysis or our Promotional Insights solution, be sure to contact us. We can help you evaluate the effectiveness of your holiday promotional spend with access to near real-time marketplace insights on the brands, categories, and products your rivals promote, including discounts, campaign frequency and duration and more.

  • Manage Your Supply Chain Like a Pro

    Manage Your Supply Chain Like a Pro

    To make faster, seamless deliveries possible, brands need to tighten their supply chain. The pandemic has put a lot of stress on the global supply chain. The supply shock that began in China in February and the demand shock that followed as the global economy shut down uncovered weaknesses in production strategies and supply chains. Temporary trade restrictions and shortages of pharmaceuticals, critical medical supplies, and other products, further added to the problem. 

    As a consequence of all this, brands have to reduce or even eliminate their dependence on sources that are perceived as risky and rethink their use of lean manufacturing strategies that involve minimizing the amount of inventory held in their global supply chains. In the post-pandemic world, the supply chain will take center stage, and managing it efficiently with technical support is going to be what gives one brand an upper hand over the others.

    1. Micro fulfillment is emerging as the need of the hour

    Micro fulfillment
    Emerging Micro Fulfillment

    Retailers are now faced with unprecedented omnichannel fulfillment complexities. Not only do customers expect faster order fulfillment and delivery, but they’re also opting to ‘buy online and pick up in-store (BOPIS)’ or ‘click-and-collect’. Amazon has spent billions of dollars on building its shipping infrastructure, including its existing operating 175+ fulfillment centers across the world and investing nearly $1.5 Bn to build an air hub in the US. Walmart, on the other hand, is relying on its existing footprint across 5000+ US stores to help deliver online orders faster.

    All this is hinting towards micro-fulfillment emerging as a strategy retailers are using to make the fulfillment process more efficient and their supply chain more ready — from receiving an online order to packing it and offering last-mile delivery. This approach will certainly work towards imparting speed to localized, in-store pick-up and combine it with the efficiency of large, automated warehouses. Delivery speed and costs are more important than ever to retain customers and foster brand loyalty. In fact, this will become a big differentiator for grocery e-commerce as the number of people making online grocery purchases has increased drastically the world over and a recent report indicated that in the US, 46% of people use online delivery more now than before the COVID crisis, and 40% use online pickup more.

    2. Use big data to tie-in loose nodes

    The landscape of supply

    Supply chain management is held at the heart of every successful e-commerce company. Supply Chain efficiency always ensures that the right product reaches the right place at the right time. It ensures cost reduction and enhancement of cash utilization. That is why it is important to stay alert and tie-in all loose ends in the supply chain architecture. Big data can come in handy here and it is that quantitative method and structure that can be used to improve decision-making for all activities across the supply chain. While the role of big data is extremely exhaustive and full-pronged across the entire supply chain design, it is important to understand it in theory in a simplified way so that brands can incorporate it to make their backend operations seamless.

    Big data is all about real-time analytics and it primarily does two very important things in making supply chain management easy

    • It expands the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems. 
    • Big data apply powerful statistical methods to both new and existing data sources. This helps give structure to new insights. This in turn allows forecasting and helps improve supply chain decision-making capabilities for your brand, all the way from the improvement of front-line operations, to strategic choices such as the selection of the right supply chain operating models.

    3. Improve ROI by introducing automation to the mix

    Introducing automation
    Introducing Automation to Improve ROI

    Introducing automation will help take care of tasks usually done manually, such as placing orders, processing changes, data entry, and much more. This frees up time and cuts down on human errors leading to error-free, faster processes. Adidas for instance has been able to reduce 60% of its operational supply chain costs just by switching to end-to-end automation. The largest sportswear manufacturer used automation across 400 factories by bringing in standardized, reusable processes to deliver the best results in a cost-effective way across the supply chain, marketing, finance, retail, and eCommerce. On the supply chain part, with automation, the brand was able to globally attend to supply chain service desk management, vendor onboarding, PO change management, Contract form approval, product data verification, and other such tasks in real-time. This highly successful initiative helped the brand save a lot of time, it earlier lost in manually attending to internal processes and reduced the time to market for Adidas by two-thirds. Moreover, automating systems helps cut down slacks and in return allows the supply chain to stay agile and alert for any unforeseen situations. This readiness further boosts the framework towards growth. 

    4. Eye the future and introduce robotics

    Introduce Robotics
    Robotics is the next big thing in Future

    Autonomous technology is not the next big thing of the future but is the most important thing at present defining the face of the supply chain. Autonomous robots are expected to see strong growth over the next five years. In fact, according to the Boston Consulting Group (BCG), the global robotics market is estimated to reach USD 87 Billion by 2025. It is believed that more than half of this will be allocated for the retail market. In fact, it is not uncommon to find giant beetle-like robots moving around busily with vertical shelves stacked on them inside Amazon’s warehouse in southern New Jersey, US. Tesco for instance uses Radio Frequency Identification (RFID) robots who are used to scan inventories for entire stores in just an hour (as against seven hours for a store employee) with far fewer errors. 

    Even though every word of this sounds too futuristic to be believable, this is the reality for now and retailers are beginning to realize that innovation must set in holistically and extend far beyond just the warehouse or supply chain. Autonomous mobile robots (AMRs) are fast becoming commonplace in warehouses, helping warehouse workers to fulfill orders quickly and efficiently. There are a few different types of robots that companies are considering, and each has its own unique set of advantages. AMRs in totality enable workers to be more productive due to constant collaboration and promote agility, cutting down on slacks and errors. 

    A cohesive and well-defined supply chain where you can leave enough room for tweaks in the future owing to evolving trends will surely help you gain an edge over your competitors through the entire lifecycle of your product. Getting a grip over the supply chain is necessary now as, by 2025, many supply chains may shift from global flows of goods and services to national, regional, and local networks of buyers and suppliers. So, integrating the supply chain keeping an eye on the global and local is the real deal!

  • How Brands Boost Sales & Satisfaction on Walmart.com

    How Brands Boost Sales & Satisfaction on Walmart.com

    The explosive growth of online shopping has forced brands to re-examine their e-commerce processes to stay competitive and profitable. In particular, out-of-stocks are a common, costly retail challenge, as product shortages frustrate online shoppers – and even prompt them to leave brands.

    According to McKinsey & Company, forty-eight percent of consumers switched to a different brand in 2020 because those products were in stock. Among these consumers, seventy-three percent plan to keep using the new brands, linking product availability gaps to the erosion of sales and loyalty. Conversely, brands with effective inventory planning and replenishment can keep items in stock, drive sales and improve the customer experience.

    Retailers like Walmart, collaborating with these brands to meet customer demand, are still facing inventory challenges but, as noted in 2021 Q3 earnings, inventory was up almost twelve percent year-over-year as they worked to stay ahead of increased holiday demand. They have also adjusted in-store operations to accommodate ever-growing e-commerce demands, especially within grocery-centric categories, as digital grocery buyers now amount to more than half the U.S. population.

    Maximizing Conversions with Category Insights

    Walmart’s dot-com strategy is paying off in spades, considering they surpassed Amazon as the leading U.S. grocery e-commerce retailer in 2020 and grew another forty-one percent in Q3, 2021. Our team has been actively tracking digital shelf analytic KPIs on Walmart.com to identify inventory and promotional performance improvement opportunities at a category level to support brands in capitalizing on these digital growth opportunities.

    The latest analysis is summarized below, reviewing average category availability and discount trends occurring each week of the month, from May to August 2021, at a category level. A recent report found the 29th of each month to be the busiest day for online sales because consumers often get paid at the end of the month, which made DataWeave analysts wonder:

    • Which categories are maximizing their growth potential on Walmart.com and where are the greatest opportunities for improvement during periods of increased demand?
    • How do increased demand periods (like payday) impact category online availability?
    • Are category promotions offered at the right times throughout the month to best support demand?

    When Seasonal Demand for Groceries and Payday Merge

    Across all Walmart.com food categories tracked, Week 5 – where payday commonly falls for most consumers, had the lowest average product availability, while Week 4 had the highest average product availability for all categories except Deli and Fruits and Vegetables. These findings may inspire Walmart’s brand partners to rethink their inventory and assortment planning, replenishment and even pricing efforts to maintain a healthy stock closer toward the end of the month to match higher demand.

    The categories with the greatest difference in average availability during Week 5 versus the rest of the month were Snacks & Candy, Beverages and Alcohol, indicating consumers consistently made these types of purchases closest to payday, when income was highest throughout the month. Seasonality is a secondary factor that influenced demand for these items given events like Memorial Day, Fourth of July, Summer Break, and Back-to-School shopping all took place during our analysis. Additionally, most holidays overlapped payday, which also furthered Week 5 demand.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Coupling availability with discounts allows us to consider whether consumers buy more in Week 5 due to high discounts or increased purchasing power, or both. In reviewing the average category discounts offered within the same grocery-centric categories analyzed above, we found almost every grocery category showed a higher discount in Week 5 compared to the rest of the month, except for Bread & Bakery and Alcohol.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Regarding Alcohol, during Week 4, when average availability was the highest, the average discounts offered were the lowest. This can indicate inventory was primed for payday shoppers (and the holidays of course). Bread & Bakery offers the greatest average discounts when inventory levels are lowest on average, indicating Week 3 is a great time to stock up, while Week 4 might be a great time to buy the freshest inventory.

    The greatest average discounts in Week 5 were in Snacks & Candy, Pantry and Fruits & Vegetables. Deeper discounts for Snacks & Candy in Week 5 may have helped brands compete for consumers’ disposable income despite being a discretionary category. Pantry brands’ discounts may have reflected a need to compete for shoppers’ attention. During this period, consumers were out of the house more and less likely to use these grocery staples compared to earlier lockdown periods and cooler months.

    Making Specialty Categories and Health a Priority for Online Shoppers

    Interestingly, the only two categories where inventory was higher in Week 5 versus all other weeks each month were ‘Special Diets’ foods and ‘Summer Flavors’, although ‘Special Diets’ foods consistently maintained the lowest level of average availability each week across all food categories analyzed. This consistent lack of inventory could indicate a great opportunity for brands to increase inventory for dietary products sold on Walmart.com.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    The average availability for ‘Summer Flavors’ foods verifies brands are maintaining a solid replenishment strategy for these seasonal items, and a high likelihood consumers will happily find what they need to plan their Summer gatherings on Walmart.com. One alarming factor we found was the change in average discounts offered during Week 5 versus Weeks 1 through 4, indicating promotions surrounding payday may be driving sales volume versus organic demand.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Digital Growth Opportunity in Meal Kits and Kids’ Meals

    Two categories primed for growth, according to Statista, are meal kits and kids’ food and beverages. Their research indicates retail sales for kids’ food has grown steadily year-over-year since 2013, and a recent report also indicates meal kit sales are expected to more than double 2017 sales in 2022, reaching $11.6 billion in the U.S., spurred by pandemic-induced demand. A concerning find in our research indicates both categories, ‘Easy Meal Solutions’ and ‘Kid Friendly Foods’ on Walmart.com, showed great volatility when it comes to in-stock availability. For example, in Week 1, ‘Easy Meal Solutions’ had an average availability nearly half the average of the rest of the month (around nineteen percent versus nearly thirty-eight percent), and in Week 5, payday week, ‘Kid Friendly Foods’ saw the biggest drop in average availability compared to Weeks 1 through 4 (over sixty-seven percent versus seventy-five percent) indicating supply may not be keeping up with the heightened demand.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    The heightened average discounts offered during Week 5 for ‘Baby’ and ‘Pets’ items indicate two categories consumers will most likely stock up on during payday.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Back to School Stock-Outs

    U.S. retail sales unexpectedly increased in August, likely boosted by back-to-school shopping and child tax credit payments. Meanwhile, product shortages and other supply chain issues slowed 2021’s back-to-school sales, possibly affecting school supplies’ and clothing availability on Walmart.com. According to our analysis, the average product availability in Walmart.com’s school supplies category fell from over sixty-two percent during Weeks 1 through 4 to nearly forty-two percent in Week 5.

    Warmer weather, seasonal events, reduced lockdowns, and vaccination efforts led more Americans to resume in-person socializing, giving reason to update their spring and summer wardrobes. In July, Forbes shared that three-quarters of shoppers are purchasing apparel, accessories and shoes the most. On average, only around sixty-three percent of clothing items were available on Walmart.com during Weeks 1 through 4. However, in Week 5, that figure plummeted to just over thirty-eight percent, the most significant drop among all categories.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Demand for new fashion remained high throughout this period, seemingly fueled organically, as only moderate additional discounts took place in Week 5, and although the average discount on school supplies was only around twenty-seven percent during Weeks 1 through 4, it surged to just over forty-seven percent in Week 5. Generous additional discounts in Week 5 may have inspired online shoppers to shift spending from clothing to school supplies in late July and August ahead of students’ return to the classroom.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Prioritizing Product Availability with Digital Advertising Strategies

    Seventy-eight percent of B2C marketers increased their 2021 digital advertising spend to fuel online product discoverability (Share of Search), and sales and market share, but out-of-stock experiences simultaneously surged 172% this year from pre-pandemic levels. Paying for ads that drive traffic to your out-of-stock products can be as detrimental to your brand as a bad user experience. Our review of the ‘Featured Products’ sold on Walmart.com show consistent, low-levels of product availability each week throughout the months reviewed.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Additionally, the average discount offered on these products tended to be higher than most other categories reviewed, indicating brands participating in the featured product section of the website were not only investing in digital ads, but also doubling down with promotional activity as well.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    How Brands can Replenish Their Digital Shelf

    It is well known just how important it is to have products available during the right time of day, week, month, or season to improve customer satisfaction rates, but with your e-commerce store open 24/7 and omnichannel fulfillment strategies in place, it drastically changes the way in which strategic execution is prioritized for a retailer to reduce basket abandonment and for brands to build loyalty.

    Our greatest takeaway from this analysis is realizing how crucial it is for brands to proactively track product availability and competitive pricing insights to stay ahead of the curve and achieve their digital growth goals. Early visibility to stock replenishment could help brands align with heightened cyclical and seasonal demand to avoid out-of-stocks and grow e-commerce sales.

    This is why more leading brands now rely on our Digital Shelf Analytics solutions, including Pricing and Availability insights, to keep eCommerce planning agile, to maximize online conversions, and ultimately maintain shopper satisfaction and loyalty.

  • Top 7 AI tools for your eCommerce business

    Top 7 AI tools for your eCommerce business

    The 2020 global health crisis sped up the adoption of omnichannel shopping and fulfillment. Consumers spent $791.70 billion online with U.S. merchants in 2020, a 32.4% rise compared to 2019. To keep up with this digital shift, offline businesses have substantially moved investments to online infrastructures for everything from e-commerce platforms, product recommendations, inventory management, and communications. AI tools for eCommerce have played a major role in helping businesses in the digital shift. 

    However, the benefits of setting up e-commerce stores are potentially outweighed by the increased costs. As markets transition to online retailers, they must learn to efficiently collect, secure, and analyze data coming in from multiple sources. Strategically approaching the data problem with artificial intelligence (AI) can help better serve customers, gain a competitive advantage, and drive loyalty.

    In this blog, you will learn about seven data and AI tools for eCommerce businesses:

    Seven data and AI tools for eCommerce businesses
    Seven Data and AI tools for eCommerce businesses

    1. Data Warehouse

    Data is the one advantage that eCommerce merchants and marketers have over brick and mortar retailers. When buyers are from the internet, eCommerce retailers can collect data and measure almost every aspect of their interactions. However, that advantage is worthless unless there is a system to make sense of the data they collect. Companies assume that they have a sound system in place. But, what they have is a network of silos. In such a system, data sticks to different platforms like Google Analytics, Shopify, or Klaviyo and can’t move to deliver valuable insights. Funneling all your data into a single location for your eCommerce stores is the right way to go. Data warehouses centralize and merge a plethora of data from various sources, helping organizations to derive valuable business insights and improve decision-making. 

    Data Warehouses support real-time analytics and ML operations quickly & are designed to enable and support business intelligence (BI) activities like performing queries and analysis on a colossal amount of data. Data could range from customer-related data, product or pricing data, or even competitor data. 

    However, the time needed to gather, clean, and upload the data to the warehouse is a time-consuming process. Here’s where DataWeave’s AI-Powered Data Aggregation & Analysis Platform can help! Get critical insights on your competitor’s pricing, assortment, and historical sale trends with a real-time dashboard. Build a winning eCommerce strategy with market intelligence without the need to store your data. 

    2. Data Lake

    Data Lake

    A data lake is a centralized repository that can store structured and unstructured data at any scale. Companies don’t have to provide a schema to the data before storing it, but they still can run different analytics and ML-related operations. However, it takes more time to refine the raw data and then analyze or create ML models for predictions. 

    An Aberdeen survey saw businesses implementing a Data Lake outperforming similar companies by 9% in organic revenue growth. The organizations that implemented Data Lake could perform various analytics over additional data from social media, click-streams, websites, etc. A Data Lake allows for the democratization of data and the versatility of storing multi-structured data from diverse sources, improving insights and business growth. 

    eCommerce businesses can collect competitors’ data in data lakes like their popular products, categories, landing pages, and ads. Analyzing competitors’ data helps retailers price their products correctly, helps with product matching, historical trend analysis, and much more. However, data lakes can also be used to store consumer data such as who they are, what they purchase, how much they spend on average, and how they interact with a company. Successful retailers leverage both competitor and consumer data to understand their consumers better, what brands to carry, how to price each product, and what categories to expand or contract. Retailers also store identity data such as a person’s name, contact information, gender, email address, and social media profiles. Other types of data stored are website visits, purchase patterns, email opens, usage rates, and behavioral data. 

    The major challenge with a data lake architecture is that it stores raw data with no oversight of the contents. Without elements like a defined mechanism to catalog and secure data, data cannot be found, or trusted resulting in a “data swamp.” Consequently, companies need teams of data engineers to clean data for data scientists or analysts to generate insights. This not only increases the turnaround time of gaining valuable information but also increases operational costs.

    However, you can rely on platforms like DataWeave that stores competitor pricing & assortment information at a centralized location. You can leverage intelligently designed dashboards to get real-time insights into the collected data and make data-driven decisions without the need for storing, cleaning, and transforming the data.

    3. Data Ingestion & ETL

    To churn out better insights, businesses need access to all data sources. An incomplete picture of data can cause spurious analytic conclusions, misleading reports and inhibit decision-making. As a result, to correlate data from multiple sources, data must be in a centralized location—a data warehouse or a data lake. However, extracting and storing information into these systems require data engineers who can implement techniques like data ingestion and ETL.

    While data ingestion focuses on getting data into data lakes, ETL focuses on transforming data into well-defined rigid structures optimized and storing it into a data warehouse for better analytics workflows. Both processes allow for the transportation of data from various sources to a storage medium that an organization can access, use, and analyze. The destination can be a data warehouse in the case of ETL and a data lake in case of data ingestion. Sources can be almost anything from in-house apps, websites, SaaS data, databases, spreadsheets, or anywhere on the internet.

    Data ingestion & ETL are the backbones of any analytics/AI architecture since these processes provide consistent and convenient data, respectively. 

    4. Programming languages

    Programming languages

    Programming languages are tools used by programmers to write instructions for computers to follow since they “think” in binary—strings of 1s and 0s. It serves as a bridge that allows humans to translate instructions into a language that computers can understand. Some common and highly used programming languages for building AI models are Python and R.  

    While Python is the most widely used language for training and testing models, R is mostly embraced for visualizations and statistical analysis. However, to productize the ML models, you would require Java programming language so that models can be integrated with your websites to provide recommendations.

    5. Libraries/AI frameworks

    An AI framework is a structure that acts as a starting point for companies or developers to add higher-level functionality and build advanced AI software. A framework serves as a foundation, ensuring that developers aren’t starting entirely from scratch.

    Using AI frameworks like TensorFlow, Theano, PyTorch, and more saves time and reduces the risk of errors while building complex deep learning models. Libraries and AI frameworks also assist in building a more secure and clean code. They future aid developers in simpler testing and debugging.

    Various open-source frameworks in the market also come with pre-trained models for specific use cases. Organizations can leverage off-the-shelf models and tweak with existing data to enhance the accuracy of the predictions.

    6. IDE & Notebooks tools

    IDE or Integrated Development Environment is a coding tool that allows developers to write and test their code more efficiently. However, notebooks are one of the most popular AI tools for organizations to execute analysis and other machine learning tasks. It offers more flexibility over IDEs in terms of exploratory analysis.

    All the features, including auto-complete, that IDEs or notebooks offer are beneficial for development as they make coding more comfortable. IDEs/Notebooks increase developers’ productivity by combining common software activities into a single application: building executables, editing code, and debugging.

    7. Analytics tools

    Competitive Pricing

    Data Analysis transforms raw data into valuable statistics, insights, and explanations to help companies make data-driven business decisions. Data analytics tools like PowerBI and Tableau have become the cornerstone of modern business for quickly analyzing structured and semi-structured data. 

    However, these platforms aren’t optimized specifically for the eCommerce industry. Consequently, you should embrace analytical tools particularly designed for eCommerce companies to make better decisions about product assortment, pricing, and promotions. With data analytics, companies can gain insights into the most popular and discoverable brands on their own and competitors’ platforms. Paired with attribute matching, competitive intelligence gives a deeper understanding of the latest trends and why certain products are popular with your customers. Some more meaningful metrics that retailers can track are discount gap, price gap, catalog strength, and product type gaps. 

    Competitive pricing is another benefit of data analytics with which retailers can identify gaps and keep up with actionable pricing insights. Retailers get to maximize profits and respond to demand by cashing in on insights into rivals’ pricing. With the right analytics tools, they can also track changes in pricing across crucial metrics such as matched products, recent price changes, highest price positions, stock status, and much more. 

    Analytics tools can also help eCommerce companies to capture information about competitors’ promotional banners through AI-powered image analysis. It can provide insights into how and where to spend promotional expenditure. 

    Conclusion

    This listicle discusses some of the AI and data tools commonly used by the eCommerce industry. Data analytics has become a popular method for retailers to understand their customers and boost productivity. Data analytics help companies improve customer experience, improve customer loyalty, generate insights, and advise on data-driven actions. Business intelligence tools can help companies monitor key performance indicators (KPIs), perform proper data analyses, and generate accurate reports. 

    Want to learn how DataWeave can help make sense of your and your competitor’s pricing, promotional, and assortment data? Sign up for a demo with our team to know more.

  • How Brands Can Outperform Rivals With Next-Gen Digital Shelf Analytics

    How Brands Can Outperform Rivals With Next-Gen Digital Shelf Analytics

    As eCommerce grows in complexity, brands need new ways to grow sales and market share. Right now, brands face urgent market pressures like out-of-stocks, an influx of new competition and rising inflation, all of which erode profitability. As online marketplaces mature, more brands need to make daily changes to their digital marketing strategies in response to these market pressures, shifts in demand, and competitive trends.

    eMarketer forecasts 2021 U.S. eCommerce will rise nearly 18% year-over-year (vs. 6.3% for brick-and-mortar), led by apparel and accessories, furniture, food and beverage, and health and personal care. The eCommerce industry is also undergoing fundamental changes with newer entities emerging and traditional business models evolving to adapt to the changed environment. For example, sales for delivery intermediaries such as Doordash, Instacart, Shipt, and Uber have gone from $8.8 billion in 2019 to an estimated $35.3 billion by the end of 2021. Similarly, many brands have established or are building out a Direct to Consumer (D2C) model so they can fully own and control their customer’s experiences.

    In response, DataWeave has launched the next generation of our Digital Shelf Analytics suite to help brands across retail categories directly address today’s costly market risks to drive eCommerce growth and gain a competitive advantage.

    “Our new enhancements help brands improve online search rank visibility and quantify the impact of digital investments – especially in time for the busy holiday season.”  
    ~ Karthik Bettadapura, CEO and co-founder, DataWeave

    The latest product enhancements provide brands access to tailored dashboard views that track KPI achievements and trigger actionable alerts to improve online search rank visibility, protect product availability and optimize share of search 24/7. Dataweave’s Digital Shelf Analytics platform works seamlessly across all forms of eCommerce platforms and models – marketplaces, D2C websites and delivery intermediaries.

    Dashboard for Multiple Functions

    While all brands share a common objective of increasing sales and market share, their internal teams are often challenged to communicate and collaborate, given differing needs for competitive and performance data across varying job functions. As a result, teams face pressure to quickly grasp market trends and identify what’s holding their brands back.

    In response, DataWeave now offers executive-level and customized scorecard views, tailored to each user’s job function, with the ability to measure and assess marketplace changes across a growing list of online retail channels for metrics that matter most to each user. This enhancement enables data democratization and internal alignment to support goal achievement, such as boosting share of category and content effectiveness. The KPIs show aggregated trends, plus granular reasons that help to explain why and where brands can improve.

    Brands gain versatile insights serving users from executives to analysts and brand and customer managers.

    Prioritized, Actionable Insights

    As brands digitize more of their eCommerce and digital marketing processes, they accumulate an abundance of data to analyze to uncover actionable insights. This deluge of data makes it a challenge for brands to know exactly where to begin, create a strategy and determine the right KPIs to set to measure goal accomplishment.

    DataWeave’s Digital Shelf Analytics tool enables brands to effectively build a competitive online growth strategy. To boost online discoverability (Share of Search), brands can define their own product taxonomies across billions of data points aggregated across thousands of retailer websites. They can also create customized KPIs that track progress toward goal accomplishment, with the added capability of seeing recommended courses of action to take via email alerts when brands need to adjust their eCommerce plans for agility.

    “Brands need an integrated view of how to improve their discoverability
    and share of search by considering all touchpoints in the digital commerce ecosystem.”
    ~ Karthik Bettadapura, CEO and co-founder, DataWeave

    Of vital importance, amid today’s global supply chain challenges, brands gain detailed analysis on product inventory and availability, as well as specific insights and alerts that prompt them to solve out-of-stocks faster, which Deloitte reports is a growing concern of consumers (75% are worried about out-of-stocks) this holiday season.

    User and system generated alerts provide clarity to actionable steps to improving eCommerce effectiveness.
    You also have visibility to store-level product availability, and are alerted to recurring out-of-stock experiences.

    Scalable Insights – From Bird’s Eye to Granular Views

    DataWeave’s Digital Shelf Analytics allows brands to achieve data accuracy at scale, including reliable insights from a top-down and bottom-up perspective. For example, you can see a granular view of one SKUs product content alongside availability, or you can monitor a group of SKUs, say your best selling ones, at a higher level view with the ability to drill down into more detail.

    Brands can access flexible insights, ranging from strategic overviews to finer details explaining performance results.

    Many brands struggle with an inability to scale from a hyper-local eCommerce strategy to a global strategy. Most tools available on the market solve for one or the other, addressing opportunities at either a store-level basis or top-down basis – but not both.

    According to research by Boston Consulting Group and Google, advanced analytics and AI can drive more than 10% of sales growth for consumer packaged goods (CPG) companies, of which 5% comes directly from marketing. With DataWeave’s advanced analytics, AI and scalable insights, brands can set and follow global strategies while executing changes at a hyper-local level, using root-cause analysis to drill deeper into problems to find out why they are occurring.

    As more brands embrace eCommerce and many retailers localize their online assortment strategies, the need for analytical flexibility and granular visibility to insights becomes increasingly important. Google reports that search terms “near me” and “where to buy” have increased by more than 200% among mobile users in the last few years, as consumers seek to buy online locally.

    e-Retailers are now fine-tuning merchandising and promotional strategies at a hyper-local level based on differences seen in consumer’s localized search preferences, and DataWeave’s Digital Shelf Analytics solution provides brands visibility to retailer execution changes in near real-time.

    Competitive Benchmarking

    Brand leaders cannot make sound decisions without considering external factors in the competitive landscape, including rival brands’ pricing, promotion, content, availability, ratings and reviews, and retailer assortment. Dataweave’s Digital Shelf Analytics solution allows you to monitor share of search, search rankings and compare content (assessing attributes like number of images, presence of video, image resolution, etc.) across all competitors, which helps brands make more informed marketing decisions.

    Brands are also provided visibility into competitive insights at a granular level, allowing them to make actionable changes to their strategies to stay ahead of competitors’ moves. A new module called ‘Sales and Share’ now enables brands to benchmark sales performance alongside rivals’ and measure market share changes over time to evaluate and improve competitive positioning.

    Monitor competitive activity, spot emerging threats and immediately see how your performance compares to all rivals’, targeting ways to outmaneuver the competition.

    Sales & Market Share Estimates Correlated with Digital Shelf KPIs

    In a brick-and-mortar world, brands often use point of sale (POS) based measurement solutions from third party providers, such as Nielsen, to estimate market share. In the digital world, it is extremely difficult to get such estimates given the number of ways online orders are fulfilled by retailers and obtained by consumers. Dataweave’s Digital Shelf Analytics solution now provides sales and market share estimates via customer defined taxonomy, for large retailers like Amazon. Competitive sales and market share estimates can also be obtained at a SKU level so brands can easily benchmark their performance results.

    Additionally, sales and market share data can also be correlated with digital shelf KPIs. This gives an easy way for brands to check the effect of changes made to attributes, such as content and/or product availability, and how the changes impact sales and market share. Similarly, brands can see how modified search efforts, both organic and sponsored, correspond to changes in sales and market share estimates.

    Take Your Digital Shelf Growth to the Next Level

    The importance of accessing flexible, actionable insights and responding in real-time is growing exponentially as online is poised to account for an increasing proportion of brands’ total sales. With 24/7 digital shelf accessibility among consumers comes 24/7 visibility and the responsibility for brands to address sales and digital marketing opportunities in real-time to attract and serve online shoppers around the clock.

    Brands are turning to data analytics to address these new business opportunities, enhance customer satisfaction and loyalty, drive growth and gain a competitive advantage. Companies that adopt data-driven marketing strategies are six times more likely to be profitable year-over-year, and DataWeave is here to help your organization adopt these practices. To capitalize on the global online shopping boom, brands must invest in a digital shelf analytics solution now to effectively build their growth strategies and track measurable KPIs.

    DataWeave’s next-gen Digital Shelf Analytics enhancements now further a brand’s ability to monitor, analyze, and determine systems that enable faster and smarter decision-making and sales performance optimization. The results delight consumers by helping them find products they’re searching for, which boosts brand trust.

    Connect with us to learn how we can scale with your brand’s analytical needs. No project or region is too big or small, and we can start where you want and scale up to help you stay agile and competitive.

  • Top 4 ways to optimize content to drive e-commerce sales

    Top 4 ways to optimize content to drive e-commerce sales

    Content is the reigning king for e-commerce & plays a big role in driving sales and conversions. And, consumer-centric content that drives traffic is vital for e-commerce sales. Unlike offline retail where the sales staff on the ground is always available to answer customer queries, online that is not the case. When shopping online, customers rely on audio & visual product content to give them the information they need in order to make purchase decisions. Understanding that your product speaks to your customers directly on online channels is critical – so optimizing your product content to represent your brand in the best light is very important.

    Here are the Top 4 ways to optimize content & drive ROI.

    1. Focus on your customer & set a brand tone

    Who is your customer? And what type of product is your brand selling? The golden rule to getting the right content for your brand is to answer these two questions right. For instance, if you’re selling furniture and focusing on a family audience then using flowery language will not help your cause. You need to share factual, product-specific content, calling out furniture specs from color, fabric, size, and so on.

    Flower Glossary Categories

    Take for instance ProFlowers – a US-based flower retailer who created an entire Florapedia® – an in-depth flower guide. This content helped their customers learn more about the various flowers & discover new flowers they never knew of when making purchase decisions. To drive e-commerce sales, ProFlowers set the brand tone using educational content.
    On the other hand, if you are selling clothing or lingerie, you need to be extremely specific about the details of each product. Let’s look at reputed outdoor clothing brand Jack Wolfskin – they use high-quality images for content optimization and showcase real instances and moods in which the clothing can be worn or what they can be paired with. This is a good way to allow customers to picture themselves owning the item, as well as research their unique qualities.

    Clothing brand Jack Wolfskin
    Educational, visual-heavy, or fun & quirky – pick your content style based on your brand tone.

    2. Use videos as a powerful content optimization tool

    Videos empower content and hook your customers in. In a report, Cisco had earlier projected that by the year 2022, videos will be responsible for 82% of all consumer internet traffic. For e-commerce, video content can not only deliver a message but is easily shareable across all platforms. Videos not only possess the power to captivate people for extended amounts of time, but according to research, if a video is embedded on your website, you’re 53 times more likely to rank on the first page of Google. 

    Videos work as a descriptive medium to give more details about your product. Further, explanatory videos relieve consumer fears regarding the quality of the product by allowing viewers to visually experience its usages and benefits. MAC for instance uses a host of make-up tutorials and other video content on their website.

    videos as a powerful content optimization tool

    Videos bring brand storytelling to life and keep visitors informed. In the case of the videos created by MAC, they are not only informative and engaging, but they also help the brand answer common shopper questions with live examples. Customers who land upon the MAC website can watch videos relevant to the products they want to shop for, understand the product details and then decide if the product is for them. Thus, brands using videos can create a better customer experience by giving the visitors an immersive brand exposure online, just like they could have got offline. 

    3. Focus on making your product page consumer-centric

    A product page can be highly discoverable if it aligns with the best practices & standard e-commerce algorithms put in place by popular marketplaces and e-commerce channels. This is because the organic product ranking algorithms vary across channels and are composed of direct and indirect factors used to match a consumer’s popular search queries to products they are most likely to purchase. For better content optimization that ensures visibility, start by mapping platform-specific content standards. Then follow the SEO trends and tweak your product titles and description, to give your brand content a boost.

    LOVE Hair product
    Love Hair Product

    Take for instance the LOVE Hair product pages – the product titles are crafted using product features and benefits like revitalizing, nourishing, volumizing. Consumers searching for shampoos normally type in these attributes to look for shampoos that may suit their requirements – so using attributes as a hook in the product title is a great idea to make the product more discoverable against that keyword or attribute. 

    Next, keeping these standards as a backbone, fine-tune the product details you are putting out on the page. Your product features are the reasons why your consumer will buy your brand as compared to your competitors, so your descriptions should be crisp, easy to read, highlighting all the product features & facts that help them make that purchase decision. Let’s look at the Fitbit product page…

    Fitbit product page
    Fitbit Product Page

    4. Optimize content based on devices

    With mobile commerce reaching the tipping point in the e-commerce sales funnel, you cannot ignore attending to content optimization for hand-held devices. Many times, buyers on the go use mobile devices to conduct their research and your success lies in being able to entice them with a perfectly optimized e-commerce page even on their mobile device. Here are a few tips to keep in mind:

    • Keep their reading experience in mind. Use shorter titles, and bite-sized product information so key points are upfront and visible on a tinier screen 
    • Be concise with your content presentation
    • Video content should not autoplay on mobile. The less invasive your content, the better
    • Keep video & image file sizes small so that page load time is quick
    Optimize content based on devices
    E-Commerce Product Page

    A perfectly balanced e-commerce product page is even more vital in the new normal, given that COVID has accelerated e-commerce, globally. So, whether you are selling furniture, books, clothes, or health juices, with the right focus on product content, you can convert shoppers into customers more easily and increase your sales & revenue. Feel free to take inspiration from some of the examples above to apply some of these strategies to your online store.

    And if you need to get your brand discovered with content optimization, here’s how DataWeave can help! 

    Building the right product page with the right content is not enough. You will also need to keep rehashing your product pages with reviews, offers, and other such relevant nodes to deliver the right punch. After all, delivering the right customer experience starts with a product page done right. 

    Want to see first-hand how DataWeave can help brands with content optimization? Sign up for a demo with our Digital Shelf experts to know more. 

  • Win Search. Win the Digital Shelf this Holiday Season!

    Win Search. Win the Digital Shelf this Holiday Season!

    With the holiday shopping frenzy right around the corner, brands need to do everything they can to win their customer’s share of wallets. ‘Tis the season shoppers have longer shopping lists and will likely buy products they’ve never purchased before for gift giving. This makes it even more critical for brands to make sure they make it easy for shoppers to find their product at the time right time, with the right deals and discounts.
    Watch the webinar with Karthik Bettadapura – CEO, Co-Founder at DataWeave & Vladimir Sushko– E-Retail Director at Anheuser-Busch InBev & learn about the key levers brands need to pull to get their Digital Shelf ready for the Holiday Season

    Let’s start with Product Search…

    Search Optimization

    Organic levers you can pull for Search Optimization

    Key Highlights:

    • Product content is not a one-time fix. It’s seasonal. Seasons change and so does your product. Your content needs to reflect these dynamic changes. 
    • Fact – Search rankings drop when product availability starts dipping.
      Lesser known fact – even after stock replenishing, your search ranking does not bounce back immediately. The opportunity cost of dwindling product stock is high. 
    • Being optimized for the right keyword is good. Being optimized for the right keyword, with a higher ranking than your competitor is great! 
    • Ratings & Reviews have a large correlation with search ranking and impact on sales.

    We then asked the audience what factors they thought had the biggest impact on search rankings… 

    Search Ranking

    Listen to the experts answer questions that have been on everyone’s mind this Holiday Season! 

    Win Search Wn the Digital Shelf
    • Vladimir: How do you approach Search at Ab InBev? 
    • Vladimir: Favourites have an impact, but is this something you can influence? If yes, how?
    • Vladimir: When it comes to growing your online sales via marketplaces what’s the one lever you pull most aggressively?
    • Karthik: Optimizing Share of Search on Marketplaces v/s traditional online retailers 
    • Vladimir: What organic efforts do you use to improve your share of search?
    • Vladimir: When it comes to sponsored search do you use an always-on strategy or are ads spent strategically? 
    • Karthik: Sponsored or Organic? What’s your advice to brands? 
    • Vladimir: Are you tracking your competitor’s Digital Shelf? 
    • Karthik: Which competitor KPIs do you recommend brands should track?
    Bonus Content

    Vladimir: What’s your strategy to make sure you have a high share of search during Christmas – the busiest shopping season. 

    Karthik: What’s your advice for brands for the festive season?

    Click here to register for the On-Demand Webinar

    Do you know if your brand is prepped and ready to make an impact during the biggest holiday season of the year? Or simply just wondering if your Digital Shelf is optimized with the right price, discounts, reviews, and keywords? Our team can DataWeave can help! Reach out to our Digital Shelf experts to learn more.

  • 2021 Cyber Weekend Preliminary Insights

    2021 Cyber Weekend Preliminary Insights

    The exponential growth of eCommerce has forever changed holiday shopping as we know it. What was once led by the launch of Cyber Monday in 2005, has since expanded to ‘Cyber Five’ in 2018, now spans beyond an eight-week period, and is collectively the busiest digital shopping period of the year. Most retail websites have launched a ‘Thanksgiving Comes Early’ sales event for a mosaic of products, causing one to wonder how this ‘early start’ to holiday shopping will impact the traditional promotional cadence consumers have grown to expect to see launch closer to the holidays. Given today’s environmental challenges, threats of scarcity are also encouraging consumers to buy early, which could also impact traffic on the shopping days that have traditionally seen the highest sales volume from digital shoppers.

    In the current environment, the onus will be on consumers to keep a watch for their categories of interest and buy them as and when they appear on sale in their favorite store, because there is no guarantee of sustained availability. Of course, they might return and buy at a different store if a better deal comes up, but there’s a time cost for the dollars saved. More broadly, there has been enough noise made about deals and discounts to keep consumer interest and curiosity going.

    The early promotional start and heightened demand has influenced our team to get a jump start on our 2021 Black Friday analysis to look deeper at trends seen pre-Black Friday 2021 versus 2020. With this assessment, we can track how promotional prices and product availability rates may have changed throughout the event leading in to 2021 Cyber Five, and compare it to last year’s activity to understand how 2021 holiday sales may be impacted.

    We reviewed popular holiday categories like apparel, electronics, and toys (for kids and pets), to have a broad sense of notable trends seen consistently throughout various, applicable marketplaces. What we found is a consistent decline in product availability over the last six months and as compared to last year, alongside an increase in prices.

    We first analyzed availability changes for popular categories on Amazon, noted in the chart below, to understand how inventory may have changed throughout the year, and also compared to 2020. With the exception of batteries and solar power goods and books and maps, there appears to be consistency in greater product availability in 2021 versus 2020, but a slow decline in availability throughout 2021, leading into the holiday season.

    Source: DataWeave Commerce Intelligence – Product Availability in-stock percentage from July 2020 through September 2021 for a sample size of 1000+ products on Amazon.com

    When it came to our pricing analysis, we reviewed select categories on Amazon and Target.com, and found around fifty percent of products on both websites to have seen a price increase year-over-year, while only thirty-seven percent and sixteen percent of products saw a price decrease on Amazon and Target.com, respectively. We also see an increase in the manufacturer’s retail price (MRP) in 2021 versus 2020 for a very high proportion of products (forty-eight percent of products on Amazon and thirty-five percent of products on Target.com), but the discount percentages have remained the same.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: MRP and promotional pricing for 1000+ products on Amazon and Target.com were analyzed from November 13th – 15th, 2021 versus Pre-Black Friday November 24th & 25th 2020

    *Please reach out to our Retail Analytics experts for access to sub-category details available within the above analysis conducted on Amazon and Target.com.

    This indicates 2021 discounts may appear to be greater than or equivalent to 2020, but in reality, consumers will end up paying higher prices than they would have for the same items in 2020. The remainder of this article highlights our key findings found within each key category reviewed – Electronics, Apparel and Toys.

    Electronics Category Analysis

    The television category showcases a great example of how pricing fluctuations impact holiday promotional cadences. Based on our analysis, we found the average television price to have increased around seven percent from April to October 2021, as seen below and as noted within our analysis conducted with NerdWallet.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: The change in average price captured for televisions sold on Amazon from May 2021 through October 2021.

    In fact, on Amazon and Target.com, we see around eighty-four percent of the SKUs listed show both an MRP and promotional price increase in 2021 versus 2020 during pre-Black Friday times. One specific example found on Amazon is noted below for Samsung TV model QN65LS03TAFXZA, a 65 inch QLED TV that was priced at $1697 during this analysis at a fifteen percent discount from MRP, but was priced last year at $1497 without a discount from MRP. In essence, even though the TV offers a greater discount this year, it is actually more expensive than it was in 2020 at this same time of year.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: MRP and promotional pricing analysis on Amazon.com comparing prices from November 13th – 15th, 2021 versus Pre-Black Friday November 24th & 25th 2020

    Unlike TVs, the price of laptops has experienced a decrease over time based on our analysis conducted during the same timeframe, indicating these are a great buy for consumers this holiday season versus promotional offers seen in 2020.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: The month-over-month change in average price captured for televisions sold on Amazon from April 2021 through September 2021.

    Overall, our prediction is that within the electronics category, promotions during Cyber Five may be equivalent to last year’s offers, however, supply will be limited and the total spend versus last year will be greater to the consumer outside of Doorbuster deals offered on select models.

    Apparel Category Analysis

    The Luxury market is seeing a Roaring 20s-like feeling this season given the Covid-induced changes in work and lifestyle and higher disposable income. Therefore, our prediction is that prices for these goods are likely to remain flat, or offer very little discounts this season both due to supply constraints as well as higher demand. For example, our analysis on shoe pricing changes shows relative stability from April to October 2021.

    Source: DataWeave Commerce Intelligence – Pricing Intelligence: The change in average price captured for shoes sold on Amazon from May 2021 through October 2021.

    Given heightened demand and the Global shipping crisis, we anticipate luxury apparel categories to face out-of-stock challenges this holiday season, and therefore we also anticipate seeing less promotional activity for these items as well during Cyber Five 2021. To dive deeper into the severity of the impact, we looked at availability for clothing, accessories, and footwear categories from August 2020 until present to verify our thesis.

    Focusing only on clothing, accessories, and footwear, these categories followed the same downward trending pattern regarding product availability decreases this year with a decline from June (seventy-six percent versus eighty-six percent in May 2021) to September 2021 (the lowest rate seen at sixty-eight percent availability), followed by a partial recovery in October and November (achieving seventy-seven percent availability).

    Source: DataWeave’s Commerce Intelligence – Product Availability: 10k SKUs tracked across 11 retailers US websites (Farfetch, Brownsfashion, NetAPorter, EndClothing, 24s, Selfridges, Ssense, Harrods, Luisaviaroma, MyTheresa, MrPorter) tracked daily stock status in apparel categories; Availability is calculated as percent of instances when product is in stock against all instances tracked.

    Not all recoveries were the same however, and given this, we predict accessories to have the lowest availability rate and greatest risk of facing out of stocks heading into Cyber Five. From May through November 2021, accessories availability continued to decline significantly from month to month, beginning at eighty-three percent in May and ending at seventy-four percent in November. Given this continued decline and with Black Friday right around the corner, we don’t anticipate inventory levels to increase enough to meet the increased holiday demand.

    Source: DataWeave’s Commerce Intelligence – Product Availability: 10k SKUs tracked across 11 retailers US websites (Farfetch, Brownsfashion, NetAPorter, EndClothing, 24s, Selfridges, Ssense, Harrods, Luisaviaroma, MyTheresa, MrPorter) tracked daily stock status in apparel categories; Availability is calculated as percent of instances when product is in stock against all instances tracked.

    Toys & Games Category Analysis

    As noted by DigitalCommerce360, we also anticipate toys to be one of the greatest impacted categories this holiday season given the continued decline in overall availability for these items on Amazon.com, as one great example. Within our category analysis, we saw a steady decline in availability from March 2021 through June (eighty percent to sixty-one percent), followed by a period of stability from June through August (approximately sixty percent), followed by another decline from September through October, finally reaching the lowest availability of fifty-six percent (down twenty-four percent from March 2021).

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Toys & Games SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    The biggest sub-category within the toys department on Amazon, Sports and Outdoor Play, followed the same trend as Toys and Games overall through June 2021, also reaching its lowest availability of fifty-six percent. Instead of continuing along that pattern, Sports and Outdoor Play started on a recovery path, ending at a relatively high availability level of sixty-seven percent in October, which is only five percent lower than its highest availability (seventy-two percent in March 2021). Games and Accessories, the second largest sub-category in Toys and Games, had a continuous decline starting with eighty-nine percent in March 2021, reaching its lowest availability of fifty-four percent in October.

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Toys & Games SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    The sub-category Tricycles, Scooters and Wagons interestingly had its highest availability from July to September 2021 (around eighty percent), unlike other sub-categories which as a whole, had their lowest availability during the same timeframe. From September through October, there was a significant decline (fourteen percent), reaching its lowest availability of sixty-seven percent. The sub-category Babies & Toddlers started on a continuous decline from its highest availability of eighty percent in April to its lowest availability of fifty-six percent in October.

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Toys & Games SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    *Please reach out to our Retail Analytics experts for access to sub-category details available within the above analysis on the Toys and Games category on Amazon.com.

    Pet Toys Category Analysis

    When it comes to in demand holiday toys, you can’t forget about the needs for gifts for our furry friends and family. We also tracked sub-categories such as dog, cat, and bird toys, following the same methodology as tracked within Toys and Games to track pet toy availability changes.

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Pet Toys SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    Dog toys, the biggest sub-category out of the three pet toys analyzed, had high availability – ninety percent in March 2021, but started to decline reaching a low of sixty-five percent in October. There was a period of stability from April to August (averaging seventy-seven percent), followed by a significant decline of over thirteen percent in from September to October. Cat toys, the second largest sub-category, also had its highest availability in March (eighty-nine percent) followed by a steady decline to sixty-six percent in June, a recovery from July to August (achieving seventy-three percent), followed by another decline during September and October, reaching its lowest availability of sixty-three percent (down twenty-six percent from eighty-one percent in March). Interestingly, dog toys which has a product count eight times greater than cat toys, had higher availability than cat toys during each of the months considered during the analysis.

    Source: DataWeave’s Commerce Intelligence – Product Availability – hundreds of Pet Toys SKUs tracked on Amazon.com on a weekly basis from March 2021-October 2021

    In Conclusion

    If we consider discounts and availability to be a good indicator of sales for the 2021 holiday season, with the Global shipping crisis looming over this year’s event, we expect retailers to have trouble keeping their inventory well stocked, which might affect growth rates. That being said, while discounts may be muted and popular items may come on very limited sales given constraints, we believe digital sales on Black Friday will see the highest year-over-year growth to date, given a number of supporting factors: scarcity threats increasing demand and the reason to buy, and consumers waiting to see if holiday offers surpass those see in the early start promotions, followed by the sudden rush to buy on Black Friday so as not to risk a given product being out of stock beyond this time period.

    We also anticipate seeing a continued decline in product availability day-to-day as we progress throughout Cyber Five 2021. Given the analysis conducted on 2020 trends, (we tracked nearly a one percent decline in availability on Black Friday 2020 vs. Thanksgiving Day, followed by a two percent decline on Cyber Monday), our data indicates products went out-of-stock at a faster rate then also.

    Ultimately only the digital-savvy retailers and brands will thrive during these opportune times, while others will continue to be in catch-up mode. Access to real-time marketplace insights can enable a first-to-market strategy, while having access to historical patterns can also help react faster to commonly seen future market factors, such as another pandemic or Global shipping crisis. These types of insights also support day-to-day operations, enabling retailers and brands to accelerate eCommerce growth, determine systems to distinguish their online strategies, discover efficiencies and drive profitable growth in an intensifying competitive environment.

    Continue to follow us in the coming weeks to see the insights we track through Cyber Five 2021, and be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.

  • Top 10 Retail Analytics that You Must Know

    Top 10 Retail Analytics that You Must Know

    Customers expect personalization. Unless they have a seamless experience on your online channels, they’ll leave for a different retailer. Retail analytics can solve these problems for merchants looking to increase customer satisfaction and sales. It provides insights into inventory, sales, customers, and other essential aspects crucial for decision-making. Retail analytics also encompasses several granular fields to create a broad picture of a retail business’s health and sales, along with improvement areas.

    Big data analytics in the retail market
    Big data analytics in the retail market

    Big data analytics in the retail market is expected to reach USD 13.26 billion by the end of 2026, registering a CAGR of 21.20% during the forecast period (2021-2026). The growth of analytics in retail depicts how it can help companies run businesses more efficiently, make data-backed choices, and deliver improved customer service.

    In this blog, we’ll discuss the top 10 analytics that retailers are using to gain a competitive advantage in accurately evaluating business & market performance.

    Top 10 of Retail Analytics You Must Know
    Top 10 of Retail Analytics You Must Know

    1. Assortment

    Assortment planning allows retailers to choose the right breadth (product categories) and depth (product variation within each category) for their retail or online stores. Assortment management has grown beyond simple performance metrics like total sales or rotation numbers. Instead, retail analytics offers a comprehensive analysis of product merchandise and an estimated number of units at the push of a button. Retailers that effectively apply assortment analytics can enjoy increased gross margins and prevent significant losses from overstocks sold at discounted prices or out-of-stock inventory leading their customers to buy from competitors. 

    It also helps retailers gain insights into the trendy and discoverable brands and products on all e-commerce websites across the globe. They can boost sales by making sure they have an in-demand product assortment. They can also track pricing information and attributes common across popular products to drive their pricing and promotion strategies.

    2. Inventory Management

    An inadequately maintained inventory is every retailer’s worst nightmare. It represents a poor indicator of inadequate demand for a product and leads to a loss in sales. Data can help companies answer issues like what to store and what to discard. It’s beneficial to discard or increase offers on products that are not generating sales and keep replenished stocks of popular items. 

    Worldwide Inventory Distribution

    In 2020, the estimated value for out-of-stock items ($1.14 trillion) was double that of overstock items ($626 billion). A similar trend was especially prominent in grocery stores, where out-of-stock items were worth five times more than overstock items.

    Unavailability of high-selling products can lead to reduced sales, ultimately generating incorrect data for future forecasting and producing skewed demand and supply insights. Retailers can now use analytics to identify which products are in demand, which are moving slowly, and which ones contribute to dead stock. They can know in real-time if a high-demand product is unavailable at a specific location and take action to increase the stock. Retailers can use this historical data to predict what to stock, at what place, time, and cost to maintain and optimize revenue. It helps satisfy consumer needs, prevents loss of sales, reduces inventory cost, and streamlines the complete supply chain.

    3. Competitive Intelligence

    Market intelligence & Competitive Insights
    Market intelligence & Competitive Insights

    The ability to accurately predict trends after the global pandemic and with an unknown economic future is becoming the cornerstone for successful retailers. Smart retailers know how important it is to Pandemic-Proof their retail strategy with Market Intelligence & Competitive Insights 

    With 90% of Fortune 500 companies using competitive intelligence, it’s an essential tool to gain an advantage over industry competitors. Competitive Intelligence allows you to gather and analyze information about your competitors and understand the market–providing valuable insights that you can apply to your own business. A more strategic competitor analysis will explain brand affinities and provide insights on what to keep in stock and when to start promotions. Customer movement data will also give you access to where your customers are shopping.

    4. Fraud Detection

    Fraud Detection
    Fraud Detection

    Retailers have been in a constant struggle with fraud detection and prevention since time immemorial. Fraudulent products lead to substantial financial losses and damage the reputation of both brands and retailers. Every $1 of fraud now costs U.S. retail and eCommerce merchants $3.60, a 15% growth since the pre-Covid study in 2019, which was $3.13. Retail Analytics acts as a guardian against fraudsters by constantly monitoring, identifying, and flagging fraud products and sellers. 

    5. Campaign Management

    Some of the challenges of the retail industry are that it’s seasonal, promotion-based, highly competitive, and fast-moving. In today’s competitive marketplace, consumers compare prices and expect personalized shopping experiences. Campaign management allows marketing teams to plan, track, and analyze marketing strategies for promoting products and attracting audiences. Retail analytics can help businesses predict consumer behavior, improve decision-making across the company, and determine the ROI of their marketing efforts. 

    According to Invesp, 64% of marketing executives “strongly agree” that data-driven marketing is crucial in the economy. Retail analytics can help businesses analyze their data to learn about their customers with target precision. With predictive analysis, retailers can design campaigns that encourage consumers to interact with the brand, move down the sales funnel, and ultimately convert.

    6. Behavioral Analytics

    Retail firms often look to improve customer conversion rates, personalize marketing campaigns to increase revenue, predict and avoid customer churn, and lower customer acquisition costs. Data-driven insights on customer shopping behaviors can help companies tackle these challenges. However, several interaction points like social media, mobile, e-commerce sites, stores, and more, cause a substantial increase in the complexity and diversity of data to accumulate and analyze. 

    Insider Intelligence forecasts that m-eCommerce volume will rise at 25.5% (CAGR) until 2024, hitting $488 billion in sales, or 44% of all e-commerce transactions. 

    Data can provide valuable insights, for example, recognizing your high-value customers, their motives behind the purchase, their buying patterns, behaviors, and which are the best channels to market to them and when. Having these detailed insights increases the probability of customer acquisition and perhaps drives their loyalty towards you. 

    7. Pricing

    competitive pricing in retail
    Competitive pricing in retail

    Market trends fluctuate at an unprecedented pace, and pricing has become as competitive as it’s ever been. The only way to keep up with competitive pricing in retail is to use retail analytics that enables retailers to drive more revenue & margin by pricing products competitively. 

    A report from Inside Big Data found companies experience anywhere from 0.5% up to 17.1% in margin loss purely because of pricing errors. Pricing analytics provides companies with the tools and methods to perceive better, interpret and predict pricing that matches consumer behavior. Appropriate pricing power comes from understanding what your consumers want, which offers they respond to, how and where they shop, and how much they will pay for your products. 

    In 2021, the price optimization segment is anticipated to own the largest share of the overall retail analytics market. Retailers can identify gaps and set alerts to track changes across crucial SKUs or products with pricing analytics. Knowing your customer’s price perception will increase sales and also allow you to design promotions that’ll attract customers. Pricing analytics also accounts for factors like demographics, weather forecasting, inventory levels, real-time sales data, product movement, purchase history, and much more to arrive at an excellent price.  

    8. Sales and Demand Forecasting

    Sales and demand forecasting allow retailers to plan for levels of granularity—monthly, weekly, daily, or even hourly—and use the insights in their marketing campaigns and business decisions. The benefits of a granular forecast are apparent since retailers don’t have to bank on historical data of previous clients and customers to predict revenues. Retailers can plan their strategies and promotions that suit their customer’s demands. 

    With sales and demand forecasting, retailers can also consider the most recent, historical, and real-time data to predict potential future revenue. Sales and demand analytics can predict buying patterns and market trends based on socioeconomic and demographic conditions. 

    9. Customer Service and Experience

    With the development of eCommerce, more and more customers prefer to browse and interact with the product before purchasing online. They look for better deals and discounts across stores and platforms. 3 out of 5 consumers say retail’s investment in technology is improving their online and in-store shopping experiences. To enhance merchandising and marketing strategies, retailers can gather data on customer buying journeys to understand their in-store and online experiences. 

    Retailers can run test campaigns to know the impact on sales and use historical data to predict consumers’ needs based on their demographics, buying patterns, and interests. Retail analytics help retailers to bring more efficiency in promotions and drive impulsive purchases and cross-selling.

    10. Promotion

    Analyze competitors' promotions
    Analyze Competitors’ Promotions

    Promotions are potent sales drivers and need to be cleverly targeted towards specific customers with precise deals to generate outstanding sales. Retail analytics allows companies to study their customers and competitors to a vastly elevated level. 

    To be an industry leader, retail companies not only have to understand their customers, but they must also analyze competitors’ promotions to improve their marketing strategies. Analyzing your competitor’s promotional banners, ads, and marketing campaigns are no more associated with imitation. 

    With data analytics and AI, retailers can watch their competitors’ commercialization strategies. It can uncover vital information about their target audience, sales volume fluctuations, popular seasonal product types, product attributes of popular items, and significant industry trends.  Knowing exactly which products and brands are popular among your competitor’s campaigns can help retailers improve their promotional strategies. 

    Conclusion

    The benefits of retail analytics are spread across various verticals, from merchandising, assortment, inventory management, and marketing to reducing losses. The need for analytics has become even more apparent considering the growing eCommerce platforms, changing customer buying journeys, and the complexity of the industry. Understanding which products sell best among which customers will help retailers to deliver an optimized shopping experience.

    Want to drive profitable growth by making smarter pricing, promotions, and product merchandising decisions using real-time retail insights? DataWeave’s AI-powered Competitive Intelligence can help! Reach out to our Retail Analytics experts to know more.

  • How Artificial Intelligence is giving the Indian Beauty Industry a Facelift

    How Artificial Intelligence is giving the Indian Beauty Industry a Facelift

    With the help of artificial intelligence and machine learning, beauty and cosmetics companies are exploring new possibilities. According to a report by Avendus, the global beauty and personal care market are expected to touch US$725 billion by 2025 and the young Indian market is expected to grow to $28 billion by then. This segment is a space of opportunity and today we have more than 80 Indian brands in this domain. 

    D2C beauty brand logos
    D2C beauty brand logos

    While technology in this space plays a very important role, Artificial Intelligence (AI) amongst everything else is giving the beauty industry a makeover. This is because, AI can create an impact on all stages of the beauty value chain — from research & development to supply chain management to product selection, marketing, and more! Resonating this thought, Chaitanya Nallan, CEO & Co-Founder, SkinKraft Laboratories mentions “As a digital-first brand, we sell across multiple e-commerce platforms as well as through our own website. Thus, it is very important that we track and maintain inventory across all channels in real-time to avoid stock-outs and loss of sales. We use AI for this. We have built an in-house data tracking dashboard that pulls in inventory information from all warehouses and maps them against sales to give us an accurate estimate of days of inventory across all SKUs and across all platforms. This information directly feeds into our procurement dashboard and also helps the marketing team to create the right sales strategy.”

    Stock availability is crucial to driving sales. If you need help tracking your online inventory – DataWeave can help give you a near real-time view of your product stock status across marketplaces. 

    With AI being a powerful technology wand, here is how it can drive the future of beauty brands within the D2C segment in India.

    Making Virtual Product trials a reality

    virtual product trial
    Virtual Product Trial

    Augmented Reality (AR) is a prevalent term and many companies are already using it on an everyday basis. More commonly, the Snapchat and Instagram filters we use are all powered by AR. In a similar vein, virtual images can be laid over actual images in real-time using AI. And keeping this concept handy, beauty brands are bringing to the front the AR-powered ‘virtual mirrors’ that let consumers try on cosmetic products in real-time. Modiface by L’Oréal is a perfect example of VR-mirrors, which has pioneered the AR-powered makeup try-ons in the market. These virtual mirrors use AI algorithms to detect the user’s face through a camera by focal points and map the face. Then using AR, images of makeup are adjusted according to the terms obtained and overlaid over the features on the face giving consumers a virtual feel of what they’d look like wearing the product. 

    Virtual try on
    Virtual Try-on

    Much recently, Indian brand Lakme has made ‘virtual try on’ possible by creating a smart mirror on its official website that allows customers to watch their reflection, try on different shades, and customize those shades according to their preferences. Shade matching until a few years back was an entirely on-ground phenomenon and customers visiting a local cosmetics store were able to choose and match the shade of compact, eye shadow, and lipstick against their true skin tone. Today AI can allow you to narrow down on products based on a virtual shade card, put them against your skin in real-time. 

    Make it Truly Personalised

    Every customer is unique, and one size does not fit all. Everyone has a personalized beauty regime they follow & understanding this could be the key to success for beauty brands. For this reason, the future of beauty lies in harnessing AI and AR solutions to tailor the beauty shopping experience to match the needs of the individual consumer. This not only enhances digital engagement but also increases purchasing confidence which in turn helps brands drive conversion and brand loyalty.  

    Pre-pandemic, offline beauty advisors played a consultative role when customers were making purchase decisions. A lot of this has moved online – take for instance Olay. It launched an online “Skin Advisor” app based on a deep-learning algorithm that analyses a consumer’s skin using a simple selfie! Armed with information on their skin type, customers can make an informed, personalized purchase that’s right for their specific skin type. 

    Skin Advisor App
    Skin Advisor App

    Understanding customer preferences and using data from their past purchases also help with personalized marketing in a big way. “Data-driven personalization gives brands insight into what their customers are interested in. We integrate this data into our marketing campaigns and deliver specific, personalized, and relevant content. This way, we make sure to target the right audience with the right messaging. This, in turn, helps us increase engagement and retain customers. Moreover, this combined data, allows us to get repeat sales through upselling and cross-selling. Further, knowing customers beyond just simple demographics helps us improve our targeting and helps us predict future behaviour. We’d like to know, for instance, if a customer clicked on our advertisement, liked, or commented on our social media product displays, signed up to our email list, etc. These analytics reveal a customer’s interest. Combine it with demographics – and you get a sense of what the customer is interested in,” Dhruv Madhok, Co-Founder, ARATA highlights. 

    Boost Product Development

    Social listening
    Social listening

    AI algorithms can be used to study and analyse customer feedback. The algorithm works towards interpreting customer comments, reviews, and feedback on a brand’s website, social media channels, and other online platforms. Artificial Intelligence can also decode and analyse questionnaires and feedback forms that the customers may have responded to online or offline. 

    The beauty and personal care industry is largely driven by usage and customer preferences, so gauging how customers feel about key products can help businesses create & develop products that customers will most likely prefer to buy. For instance, reputed beauty brand Avon recently mentioned that it developed the True 5-in-1 Lash Genius Mascara based on actual consumer feedback! They used machine learning & artificial intelligence to read, filter, process & rank thousands of online consumer comments to determine the top features they crave in a mascara. Using this customer gathered intelligence, they developed a unique product that consumers we’re “asking for”!

    True 5-in-1 Lash Genius Mascara by beauty brand Avon
    True 5-in-1 Lash Genius Mascara by beauty brand Avon

    Need help listening to what your consumers are saying about your brand online? Read more about DataWeave’s AI Powered Sentiment Analysis solution.

    More and more brands are listening to customer responses closely to give way to new products, bring in tweaks to their existing basket, and innovate further. “Our ORM team is leading the knowledge accumulation as far as social listening is concerned. They are not just responsible for responding to customer queries, they are also instrumental in highlighting key insights based on user behaviour being observed,” Chaitanya of SkinKraft Laboratories further asserts. 

    Bombay Shaving Company too with its data-centric culture leverages customer responses for decision making & product development. “In-home personal care and hygiene exploded during the pandemic. We used data analytics to explore different dimensions of in-home experience-driven needs (new usage occasions, need for convenience and DIY, etc.). We listened to our customers & were able to introduce our women’s brand, with innovative hair removal products in a big way during this period. Which today contributes to a significant percentage of our business,” Shantanu Deshpande, Founder & CEO, Bombay Shaving Company mentions.

    Given the scope and scale of the beauty and personal care industry that is major ‘usage’ driven, Artificial Intelligence with its diverse potential can bring a paradigm shift in the industry. AI can help not only with virtual trials, personalization, listening in to customers’ feedback but also with monitoring a brand’s Digital Shelf. Brands can amplify their online sales by tracking Digital Shelf KPIs like share of search & product visibility, pricing & discounting, product content, availability & assortment. Reach out to our Digital Shelf experts to learn more.

  • Gold, Gift Hampers & Gadgets – brands that sparkled this Diwali!

    Gold, Gift Hampers & Gadgets – brands that sparkled this Diwali!

    The festival of lights symbolized the victory of light over darkness, good over evil & knowledge over ignorance. Over the years, Diwali has become all that and more. It has single-handedly become the biggest shopping season in India! Splurging on a new Smart TV or Fridge, or a furniture upgrade at home has become customary during Diwali. Not to forget buying gold and gifts for all your loved ones! 

    As more and more people are doing their Diwali shopping online, we decided to look at the data, see what people were browsing and buying. And more importantly, which brands spruced up their Digital Shelf & put their best foot forward this Diwali Season. 

    Methodology

    • We tracked the first 250 products on Amazon & Flipkart against specific keyword searches & product categories. 
    • Share of Search (SoS): The percentage of products that appeared on the search results page on Amazon or Flipkart belonging to a brand, against a specific keyword or category. 
    • Dates of Crawl during the Flipkart Big Billion Day / Amazon Great Indian Festival.
      – Pre-sale period: 1st October 2021
      – Sale Period: 3rd to 10th October 2021
      – Post Sale Period: 11th – 18th October 2021

    India’s E-Commerce Gold Rush

    YouGov reported that almost three in ten urban Indians (28%) are planning to spend on gold in the next 3 months. Seven in ten (69%) of these prospective gold buyers agreed with the statement, “Diwali is the best time to buy gold”, highlighting their inclination to spend during the festive season. Also, the same survey showed that Tanishq was the most trusted gold brand. With Kalyan Jewellers, Malabar Gold & Diamonds and PC Jewellers also making it to the top 5 list.

    E-Commerce Gold Rush

    While traditionally Gold was mostly sold offline, that trend has fast changed. We tracked brands that had the highest Share of Search against the keyword “Gold Coin” on both Amazon & Flipkart to see if Tanishq, Malabar Gold, PC Jewellers – the big trusted names in jewellery were making their mark online. 

    Search Insights for Gold Coin
    Search Insights for Gold Coin
    • On Amazon, MMTC-PAMP (a joint venture between Switzerland-based PAMP SA & MMTC Ltd, a Government of India undertaking) and Kundan had the highest visibility for the keyword “Gold Coin” at 10%, followed by Malabar Gold at 9%. (Refer to above graph of Search Visibility on Amazon)
    • MMTC-PAMP used the help of Sponsored ads to get this visibility. They sponsored 9 products during the sale, while ACPL, the largest supplier of silver in India sponsored 26 products and New Delhi-based PC Jewellers sponsored 12 products. (Refer to above graph of Sponsored Products on Amazon)

    As recently as 2 weeks ago, MMTC-PAMP launched their e-commerce portal following in the footsteps of other jewellery brands. According to a report by the World Gold Council, the jewellery industry went through a massive slowdown amid the pandemic and prepping their e-commerce & digital strategies are likely going to be the only way forward.

    • On Flipkart, PC Jewellers, Malabar Gold & Kundan occupied the top 3 spots on the search results page. While PC Jewellers sponsored 12 products on Amazon, on Flipkart they sponsored zero. Malabar Gold on the other hand sponsored a whopping 25 products on Flipkart! Interestingly Malabar Gold sponsored no products on Amazon for the keyword Gold Coin. (Refer to above graph of Sponsored Products on Flipkart)

    Unboxing the love – Branded Diwali Gift Hampers

    Branded-Diwali-Gift-Hampers
    Branded-Diwali-Gift-Hampers

    Now let’s talk about Diwali Gifts. How often have you thought of buying someone a Diwali gift but had absolutely no idea what to get them? You’re not alone! A lot of consumers would simply run a search for “Diwali Gift Hampers” or Diwali Gifts” in the hope to stumble across a great gifting idea and make an instant purchase! Smart brands who know this make sure their products have organic or sponsored visibility against these keywords

    On Amazon, Tied Ribbons, a D2C gift and Décor company had the highest number of Sponsored products (15) against the keyword Diwali Gift followed by the iconic Brand Archies with (14) products.
    Flipkart had a whole bunch of smaller brands and sellers optimizing their products for this keyword. Some bigger, more known brands like Chaayos, Cadbury, D2C Tea brand Vahdam did have visibility for the keywords “Diwali Gift Hampers/ Diwali Gifts” but they were way down on the list, at the bottom of the search results page, or on Page 2.

    Was this a missed opportunity for them?

    Give your home a festive upgrade!

    Diwali is a perfect time to upgrade or buy new electrical appliances for your home. Great prices, new product launches, and an unmatched festive feeling make it even more ideal to make new purchases. If you’re eyeing smart innovative electrical appliances for your home this year and decided to go make your purchase during the Flipkart Big Billion Day or Amazon Great Indian Festival, let’s take a look at which brands made sure they showed up right on top in your online search. 

    We tracked search visibility for 5 keywords in the home appliance space – Smart TV, Washing Machine, Microwave, Air Conditioner & Refrigerators to see which brands had the highest share of search

    Brands with the highest Share of Search on Amazon
    Brands with the Highest Share of Search on Amazon
    • On Amazon, both Samsung & LG had high visibility across all products except Air Conditioners!
    • For ACs, Voltas had the highest share of search even though they sponsored 0 products! And that’s definitely noteworthy. So what really gave them the edge and put them in this winning position?

    We took a look at their product reviews to draw an analysis. Voltas ACs had close to 10k reviews! The highest in the AC category. Ratings & Reviews play a key role in helping brands drive their Digital Shelf experience. Customers trust user-generated content more than information brands share with them. Also, Amazon’s A9 algorithm prioritizes products with better reviews & shows them higher up in search – a low-cost & organic way for brands to get to the top without spending money on Sponsored ads!

    Most loved AC brand
    Most loved Air Conditioner brand
    • When it comes to washing machines, Lloyd & White Westinghouse (trademark by Electrolux) sponsored the maximum number of products in the category, this gave them the highest Sponsored SoS (13%) on the first page. 
    • While their sponsored visibility was high, their overall SoS was low which is why they didn’t organically feature in the top 5. Sponsoring products is a great but expensive way to artificially boost product visibility during sale periods. Brands need to go the Voltas route by optimizing their reviews & rating or content, to organically gain and sustain product visibility.

    … & here are the brands that made it to the top on Flipkart.

    Brands with the highest Share of Search-on-FLIPKART
    Brands with the Highest Share of Search-on-FLIPKART

    Gift-worthy gizmos!

    Buy the latest gadgets and pamper yourself this Diwali or gift them to your loved ones! You could be looking to upgrade your laptop, or buying a fancy DSLR or Smartwatch, buying it online may be your best bet. Discounts have dwindled over the years but you may still get the most lucrative discounts online. Let’s look at the discounts offered on Amazon & Flipkart for some gift-worthy gizmos like Laptops, Cameras, Smart Watches & Headphones this festive season. 

    The platform that offered the highest number of products in their catalog at a discount

    Flipkart had the higher number of gizmos on Discount this Diwali

    On Amazon, during the sale, the headphones category offered a 75% of products on discount as compared to the pre-sales period. That number was just around 51% for cameras. Far more number of products were discounted on Flipkart – 87% for headphones & laptops. And cameras 77%. So if you were looking to shop for gadgets around Diwali, Flipkart would’ve been a better bet. 

    Let’s look at which platform offered the highest percentage of discounts on products. 

    Discounts were higher across all 4 product categories!
    Discounts were higher across all 4 product categories!

    Apart from more products being discounted on Flipkart, Flipkart also offered higher discounts across these 4 categories. Discounts were higher across all 4 product categories!  

    Do you know if your brand is prepped and ready to make an impact on a Big Festival Sale Day? Or simply just wondering if your Digital Shelf is optimized with the right price, discounts, reviews and keywords? Our team can DataWeave can help! Reach out to our Digital Shelf experts to learn more.

  • Discounts continue to fizzle out on Amazon-Flipkart as e-commerce gathers steam

    Discounts continue to fizzle out on Amazon-Flipkart as e-commerce gathers steam

    Around 30 percent of electronic products across Amazon and 19 percent across Flipkart continued to be sold without any discount during October which is ironically seen as the festive month where the two companies make tall claims about deals and discounts offered around Indian festivals.

    Out of the 70-80 percent products where discounts were available, Flipkart surprisingly turned out to be more generous than Amazon during the period under review.

    As per the data exclusively shared with Moneycontrol by digital commerce analytics platform DataWeave, Flipkart on an average offered 26.3 percent discounts across the categories mentioned as compared to Amazon which had just 10.6 percent discounts.

    What makes it more interesting is that while Amazon was officially running its flagship sale The Great Indian Festival for the entire month, Flipkart had concluded its The Big Billion Days on October 10th itself.

    However, it looks like the latter was in no mood to let the competition have it all.

    The pattern is slightly different from the week-long data which was reported by Moneycontrol last month. During the first week of the festive sale, both the two companies offered no additional discounts across 30 percent of the products across the electronics category which houses products like refrigerators, air-conditioners, and laptops.

    While Amazon continues to stick to the trend, Flipkart seems to have become a little aggressive there.

    On a product level, across air conditioners, while Flipkart offered discounts across 84 percent of the products, Amazon offered it only across 73.1 percent of products. Laptops saw at least 87 percent of products on Flipkart having discounts while on Amazon it was across 76.3 percent.

    Smart TV interestingly had a different pattern. While Amazon had 72.1 percent of the products at a discounted price, Flipkart had just 63.7 percent of smart TV’s on discount.

    Interestingly, on Amazon and Flipkart at least 2.5 percent and 3.1 percent of electronic products also had a price hike during the period under review respectively.

    This is a far cry from discounts in the 60-70 percent range that the two companies advertise across electronics and appliances categories on their platforms during the sale period to lure customers.

    “Sellers decide the price of their products on Amazon. Our investment in technology and infrastructure has allowed them to save costs and consistently offer great prices to customers. Our partnership with banks, sellers, and ecosystem partners allow us to add further value through exchange offers, no-cost EMI, instant bank discounts among others, ” said an Amazon spokesperson.

    Flipkart did not respond to queries.

    Bengaluru-based Dataweave counts Japanese ad-tech firm FreakOut Group and domestic venture capital firm Blume Ventures among its investors. The data was shared exclusively with Moneycontrol.

    The price comparisons were made with rates displayed on October 1, the last business-as-usual day before the sale started and the month-long sale period beginning October 3.

    For this analysis, DataWeave crawled pages of the electronics category, which houses products, including air-conditioners, cameras, headphones, laptops, microwave ovens, refrigerators, smart televisions, smartwatches and washing machines. The firm scanned 2,285 products on Amazon and 3,131 on Flipkart.

    According to experts,…Continue reading the article here
    This article was originally published on Moneycontrol on November 3, 2021

  • Prioritizing Brand Protection Before the Holiday Rush

    Prioritizing Brand Protection Before the Holiday Rush

    Counterfeits pose a dangerous threat to any retail brand. Since every single sale is a pivotal branding opportunity, especially for young, burgeoning eCommerce brands, an online marketplace flooded with counterfeits can be particularly dangerous. One in five customers will boycott a brand after mistakenly purchasing a counterfeit product, and that’s not the kind of ratio that any retailer –– from the smallest Direct-to-Consumer (DTC) site to the behemoths like Amazon –– can afford to ignore.

    In the age of online reviews, it’s especially dangerous to have counterfeits floating around. Customers that have a bad experience with a counterfeit can take to the internet to disparage your brand without ever actually interacting with your company or trying your product. That’s why consistent and thorough content audits are paramount to ensuring your brand’s authentic products are highly discoverable, and brand protection and governance processes are in place to safeguard brand integrity across all applicable eCommerce websites.

    The Holiday Counterfeit Boom

    The holidays are a time when customers search for gifts for their friends and family, which means exploring brands outside of their usual fare. Many consumers will be exposed to your brand’s Digital Shelf for the first time over the holiday season, creating an opportunity for brand growth. But if you don’t have eCommerce brand protection initiatives in place, the holidays can be detrimental to brand positioning, customer trust, and your bottom line.

    As consumers boost their online spending and web traffic increases over the holidays, so does the likelihood of them purchasing counterfeit goods online. eMarketer predicts that retail eCommerce sales will comprise almost 20 percent of total holiday retail sales this year. As such, there will also be a surge in counterfeit inventory. So, this is an ideal time to invest in a brand protection solution to help you stay ahead of unauthorized sellers entering the marketplace.

    Brand Integrity Helps Suppliers Save

    Implementing a solution to mitigate the risks of counterfeit products should be at the top of every retailer’s “To-Do” list this year. However, for many retailers, this means manually reviewing numerous websites and third-party marketplaces for violations. Not only is manually reviewing content, images, and seller authenticity a time-consuming process, but it also leaves a lot of room for human error – making it possible for counterfeits to slip through the cracks and into the hands of unsuspecting customers. Not to mention your time should be spent fulfilling orders and increasing customer satisfaction during the high-traffic holiday season, not distracted by monitoring counterfeits.

    Fortunately, that’s not the only way to identify counterfeits and protect your brand online. An effective content auditing tool can help you monitor, detect, and determine systems to identify and act on identified violations, saving time and labor hours normally spent on manual auditing processes. Content audit software also often contains helpful features to help you run your business more strategically by monitoring online hygiene factors like product titles and description. It works across all online channels by highlighting content gaps, which can then be remedied to improve product visibility and conversions. Through online content optimization, you can save money (in unnecessary labor costs), improve your Share of Search, and increase sales and share, with a modest up-front investment.

    Brand Value Protection Boosts Consumer Confidence

    Brand image protection doesn’t just protect retailers, it also protects customers from unintentionally buying dangerous counterfeit goods. Counterfeiting has skyrocketed during the pandemic. The International Chamber of Commerce reports that, by 2022, counterfeit goods will be a $4.2 trillion industry, and global damage from counterfeit goods is projected to exceed $323 billion. Studies show one in four customers has unknowingly purchased a counterfeit item online.

    As counterfeits increase in number, so does the risk of counterfeit consumption by unwitting consumers. Counterfeit goods are as dangerous as they are ubiquitous. Customs and Border Patrol has found ingredients such as cadmium, arsenic, lead, and cyanide inside of counterfeit cosmetics. Consumers are aware of these risks. So, as a retailer, you need to be able to reassure customers that they can trust the authenticity of the goods they are purchasing at your online store.

    A counterfeit detection tool can help you identify fakes and image replicas across multiple online marketplaces, so you can get fake products delisted. Automated counterfeit solutions can increase customer satisfaction in their purchasing experience, since they know they’re getting an authentic product right off the bat. This type of online brand protection creates increased brand loyalty over time, as well as more positive first-time product interactions.

    Making a Measurable Impact: A Counterfeit Detection Case Study

    Classic Accessories is a leading manufacturer of high-quality furnishings and accessories. The company’s investment in a counterfeit detection tool paid off in spades for their organization. After noticing a surge in counterfeit versions of their goods being sold via online, global marketplaces, they decided they needed to change their manual counterfeit and image violation detection process to an automated one to proactively respond to concerned activity in a timely manner.

    Their goal was to achieve streamlined, actionable insights across all retail websites to account for varied violation submission processes, and to reduce the timespan in which insights were generated, ultimately eliminating the need to conduct daily, manual audits. They partnered with DataWeave, who built out a fully customized program to automate Classic Accessories’ content inventory management process, and identified SKU-level violations by matching names and images in diverse online marketplaces.

    During the first three months of onboarding, Classic Accessories was able to detect more than 25,000 violations, submitting notices to each marketplace, and even achieved a 100% removal rate across all Amazon sources. Additionally, they also achieved their goal of saving time (22 hours per week) in automation processes, translating to a $68,000 savings opportunity in labor costs.

    Closing Thoughts

    Prioritizing your online brand protection strategy is imperative to growing your online presence and achieving customer satisfaction and brand loyalty. Fortunately, there are options like DataWeave’s brand protection tool available to help curate your online content, provide consistency across online channels, and improve consumer confidence by addressing and removing counterfeit violations. Implementing the right solution can help find counterfeit products in real-time to keep your brand safe –– and your reputation intact –– throughout the 2021 holiday season. The right brand protection software will provide both Brand Protection and Content Audits, so your brand is optimized from every possible angle for truly competitive results.

  • Amazon-Flipkart sops war in festive sales fizzles out, shows data

    Amazon-Flipkart sops war in festive sales fizzles out, shows data

    Discounts may have been scaled down as the e-commerce market matures and the government looks out for alleged malpractices.

    Radhika Subramanium made umpteen trips to the shiny black Bosch mixer-grinder on her phone in the last few weeks. She put it in her shopping cart and waited for the festive season sale to begin, hoping to get a good deal. At the end of the day, who doesn’t want to save a few extra bucks?

    But, on October 3, the big day when e-commerce giants Amazon and Flipkart locked horns and launched The Great Indian Festival and Big Billion Days, Subramaniam was sorely disappointed. Her cart barely showed any discount. She bought the appliance anyway because it was needed, but her excitement was gone.

    It was largely the same story for Vaibhav Jaiswal. His Boat headphones didn’t even fetch a Rs 200 discount.

    Revati Krishna, in fact, checked out with zero discount on the sit-and-bounce ball she had picked up for her nephew.

    Subramanium, Jaiswal and Krishna are among hundreds of Indians who realised that e-commerce sales no longer offer the lucrative discounts they used to, except for select products such as mobile phones.

    On average, 30 percent of the products sold across the electronics category which houses products like refrigerators, air-conditioners and laptops on Amazon and Flipkart had no discount during their week-long festive sale season, according to a study by a data analytics company.

    Higher prices

    Interestingly, 8-11 percent of the products across categories such as washing machines, microwave ovens and laptops even showed higher prices during the sale across the two platforms.

    The price comparisons were made with rates displayed on October 1, the last business-as-usual day before the sale started.

    The data was compiled by Bengaluru-based digital commerce analytics platform DataWeave, which counts Japanese ad-tech firm FreakOut Group and domestic venture capital firm Blume Ventures among its investors. The data was shared exclusively with Moneycontrol.

    The discounts were lean even on lower-priced products. Amazon dangled a 6.4 percent discount on air-conditioners priced at Rs 33,500-34,000 during the sale, while Flipkart offered barely a 5 percent discount, according to the data.

    This is a far cry from discounts in the 60-70 percent range that used to be advertised across electronics and appliances categories on online marketplaces.

    For this analysis, DataWeave trawled the first five pages of the electronics category, which houses products, including air-conditioners, cameras, headphones, laptops, microwave ovens, refrigerators, smart televisions, smartwatches, and washing machines. The firm scanned 1,184 products.

    Gone are the days when discounts were offered for habit-forming. According to experts, with the markets maturing, companies no longer fancy hoarding deal hunters.

    “As people have got used to buying online, the companies have decided to focus on convenience rather than price,” said Harish HV, managing partner at ECube Investment Advisors. “You won’t even find a significant difference between the price of a product across the two marketplaces Amazon and Flipkart, which have a clear duopoly. It will go on like this unless a big new entrant starts disrupting prices again.”

    According to Harish Bijoor, … Continue reading the article here
    This article was originally published on Moneycontrol on October 27, 2021

  • 6 ways Reviews & Ratings can Skyrocket your eCommerce sales

    6 ways Reviews & Ratings can Skyrocket your eCommerce sales

    As per recent research conducted by Deloitte, approximately 81% of consumers use reviews to make purchase decisions. Reviews work like social testimonials. They are credible recommendations, as a vote of confidence from an existing customer. And when satisfied customers express themselves through the right words, automatically your product gets a boost.

    In case you’re thinking, ‘who has time to read through each and every review?’ Put a pause to your thought, because more than 70% of people regularly or occasionally read online reviews, and 19% of US shoppers trust online reviews as much as a personal recommendation. Online reviews matter and for brands that are selling online, this is becoming a big deciding ground, contributing to sales.

    Let’s go a little deeper and take a look at why good Ratings and Reviews are important for your eCommerce sales.

    1. Use your customer’s voice as a marketing tool!

    Reviews have emerged as a new and effective product promotional tool that never fails to attract the right audience. Even standalone, reviews or word-of-mouth from real users have always been the hook for consumers, so using reviews in your marketing amps up the impact. And the best part is, that it is absolutely free and user-generated!

    Here’s how Fabletics in the UK is using reviews for marketing – they’ve brought these customer testimonials right onto their website homepage! These attention-grabbing reviews showcase the voice of their existing customers and serve as the main influence for future customers that visit their website and want to know more about their brand offerings.

    Fabletics
    Fabletics website

    Using reviews in Search ads is another really impactful way to amplify your customer’s voice and confidence in your brand. Here’s a sample of how we at DataWeave could use our fantastic G2 reviews to build out a Search ad.  

    G2 Review
    G2 Review

    2. Use Reviews & Ratings to influence buying decisions

    Suja Website
    Product page from Suja website

    Display your reviews upfront. Help consumers make their purchase decision easier. Take for instance Suja, a cold-pressed juice brand. Suja converts user ratings and reviews into scores for each of their organic drinks and displays it right below the product, so at one glance users know which products have high reviews and which don’t. This further eases purchase decisions and every customer can decide on the variant right at the product page and then add it to the cart if it meets their expectations.  

    3. Positive reviews impact your brand’s conversion rate

    Improved star rating

    Experts say that 50 or more reviews per product can mean a 4.6% increase in conversion rates. McKinsey has attempted to quantify the relationship between reviews and conversion rates by analyzing reviews and ratings across the 70 highest-selling categories on a major online platform. After tracking hundreds of thousands of individual SKUs over a two-year time span, they found out that the correlation between star ratings and product sales was positive in 55 of the 70 categories they examined. In fact, a jump in rating was also seen to add to the conversion rates growing. Loyalty drives ratings and that, in turn, leads to positive conversions.

    Negative or fewer ratings can directly impact sales. We at DataWeave can help Brands adapt to consumer feedback by tracking their reviews and rating.

    4. Use honest & transparent reviews to build trust, including negative reviews.

    Product Review

    Take for instance this detailed review for a Lancôme mascara on Ulta Beauty. It not only gives the user a ready guide to the product they are eyeing but also makes the brand come out very transparent and believable, courtesy of the cons & negative reviews on display. This helps build a relationship of trust with customers across the board. Various studies have been conducted where consumers said when looking at reviews of businesses, they would trust the company less if there were no negative reviews on display. And they said the probability of every single customer having a four or five-star experience just isn’t believable – this would cause suspicion and has a strong potential of turning them away from making a purchase. Consumers clearly want the real story about a brand or business and not just a rosy picture.

    5 Ratings and reviews can boost SEO

    Ratings and reviews to structure
    Use ratings and reviews to structure the entire listing

    Online reviews are estimated to make up 10% of the criteria Google algorithms use when displaying search results. Every brand understands the importance of putting SEO-optimized content online via blogs & an array of other content marketing activities. Reviews can contribute to that cause too! User-generated content like reviews can work as a ready stream of optimized content, which Google can crawl to rank products higher in search. What is interesting is that buyers when posting a review for products are bound to mention the brand name and use certain words to describe their experience, which subconsciously in most cases become the right keywords! This actually then turns into organically generated authentic, keyword-optimized content. In fact, brands can collect and use rich snippets of reviews on their website or use it for marketing purposes to further optimize listings on Google. Take for Instance Face Theory, they use ratings and reviews to structure the entire listing for their own e-commerce website. This helps them rank higher in search on Google and even on Amazon.

    #6 Understand Customer Sentiment via reviews

    Consumers use reviews to make purchase decisions. On the flip side, what’s interesting is that brands can also gauge their consumers through reviews or feedbacks they submit. This feedback helps brands align with the ground reality of how consumers really feel about their products. And by synthesizing & breaking down reviews across channels, brands can work towards bringing more innovation and personalization for their customer, just the way they want.

    Understand Customer Sentiment via reviews
    Understand Customer Sentiment via reviews

    Take, for instance, Starbucks, a leading international coffee chain introduced MyStarbucksIdea in 2008. This was an instant hit and Starbucks customers within just the first five years of operation, shared over 150,000 ideas and recommendations to the brand, and the company put hundreds of them to use. This is a real case of a brand becoming an advocate to customer sentiments to drive its innovations directly from the core of ideas and reviews submitted by discerning customers.

    In today’s scenario, brands do not need elaborate programs like MyStarbucksIdea, they can simply ask customers for their ideas, thoughts, and suggestions via online reviews across numerous platforms! The only task from there is on is collecting and analyzing these reviews to glean insights.

    explosion of product reviews

    The new normal has led to an explosion of product reviews as more and more people shop online. In the US alone reviews were 40 – 80% higher during the core months of the pandemic in 2020 as compared to 2019.

    Reviews matter, and even more so now. Brands need to build it as part of their actionable strategies and incentivize consumers to rate and review products with each purchase.

    Need help tracking your online ratings? Or decoding customer sentiment from reviews they’ve left for your products? Sign up for a demo with our team to know how DataWeave can help!  

  • Online Halloween Shopping Is Here to Stay – Winning Share of Search

    Online Halloween Shopping Is Here to Stay – Winning Share of Search

    Lessons from Kroger, Albertson’s, and Safeway’s Optimized Online Positioning   

    As consumers continue their migration to online shopping through and after the pandemic, Halloween shopping is no exception. 

    If that’s the new paradigm, what clues should retailers and brands be looking for to enhance their sales? With Halloween around the corner, the analyst team at DataWeave wanted to see how successfully grocers are partnering with brands to prepare for the influx of online Halloween shoppers. We tracked insights from September 14 to 24, 2021, using data from Kroger, Albertson’s, and Safeway websites to understand the preparedness of each retailer, their partnered brands, and how their online strategies compare with one another.

    There are hundreds of ways for a consumer to search for a brand’s products online and of critical importance, almost 50 percent of traffic across the top 1000 retailers come through search. At the same time, consumers are becoming less brand conscious. This is a significant development, and there are significant ramifications to consumers searching for products using generic category specific keywords without including brand names in the search.  Consequently, we can’t sufficiently stress how understanding online channel experiences is critical to successful outcomes. Retailers and brands alike need an integrated view of how to improve their discoverability and share of search by considering all touchpoints in the digital commerce ecosystem.

    The Importance of Product Descriptions, Assortment, Sizes, Price Points

    With 75 percent of people never scrolling past the first page of a website when searching for the goods they desire, getting products to page one is imperative to a brand’s success. While in-store, festive displays will help drive traffic and availability awareness, the ‘digital shelf’ is a totally different locus of opportunity. Here, brands rely on proper product descriptions, the right assortment, sizes, and competitive price points to stand out among the crowd and modify their positioning, given each retailer’s consumer base and assorted competitive brands.

    Optimizing the Digital Shelf and leading Share of Search for page one across all retail websites isn’t achievable overnight, but it is never too early or too late to start, given the 24/7 visibility your products have online. When it comes to Halloween candies, confectionery brands must consider many factors when differentiating their online positioning, such as finding the ‘sweet spot’ for pricing, size, and variety within each product offered, and knowing the right and wrong times to drive promotions. Additional elements to consider when introducing seasonal candies include cannibalization of non-holiday inventory, which can increase spoilage for aged inventory, or if holiday items are successful, could cause an abundance of markdown items to be sold before replacement inventory can be ordered.

    To better understand what retailers are doing—or should be doing—to optimize their Halloween holiday sales, we turned to our DataWeave Digital Shelf Analytics data to answer these questions:

    • Which brands and products are dominating “Share of Search” page one results across all three retailer websites?
    • How do discounts and promotions vary among candy brands and retailers?
    • How does each retailer use Halloween-specific and ‘variety’ labeling within the product descriptor to differentiate their holiday season assortment?
    • What sizes of candy packages is each retailer offering, and how does this play out in online positioning?

    Winning Candy Brands

    Which Halloween candies are people searching for—and presumably buying? Our data shows that Hershey’s branded candies achieved the greatest page one ‘Share of Search’ results across all three retailers’ websites—Albertson’s, Kroger and Safeway. This was unsurprising, given their total SKU count as well as the brand loyalty Hershey’s steadily maintains throughout the year. There is a high likelihood of consumers buying what they see on page one, so in our analysis, Hershey’s has the best chance of ‘winning’ this holiday season within all three of these retail channels. 

    That said, looking more specifically at how candy items are labeled and bundled adds another layer of insight to how candy brands are performing at each of these retailers.

    Historically speaking, Snickers is almost always within the top five confectionery brands sold during the Halloween season, but with the migration of more consumers shopping online, Mars may be leaving opportunity on the table this year. Our data shows that Snickers had the lowest Share of Search percentage on page one results on Safeway.com and Albertson’s.com for brands carrying 8 or more SKUs each, indicating they will most likely not make the first page results—and therefore may end up as a clearance item after Halloween if relying on online promotional efforts to achieve sales goals.

    Source: DataWeave’s Digital Shelf Analytics Solution: Data aggregated from 9/14/21-9/24/21 for Albertson’s.com and Kroger.com, and 9/17-9/24 for Safeway.com; Analysis was conducted reviewing product information for items falling within the ‘Halloween Candy’ listing category

    What Size Candy Packages Are Retailers Carrying/Betting On?

    For example, Albertsons.com and Safeway.com’s assortment includes 124 SKUs and 108 SKUs respectively with most of those items falling within the 5 to 16-ounce (averaging 25 percent) and 32 to 64-ounce (averaging 29 percent) sizes, Kroger.com is betting on a ‘smaller is better’ strategy, with a majority (63 percent) of their candies sold in the 5 to 16-ounce package size. 

    The average Hershey candies available through all three retailers happen to be much greater in size and price point, on average, than other top ranked items, and while these larger items appear to mostly be variety packs, a majority are not labeled as ‘Halloween’ candy.

    Source: DataWeave’s Digital Shelf Analytics Solution: Data aggregated from 9/14/21-9/24/21 for Albertson’s.com and Kroger.com, and 9/17-9/24 for Safeway.com; Analysis was conducted reviewing product information for items falling within the ‘Halloween Candy’ listing category

    How Important Is Halloween-Specific Branding?

    Our data shows that Kroger.com included the name ‘Halloween’ within the product description for most (around 80 percent) of the candies sized 16 ounces or smaller, and overall have labeled more than two-thirds of their total candy items sold as ‘Halloween.’ This indicates they are staged well for the peak of the seasonal demand and anticipate their shoppers to buy smaller unit sizes, comparatively speaking.

    Source: DataWeave’s Digital Shelf Analytics Solution: Data aggregated from 9/14/21-9/24/21 for Albertson’s.com and Kroger.com, and 9/17-9/24 for Safeway.com; Analysis was conducted reviewing product information for items falling within the ‘Halloween Candy’ listing category

    Taking a closer look at all items positioned as ‘Halloween’ across the three retailer websites, Hershey’s brand Reese’s is set for success at Kroger.com for total Share of Search percentage, considering they carry eight Reese’s, non-variety SKUs. Competing in the audience of others leading with variety packs indicates the weight the Reese’s brand carries and also indicates they will also have a great likelihood of success for increased sales this Halloween season. 

    Mars M&M’s brand came out on top at Safeway.com and Albertsons.com within the ‘Halloween’ labeled SKUs, but a majority (around 70 percent) of these are variety packs that leads with the M&M’s brand versus an M&M’s only bag.

    Source: DataWeave’s Digital Shelf Analytics Solution: Data aggregated from 9/14/21-9/24/21 for Albertson’s.com and Kroger.com, and 9/17-9/24 for Safeway.com; Analysis was conducted reviewing product information for items falling within the ‘Halloween Candy’ listing category

    How Much (Less) Are People Paying for Halloween Candy?

    To determine whether candy promotions are increasing Share of Search, DataWeave measured the average promotional discount these retailers and top candy brands are offering online. When looking only at brands offering discounts on 100% of the SKUs they carry within each retailer, Brach’s brand is performing best on Albertson’s.com, and Hershey products are positioned at the top for Kroger.com and Safeway.com. 

    Source: DataWeave’s Digital Shelf Analytics Solution: Data aggregated from 9/14/21-9/24/21 for Albertson’s.com and Kroger.com, and 9/17-9/24 for Safeway.com; Analysis was conducted reviewing product information for items falling within the ‘Halloween Candy’ listing category

    Do Consumers Search For ‘Variety’ Candy Bags, or One-Product-Only Bags?

    DataWeave tagged the word ‘variety’ and found that across all three retailers’ websites, non-variety candy bags take up a greater overall Share of Search than ‘variety’ bags. Either this isn’t an important search word or retailers could try adding ‘variety’ to product descriptions to increase Share of Search.

    Source: DataWeave’s Digital Shelf Analytics Solution: Data aggregated from 9/14/21-9/24/21 for Albertson’s.com and Kroger.com, and 9/17-9/24 for Safeway.com; Analysis was conducted reviewing product information for items falling within the ‘Halloween Candy’ listing category

    Time to Make a Change

    Getting products to page one on retailers’ websites can improve sales by as much as 50 percent, but determining the right levers to pull to get there is no easy feat. Based on our preliminary analysis of Halloween insights, our advice to confectionery brands this Halloween season is to invest now to increase visibility to the fast-changing market, to get orders right and on time, establish effective pricing and promotional plans, and get the right candies in stock, to the right locations. Retailers able to get an end-to-end view of the online competitive landscape will be able to make calculated marketing decisions that stand to help generate growth and profitability.

    We are now within the prime Halloween shopping season, given that 55 percent of candy sales usually happen in the last two weeks of October (According to Timothy LeBel, President of U.S. Sales for Mars Wrigley). With online sales still growing as consumers have shifted their comfort level in buying more online, retailers should be looking for ways to optimize their product positioning, increase their Share of Search, to improve the likelihood of consumers ordering their brand’s candy to ply those Trick-or-Treaters knocking on their doors.

    About DataWeave

    DataWeave is a leading provider of advanced sales optimization solutions for e-commerce businesses, consumer brands and marketplaces. The AI-driven proprietary technology and language-agnostic platform aggregates consumable and actionable Competitive Intelligence across 500+ billion data points globally, in 25+ languages, with insights to performance for more than 400,000 brands across 1,500+ websites tracked across 20+ verticals, to ensure online performance is always optimized.

  • 4 Hacks to improve your online Product Visibility

    4 Hacks to improve your online Product Visibility

    With online shopping becoming increasingly important for brands, the concept of ‘product visibility’ within this ecosystem has emerged as the most noteworthy path to generate sales & win the Digital Shelf. Research shows that on Amazon, the first 3 products garner 64% of business generated. And post-pandemic, more and more people are now shopping online, which means your ‘digital product visibility’ becomes as critical as your in-store product visibility. What’s more, this digital shopper loves to start their search for products directly at leading marketplaces like Amazon, eBay, and the likes. As per the Shopper-First Retail report released by Salesforce almost 87% of shoppers begin product searches online. So, this is the window that brands have and this is where they need to hold their customer’s attention. But the primary question here is how to appear high up on your customer’s search?

    Imagine the pages of an e-commerce marketplace as Digital Shelves and correlate that with the offline space. Will the customer sieve through a rack-full of products to reach for your brand, lying at the back of the shelf? No, they won’t. If they have to buy a personal care item, they will choose the brand that is visible to them at the front of the rack. In a similar vein, when they search for a product online on popular marketplaces, they will quickly click “add to cart” for the ones that come up top in the search. 

    Algorithms of popular e-commerce marketplaces are usually well-kept secrets, but here are 4 hacks brands can consider to increase product visibility on their Digital Shelf.

    1. Optimize what you say about your products

    Content Quality
    Work on the product listing content

    To ensure that your products are visible, you will have to work on the product listing content judiciously. And it’s all about using the right keywords. Here, you will have to tighten the strength of your content to ensure that both your listing text and titles are accurate, and include natural language search keywords that consumers normally use when searching for products. Ignore, jargon or business-heavy words, and think what a real consumer may use to search for a smartwatch – for instance. ‘Bluetooth watch’ or ‘Smart Watch’ or ‘Camera Watch’ etc. The product title below has it all covered.  So no matter which of the 3 keywords a customer is searching for, this watch will come up in their search results.

    Title enables Product Visibility
    Listing Text and Title enables Product Visibility

    This aside, the title should highlight as much as possible about the product and the description should be pointed and readily related to the search one can use to look for it. For instance, in the below image Puracy, has not only used the word shampoo in its title, which normally the consumers will use to search, but has given all the major highlights as a part of the title. The consumer at a glance would get the whole picture inclusive of the quality, fragrance, quantity and the dispenser type.

    Product Features mentioned in Title
    Product features mentioned in Title ensures greater Product Visibility

    Search engines take consumer’s natural language preferences into consideration, so a bad or incomplete product description or title will never help customers see your brand easily on the marketplaces listing. Here’s a listing, which does not work at all.

    Bad or incomplete product description
    Bad or incomplete product description is bad for Product Searchability

    Let’s look at the above example closely. If the customer is a diehard fan of Calvin Klein and is searching for a Calvin Klein sweater, then this product will definitely show up in their search results, but what if he was looking to buy full sleeve polos or wanted a full sleeve sweater, or a navy blue sweater – even though this product fits the bill completely for all the searches, but it may not show up in his search results only because the right content/ keywords have not been used to describe the product. And that is why it is important to use common keywords in your product content that consumers normally use when searching for your product.

    In fact, according to a recent Forrester survey 45% of online shoppers do not complete a purchase if they cannot find what they are looking for, and insufficient product information adds to the cause. If you want to audit your current e-commerce content, DataWeave can help!

    2. Improve product ranking through the right reviews

    Reviews and Ratings
    Good Reviews and Ratings boost product page rankings

    Marketplace search engines prioritize products with good reviews and ratings and show them higher up in search. This prompts product visibility and assures your brand of organic discoverability. So, it is important to fetch as many honest reviews and ratings for your products, in order to gain marketplace visibility.

    Research shows that 88% of consumers trust online reviews as much as personal recommendations, and 72% of consumers are inspired to trust a brand based on the positive reviews it receives. Moreover, millennials, trust user-generated content 50% more than other media. Even though bad reviews are part and parcel of any business, a brand’s focus should be on ‘honest’ reviews. Real user-based reviews have the power to generate customer trust. When customers begin to trust your product and in your brand, they are more willing to add that product to their cart and finalize their purchase. Good product reviews will certainly make your Digital Shelf a lot more attractive & boost your product sales. Learn how DataWeave can helps brands monitor & stay on top of their Reviews & Ratings

    3. Give importance to pricing and attain the sweet spot with product ranking

    Pricing leads to Performance
    The right Product Pricing leads to higher rankings in Searches

    Intelligent pricing, which is not too low or too high should be your weapon to make your brand gain ample visibility among its competitors. Moreover, if the cost of your products is ominously dissimilar from other products it is competing with, it is bound to impact your position in search results. To achieve great results, either you can consider your competitors and analyse the pricing to reach a perfect mid-ground or choose a dynamic pricing strategy. This strategy will allow your products to cost less than the competition, marginally. 

    Amazon is pretty sophisticated in this department and reprices top-selling items 3 or 4 times per day and the same can be repriced up to 12 times daily. Following in line, McKinsey reports that multichannel leaders are also changing the prices on 10 to 20 percent for their online assortment daily.At DataWeave, we can help brands track prices on a daily or even every few hours during sale season, when prices are the most sensitive. Learn more here.

    4. Invest in paid advertising to improve your listing

    Paid Ads - Boost Sales
    Paid ads on Online Retail sites can boost product visibility and Sales

    Investing money in paid promotion at e-commerce marketplaces will help you gain visibility and push your products on top of the first page of your category listing. Today, e-commerce platforms allow you to pay for promoted listings that are displayed near organic search results. In fact, a paid Pay Per Click (PPC) campaign will allow you to build up your sales volume and brand awareness, which will in turn assure your brand of long-term organic search placement. 

    The idea of sponsored ads is getting well-received all across and Amazon’s accelerating ad revenue growth is a living proof of this. In the fourth quarter of 2020, Amazon’s ad revenue reached $7.95 billion, up 66% over the previous year. 

    The e-commerce marketplace is expanding and in 2020, retail e-commerce sales worldwide amounted to 4.28 trillion US dollars and e-retail revenues are projected to grow to 5.4 trillion US dollars in 2022. To be able to make the most of this growing market, make sure your brand is winning the Digital Shelf, starting by winning the appropriate ‘Share of Search’. Want to learn how DataWeave can help you win the Digital Shelf? Sign up for a demo with our team to know more.

  • How an American QSR (Quick Service Restaurants) improved its Business ROI Food Apps

    How an American QSR (Quick Service Restaurants) improved its Business ROI Food Apps

    Traditionally, Quick Service Restaurants (QSRs) such as McDonald’s or Burger King, have been strategically operating on a brick and mortar model. However, according to some studies, an average QSR generates as much as 75% of its sales from online orders.

    With the advent of delivery apps such as Uber Eats and Doordash, a significant portion of QSRs’ business has moved to these platforms. The war to top rank on one of these platforms is an even greater feat. With each brand competing for the top listing, it’s much less about the dollars you pay and much more about optimizing your investments.

    The relationship between QSR chains and food delivery apps has its advantages and disadvantages. One of the critical grouses QSRs have against food apps is the incremental marketing spend required to participate on the platform and the inability to measure the impact of their investment. What makes matters worse is the limitation in metrics even available to measure the impact – neither the food apps provide them, nor does anyone else.

    At DataWeave, we have made it our mission to enable QSRs to not only define measurable metrics to achieve a positive ROI for food app marketing investments, but we also equip QSRs with the tools to track their competitive performance at granular, zip code-based level so that localized strategies can be modified as needed. Below is an example of a 1000+ store chain QSR we partnered with to optimize a pre-existing investment made with a large food aggregator app. Within months of engagement with us, they were able to achieve a 3X increase in sales without adding any additional marketing dollars.

    Below are the pain points we identified and solved together:

    1. No Defined Metric

    Problem – No leading metric to track marketing performance

    One of the first issues we realized was that sales was not a good metric for tracking marketing performance as it’s a lagging metric and doesn’t capture the issues that help grow or suppress sales.

    Most of the sales are driven by rank in the cuisine category and searches for branded keywords. But, the QSR chain had no way to track these ranks.

    In fact, 70%+ sales go to the first five restaurants for the category and keyword

    Comparing ranking on food delivery platforms
    Comparing ranking on food delivery platforms across different categories and times

    Solution – Establish ranking as a clear marketing metric

    By aggregating data across different food app platforms comprehensively, i.e. across locations, at different times of the day, we established the ranking of the QSR chain in critical categories and for priority keywords, identifying where they under or over-performed relative to the competition. As we did this daily- this became a straightforward metric that helped establish the performance of their marketing campaign.

    2. Geographical & Categorical Challenges

    Problem: Identifying poor-performing stores and zip codes

    We realized  it was not a simple exercise to identify well performing stores on food apps since sales depend on many factors such as competition, population of the area, local cuisine preference, etc.

    Solution: Zip Code Ranking and Attributes

    We tracked the ranking of each store within each Zip Code for keywords and created a list of poor-performing stores. We also extracted attributes such as estimated time of arrival (ETAs), Delivery Fee, Ratings, Reviews, etc., for each of these poor performing stores, to identify the reasons for the poor ranking. 

    Analysing key metrics at a store level
    Analysing key metrics at a store level – identifying worst & best performing stores

    E.g., We realized 356 of the stores were not populating on first page results, primarily because of poor ratings and High ETAs. After the focused initiative, 278 of these stores started showing on the first page and increased sales by 23%. 

    3. Sensitivity Analysis Deficiency

    Problem: Not clear about the contribution of Rating, ETAs, Fees, etc. on the Ranking

    The exact ranking algorithms of these food apps are not publicly shared – so the QSR chain wasn’t clear which variable of rating, ETAs, fees, ad spend, or availability contributed more or less to the overall ranking. 

    Solution: Sensitivity analysis for measuring contribution 

    Comprehensive data for multiple zip codes in various timestamps was analyzed to determine which variable contributes most significantly to the rankings and when. We also conducted A/B testing – simultaneously testing two different variables, such as reducing ETAs at one store and improving ad spend at another, calculating which led to greater rank and sales impact.

    For example, we realized reducing publicized ETA’s (even by decreasing the delivery radius) contributed much more to improve the rankings than changes to ratings.

    4. An Unknown Competitive Landscape

    Problem: Tracking competitor performance

    For example, we found the QSR chain performed well in key urban centers, but the competition was doing even better, but there wasn’t a good way to track and compare the performance of the competitors.

    Solution:

    We started tracking the QSR chain and the competition for each of the metrics and started comparing performance.

    Analysing competitive performance
    Analysing competitive performance on key metrics such as ETA, Availability etc

    We quickly realized ranking started quickly improving as we gained a slight edge in each metric against the competitors. For example, 5 minutes less ETA adds to higher ranking.

    In six months of this exercise with the QSR chain, we improved the average ranking from 24 to 11 for the QSR chain, getting them featured on the first page.

    5. Blind Advertising Investment Opportunities

    Problem: 

    The QSR chain was not clear on which banners (Popular near you, National Favorites, etc.)  to choose to invest in, and had to depend on the recommendations of the food platforms entirely. 

    They weren’t even provided a clear view of which position made the banner visible and at what rank among those banners was their promo visible. They were at times the 7th promo in the 6th banner, which has almost zero probability of being discovered by the user – this happened despite paying heavily for the banners.

    Solution: 

    We aggregated data for all banners populated within each zip code and found out the ranking and in which position the QSR chain was visible.

    Analysing right banners
    Identifying and analysing right banners for advertising spends

    The QSR chain invested in 630 zip code-based banners with guaranteed visibility, but our assessment indicated the banners were only visible in 301 zip codes. After selecting suitable banners for promotions, we improved visibility to 533 zip codes within enhancing the budget.  

    We are now using the same strategy for refining discounts, offers, promotions, and coupons. 

    6. Lack of Campaign Performance Monitoring

    Problem: Unsure of the long-term impact of marketing spend

    In general, increasing marketing spend does give a temporary boost to sales, but the QSR chain’s question was, how can we measure the long-term impact i.e., ranking keywords and the targeted zip codes.

    Solution: 

    We created a simple widget for every marketing campaign which showed the rank for the keywords for selected zip codes before the campaign, during the campaign, and post the campaign, clearly establishing the midterm impact of the campaign. This constant monitoring allowed the QSR to also quickly pivot on their strategy on account of national holidays etc, and act accordingly.

    7. Non-Existent ROI Measurement

    Problem: Establishing the impact of ranking on sales

    Though the QSR chain could track sales that were coming via the food app channel, they had no way of knowing incremental organic volume driven by marketing efforts. 

    One missing variable here was how much of extra sales could be attributed to improvement of QSR ranking? 

    Solution: 

    By combining the sales data with aggregated insights over time, we established for the QSR chain how much increase in sales they could anticipate from an increase in ranking, also knowing which changed variables led to the percentage of change increase.

    So, in essence, we were able to tell the QSR chain that for each store how much sales would increase by improving ETAs, rating, ad visibility, availability, etc., enabling precise ROI calculations for each intervention they make for their stores.

    Increasing sales by 3x within six months was only the beginning, and the journey of driving marketing efficiency using competitive and channel data has only just begun. 

    DataWeave for QSRs

    DataWeave has been working with global QSR chains, helping them drive their growth on aggregator platforms by enabling them to monitor their key metrics, diagnose improvement areas, recommend action, and measure interventions’ impact. DataWeave’s strategy eliminates the dependence on food apps for accurate data. We aggregate food app data and websites to help you with analysis and the justification of marketing spend and drive 10-15% growth.

    DataWeave’s strategy eliminates the dependence on food apps for accurate data. We aggregate food app data and websites to help you with analysis and the justification of marketing spend and drive 10-15% growth.

    If you want to know learn how your brand can leverage Dataweave’s data insights and improve sales, then click here to sign up for a demo

  • Structured and Unstructured data – Benefits of Big Data

    Structured and Unstructured data – Benefits of Big Data

    The big buzzword of the decade has got to be data. When the untapped potentials of great data were first discovered, experts started calling it the new oil, implying that it is now the most precious resource. And then when the usage of data became more mainstream, where corporations started mining and getting access to piles of data, people started calling it the new soil, insinuating that if all this data isn’t regularly nurtured and optimally used, it would be rendered useless. 

    But amidst all this hype, all the clutter, and all the buzz around this four-letter word, data is just a bunch of numbers and statistics collected for reference and analysis. Basically, it is just what you, your company, your government, or your country make of it. So how can retailers make the most of it? 

    Before assessing the use cases, it is paramount to understand the different types of data retailers have access to today. Broadly, it is structured and unstructured. Log files, excel spreadsheets with point-of-sale figures, hierarchies, and inventory data are rich sources of structured data; and information that is derived from in-store sensors, customer reviews, social media posts and hashtags, and even conversations between the store staff and customers serve as unstructured data. While the former sits on well-organized databases for retailers to access, giving them operational robustness, unstructured data gathered from social media and personal interactions helps retailers achieve unprecedented value and gain a competitive advantage. However, the very nature of unstructured data makes the process of obtaining, analyzing and making sense of it rather difficult.

    Structuring vs Unstructured
    Structuring vs Unstructured

    In fact, according to a survey by Deloitte, only 18% of organizations reported being able to take advantage of such data. However, harnessing this data isn’t rocket science (not anymore, at least) as there are a number of tools at a retailer’s disposal today that makes this process convenient and efficient. At DataWeave, we help retailers and brands make sense of unstructured data. Read more about our tech here. 

    Unstructured data is also qualitative, rather than being quantitative, which in turn makes its use cases more effective, giving businesses a competitive edge. How? Glad you asked!

    Customer Behaviour Analytics

    What motivates a customer to buy more, or spend more time in a store or online? What is the best time to reach them and where (in an omnichannel world) would they like to be reached? Million-dollar questions, right? Big data gives you insights into this and more, which will then help improve customer acquisition and loyalty. 

    UK-based home retailer Argos uses data to find out exactly how consumers felt about them. After having embarked on an ambitious project of opening 53 new digital stores a few years back, Argos invested in tools that helped them analyze data received from various social media sites based on the demographics and location to assess the performance of each store and identify rooms for improvement. This helped them understand which stores were perceived more favorably and in which areas, quickly identify issues in-store, action feedback, and find resolutions to increase customer satisfaction.

    Want to know customer sentiment against your product? Our Sentiment Analysis solution can help! Access in-depth insights sourced from customer opinions with our constantly evolving algorithm.

    DataWeave Sentiment Analysis
    DataWeave Sentiment Analysis

    Personalization and hyper-personalization

    The fact that customers are interacting with retailers on multiple platforms today gives retailers access to a wealth of information about their individual customers that could help them tailor their products, offerings, services and communication to these individuals. According to a study conducted by BCG and commissioned by Google, customers increasingly prefer a shopping experience that’s easy and fast and that helps them make purchase decisions.

    Target’s popular pregnancy prediction score based on purchase and purchase volume of about 25 different products in-store, such as unscented lotion, large amounts of calcium, magnesium, and zinc, serves as a great example of how they use this information to then target advertising (e.g sending a booklet of coupons related for baby products) to this cohort of their customers. This algorithm got the international limelight when Target started sending such coupons to the irate father of a teenager who had no idea that his daughter was pregnant. Basically, the retailer knew about the man’s daughter’s pregnancy even before he did!

    Operations and supply chain

    Amazon Go
    Amazon Go

    A healthy mix of structured and unstructured data is key today in achieving operational excellence. Faster product life cycles and ever-complex operations cause organizations to use big data in retail analytics to understand supply chains and product distribution to reduce costs. Combining that with CRM, ERPs, and other log file data can help in real-time delivery management, improved order picking, and overall supply chain efficiency to reduce costs. 

    Amazon Go, the checkout-free convenience store by Amazon uses AI-powered cameras, computer vision, and sensors to facilitate grab-and-go systems. Now, the store wholly relies on structured and unstructured data in order to function.  The sophisticated automated system makes ordering and restocking highly efficient, given that the cameras can track inventory in real-time. The system knows how many picks-per-hour each stocker is completing and exactly when items go out of stock. 

    The fact that data enables prediction and forecasts can help cater to a prospective rise in demand by managing the supply chain in advance. For example, if a pharmaceutical company analyzed social media content and determined that people in specific geographical areas were discussing cold and flu symptoms, that could give them a heads-up that demand for products to treat those conditions is on the rise.

    Price and cost optimization

    Machine learning algorithms are not only designed to learn, but over time they get better at finding the optimal price points for retailers. Retailers can use machine learning models to set prices against sales targets. According to an IBM study, 73 percent of companies surveyed plan to optimize their pricing and promotions through smart automation before the end of 2021.

    Automation achievers outshine peers in profitability and revenue growth
    Automation achievers outshine peers in profitability and revenue growth

    Walmart has shrewdly utilized powerful proprietary algorithms to make their offers nearly impossible to beat over the last few years. It still reigns in offering the best price match policy for their customers. This strategy has helped it gain a lot of trust, good publicity, and enabled retention of customers. But how do you optimize what you charge without pricing yourself out? That is where data comes into play. You need real-time monitoring across thousands of stock-keeping units (SKUs) to identify key value categories and items. With proper data analytics in your pocket, you can ask and answer the following important questions: Which items’ prices matter most? Which items have the biggest pull on price perception?  What pricing strategies are competitors adopting, and how can you match them? And which items can you afford to reduce in price to win loyalty and boost that very perception?

    Learn more about how DataWeave can help retailers make smarter pricing decisions. 

    Seamless shopping journey
    Seamless shopping journey

    Every company uses data to achieve its own personal goals and objectives, but what makes one retailer better than the other is how they use both structured and unstructured data to provide a seamless experience and shopping journey to customers in a way that is effortless, non-intrusive, and innovative. So use your structured data and also find a way, use the tools, and leverage the power of technology to structure your unstructured big data. In today’s competitive retail landscape where retailers – both online and offline – are leveraging cutting edge technologies to deliver close-to-perfect products and services, and innovative concepts, it is only the ability to harness all forms of structured and unstructured data that will result in achieving your ever-evolving customer engagement and experience goals. 

    Want to learn how DataWeave can help make sense of your unstructured data? Sign up for a demo with our team to know more. 

  • Decoding Growth for CPG Brands in India with Data Analytics

    Decoding Growth for CPG Brands in India with Data Analytics

    It’s been a pivotal year for the CPG industry in India. Consumers were forced to stay at home due to the pandemic, leading to a surge in online CPG shopping, simultaneously increasing the expectation for safety and convenience.

    Brands and retailers needed to adjust to this new reality to meet customer expectations that were very different from the pre-Covid era. They needed to make adjustments right from the way of marketing to product assortments, communication to customer interaction. Factors such as increased competition from e-commerce platforms, the emergence of homegrown brands, traditional players making the online shift all have transformed the CPG industry. 

    Kantar study
    A survey conducted by Kantar showed the delta growth of top CPG brands in the last five years in India.

    A survey conducted by Kantar showed the delta growth of top CPG brands in the last five years in India. As per the report, among the 428 brands, 55% of brands failed to grow their penetration. 

    “Some big brands like Lux and Lifebuoy feature in this list of brands that failed to grow – each of these brands still reached over half of India, but in 2016 they were much bigger. Size alone, therefore, does not guarantee success, but it helps.”

    Going forward too, CPG sales will remain high as consumers are spending more time at home and brands must ensure they are doing everything possible to work on their strategies. Hence, CPG companies are turning to technology to increase their productivity and efficiency.

    What Is Data Analytics?

    In brief, data analytics refers to the process of drawing conclusions from any predetermined datasets. With CPG data analytics, it means any product-related or consumer behavior-related data that is relevant to the brand. However, data has long been ignored by CPG companies. Research by McKinsey shows that CPG scored below average when it comes to digitally mature industries globally. Only 40 percent of consumer-goods companies that have made digital and analytics investments are achieving returns above the cost of capital. The rest are stuck in “pilot purgatory,” eking out small wins but failing to make an enterprise-wide impact.”

    Kantar study
    A survey conducted by Kantar showed the delta growth of top CPG brands in the last five years in India.

    It is important for any company that sells products to understand the structure and needs of the consumers. The goal is to know what products they should produce and what makes it profitable for them to produce it. This is where data comes in. The more data, the better it is, and it is important for companies to understand what to study to discover the trends in their consumers’ behavior. If they can identify trends and make predictions based on that data, then they will be better prepared to make changes that will improve the business.

    Another banner trend and rightly so is using modern-day technologies such as AI & Machine Learning (ML) to spot hidden trends and opportunities. ML capabilities can help CPG companies identify anomalies that are not obvious to human intelligence so they can react accordingly.

    Data Analytics Gives You An Edge Over Your Competitors

    Big CPG companies such as PepsiCo, Unilever, and McDonald’s have been focusing on data for a long time. McDonald’s has been investing in data heavily since 2015 and also acquired analytics firm Dynamic Yield, an ML platform for retailers in 2019. Some of the data points that McDonald’s uses are historical sales data, customers’ past purchases, items in trend, and so on. For maximum efficiency, brands must focus on data across the board – from data related to sales and merchandising to price optimization, marketing, supply chain, and more.

    Key Data points for CPG

    Some of the key data points for CPG companies to reconfigure their businesses are:

    a. Sales Data And Trends

    Sales data shows the units of a product (SKUs) that are sold across different locations or channels. This data gives a better idea of what decisions, activities, assortments lead to higher sales. While the companies often have a track of their offline sales, it is important that you combine both offline and online sales data, especially now that digital channels are turning out to be a make or break for a lot of CPG brands.

    Instead of relying on traditional surveys or testimonies, brands must look to invest in tools that can present this highly complex data in a clear and communicative manner.

    Getting sales data from your own website is the easy part, but if you want to know your online sales and market share on marketplaces like Amazon v/s your competitors, get in touch with our team to know more.

    b. Competitive Analytics

    Competitor analysis provides an opportunity to go deeper and evaluate who’s operating in your space, how they operate if there is a specific competitor you don’t know of, or even a potential competitive advantage you are not aware of. 

    This data also helps to focus on the root cause for positive and negative developments, and uncover relative market positions of main competitors. Depending on the product and goals, companies should gather data about their competitors’ pricing & promotional strategies, package design, sizes, product range, etc.

    c. Market Basket Analytics

    Popularly known as assortment optimization analytics, this is one of the most important data points, from a marketing perspective. This is based on the theory that customers who buy one item are more likely to buy another specific item.

    For instance, if a customer is buying hot dogs they are typically more likely to buy buns. Grocery stores also pay attention to product placement and shelving, you will almost always find shampoos and conditioners together. Walmart’s infamous beer-and-diapers anecdote is also a classic example of Market Basket Analytics.

    If you want insights into your product assortment, bestsellers, or insights into your competitor’s assortment and bestsellers, we can help!

    d. Price And Promotional Analytics

    CPG industry is a highly fragmented market and companies focus on pricing and promotions to boost their sales. More than often, promotion spends tend to be even bigger than advertising budgets. However, many companies struggle to get their pricing right and often find that promotions are actually counterproductive. 

    Creating optimized pricing and promotional strategies especially in a digital world can be a struggle. In a world where shoppers compare prices and deals, have thousands (if not lakhs) of options to choose from, pricing agility can be the key to competitive advantage. Top retailers and CPG companies rely on data and analytics to get their strategies right.

    CPG Analytics breakup
    CPG Analytics Breakup

    e. Customer analytics

    Businesses can take full advantage of advanced analytics to map their customers’ shopping experience and make changes to their marketing strategies accordingly. Brands can create a personalized experience for their audience using information such as customer demographics, store and brand loyalty, purchase frequency, completed transactions, abandoned products, and carts, etc. This will help CPG manufacturers deliver superb customer experiences and design lean operations to meet their objectives of better understanding their consumers to enhance their experience, reduce costs, streamline the supply chain and enhance the relationships.

    Conclusion

    If used right these data points can create exponential profits and margins for CPG brands. It’s Important that businesses invest in big data and advanced analytics to focus on delivering impactful services to consumers. Businesses can use these data points to identify strengths, gaps, and opportunities. 

    For short-term goals, CPG data helps by providing an accurate and clear picture of the ongoing operations across the entire business. As a long-term plan, measuring, evaluating, and tracking this data can empower your business to make better decisions and allocate your resources better. CPG data analytics affects the entire supply chain processes and solutions and can boost sales, ROI, and YoY growth if used tactfully.DataWeave’s AI-Powered analytics solutions give CPG brands the data they need to improve customer experience and drive e-commerce sales. Sign up for a demo with our team to know more.

  • “The Rise of Digitally Native Brands (DNVB)”

    “The Rise of Digitally Native Brands (DNVB)”

    Direct-to-Consumer (D2C), Digitally Native Vertical Brands (DNVB), and brand.com serve as
    different variations of a similar concept that has blown up in the past few years fueled by factors ranging from a surge in online shopping, stay-at-home restrictions brought about by the pandemic, and a general shift in consumer behavior.

    US D2C E-commerce sales

    D2C sales were forecasted to account for $17.75 billion of total e-commerce sales in 2020, up 24.3% from the previous year, according to eMarketer. The Middle East might have been late in joining the party but the key players from across the board including brands that sold the traditional way via wholesalers and retailers or those that use online marketplaces such as Amazon and Noon, and the new brands entering this nascent market today are all realizing the potentials of communicating with and selling to customers directly.

    The Middle East has one of the highest youth populations in the world with more than 28% of the residents aged between 14 and 29. This means that a great chunk of the population is inherently digital natives, who grew up with smartphones. These young tech-savvy consumers are more informed, are massively influenced by social media for their purchases, are more value and purpose-driven compared to the older generations, as a result of which are open to experimenting with newer brands that align with their ideas and ideologies.

    This presents an opportunity for both traditional retailers as well as nascent brands to tap into their e-commerce potential and tailor their offerings to this new cohort of customers leveraging data to understand their individual needs by connecting and engaging with their customers. And the best way to “pivot” to the ever-evolving demands would be by adopting the D2C approach.

    Some of the benefits of the D2C milieu in retail would be:

    Access to Customer Data
    Access to Customer Data

    1. Complete access to customer data

    Many retailers agree that data is the real differentiator in D2C retail. Using marketplaces like noon.com and Amazon to retail products is great because of the large customer base they have access to, but the downside is, these behemoth marketplaces own the customers and hence their data. The importance of data can’t be stressed enough, but a key use case of all that complex algorithm is that it empowers retailers to customize and personalize offerings to their customers. According to a study by InstaPage, 74% of customers feel frustrated when website content is not personalised.  Not having control or access means, they are now crippled from the ‘ability to customize’.

    2. Building direct connections with customers

    Building direct connections with customers
    Building direct connections with customers

    Trust is a strong consideration for most consumers today. According to a PWC report, 60% of consumers in the Middle East shop online with companies they feel they can trust Gaining trust has proven to be an arduous task for retailers, who now must assure security and demonstrate high levels of education and awareness of their customers, which can only happen through direct connections, personal interactions, and consistent engagement. D2C brands are much better placed to respond to consumer demand to meet their expectations and more importantly address and resolve any grievances they might have. 

    3. Increasing margins by cutting out middlemen

    Increasing margins by cutting out middlemen
    Increasing margins by cutting out middlemen

    Studies have shown that successful D2C companies have a gross margin of 50 – 85%, thanks to two components – effective customer acquisition and eliminating middlemen. Brands with their own unique value proposition, voice, channels and strategies come across as more authentic, and for the millennials and Gen Z, authenticity is the name of the game. Secondly, and perhaps more evidently, getting rid of distribution partners ends up saving costs for the company tremendously.

    Also, e-commerce eliminates the high fixed distribution costs brands used to pay retailers for shelf space and replaces it with variable costs to list on their website or an e-commerce marketplace. However, one thing to keep in mind when listing on marketplaces is that digital channels provide transparency into pricing. And customers will be comparing the prices of your products against your competitors. That’s why it’s critical for D2C brands to benchmark their pricing strategies against their rivals to drive more revenue and margins by pricing products competitively. Want to know how? Read about how DataWeave’s AI-Powered e-commerce analytics solutions can help

    4. Enables to expand presence 

    Enables to expand presence
    Enables to expand presence

    While the D2C approach is proving to be profitable, it also gives brands the flexibility to expand and enhance their presence. Nike would be a prime example of how it has aggressively expanded its presence offline and online since it announced a decade back about its Customer Direct Acceleration strategy. Over the years, Nike’s D2C sales have grown from 16% of the brand’s total revenue to 35% or $12.4 billion by the end of fiscal 2020. Undoubtedly, Nike’s e-commerce focus has been strong, but what they have also mastered is its digitally integrated concept stores that have taken in-store experience to the next level. Moreover, going the D2C way has given the brand more flexibility to build on its voice and purpose, which is reflected across all of its channels and touchpoints. As a result, Nike has been able to grow its presence in existing markets, and establish the brand in new markets by widening its e-commerce penetration and opening stores that helps build communities and serve as marketing fronts instead of merely being points of sale.

    In the Middle East too, there are some strong players, that realized the benefits of D2C and are reaping the benefits now. The most prominent of them would be Huda Beauty, founded by makeup artist turned billionaire entrepreneur Huda Kattan. Beginning as a blog in 2010, Huda Beauty has fast become the number one beauty Instagram account in the world. Huda launched her brand into Sephora in the Dubai Mall in 2013 and has since expanded the range to include a vast array of beauty products. The brand has since had several record-breaking launches globally, with products now available worldwide on hudabeauty.com as well as retailers including, Sephora, Sephora in JC Penney, Harrods, Selfridges, and Cult Beauty. Equipped with a clear value proposition and an army of loyal customers, the company continues to grow as its founder continues to deliver on the brand promise and remains connected with her customers bypassing middlemen. 

    Also read how DataWeave helped Douglas, a premium beauty retailer in Germany go D2C when the pandemic forced them to focus on their ‘Digital First’ strategy. 

    Another example would be the popular eyewear brand, Warby Parker, a company that capitalized on technology, data, and strived to bring a solution to the market. It stepped into an industry that was criticized for being expensive, entered a market that was skeptical of purchasing online, and turned the whole situation around by going D2C. They designed their own frames and sourced their own raw materials, drastically bringing down the costs that would have been passed on to end consumers. They introduced virtual try-on that delivered accurate results turning customers into loyal consumers. And today, after six separate rounds of fundraising, the company is reportedly set to launch an Initial Public Offering this year.

    The playing field in the Middle East is wide open and the appetite for brands that respect value, put people over profits, care about providing suitable, cruelty-free, and ethical products, and understand their customers is only growing. Brands with a robust infrastructure, the right technical know-how and technologies, expertise to manage data, and clear strategies are already on the right path to establishing a strong D2C platform. 

    Insights from DataWeave can help D2C brands make smart, competitive assortment, promotion, and pricing decisions amongst other things to improve the customer experience and drive e-commerce sales. Sign up for a demo with our team to know more.

  • Seven tricks to win food wars on food aggregators apps

    Seven tricks to win food wars on food aggregators apps

    Food aggregators have emerged as a critical channel for Quick Service Restaurant (QSR) chains to grow their business – especially post-pandemic. Quick Service Restaurants, QSRs, as we call them, are capitalizing on the opportunity too. For many chains, as high as 50% of their revenue now comes via aggregator channels.

    However, most QSR chains are only beginning to leverage data and analytics to drive business on the food aggregator apps.

    Currently, QSRs spend vast amounts on marketing on Food apps but are always unsure of the return on their investment. Aggregators share some data, but they have an inherent motive to entice QSRs to buy more advertisements. They cannot share competitive insights as well. Moreover, as QSRs work with several platforms at once, it gets difficult to collate and analyze data from all these platforms together. These issues make leveraging data for QSR chains difficult. At Dataweave, we have collated some insights from our recent experience of working with global QSR chains helping them improve their sales on different food applications using data:

    1. Availability

    Availability of QSR and Availability Trends
    (L) Availability of QSR outlets across aggregator platforms at state, city, and outlet levels. (R) Availability trends at Lunch and Dinner slots across platforms. Such trends can highlight problem areas that need to be addressed.

    The easiest and most impactful fix is to ensure that all your outlets are available on the app at the peak slots, typically lunch and dinner. Availability increase of ~2% at peak times results in order volume increase by ~5%-7%.

    The reasons for unavailability range from lack of riders, overwhelming orders at the outlet, or just plain technical glitches. Tracking this metric and actively engaging with your stores and aggregator platforms to resolve any issues should be a daily priority.

     2. Monitoring Keyword Ranks

    High correlation between ranking and sales
    Illustrative chart showing a high correlation between ranking and sales

    If you are a Pizza chain but don’t show up among the first five ranks when your target customer is searching for Pizza, the chances of a sale are lower.

    What helps is to track the ranking for your brand, and your competitor brands, in different category listings across different keywords.

    Your ranking may differ a lot by region, markets, and Zip codes depending on consumer tastes, competitors, and your brand presence, and it’s helpful to track it granularly. 

    No surprises here – but rank is strongly correlated with your order volumes!

    3. Tracking competitors

    QSR chain rank
    Illustrative chart showing the rank of key QSR chains on the home page and various categories

    One of the tricks to rapidly gain in ranking is to monitor competitors in your category and ensure that you are doing better on each attribute – ranking, rating, ETAs (estimated time of arrival), fees, discounts, etc.

    A slight edge across your outlets translates to rapid gain in ranking and order volumes.

    4. Choosing suitable banners for promotions

    Position of banners
    Position of various banners at various zip codes. Important to choose banners that rank higher.

    Choosing banners is an essential strategy to gain visibility – but it’s vital to know two factors: 

    • At what rank does the banner you are choosing show up on the App/Website.
    • At what position does your brand show up in the banner?

    If you are on a 5th rank on the 4th banner, your marketing spend is probably going down the drain.


    5. A/B Testing

    Before starting an effective marketing campaign, it helps to do A/B testing by running two different banners in the same city one week apart to see which yields more impact.

    A/B testing can also be a tool to choose banners, discounts, offers, signature images, etc.

    6. Sensitivity analysis

    Delivery time impact
    Illustrative chart showing that ETAs are highly correlated with sales, whereas ratings do not have much impact.
    • What has more impact on sales – Ratings or ETAs? 
    • What will be the likely impact on sales of the marketing campaign in New York vs. Denver? 
    • What is the likely impact of competitors’ ad blitz on your sales?

    Data can answer these and many more questions, and this sensitivity analysis should be part of the QSR chain’s decision-making

    7. Monitoring campaign performance

    QSR chains spend millions of dollars of ad budget running campaigns on aggregator platforms combining banner ads, discounts, offers, etc.

    It’s a great idea to measure QSRs rank on these aggregator’s platforms before, during, and post the campaign in focus Zip Codes for priority keywords to see if the gain in ranking is temporary or lasts for a while.

    The ultimate factors for QSRs to win will remain the quality of food and consistency of the brand’s messaging. Leveraging the power of data can help understand the aggregator platform’s characteristics, competitor’s strengths, weaknesses, & strategy, and consumer behavior trends.

    Also, data can help better direct ad dollars and the eCommerce teams’ focus on the right initiatives to drive maximum sales and growth.

    DataWeave for QSRs

    DataWeave has been working with global QSR chains, helping them drive their growth on aggregator platforms by enabling them to monitor their key metrics, diagnose improvement areas, recommend action, and measure interventions’ impact. 

    DataWeave’s strategy eliminates the dependence on food apps for accurate data. We directly crawl food aggregators apps and websites and help you with data and analysis to solve the aforementioned issues and drive 10-15% growth.

  • G2 recognise DataWeave as Leader and High Performer in 2021.

    G2 recognise DataWeave as Leader and High Performer in 2021.

    We are really excited by the recognition that G2 has given us. G2 has awarded us 3 new badges this year in G2’s Summer 2021 Reports. Before we dive into what these awards are, let me give you a little background

    What are G2 and G2 Grid Report?

    G2 (formerly G2 Crowd) is the world’s leading B2B software and services review platform. The platform helps potential customers choose the right software and services for their business based on authentic, timely reviews from genuine users.

    Every quarter, G2 creates a report that showcases the top-rated solutions in the industry, as chosen by the real heroes, our customers

    The Grid Report represents the democratic voice of real software users, rather than the subjective opinion of one analyst. G2 rates products from the E-Commerce Analytics category and Multi-Channel Retail category algorithmically based on data sourced from:

    • Product reviews shared by G2 users
    • Data aggregated from online sources and social networks

    Who is DataWeave?

    DataWeave provides Competitive Intelligence and Digital Shelf Analytics to eCommerce businesses and consumer brands by aggregating and analyzing Web data at a massive scale.

    The company’s AI-powered technology platform enables eCommerce businesses to make smarter pricing and merchandising decisions and helps brands optimize their online channels to drive more sales.

    With that context here is a deeper look at what we have been recognized for.

    Leader Summer 2021 – E-Commerce Analytics

    Leader Summer G2 2021

    Products in the Leader quadrant in the Grid® Report are rated highly by G2 users and have substantial Satisfaction and Market Presence scores in the category of E-Commerce Analytics.

    Simply put, this means, among all the e-commerce analytics solutions listed on G2, DataWeave scored the highest on customer delight, consideration & market share along with a handful of select companies that were all ‘Leaders’ in this category.

    High Performer Summer 2021: Multi-Channel Retail

    High Performer Summer G2 2021

    Products in the High Performer quadrant in the Grid® Report have high customer satisfaction scores and Market Presence scores compared to the rest of the Multi-Channel Retail category.

    This means that in the Multi-Channel Retail category, while we’re not “Leaders” we come in at a very close second as a “High Performer”. We’re still the preferred choice and have a greater market share & customer consideration over a lot of other solutions in this category on G2.

    We have also won the Users Love Us reward badge, for receiving 20+ reviews with an average rating of 4.4 stars.

    Users Love Us G2

    We would like to thank all the users for sharing their love and giving us such amazing reviews. These awards give us the impetus to continue our journey in making customer delight our top priority and helping our customers win.

    Here is what DataWeave’s team has to say about earning these badges:

    “Winning these badges from G2 is not only a huge confidence booster but also validation from users that DataWeave’s solution and capabilities are making a difference for our customers.”

    Krishnan Thyagarajan, COO and President, DataWeave

    “DataWeave as a Leader and High Performer in these categories brings credibility and showcases the market share that the product holds amongst our valued customers.
    It also showcases that our customers value the proactive engagements driven by our customer success managers. A big kudos to our team at DataWeave and a big thank you to our customers for helping us achieve this recognition.”

    Srikanth Ramanolla, Director of Customer Success, DataWeave

    If you are one of our customers who have loved using our product, then I urge you to give us your review over here to continue providing value to wonderful customers like you.

  • Amazon Prime Day Secrets all Brands need to know.

    Amazon Prime Day Secrets all Brands need to know.

    Prime Day or not, brands need to make sure their Digital Shelf is well stocked, highly discoverable in crowded marketplaces, have the right offers and discounts to stay competitive, all while making sure their products have glowing reviews, ratings and optimized content. While this is a year-round effort, brands go the extra mile on Prime Day to make sure they’re putting their best foot forward.

    Methodology
    To understand how brands adapted their digital shelf for Prime Day, we examined data insights across Amazon in 6 markets and compared the following brand KPIs:

    • Share of voice (SOV): The percentage of a brands products that appear in the search results page for relevant keywords on Amazon.
    • Availability: The percentage of products in stock on Amazon for Prime Day.
    • Additional discounts: The reduction in the listing price of a product during Prime Day compared to before or after the event to see how brands adapted their pricing strategies to stand out from rivals

    Winning brands made sure their products were ‘highly’ discoverable

    With all the global lockdowns, home entertainment hit a new high. So we looked at the word “TV” to see which brand had the highest share against this keyword during the Prime Day event.

    • In the US – Samsung won hands down with close to 15% SOV. LG came in at a not so close 2nd with 9% SOV.
    • In the UK – Samsung won again with a whooping 16.7% SOV with Sharp at # 2 at 12%.

    Now let’s look at some key European markets

    • On Amazon Italy we saw a similar trend – Samsung & LG, neck to neck at 22% & 19% respectively.
    • Amazon Germany was no different – Samsung had the highest SOV at 15% and Philips far behind at 7%.

    Samsung has such a strong association with the keyword TV. This means, when customers are searching for TVs on Amazon in these regions – the brand that has the largest selection up on display for them to choose from is Samsung! That’s definitely going to have a positive impact on sales, don’t you think?

    Do you know which keywords you should be tracking for your brand? And do you know your Brand’s SOV against those keywords?

    … & finally, an outlier!

    • In Amazon France, LG took the lead for a change – with 15% SOV. But we have Samsung not far behind at 13%.

    Kudos to team Samsung!

    Winning Brands kept a close eye on product availability

    Poor product availability leads to lost sales. But not on Amazon Prime Day! Bigger brands that sell over 500 products created artificial scarcity by listing a chunk of products out of stock before the sale. And restocked aggressively during the sale.

    In contrast, the smaller brands that sold fewer than 100 products didn’t dare make such bold moves and stayed stocked up even pre-event to avoid even a single day of lost sales.

    Let’s look at some data from the US

    • The average availability for bigger brands selling 500+ products before the sale was 41% and then went up dramatically to 81.4% the day of the sale when they aggressively restocked.

    Similar trend in the UAE

    • Availability pre-sale was 26.2% and during the sale shot up to 87.6%!

    Various other markets displayed similar patterns. And this was only possible because these brands were able to track their availability with precision and plan their stock levels accordingly.

    How are you tracking your availability across marketplaces? Do you know when your products are out of stock and are in immediate need of replenishment?

    Winning Brands made strategic pricing and discounting decisions

    Discounts matter. Period. And brands that use discounts strategically, win.

    Let’s look at Airpods on Amazon in the US.

    • During the event, Apple had the highest SOV for the keyword Airpod at 7.5% followed by SkullCandy, an American audio accessory manufacturer at 5.6%. 
    • Here’s the interesting part – during the sale Apple offered just 3.4% additional discounts while SkullCandy offered 32.1% additional discount to try and win sales from Apple. And looks like it worked! Apples SOV dropped from 13.5% before the event to 7.5% during the event and SkullCandy’s SOV improved 

    Now let’s look at the same data cut in the UAE – a market where Apple products have a fair penetration, but not as high as in the US. They needed a more aggressive discounting strategy in this market.

    • In the US, during the sale, Apple offered additional discounts on just 30% of products. However, in the UAE that number rose to 90% – a clear strategy to make their product pricing more attractive to customers to win sales

    Discounts and markdowns aren’t always the answer to improving sales, but when used strategically can drive significant impact to your bottom line.

    Are you tracking your competitor pricing? Do you know if they’re keeping tabs on your pricing strategy to get ahead of you?

    Winning Brands made it to the Amazon Best Seller list

    Amazon Best Sellers are products that have the highest sales on Amazon. Products with a higher Amazon Best Seller Rank have higher sales.

    • In the US, Nintendo had the highest share in the Electronics Best Seller category during the sale at 18.6%. Before the sale their share stood at 22.5% – so they lost ground a little ground with a 27.2% drop in their Best Seller share. While they gave additional discounts of 17%, only 27% of their catalogue was discounted.
    • But here’s a brand that knocked it out of the park! The Razer had an SOV of just 1.18% before the sale and during Prime Day it shot up to 6%! A clear indication that sales for the Razer spiked exponentially during Prime Day. Could this be because Razer offered 100% of their catalogue on an additional discount of 31%? It’s a bold move that could have paid off and contributed to super high sales. 

    Now let’s look at France – the Fashion Capital of the World and which brand came out on top in the Fashion Best Seller Category

    • Footwear brand Havaianas had the highest SOV on Prime Day (13.48%) Not too surprising because before the sale they were at 14.09%
    • Now let’s look at the Best Seller Rank – Lacoste secured BSR #1 at the event. Pat on the back for them because before the sale they were at Rank 46! And post-sale they dropped to #6. Definitely a combination of techniques involved here that got them from #46 to #1 at super speed!

    What techniques have you tried to boost sales for your products on Amazon?

    Brands that do not optimize their Digital Shelf risk losing out on their share of basket. If you’ve been thinking about how to optimize your Brands Digital Shelf, then get in touch & learn how DataWeave can help!

  • Amazon Prime Day 2021 Discounts Set Home Leaders Apart

    Amazon Prime Day 2021 Discounts Set Home Leaders Apart

    Home is where the shopping cart is.

    After last year’s blistering pace of e-commerce sales growth in the home category, we at DataWeave wanted to know how Prime Day 2021 discounts on home products would impact retailers and brands around the world.

    We focused our analysis on how international retailers adapted their Prime Day pricing strategies to distinguish their offerings across eight home subcategories, including bed & bath, kitchen and pet supplies.

    Our Methodology
    We tracked the pricing of products among 21 leading retailers in nine countries across five regions, including:

    • The US (Ace Hardware, Amazon US, Best Buy, Home Depot, Lowe’s, Petco, PetSmart, Target and Wayfair US)
    • The UK (Amazon UK, Ebay, Etsy, OnBuy and Wayfair UK)
    • Europe (Amazon France, Amazon Germany and Amazon Italy)
    • The Middle East (Amazon Saudi Arabia and Amazon UAE)
    • Asia (Amazon Japan and Amazon Singapore)

    The results showed some surprising differences among retailers and regions. See how retailers used pricing as a competitive strategy to win Prime Day sales in the home category, as well as international home brands that stood out for the discounts on their products.

    Percentage of items with a price decrease

    The US retailer with the overall highest percentage of home products with a price decrease for Prime Day was Amazon US (26.4%).

    Home subcategories with the highest percentage of items with a price decrease per US retailer were:

    • Ace Hardware: Power & hand tools (21.2%)
    • Amazon US: Furniture (36.3%), appliances (34.1%) and kitchen (28.3%)
    • Best Buy: Appliances (0.9%)
    • Home Depot: Power & hand tools (0.2%)
    • Lowe’s: Furniture (29.2%), power & hand tools (5.5%) and appliances (4.1%)
    • Petco: Pet supplies (11.6%)
    • Target: Bed & bath (37.9%), furniture (32.5%) and kitchen (11.5%)
    • Wayfair US: Pet supplies (31.9%), home & garden (25.6%) and bed & bath (24.8%)

    The UK retailer with the overall highest percentage of items with a price decrease for Prime Day was Amazon UK (36.4%).

    Home subcategories with the highest percentage of items with a price decrease per UK retailer were:

    • Amazon UK: Appliances (41.7%), power & hand tools (39.5%) and furniture (36.4%)
    • Ebay: Smart home (10.5%), bed & bath (10.1%) and furniture (8.3%)
    • Etsy: Bed & bath (1.7%), kitchen and pet supplies (both 1.5%)
    • Wayfair UK: Kitchen (17.7%), bed & bath (10.8%) and home & garden (5.9)

    In Europe, Amazon Germany had the overall highest percentage of items with a price decrease for Prime Day (27.3%).

    Home subcategories with the highest percentage of items with a price decrease per European retailer were:

    • Amazon France: Appliances (15.9%), power & hand tools (15.8%) and furniture (14.2%)
    • Amazon Germany: Power & hand tools (40.0%), appliances (33.7%) and pet supplies (28.4%)
    • Amazon Italy: Furniture (11.8%)

    Across the Middle East & Asia, Amazon UAE had the overall highest percentage of items with a price decrease for Prime Day (41.6%).

    Home subcategories with the highest percentage of items with a price decrease per retailer were:

    • Amazon Saudi Arabia: Power & hand tools (53.8%), pet supplies (33.3%) and appliances (30.4%)
    • Amazon UAE: Appliances (55.8%), kitchen (49.9%) and pet supplies (49.0%)
    • Amazon Japan: Appliances (15.3%), power & hand tools (10.8%) and furniture (9.0%)
    • Amazon Singapore: Bed & bath (35.4%), appliances (30.2%) and power & hand tools (27.2%)

    Magnitude of price decrease

    The US retailer with the greatest overall magnitude of price decrease for Prime Day was Target (20.3%).

    The home subcategories with the greatest magnitude of price decrease per US retailer were:

    • Ace Hardware: Power & hand tools (14.3%)
    • Amazon US: Kitchen (21.0%), appliances and pet supplies (both 18.3%) and furniture (15.1%)
    • Best Buy: Appliances (8.7%)
    • Home Depot: Power & hand tools (17.7%)
    • Lowe’s: Power & hand tools (13.4%), furniture (13.0%) and appliances (10.8%)
    • Petco: Pet supplies (17.2%)
    • Target: Bed & bath (28.9%), smart home and kitchen (both 19.1%) and furniture (18.5%)
    • Wayfair US: Pet supplies (4.6%), kitchen (4.4%) and bed & bath (4.1%)

    Brands with the greatest magnitude of price decreases per US retailer included:

    • Ace Hardware: Zircon (48.6%), Smith\u0027s (36.1%) and DMT (30.0%)
    • Amazon US: Supply Guru (56.3%), Seresto (55.4%) and Advantage (53.4%)
    • Best Buy: Panasonic (29.1%), Farberware (16.6%) and Insignia™ (14.0%)
    • Home Depot: Husky (17.7%)
    • Lowe’s: Metabo HPT (43.2%), Dewalt (27.8%) and GZMR (26.5%)
    • Petco: Seresto (50.0%), Open Road Brands (45.4%) and Starmark (42.1%)
    • Target: Little Tikes (50.0%), Bobsweep (43.9%) and Shark (42.2%)
    • Wayfair US: Sorbus (57.8%), GE Appliances (45.9%) and Nu Steel (42.1%)

    The UK retailer with the greatest overall magnitude of price decrease for Prime Day was Etsy UK (19.8%).

    The home subcategories with the greatest magnitude of price decrease per UK retailer were:

    • Amazon UK: Furniture (21.8%), power & hand tools (21.5%) and appliances (20.8%)
    • Ebay: Pet supplies (12.2%), appliances and furniture (both 12.0%) and bed & bath (10.0%)
    • Etsy: Pet supplies (44.3%), kitchen (18.1%) and bed & bath (14.2%)
    • Wayfair UK: Home & garden (12.2%), bed & bath (9.2%) and furniture (8.9%)

    Brands with the greatest magnitude of price decreases across home sub-categories per UK retailer included:

    • Amazon UK: Tefal (54.0%), Caterpack (51.6%) and Nylabone (49.9%)
    • Ebay: Bob Martin (59.8%), Fridgemaster (57.5%) and Tetramin (49.3%)
    • Etsy: Celebnails and vitrifiedstudio (both 49.5%), Deco-Den UK Supplies (46.5%) and Caxo Beauty (36.9%)
    • Wayfair UK: Breakwater Bay (41.1%), Zipcode Design (33.3%) and Heritage Brass (29.7

    Among European retailers, Amazon Italy offered the greatest overall magnitude of price decrease for Prime Day (29.9%) among a total of 49 products.

    The home subcategories with the greatest magnitude of price decrease per European retailer were:

    • Amazon France: Bed & bath (11.7%), pet supplies (11.2%) and appliance (9.2%)
    • Amazon Germany: Kitchen (23.4%), power & hand tools (22.3%) and furniture (20.2%)
    • Amazon Italy: Furniture (29.9%)

    Brands with the greatest magnitude of price decreases per European retailer included:

    • Amazon France: Thermobaby (47.6%), Sinogoods (44.6%) and Tractive (40.0%)
    • Amazon Germany: Sage Appliances (56.5%), Nasum (51.7%) and Hikenture (49.2%)
    • Amazon Italy: Gifort (55.1%) and Wokkol (4.8%)

    Across the Middle East and Asia, Amazon UAE offered the greatest overall magnitude of price decrease for Prime Day (15.3%).

    The home subcategories with the greatest magnitude of price decrease per retailer were:

    • Amazon Saudi Arabia: Furniture (18.0%), pet supplies (15.9%) and power & hand tools (15.8%)
    • Amazon UAE: Pet supplies (17.8%), appliances (16.4%) and kitchen (16.2%)
    • Amazon Japan: Kitchen (25.4%), furniture (14.5%) and bed & bath (13.6%)
    • Amazon Singapore: Kitchen (14.0%), furniture (11.1%) and appliances (8.0%)

    Brands with the greatest magnitude of price decreases per retailer in the Middle East and Asia included:

    • Amazon Saudi Arabia: American Baby Company (55.5%), Charmcollection (49.0%) and LG (46.9%)
    • Amazon UAE: Knorr (54.0%), Ocean Patio (50.0%) and Bikuul (48.2%)
    • Amazon Japan: キングジム (King Jim) (59.1%), Skylight (52.0%) and Cozyone, Hbada and Bauhutte (バウヒュッテ) (all 50.0%)
    • Amazon Singapore: Trademark Home (59.8%), Gaggia (54.5%) and Ely’s & Co. (46.8%)

    Discounts before, during and after the event

    The US retailer with the biggest overall home discount before Prime Day was Amazon US (27.0%). Amazon’s biggest pre-event discounts were on power & hand tools (28.6%), kitchen (28.3%) and furniture (28.0%).

    Ace Hardware offered the biggest discounts on power & hand tools during and after the event (both 34.1%).

    Amazon UK stood out for discounts this Prime Day. It was the UK retailer with the biggest overall home discount before (26.1%) Prime Day, with the deepest discounts on appliances (29.0%), power & hand tools (27.1%) and pet supplies (25.9%).

    During Prime Day, Etsy and Amazon UK offered the biggest discounts (29.7% and 29.6%, respectively).
    Etsy’s top discounts were on pet supplies (40.0%), kitchen (32.5%) and bed & bath (28.1%), while Amazon UK’s top discounts were on power & hand tools (32.3%), appliances (31.4%) and pet supplies (28.9%).

    After the event, Etsy had the biggest discount (29.8%), led by kitchen (34.3%), pet supplies (32.9%) and bed & bath (28.9%).

    In Europe, Amazon Italy offered the biggest overall home discount before (31.6%) and during (29.4%) Prime Day. Amazon France offered the biggest discount after (21.4%) Prime Day.

    In the pre-sales event, Amazon Italy gave the most generous discounts on pet supplies (31.6%) and appliances (9.3%).

    During Prime Day, Amazon Italy offered the biggest discounts on pet supplies (31.6%), furniture (28.3%) and appliances (9.3%).

    After Prime Day, Amazon France offered the biggest discounts on kitchen (24.6%), appliances (22.9%) and pet supplies (21.4%).

    Popularity

    In the US, among home products with high popularity, Amazon US offered the highest percentage of items with a price decrease (26.8%) and Target offered the greatest magnitude of price decrease (23.6%).

    For home items with moderate popularity, Amazon US offered the highest percentage of items with a price decrease (26.9%) and Target offered the greatest magnitude of price decrease (18.9%).

    Among home merchandise with low popularity, Amazon US offered both the highest percentage of items with a price decrease (23.8%) and the greatest magnitude of price decrease (15.9%).

    Amazon UK stood out in this analysis of product popularity. In the UK, among home products with high popularity, Amazon UK offered the highest percentage of items with a price decrease (37.1%) and Etsy offered the greatest magnitude of price decrease (20.9%).

    For home items with medium popularity, Amazon UK offered the highest percentage of items with a price decrease (35.9%) and Etsy offered the greatest magnitude of price decrease (24.4%).

    Among home merchandise with low popularity, Amazon UK offered both the highest percentage of items with a price decrease (34.9%) and the greatest magnitude of price decrease (21.7%).

    In Europe, Amazon Germany stood out for discounts for home products across all levels of popularity.

    Among home goods with high popularity, Amazon Germany offered both the highest overall percentage of items with a price decrease (29.1%) and the greatest overall magnitude of price decrease (19.1%).

    For home items with medium popularity, Amazon Germany offered both the highest percentage of items with a price decrease (28.4%) and the greatest magnitude of price decrease (19.8%).

    Among home merchandise with low popularity, Amazon Germany offered the highest percentage of items with a price decrease (22.5%) and Amazon Italy offered the greatest magnitude of price decrease (55.1%) related to a product count of 9.

    In Middle East & Asia, among home items with high popularity, Amazon Singapore offered the highest overall percentage of items with a price decrease (35.6%) and Amazon Saudi Arabia had the greatest overall magnitude of price decrease (19.4%).

    For home products with medium popularity, Amazon UAE offered both the highest percentage of items with a price decrease (47.5%) and the greatest magnitude of price decrease (16.0%).

    Among home goods with low popularity, Amazon UAE offered the highest percentage of items with a price decrease (43.3%) and Amazon Japan had the greatest magnitude of price decrease (15.5%).

    Prime Day 2021 hit a global home run

    Overall, Prime Day 2021 offered consumers many generous deals on home products across every region.

    According to our analysis, the retailers whose Prime Day pricing stood the most were Amazon US and Target in the US, Amazon UK and Etsy in the UK, Amazon Germany and Amazon Italy in Europe, Amazon UAE in the Middle East and Amazon Japan in Asia.

    Check out our Prime Day 2021 pricing insights across other categories, including health & beauty, fashion and electronics.

  • Prime Day 2021’s Best Fashion Discounts Around the World

    Prime Day 2021’s Best Fashion Discounts Around the World

    Consumers are moving beyond yoga pants. After a long year of pandemic lockdowns and the casual comfort of a homebody lifestyle, shoppers are giving their wardrobes a makeover. During this year’s Prime Day sales event, retailers around the world were well prepared for fashion shoppers’ enthusiasm for a fresh look.

    That’s why we at DataWeave wanted to know how Prime Day 2021 discounts played a role in fashion marketing. We focused our analysis on how global retailers adapted their Prime Day pricing strategies to distinguish their offerings across seven fashion subcategories, including men’s and women’s shoes, and clothing & accessories.

    Our Methodology
    We tracked the pricing of products among 16 leading retailers in nine countries across five regions, including:

    • The US (Amazon US, Nordstrom, Target, Walmart and Zappos)
    • The UK (Amazon UK, Ebay, Etsy and OnBuy)
    • Europe (Amazon France, Amazon Germany and Amazon Italy)
    • The Middle East (Amazon Saudi Arabia and Amazon UAE)
    • Asia (Amazon Japan and Amazon Singapore)

    This year’s results showed some impressive discounts among retailers and across regions. Let’s see how retailers used pricing as a competitive strategy to win Prime Day sales in the fashion category, as well as international fashion brands that stood out for the generous discounts on their products.

    Percentage of items with a price decrease

    The US retailer with the overall highest percentage of fashion products with a price decrease for Prime Day was Amazon US (34.5%).

    Fashion subcategories with the highest percentage of items with a price decrease per US retailer were:

    • Amazon US: Watches (43.1%), men’s clothing & accessories (34.0%) and men’s shoes and women’s shoes (both 32.8%)
    • Nordstrom: Men’s clothing & accessories (5.2%), women’s shoes (2.1%) and women’s clothing & accessories (1.5%)
    • Target: Women’s shoes (28.3%), men’s shoes (13.0%) and women’s clothing & accessories (7.2%)
    • Walmart: Watches (6.8%), women’s clothing & accessories (6.4%) and men’s shoes (4.2%)
    • Zappos: Women’s shoes (28.2%), men’s clothing & accessories (13.6%) and men’s shoes (6.5%

    By far, the UK retailer with the overall highest percentage of items with a price decrease for Prime Day was Amazon UK (30.7%).

    Fashion subcategories with the highest percentage of items with a price decrease per UK retailer were:

    • Amazon UK: Women’s shoes (38.5%), watches (33.5%) and men’s shoes (25.0%)
    • Ebay: Men’s shoes (7.6%), women’s clothing & accessories (6.0%) and women’s shoes (5.5%)
    • Etsy: Jewellery & accessories (4.7%), men’s clothing & accessories (4.3%) and women’s clothing & accessories (2.4%)

    In Europe, Amazon Germany had the overall highest percentage of items with a price decrease for Prime Day (35.0%).

    Fashion subcategories with the highest percentage of items with a price decrease per European retailer were:

    • Amazon France: Women’s clothing & accessories (27.6%), watches (24.2%) and men’s shoes (19.4%)
    • Amazon Germany: Women’s shoes (38.7%), watches (37.6%) and men’s shoes (36.7%)

    Across the Middle East & Asia, Amazon UAE had the overall highest percentage of fashion items with a price decrease for Prime Day (49.1%).

    Fashion subcategories with the highest percentage of items with a price decrease per retailer were:

    • Amazon Saudi Arabia: Men’s clothing & accessories (54.0%), watches (52.0%) and men’s shoes (47.0%)
    • Amazon UAE: Watches (55.8%), men’s clothing & accessories (54.3%) and men’s shoes (51.0%)
    • Amazon Japan: Women’s clothing & accessories (17.5%) and men’s clothing & accessories (2.6%)
    • Amazon Singapore: Women’s clothing & accessories (50.0%), men’s shoes (44.8%) and women’s shoes (40.9%)

    Magnitude of price decrease

    The US retailer with the greatest overall magnitude of price decrease for Prime Day was Nordstrom (29.3%).

    The fashion subcategories with the greatest magnitude of price decrease per US retailer were:

    • Amazon US: Women’s clothing & accessories (16.3%), watches (14.6%) and men’s clothing & accessories (14.3%)
    • Nordstrom: Women’s clothing & accessories (35.0%), women’s shoes (29.3%) and men’s clothing & accessories (28.4%)
    • Target: Men’s clothing & accessories (21.9%), women’s clothing & accessories (19.3%) and women’s shoes (18.6%)
    • Walmart: Men’s clothing & accessories (23.5%), women’s shoes (16.7%) and women’s clothing & accessories (11.5%)
    • Zappos: Women’s clothing & accessories (11.9%), men’s clothing & accessories (10.6%) and women’s shoes (6.6%)

    Brands with the greatest magnitude of price decreases on fashions per US retailer included:

    • Amazon US: Free Soldier (57.3%), Rockport (52.0%) and Alvaq (48.9%)
    • Nordstrom: Little Words Project (60.0%), Robert Barakett and Nordstrom (50.0%) and Bonobos (49.1%)
    • Target: Cowboy Bebop, Avatar: The Last Airbender, YuYu Hakusho, Dragon Ball Z and Naruto (all 40.0%)
    • Walmart: Tredsafe (42.2%), C. Wonder and Crocs (both 30.0%) and Deer Stags (26.1%)
    • Zappos: Volcom (25.9%), O’Neill (20.8%) and Bandolino (20.5%)

    In the UK, Ebay and Etsy tied for the greatest overall magnitude of price decrease on fashion products for Prime Day (both 14.7%).

    The fashion subcategories with the greatest magnitude of price decrease per UK retailer were:

    • Amazon UK: Women’s shoes (27.9%), men’s shoes (27.5%) and men’s clothing & accessories (18.2%)
    • Ebay: Men’s clothing & accessories (19.1%), men’s shoes (17.7%) and watches (17.6%)
    • Etsy: Women’s shoes (30.2%), men’s shoes (17.1%) and jewellery & accessories (16.3%)

    Brands with the greatest magnitude of price decreases across fashion sub-categories per UK retailer included:

    • Amazon UK: Invicta (43.9%), Boss (42.7%) and Accurist (41.2%)
    • Ebay: Dickies (55.6%), Havaianas (55.0%) and Branded (46.7%)
    • Etsy: Mirugb (50.0%), LilisLeatherShop (41.6%) and OnaieShop (40.0%)

    Among European retailers, Amazon Germany offered the greatest overall magnitude of price decrease on fashion products for Prime Day (16.1%).

    The fashion subcategories with the greatest magnitude of price decrease per European retailer were:

    • Amazon France: Women’s shoes (22.4%), men’s clothing & accessories (12.1%) and watches (9.1%)
    • Amazon Germany: Women’s clothing & accessories (17.7%), watches (17.3%) and men’s clothing & accessories (15.0%)

    Brands with the greatest magnitude of price decreases per European retailer included:

    • Amazon France: Converse (58.3%), Alsino (43.2%) and Scuderia Ferrari (35.6%)
    • Amazon Germany: Truth & Fable Damen Kleider (59.6%), Victorinox (59.5%) and Sockenkauf24 (56.7%)

    Across the Middle East and Asia, Amazon Japan offered the greatest overall magnitude of price decrease on fashion products for Prime Day (18.4%).

    The fashion subcategories with the greatest magnitude of price decrease per retailer were:

    • Amazon Saudi Arabia: Men’s clothing & accessories (20.3%), men’s shoes (18.4%) and women’s shoes (16.7%)
    • Amazon UAE: Men’s shoes (20.5%), men’s clothing & accessories (19.0%) and women’s shoes (18.9%)
    • Amazon Japan: Men’s clothing & accessories (23.7%) and women’s clothing & accessories (18.0%)
    • Amazon Singapore: Women’s shoes (12.2%), watches (7.6%) and men’s shoes (5.0%)

    Brands with the greatest magnitude of price decreases per retailer in the Middle East and Asia included:

    • Amazon Saudi Arabia: Dorina (48.7%), Cole Haan (40.2%) and Boss (39.7%)
    • Amazon UAE: Aldo (53.2%), Mvmt (51.4%) and Inkast Denim Co. (39.4%)
    • Amazon Japan: Face Trick Glasses (30.2%), モアプレッシャー (More Pressure) (23.7%) and
    アツギ (Atsugi) (21.7%)
    • Amazon Singapore: Bloch (49.0%), Adidas (38.5%) and Chums (31.4%)

    Discounts before, during and after the event

    Nordstrom was the US retailer with the biggest overall fashion discount before (39.0%), during (40.5%) and after (41.2%) Prime Day.

    Nordstrom’s biggest pre-event discounts were on women’s shoes (44.2%), women’s clothing & accessories (36.6%) and men’s clothing & accessories (36.1%).

    Women’s shoes (43.1%), men’s shoes (42.6%) and men’s clothing & accessories (39.5%) were the leading subcategories for Nordstrom’s discounts during Prime Day.

    After the event, Nordstrom’s biggest discounts were for women’s shoes (42.5%), men’s shoes (42.2%) and men’s clothing & accessories (41.5%).

    In the UK, Ebay offered the highest discounts before (43.7%), during (42.1%) and after (42.3%) Prime Day.

    Before Prime Day, Ebay biggest discounts were on men’s clothing & accessories (47.9%), men’s shoes (43.9%) and watches (43.3%).

    Ebay’s top discounts during Prime Day were on men’s clothing & accessories (45.3%), men’s shoes (44.6%) and women’s shoes (39.5%).

    After the event, Ebay had the biggest discounts on men’s clothing & accessories (45.6%), women’s shoes (42.9%) and men’s shoes (41.9%).

    In Europe, Amazon Germany dominated with the biggest overall fashion discounts before (27.1%), during (31.8%) and after (26.5%) Prime Day.

    In the pre-sales event, Amazon Germany offered its most generous discounts on watches (27.5%), women’s shoes (12.6%) and men’s shoes (5.7%).

    During Prime Day, Amazon Germany’s biggest discounts were on women’s clothing & accessories (36.8%), men’s clothing & accessories (32.5%) and watches (32.0%).

    After Prime Day, Amazon Germany had the highest discounts on women’s clothing & accessories (34.1%), men’s clothing & accessories (27.6%) and watches (26.6%).

    Popularity

    In the US, among fashion products with high popularity, Amazon US offered the highest percentage of items with a price decrease (36.8%) and Nordstrom offered the greatest magnitude of price decrease (29.8%).

    For fashion items with medium popularity, Amazon US offered the highest percentage of items with a price decrease (35.2%), and Nordstrom offered the greatest magnitude of price decrease (28.1%).

    Among fashion merchandise with low popularity, Amazon US offered the highest percentage of items with a price decrease (27.2%) and Nordstrom offered the greatest magnitude of price decrease (31.6%).

    In the UK, among fashion products with high popularity, Amazon UK offered the highest percentage of items with a price decrease (31.6%) and Ebay offered the greatest magnitude of price decrease (14.3%).

    For fashion items with medium popularity, Amazon UK offered the highest percentage of items with a price decrease (32.5%) and Ebay offered the greatest magnitude of price decrease (17.6%).

    Among fashion merchandise with low popularity, Amazon UK offered the highest percentage of items with a price decrease (24.5%) and Etsy offered the greatest magnitude of price decrease (23.1%).

    In Europe, Amazon Germany dominated discounts for Prime Day 2021 across all levels of popularity.

    Among fashion goods with high popularity, Amazon Germany offered both the highest overall percentage of items with a price decrease (40.5%) and the greatest overall magnitude of price decrease (16.3%).

    For fashion items with medium popularity, Amazon Germany offered both the highest percentage of items with a price decrease (32.5%) and the greatest magnitude of price decrease (16.5%).

    Among fashion merchandise with low popularity, Amazon Germany offered the highest percentage of items with a price decrease (30.8%) and the greatest magnitude of price decrease (15.1%).

    In Middle East & Asia, among fashion items with high popularity, Amazon Saudi Arabia offered the highest overall percentage of items with a price decrease (55.9%) and Amazon UAE had the greatest overall magnitude of price decrease (20.6%).

    For fashion products with medium popularity, Amazon UAE offered both the highest overall percentage of items with a price decrease (49.3%) and the greatest overall magnitude of price decrease (17.8%).

    Among fashion items with low popularity, Amazon UAE offered the highest percentage of items with a price decrease (46.1%) and Amazon Japan had the greatest magnitude of price decrease (21.5%).

    Prime Day fashion deals galore

    Overall, Prime Day 2021 gave global shoppers an abundance of generous discounts on fashion items.

    According to our analysis, the retailers whose Prime Day pricing stood out the most were Amazon US and Nordstrom in the US, Amazon UK and Ebay in the UK, Amazon Germany in Europe, Amazon UAE in the Middle East and Amazon Japan in Asia.

    For more global Prime Day 2021 pricing insights, see our analysis of electronics and health & beauty products.

  • Prep, Prime and Plenish For Prime Day India 2021

    Prep, Prime and Plenish For Prime Day India 2021

    After demonetization, Covid-19 has probably been one of the worst scenarios for the retail sector in India. The entire nation went into lockdown and the industry noticed some big changes around the entire globe. From remote working to shopping, everything turned to digital and Bharat witnessed new trends across payments, e-commerce, and more.

    Not surprisingly, D2C has been a favorite amongst businesses thanks to its agility. More than 800 brands have joined the direct-to-consumer bandwagon in order to reach their audience quickly and in an efficient way. Where brands such as MamaEarth, Clovia, Bewakoof, Lenskart have been some of the popular brands in the sector, last year even traditional giants such as LG, Ajanta-Orpat, Piaggio, Havells also adopted the D2C model.

    Ramp up in D2c Brand Activity
    Source: Avendus

    Brands are more focused on making the user experience better and it will be safe to say that this year, D2C will be the highlight of the e-tail ecosystem. Naturally, e-commerce giants such as Amazon, Flipkart have played an important role in this revolution. Amazon, which has over 100 Million registered users in India, announced that it will host its flagship event, Prime Day this year on 26-27 July.

    Let’s look at some of the things brands can do to leave their mark this Prime Day in India.

    Digital Shelf Optimisation: Need Of The Hour

    Given that the pandemic has accelerated online shopping nationwide, Digital Shelf Optimisation (DSO) should be the key lever for any brand to accelerate its digital commerce growth. Events such as Prime day are significant for a brand’s reputation, customer experience, overall sales and can help you build a loyal customer base.

    With that in mind, we have prepared a list of things to consider, in order to help brands stand out from the crowd.

    1. Pricing And Discounting

    Pricing and Discounting
    Pricing and Discounting: Offer discounts and deals to attract customers.

    It is obvious that Prime Day will see a tremendous influx of shoppers. Noticeably, impulsive shopping is a trend during these sales, as everybody loves a good product for a discounted price. Make sure to offer discounts and deals to attract customers.

    Another suggestion is to keep a track of competition, their pricing and promotional strategies and keep an eye on price changes happening across relevant categories or SKU’s (Stock Keeping Unit). Competition analysis is a powerful tool and having accurate data on their sales, market share is a critical part of this.

    2. Optimise Product Visibility

    Product Visibility
    Product Visibility: Lakhs of sellers & brands are vying for the same spot

    Marketplaces are crowded, and getting discovered is already hard. Lakhs of sellers & brands are vying for the same spot. And with more people moving online, it’s going to get increasingly harder for brands to stand out. Optimize your search visibility using the right keywords relevant to your brand, strategically spend on Sponsored Ads to secure high visibility placements on Amazon and lastly make sure your online product packaging via product pages contain attractive images to position your product in the best light.

    3. Product Availability

    Product Availability
    Product Availability: Have plenty of stock available

    Make sure to have plenty of stock available as shoppers are likely to turn to other brands/products in case your product is unavailable. Also, keep in mind that people are generally more open to trying new products during a sale as it offers discounts. Track your products’ stock status to make sure they’re available 24 x 7.

    As the foremost goal during sales is to move inventory as much as possible, offering a large assortment is a good idea. Create product bundles that complement each other.

    4. Use A + Content

    A+ Content
    A+ Content is King: The new age packaging for your product

    Content is the new age packaging for your product. Content is crucial to change consumer shortlists & considerations into conversions.

    Your content tells your product story & gives customers the information they need to make a purchase. Use high resolution and accurate images, add features, benefits, USPs of your products clearly. It is advisable to use more than one image to show your product more clearly. Make sure all your brand & product pages on Amazon are optimized.

    5. Ratings And Reviews

    Ratings and Reviews
    Reviews and Ratings: Feedback is a very important e-commerce tool.

    Why would shoppers rely on word-of-mouth when they can take help from millions of people from the community? Not said enough, feedback is a very important e-commerce tool. Amazon’s A9 algorithm presents the choices to the consumers but reviews and star ratings still play an influential role in the journey from consideration to conversion.

    Brands could consider partnering with Dataweave, to keep track of reviews and manage negative ratings on Amazon.

    Summary

    According to a report by EY-IVCA Trend Book 2021, “ The e-commerce industry in India is expected to reach $99 Bn by 2024 and penetration of retail is expected to be 10.7% by 2024, compared to 4.7% in 2019.”

    Internet penetration rate in India 2007-2021 Published by Sandhya Keelery, Apr 27, 2021 Internet penetration rate in India went up to nearly around 45 percent in 2021, from just about four percent in 2007. Although these figures seem relatively low, it meant that nearly half of the population of 1.37 billion people had access to internet that year. This also ranked the country second in the world in terms of active internet users. Internet penetration rate in India from 2007 to 2021
    Source: Statista

    The same report also revealed that India will have 220 Million online shoppers by 2025. With e-commerce growing at an exponential rate, brands are advised to be more statistical & data-driven to win a larger % of online sales. 
    If you think this is the right time to optimize your digital shelf, take a look at our products and services.

    We at DataWeave would be happy to be a part of your e-commerce and digitization journey. You can sign up for a demo with our team to know more

  • Prime Day 2021 Reflected the Global Health & Beauty Category

    Prime Day 2021 Reflected the Global Health & Beauty Category

    As consumers socialize more this year, retailers around the world are competing for sales in the torrid health & beauty category.

    That’s why we at DataWeave wanted to know how Prime Day 2021 discounts played a role in the pricing strategies for health & beauty products. We focused our analysis on how global retailers adapted their Prime Day pricing strategies to distinguish their offerings across seven health & beauty subcategories, including makeup, health care and baby care.

    Our Methodology
    We tracked the pricing of products among 16 leading retailers in nine countries across five regions, including:

    ● The US (Amazon US, Sephora, Target, Ulta and Walmart)
    ● The UK (Amazon UK, Ebay, Etsy and OnBuy)
    ● Europe (Amazon France, Amazon Germany and Amazon Italy)
    ● The Middle East (Amazon Saudi Arabia and Amazon UAE)
    ● Asia (Amazon Japan and Amazon Singapore)

    The results showed some surprising differences among retailers and regions. See how retailers used pricing as a competitive strategy to win Prime Day sales in the health & beauty category, as well as international health & beauty brands that stood out for the discounts on their products.

    Percentage of items with a price decrease

    The US retailer with the overall highest percentage of health & beauty products with a price decrease for Prime Day was Amazon US (23.9%).

    Health & beauty subcategories with the highest percentage of items with a price decrease per US retailer were:

    ● Amazon US: Fragrance (32.4%), oral care (27.1%) and skin care (22.7%)
    ● Sephora: Makeup (0.2%)
    ● Target: Oral care (2.7%), baby care (1.6%) and hair care (0.8%)
    ● Ulta: Makeup (3.7%), skin care (0.8%) and hair care (0.1%)
    ● Walmart: Fragrance (35.3%), hair care (27.0%) and skin care (23.1%)

    By far, the UK retailer with the overall highest percentage of items with a price decrease for Prime Day was Amazon UK (41.9%).

    Health & beauty subcategories with the highest percentage of items with a price decrease per UK retailer were:

    ● Amazon UK: Oral care (62.5%), fragrance (46.2%) and hair care and skin care (both 45.4%)
    ● Ebay: Makeup (8.1%), fragrance (7.1%) and skin care (4.7%)
    ● Etsy: Oral care (3.4%), skin care (2.3%) and makeup (0.9%)

    In Europe, Amazon Germany had the overall highest percentage of items with a price decrease for Prime Day (33.1%).

    Health & beauty subcategories with the highest percentage of items with a price decrease per European retailer were:

    ● Amazon France: Skin care (27.2%), fragrance (25.0%) and hair care (24.4%)
    ● Amazon Germany: Skin care (49.7%), fragrance (41.3%) and hair care (37.2%)
    ● Amazon Italy: Skin care (5.9%)

    Across the Middle East & Asia, Amazon UAE had the overall highest percentage of health & beauty items with a price decrease for Prime Day (54.1%).

    Health & beauty subcategories with the highest percentage of items with a price decrease per retailer were:

    ● Amazon Saudi Arabia: Health care (56.4%), hair care (48.8%) and fragrance (46.2%)
    ● Amazon UAE: Skin care (64.3%), fragrance (64.0%), and hair care (58.4%)
    ● Amazon Japan: Oral care (5.3%), skin care (4.5%) and makeup (3.3%)
    ● Amazon Singapore: Baby care and health care (both 35.6%), makeup (32.6%) and fragrance (31.3%)

    Magnitude of price decrease

    The US retailer with the greatest overall magnitude of price decrease for Prime Day was Ulta (33.3%).

    The health & beauty subcategories with the greatest magnitude of price decrease per US retailer were:

    ● Amazon US: Hair care (18.0%), baby care (15.5%) and health care (15.4%)
    ● Sephora: Makeup (24.5%)
    ● Target: Hair care (46.6%), oral care (28.4%) and skin care and baby care (both 15.0%)
    ● Ulta: Hair care (40.8%), skin care (34.0%) and makeup (32.5%)
    ● Walmart: Baby care (11.5%), skin care (11.3%) and hair care (11.0%)

    Brands with the greatest magnitude of price decreases per US retailer included:

    ● Amazon US: Cerave (54.5%), Aquaphor (54.4%) and Yankee Candle (50.7%)
    ● Sephora: Nars (25.9%) and Marc Jacobs Beauty (23.1%)
    ● Target: Kristin Ess (50.0%), Hot Tools (48.6%) and Arc Oral Care (both 40.1%)
    ● Ulta: KKW Beauty, Lime Crime, Ulta and NYX Professional Makeup (all 50.0%), CoverGirl (47.8%) and Biolage (40.8%)
    ● Walmart: Whitening Toothpaste (57.6%), Absolute New York (56.7%) and Zdmathe (48.5%)

    The UK retailer with the greatest overall magnitude of price decrease on health & beauty products for Prime Day was Amazon UK (18.6%).

    The health & beauty subcategories with the greatest magnitude of price decrease per UK retailer were:

    ● Amazon UK: Oral care (23.5%), makeup (22.0%) and skin care (20.2%)
    ● Ebay: Hair care (16.0%), fragrance (14.4%) and makeup (11.5%)
    ● Etsy: Makeup (20.0%), oral care (16.1%) and skin care (13.3%)

    Brands with the greatest magnitude of price decreases across health & beauty sub-categories per UK retailer included:

    ● Amazon UK: Philips Sonicair (56.8%), BaByliss For Men (53.0%) and Dr. PawPaw (52.9%)
    ● Ebay: Oral-B Braun (50.3%), Clean (50.1%) and Versace (46.0%)
    ● Etsy: Valdenize (both 48.4%), Allure Wedding Jewelry (32.8%) and Moroccan White (30.0%)

    Among European retailers, Amazon Germany offered the greatest overall magnitude of price decrease on health & beauty products for Prime Day (20.2%).

    The health & beauty subcategories with the greatest magnitude of price decrease per European retailer were:

    ● Amazon France: Skin care (20.4%), baby care (17.8%) and makeup (16.2%)
    ● Amazon Germany: Skin care (28.2%), makeup (22.6%) and health care (21.3%)
    ● Amazon Italy: Skin care (9.9%)

    Brands with the greatest magnitude of price decreases per European retailer included:

    ● Amazon France: Look Concept (59.8%), Douyao (57.5%) and Eco Styler (57.4% for both hair care and health care)
    ● Amazon Germany: Le Cuisinier (58.4%), Beurer (47.5%) and Solida (45.9%)
    ● Amazon Italy: Bezox (9.9%)

    Across the Middle East and Asia, Amazon Japan offered the greatest overall magnitude of price decrease on health & beauty products for Prime Day (18.0%).

    The health & beauty subcategories with the greatest magnitude of price decrease per retailer were:

    ● Amazon Saudi Arabia: Health care (25.1%), skin care (19.3%) and baby care (16.5%)
    ● Amazon UAE: Makeup (23.1%), hair care (18.3%) and baby care (18.1%)
    ● Amazon Japan: Skin care (27.6%), hair care (17.2%) and makeup (14.7%)
    ● Amazon Singapore: Skin care (10.1%), hair care (8.9%) and health care (8.2%)

    Brands with the greatest magnitude of price decreases per retailer in the Middle East and Asia included:

    ● Amazon Saudi Arabia: Tide (59.8%), bblüv (58.3%) and Mas (55.0%)
    ● Amazon UAE: Syoss (59.7%), Vertex (59.1%) and Onesea (58.7%)
    ● Amazon Japan: ドクターブロナー (Dr. Bronner’s) (54.8%), ゼルマ (Zelma) (50.0%) and いち髪 (47.8%)
    ● Amazon Singapore: Changing Lifestyles (56.6%), Dynarex (53.0%) and Grohe (52.5%)

    Discounts before, during and after the event

    In the US, specialty beauty retailers’ discounts stood out during Prime Day sales. The US retailer with the biggest overall health & beauty discount before (43.4%), during (39.3%) and after (39.4%) Prime Day was Ulta. During and after Prime Day, Sephora was a close second at 38.2% for both periods.

    Ulta’s biggest pre-event discounts were on makeup (44.2%) and skin care (33.0%). Hair care (40.8%). makeup (39.5%) and skin care (36.0%) were the leading subcategories for Ulta’s discounts during Prime Day. After the event, Ulta’s biggest discounts were for hair care (40.8%), makeup (39.6%) and skin care (37.3%)

    In the UK, OnBuy offered the highest discounts before, during and after Prime Day at 70.0% off baby care products. Yet the total product count was only 2.

    Among the remaining rivals, all of whom had a product count above 1000, Ebay had the highest discounts before (30.8%), during (33.8%) and after (35.0%) Prime Day.

    Before Prime Day, Ebay biggest discounts were on hair care (48.9%), fragrance (23.9%) and makeup (23.4%). Ebay’s top discounts during Prime Day were on hair care (50.3%), makeup (24.9%) and fragrance (24.8%). Similarly, after the event, Ebay had the biggest discounts on hair care (49.9%), makeup (26.7%) and fragrance (26.2%).

    Across retailers in the Middle East & Asia, Amazon UAE offered the biggest overall health & beauty discounts before (26.0%), during (30.7%) and after (26.0%) Prime Day.

    In the pre-sales event, Amazon UAE offered the most generous discounts on makeup (30.7%), fragrance (29.9%) and health care (29.2%).

    During Prime Day, Amazon UAE’s biggest discounts were on makeup (37.2%), fragrance (31.6%) and health care (31.3%).

    During Prime Day, Amazon UAE offered the biggest discounts on fragrance (30.5%), makeup (30.0%) and health care and baby care (both 26.8%).

    Popularity

    In the US, among health & beauty products with high popularity, Amazon US offered the highest percentage of items with a price decrease (24.3%) and Target offered the greatest magnitude of price decrease (29.3%).

    For health & beauty items with medium popularity, Amazon US offered the highest percentage of items with a price decrease (26.0%), and strategic partners Target and Ulta both offered the greatest magnitude of price decrease (33.4%).

    Among health & beauty merchandise with low popularity, Walmart offered the highest percentage of items with a price decrease (18.5%) and Ulta offered the greatest magnitude of price decrease (37.4%).

    Amazon UK stood out among all levels of health & beauty product popularity.

    In the UK, among health & beauty products with high popularity, Amazon UK offered both the highest percentage of items with a price decrease (42.8%) and the greatest magnitude of price decrease (18.6%).

    For health & beauty items with medium popularity, Amazon UK offered the highest percentage of items with a price decrease (42.8%) and Etsy offered the greatest magnitude of price decrease (20.1%).

    Among health & beauty merchandise with low popularity, Amazon UK offered both the highest percentage of items with a price decrease (35.4%) and the greatest magnitude of price decrease (17.6%).

    In Europe, Amazon Germany dominated discounts for health & beauty products across all levels of popularity.

    Among health & beauty goods with high popularity, Amazon Germany offered both the highest overall percentage of items with a price decrease (34.6%) and the greatest overall magnitude of price decrease (20.6%).

    For health & beauty items with medium popularity, Amazon Germany offered both the highest percentage of items with a price decrease (32.7%) and the greatest magnitude of price decrease (20.0%).

    Among health & beauty merchandise with low popularity, Amazon Germany offered the highest percentage of items with a price decrease (33.5%) and the greatest magnitude of price decrease (20.6%).

    In Middle East & Asia, among health & beauty items with high popularity, Amazon UAE offered the highest overall percentage of items with a price decrease (53.0%) and Amazon Saudi Arabia had the greatest overall magnitude of price decrease (18.7%).

    For health & beauty products with medium popularity, Amazon UAE offered the highest overall percentage of items with a price decrease (55.3%) and Amazon Saudi Arabia had the greatest overall magnitude of price decrease (19.7%).

    Among health & beauty goods with low popularity, Amazon UAE offered the highest percentage of items with a price decrease (54.5%) and Amazon Japan had the greatest magnitude of price decrease (18.7%).

    Health & beauty’s stunning Prime Day deals

    Overall, Prime Day 2021 gave shoppers around the world the opportunity to score generous discounts on health & beauty products.

    According to our analysis, the retailers whose Prime Day pricing stood out the most were Amazon US and Ulta in the US, Amazon UK and Ebay in the UK, Amazon Germany in Europe, Amazon UAE in the Middle East and Amazon Japan in Asia.

    Stay tuned for Prime Day 2021 international fashion and home goods pricing insights.

  • Who Won Prime Day 2021’s Consumer Electronics Price War?

    Who Won Prime Day 2021’s Consumer Electronics Price War?

    Amazon’s Prime Day 2021 global shopping event took place June 21 and 22, 2021 and smashed previous sales records. At DataWeave, we wanted to know how Prime Day 2021 deals and discounts on electronics compared across retailers and regions. We focused on how retailers adapted their Prime Day pricing strategies to stand out in the competitive consumer electronics category.

    Our Methodology
    We tracked the pricing of several leading retailers in nine countries across five regions, including:

    • The US (Amazon US, Best Buy, Target and Walmart)
    • The UK (Amazon UK, Ebay, Etsy and OnBuy)
    • Europe (Amazon France, Amazon Germany and Amazon Italy)
    • The Middle East (Amazon Saudi Arabia and Amazon UAE)
    • Asia (Amazon Japan and Amazon Singapore)

    Let’s see how retailers used pricing tactics to gain a competitive advantage during Prime Day, as well as which electronics brands had the highest discounts around the world.

    Percentage of items with a price decrease

    The US retailer with the overall highest percentage of items with a price decrease for Prime Day was Amazon US (26.3%).

    Electronics subcategories with the highest percentage of items with a price decrease per US retailer were:

    • Amazon US: Headphones (36.3%), video games and electronics (both 35.8%) and cameras (35.7%)
    • Best Buy: Wearables (10.1%) and electronics (6.8%)
    • Target: Electronics (21.4%), Bluetooth & wireless speakers (19.4%) and tech accessories (14.8%)
    • Walmart: Cell phones & accessories (25.8%), TV & video (14.9%) and cameras (9.4%)

    The UK retailer with the overall highest percentage of items with a price decrease for Prime Day was Amazon UK (30.4%), closely followed by OnBuy (30.0%). Of note, Amazon UK offered price increases on 10 times as many products as OnBuy (2379 vs. 237).

    Electronics subcategories with the highest percentage of items with a price decrease per UK retailer were:

    • Amazon UK: Electronics (53.2%), cameras (41.8%) and headphones (41.5%)
    • Ebay: TV & video (19.2%), computers & office (12.2%) and cell phones & accessories (7.5%)
    • Etsy: Electronics (1.4%)
    • OnBuy: Cell phones & accessories (35.5%) and wearables (4.8%)

    In Europe, Amazon Germany had the overall highest percentage of items with a price decrease for Prime Day (28.9%).

    Electronics subcategories with the highest percentage of items with a price decrease per European retailer were:

    • Amazon France: Cell phones & accessories (35.2%), electronics (21.0%) and cameras (19.6%)
    • Amazon Germany: Cell phones & accessories (43.3%), electronics (42.5%) and headphones (39.2%)
    • Amazon Italy: Headphones (25.0%), cell phones & accessories (14.3%) and Bluetooth & wireless speakers (8.3%)

    Across the Middle East & Asia, Amazon UAE had the overall highest percentage of items with a price decrease for Prime Day (36.1%).

    Electronics subcategories with the highest percentage of items with a price decrease per retailer were:

    • Amazon Saudi Arabia: Video games (47.8%), electronics (46.8%) and cell phones & accessories (41.2%)
    • Amazon UAE: Electronics (63.4%), tech accessories (60.3%) and cell phones & accessories (60.1%)
    • Amazon Japan: TV & video (14.9%), musical instruments (11.8%) and electronics (10.3%)
    • Amazon Singapore: Wearables (32.2%), car electronics (30.4%) and musical instruments (30.2%)

    Magnitude of price decrease

    The US retailer with the greatest overall magnitude of price decrease for Prime Day was Target (18.6%).

    The electronics subcategories with the greatest magnitude of price decrease per US retailer were:

    • Amazon US: Cell phones & accessories (20.4%), electronics (20.1%) and headphones (18.7%)
    • Best Buy: Wearables (16.9%) and electronics (13.3%)
    • Target: Video games (27.9%), tech accessories (25.6%) and headphones (24.4%)
    • Walmart: TV & video (10.6%), computers & office (10.3%) and home audio & theater (10.1%)

    Brands with the greatest magnitude of price decreases per US retailer included:

    • Amazon US: JBL (57.2%), Hape (55.0%) and Falcon (52.8%)
    • Best Buy: Bower (40.0%), Vtech (36.7%) and Wingthings (30.0%)
    • Target: 2k Sports and 2K Games (both 56.7%), Little Tikes (50.0%)
    • Walmart: Moonlite (54.3%), Sceptre (45.9%) and Polaroid (38.5%)

    The UK retailer with the greatest overall magnitude of price decrease for Prime Day was OnBuy (22.1%).

    The electronics subcategories with the greatest magnitude of price decrease per UK retailer were:

    • Amazon UK: Home audio & theater (28.4%), electronics (21.7%) and cell phones & accessories (20.1%)
    • Ebay: TV & video (19.5%), wearables (18.7%) and computer & office (16.6%)
    • Etsy: Electronics (14.8%)
    • OnBuy: Cell phones & accessories (22.3%) and wearables (5.9%)

    Brands with the greatest magnitude of price decreases across electronics categories per UK retailer included:

    • Amazon UK: Amazon (58.3% for both cell phones & accessories and headphones), Flexson (55.4%) and Ibra (47.9% for both cameras and TV & video)
    • Ebay: Falcon (52.0%), Ticwatch (48.4%) and Grougs by Live Lead (47.9%)
    • OnBuy: Sony (33.1%), Apple (23.1%) and Samsung (21.3%)

    Among European retailers, Amazon Italy offered the greatest overall magnitude of price decrease for Prime Day (18.9%) among a total of 66 products.

    The electronics subcategories with the greatest magnitude of price decrease per European retailer were:

    • Amazon France: Home audio & theater (13.1%), video games (10.5%) and TV & video (10.3%)
    • Amazon Germany: Video games (22.0%), electronics and musical instruments (both 20.3%) and cell phones & accessories (20.2%)
    • Amazon Italy: Cell phones & accessories (41.2%) and headphones (28.1%)

    Brands with the greatest magnitude of price decreases per European retailer included:

    • Amazon France: DCSk (59.0%), Amazon Basics (58.7%) and Qoosea (46.3%)
    • Amazon Germany: Meister (53.5%), Rampow (51.4%) and Gewa (51.1%)
    • Amazon Italy: Homscam (41.2%) and Gamurry (15.1)

    Across the Middle East and Asia, Amazon Japan offered the greatest overall magnitude of price decrease for Prime Day (11.9%) among a total of 66 products.

    The electronics subcategories with the greatest magnitude of price decrease per retailer were:

    • Amazon Saudi Arabia: Video games (16.8%), electronics (12.8%) and cell phones & accessories (12.4%)
    • Amazon UAE: Car electronics, headphones and tech accessories (all 13.1%)
    • Amazon Japan: Headphones (25.4%), home audio & theater (23.6%) and cameras (14.9%)
    • Amazon Singapore: Car electronics (9.4%), cell phones & accessories (8.6%) and electronics (8.3%)

    Brands with the greatest magnitude of price decreases per retailer in the Middle East and Asia included:

    • Amazon Saudi Arabia: Belkin (52.9%), Topoint (49.3%) and Promate (45.7%)
    • Amazon UAE: Amazon Basics (56.3%), Bettyliss (52.1%) and Acreate (50.3%)
    • Amazon Japan: タニタ(Tanita) (49.7%), Laza-Vally (47.8%) and Nebula (33.3%)
    • Amazon Singapore: Pintech Percussion (55.0%), Pac (53.4%) and Goldwood Sound Inc. (52.9%)

    Discounts before, during and after the event

    The US retailer with the biggest overall electronics discount before Prime Day was Amazon US (26.6%). Amazon’s biggest discounts were on home audio & theater (30.5%), TV & video (29.1%) and cell phones & accessories (28.6%).

    Walmart offered the biggest discounts during (32.1%) and after (31.5%) the event. During the event, Walmart’s biggest discounts were on cell phones & accessories (46.3%), home audio & theater (35.5%) and computers & office (31.0%). Similarly, after the event, Walmart’s biggest discounts were on cell phones & accessories (45.8%), home audio & theater (35.3%) and computers & office (30.8%).

    OnBuy was the UK retailer with the biggest overall electronics discount before (65.3%), during (68.6%) and after (69.1%) Prime Day with a product count of 237. OnBuy’s biggest discounts were on cameras (69.5% before, during and after the sales event), cell phones & accessories (rising from 67.6% before the sales event to 71.6% during and 71.8% after the event) and wearables (65.2% before and after the event yet 35.5% during Prime Day).

    In Europe, Amazon Germany offered the biggest overall electronics discount before (21.4%), during (25.6%) and after (20.2%) Prime Day. Amazon France and Amazon Italy also offered comparable overall discounts (21.1%) before Prime Day.

    In the pre-sales event, Amazon Germany gave the most generous discounts on cameras (34.1%), wearables (24.7%) and headphones (24.3%). During Prime Day, Amazon Germany offered the biggest discounts on video games (30.7%), headphones (30.1%) and electronics (28.3%). After Prime Day, Amazon Germany offered the biggest discounts on headphones (24.2%) electronics (22.6%) and cell phones & accessories (22.1%).

    Across retailers in the Middle East & Asia, Amazon UAE offered the biggest overall electronics discount before Prime Day (24.3%), whereas Amazon Japan offered the biggest discount during (32.0%) and after (32.3%) Prime Day.

    In the pre-sales event, Amazon UAE offered the most generous discounts on TV & video (31.3%), musical instruments (31.0%) and headphones (25.4%). During and after Prime Day, Amazon Japan offered the biggest discounts on Bluetooth & wireless speakers and electronics (both 99.0%).

    Popularity

    In the US, among electronics with high popularity, Amazon US offered the highest percentage of items with a price decrease (27.8%) and Target offered the greatest magnitude of price decrease (21.3%).

    For electronics with moderate popularity, Amazon US offered the highest percentage of items with a price decrease (24.7%) and Best Buy offered the greatest magnitude of price decrease (15.4%).

    Among electronics with low popularity, Amazon US offered the highest percentage of items with a price decrease (25.2%) and Best Buy offered the greatest magnitude of price decrease (13.4%), closely followed by Amazon US (13.3%).

    In the UK, among electronics with high popularity, OnBuy offered the highest percentage of items with a price decrease (44.0%) among 84 products and Etsy offered the greatest magnitude of price decrease (28.5%) among 150 products.

    For electronics with moderate popularity, Amazon UK offered the highest percentage of items with a price decrease (29.3%) and OnBuy offered the greatest magnitude of price decrease (26.7%).

    Electronics with low popularity, Amazon UK offered the highest percentage of items with a price decrease (23.9%) and OnBuy offered the greatest magnitude of price decrease (26.3%).

    In Europe, among electronics with high popularity, Amazon Germany offered the highest overall percentage of items with a price decrease (30.1%) and the greatest overall magnitude of price decrease (20.2%).

    For electronics with moderate popularity, Amazon Germany offered the highest percentage of items with a price decrease (29.7%) and Amazon Italy offered the greatest magnitude of price decrease (41.2%) among 12 products.

    Among electronics with low popularity, Amazon Germany offered the highest percentage of items with a price decrease (26.7%) and the greatest magnitude of price decrease (17.2%).

    In Middle East & Asia, among electronics with high popularity, Amazon Singapore offered the highest overall percentage of items with a price decrease (32.2%) and Amazon Saudi Arabia had the greatest overall magnitude of price decrease (10.4%).

    For electronics with moderate popularity, Amazon UAE offered the highest percentage of items with a price decrease (48.8%) and Amazon Japan offered the greatest magnitude of price decrease (11.5%).

    Similarly, among electronics with low popularity, Amazon UAE offered the highest percentage of items with a price decrease (36.9%) and Amazon Japan the greatest magnitude of price decrease (13.3%).

    Consumers won big on Prime Day 2021

    Overall, Prime Day 2021 offered a wide range of deals across the competitive electronics category in each region. Almost all of the retailers we studied (except for Ebay) showed up in the analysis for offering notable discounts and pricing strategies this year. Amazon US, OnBuy, Amazon Germany, Amazon Japan and Amazon UAE appeared in the results most often among their respective regions. Stay tuned for Prime Day 2021 pricing insights across other categories, including home, health & beauty and fashion.

  • Dazzle Dad With Electronics & Home Goods for Father’s Day

    Dazzle Dad With Electronics & Home Goods for Father’s Day

    This year, shoppers will skip neckties and celebrate Dad with gifts for his home office or man cave.

    As our personal and professional lives grow increasingly digital and tied to our homes, retailers face new seasonal sales opportunities. Retailers whose assortments contain in-demand electronics and home products can drive more e-commerce sales revenue and gain a competitive edge in time for Father’s Day 2021.

    According to the NRF, Father’s Day spending is expected to hit $20.1 billion, up 18% from 2020’s total of $17 billion. The vast majority (75%) of Americans plan to celebrate the fathers, husbands and other paternal figures in their life this Father’s Day.

    Popular products dads will love

    Retailers can inspire Father’s Day shoppers by filling their assortments with in-demand electronics and home products, as these two categories continue to boom.

    Consider these recent results related to electronics and home goods:

    • Online sales of consumer electronics grew 18% year-over-year in 2020 as more consumers work, shop and enjoy entertainment in the comfort of their homes. 
    • To win more sales on Black Friday 2020, certain retailers offered attractive deals and deep discounts on electronics like laptops, mobiles, wearables, USB flash drives, tablets and headphones.
    • Home furnishings sales rose 12% year-over-year in 2020 as homebound consumers invested in products for domestic comfort, organization and functional purposes. 
    • On Cyber Monday 2020, home merchandise saw bustling sales, as storage items, cabinets and bookcases were among the most competitively priced products in the category.

    Since home is the new hub, retailers can plan their assortments to align with this enduring consumer trend to outplay rivals. Optimizing their product mix involves making decisions on the right balance among bestsellers, hot trends, unique products and essential items to gain a competitive advantage.

    Grab shoppers’ attention with desirable promotions 

    Although shoppers appreciate variety, the abundance of product choices available online can overwhelm consumers. In response, retailers can craft persuasive and timely digital campaigns to help simplify the customer experience.

    Digital promotions, including banner ads and search campaigns, can help retailers spark a sense of urgency that motivates shoppers to buy. The key is for retailers to connect to consumers with the right messaging, timing and targeting to earn their attention, trust and sales. Retailers need effective promotions to optimize their ad spend.

    Pricing secures the sale

    To maximize top line performance, retailers also need to nail their Father’s Day pricing strategies.

    Notably, consumption habits and loyalty have dramatically shifted during the pandemic, which has affected retailers’ pricing strategies. Value pricing continues to soar due to economic uncertainty, job losses and a growing desire for value for money. Last year, 30% of consumers switched to a new brand due to better prices, while 25% cited better value as the reason they switched, according to McKinsey & Company. 

    On the other side of the socioeconomic spectrum, premium pricing is also on the rise. Upscale shoppers are now more willing to splurge on high quality goods, including home furnishings and electronics. These consumers will pay more for merchandise that adds value or purpose to their lives. In addition, digitally-savvy Gen Z and Millennial consumers are spending 125% as much as they did in 2019. As a result, retailers that capitalize on consumers’ enthusiasm and price elasticity will drive incremental e-commerce revenue gains.

    As e-commerce competition intensifies and informed, empowered shoppers know where to find the best prices, more retailers now seek a new pricing approach to stand out, drive sales growth and protect against price wars.

    Drive revenue with the right products, promotions and prices 

    To win the attention and sales of Father’s Day shoppers, more leading retailers now use data insights to make faster, more effective assortment and pricing decisions that maximize their e-commerce sales.

    Data analytics help retailers know which products consumers will actually buy. Leading global retailers rely on Assortment Analytics from DataWeave to ensure their online assortments keep up with evolving consumer needs. Building a competitive product mix can set retailers apart and boost e-commerce sales by offering in-demand merchandise. Assortment analytics give retailers insights on the most popular brands and products on any e-commerce website, and help them spot and fill any assortment gaps to capture more sales.

    To captivate online shoppers’ attention, retailers use DataWeave’s Promotional Insights to lower acquisition costs with marketing promotions that resonate. As online shoppers increasingly seek timely offers, these insights help retailers quickly evaluate the effectiveness of their promotions and optimize their digital ad spend. Retailers gain near-real-time insights on the brands, categories and products their rivals promote, including campaign frequency, duration and messaging for promotions that convert.

    Major retailers also turn to Pricing Intelligence from DataWeave to promptly adapt to rivals’ online price changes and shifts in consumer demand. Retailers drive more revenue and margin by easily identifying fluctuations in consumer demand and rivals’ pricing, as well as any gaps. Retailers gain an edge by seeing pricing patterns that their rivals miss. They gain accurate exact and similar product matching, and near real-time pricing updates to stay competitive and fuel e-commerce conversions.

    Data insights help retailers delight dads

    This Father’s Day, retailers can apply data insights to offer consumers eye-catching promotions of in-demand electronics and home products at the right price to wow dads. Insights from DataWeave can help retailers make smart, competitive assortment, promotion and pricing decisions that boost agility, improve the customer experience and drive e-commerce sales for this special occasion – and all year long.

  • As Value Shopping Soars, Pricing Matters More

    As Value Shopping Soars, Pricing Matters More

    The pandemic’s profound economic impact sparked a surge in value shopping.

    Between February and December 2020, 10 million Americans lost their jobs.1 Due to the pandemic, 36% of lower-income adults and 28% of middle-income adults lost a job or took a pay cut (vs. 22% of upper-income adults). In addition, less than a quarter of lower-income adults have three months’ worth of emergency funds (vs. 48% of middle-income adults and 75% of upper-income adults).2

    These financial shifts matter to retailers, as lower- and middle-income households account for 81% (29% and 52%, respectively) of the total U.S. population.3 Reduced disposable income among households like these has led more consumers to embrace bargain-hunting as a shopping habit.

    We’ll see why price sensitive consumers are influencing retailers to adjust their e-commerce pricing strategies to stay competitive and responsive.

    Consumers seek value across retail categories

    Recent research shows 50% of U.S. adults are more sensitive to product prices now than before the pandemic. Also, 80% of U.S. shoppers are taking at least one action to seek more value when they shop for groceries, prioritizing value for money over speed.4

    According to McKinsey, 65% of consumers cited value as one of their top three reasons they switched brands during the pandemic. Also, 40% of shoppers cited a desire for better value and 38% cited better prices or promotions as reasons for choosing new products.5

    Value-oriented pricing influences purchases, as 70% of consumers said product discounts are more important today compared to a year ago. In addition, 54% of consumers said better online deals and discounts are a leading factor that persuades them to choose a specific retailer.6

    As e-commerce explodes, consumers have greater access to information. They can find the best price across online sites and receive notifications when a product’s price drops before they buy.

    Retailers face intense pricing pressure

    Similar to the aftermath of the 2008 recession, discounters and dollar chain retailers are now thriving as consumers seek superior value for money. Consumers need new products yet they no longer want to spend as much as before.

    That’s why bargain retail is poised to be among the biggest winners in 2021 as consumers get out and socialize more. 7

    Dollar General continues to aggressively expand its omnichannel reach as value shopping soars.8 To stay competitive, Family Dollar has partnered with Instacart on same-day delivery.9 In the fierce grocery sector, hard discounter Aldi’s omnichannel expansion includes a focus on private labels and efficient operational processes that improve cost effectiveness and competitive pricing.10

    Across retail categories, a remarkable 50 million price changes take place online every day. Given consumers’ shift to value shopping, more retailers are changing their pricing to offer discounts both online and in-store.11 However, to avoid costly price wars, more retailers are now taking a renewed approach to their pricing strategies to protect their margins as they compete.

    Specifically, to optimize their e-commerce business for profitable growth, more retailers are modernizing their pricing strategies with data insights.

    Pricing intelligence is retailers’ secret weapon 

    As e-commerce rivalry heats up, retailers must evaluate pricing across more online websites to keep their own prices competitive. This process is becoming increasingly complex and time consuming. Meanwhile, retailers may consider adopting aggressive pricing tactics to win online sales. Yet this pricing strategy is unsustainable over the longer term, as it erodes profit margins.

    Today’s heated e-commerce rivalry means retailers can no longer afford to guess at price points or use the same pricing tactics that relied on before the pandemic.

    That’s why leading retailers turn to data insights for their pricing strategies to stay agile and flexible while rapidly adapting to fluctuations in consumer demand and competitors’ pricing.

    Now more retailers turn to DataWeave’s Pricing Intelligence to drive more revenue and margin.

    To optimize profit margins, retailers use our actionable insights to make pricing decisions according to data-driven recommendations. They also make decisions to protect their desired price perception.

    Monitoring competitors’ pricing moves helps retailers benchmark their own performance, identify gaps and respond to market trends faster. They can also refer to historic pricing data analytics to accurately anticipate and counter rivals’ next moves to gain an edge.

    Retailers that apply data insights to optimize their pricing can drive more online revenue by finding the ideal price consumers are willing to pay while still maintaining profitability. Pricing intelligence can make customer acquisition more efficient, and help retailers grow online sales and market share. 

    Amid greater price sensitivity, retailers’ pricing strategies are evolving to use data to adapt to consumers’ needs and drive e-commerce sales and profitability. DataWeave’s Pricing Intelligence gives retailers an edge so they stay agile and competitive, and maximize e-commerce sales across consumers of all economic levels.

    1. Howland, Daphne. The middle class is stressed and the pandemic isn’t helping. Retail Dive. January 20, 2021.
    2. Howland, Daphne. The middle class is stressed and the pandemic isn’t helping. Retail Dive. January 20, 2021.
    3. Bennett, Jesse, Richard Fry and Rakesh Kochhar. Are you in the American middle class? Find out with our income calculator. Pew Research Center. July 23, 2020.
    4. Maake, Katishi. DoorDash, Instacart Eye Launching Credit Cards. The Harris Poll. April 9, 2021.
    5. Charm, Tamara, Harrison Gillis, Anne Grimmelt, Grace Hua, Kelsey Robinson and Ramiro Sanchez Caballero.Survey: US consumer sentiment during the coronavirus crisis. McKinsey & Company. May 13, 2021.
    6. Berthiaume, Dan. Survey: Deals drive purchases during pandemic. Chain Store Age. March 18, 2021.
    7. Thomas, Lauren. Beauty and bargain retail could be the biggest winners in 2021, Wells Fargo predicts. CNBC. March 25, 2021.
    8. Unglesbee, Ben. Dollar General ramps up expansion of Popshelf concept. Retail Dive. March 19, 2021.
    9. Ryan, Tom. Will same-day delivery pay off for dollar stores? RetailWire. February 8, 2021.
    10. Anderson, George. Should Aldi’s growing store count and digital progress keep rivals up at night? RetailWire. February 11, 2021.
    11. Berthiaume, Dan. Survey: Deals drive purchases during pandemic. Chain Store Age. March 18, 2021
  • Food Delivery Gives Moms a Delicious Break On Mother’s Day

    Food Delivery Gives Moms a Delicious Break On Mother’s Day

    Moms deserve a scrumptious celebration. In time for Mother’s Day, restaurants and their food delivery partners can unburden mothers from the chore of cooking by delivering the gifts of ease, convenience and nourishment.

    Over the past year, moms have been starved for time amid the disruption of working from home and supporting their children’s virtual schooling. Meanwhile, grandmothers have been starved for social connection, as many of them have only seen their loved ones on Zoom.

    Restaurants can satisfy consumers’ unmet needs. Using timely, empathetic digital marketing can help restaurant operators stand out on food delivery apps (like DoorDash, Uber Eats, Grubhub and Postmates) and sell more online this Mother’s Day – and all year round.

    Delight moms with what they really want

    According to the NRF, 83% of consumers plan to celebrate Mother’s Day in 2021. On average, shoppers plan to spend $220.48 (up $16 since last year), the highest amount in the history of NRF’s Mother’s Day surveys. 1


    Most (62%) moms say they would love to eliminate the chore of cooking on Mother’s Day. Dinner is the most important meal on Mother’s Day, and most moms prefer restaurant meals (53%) to home cooked meals (39%). 2

    Given consumers’ willingness to spend and Mom’s appetite for restaurants, Mother’s Day 2021 is poised to be a powerful sales event for restaurants.

    Restaurants need new ways to navigate market trends

    The restaurant industry faces consolidation, as 17% (110,000) of U.S. restaurants permanently closed in 2020, and 87% of full-service restaurants reported an average 36% drop in revenue. 3 These figures prove restaurant operators need help to boost their top line and cut costs as they adapt to intense rivalry and shifting market conditions.

    During the pandemic, many consumers have embraced home for health or financial reasons or a creative outlet. Although 55% of consumers have been eating at home more often since the pandemic began, 65% say they are tired of cooking at home. 4

    Fortunately, consumers are in a celebratory mood. Last year, Mother’s Day was a top sales day, as consumer spending at restaurants soared 103% on Mother’s Day Sunday and 63% on Saturday. 5 Restaurants can relieve consumers of the chore of cooking and add variety to dining occasions like Mother’s Day.


    Successful restaurants gain a digital data advantage

    To satisfy consumers’ needs and outplay rivals, restaurants now turn to data analytics from DataWeave to protect their profitability with effective pricing, menu and promotion decisions. 

    Pricing analytics

    Restaurant operators can optimize their pricing to stay competitive. For instance, restaurants can compare their offerings and delivery fees with those of rivals to pinpoint and fill any gaps. Monitoring rivals’ pricing moves also helps restaurant operators stay flexible by keeping their prices affordable, so they can attract online sales growth.

    Menu analytics

    To minimize costs, more restaurants are streamlining their menus. Menu analytics can help operators spot the optimal mix of bestselling items and emerging food trends, like plant-based, vegan, gluten-free and local sourcing. To know which items to keep, operators can even use data insights on menu items down to the ZIP code level to localize their offerings and adapt to diverse tastes to drive online sales.

    Promotion analytics

    As consumers embrace home entertaining this Mother’s Day, restaurant operators can use data insights to boost sales. They can monitor rivals’ moves and compare their promotional strategies with those of competitors. Evaluating their digital marketing performance (like their brand’s discoverability and visibility ranking on food apps’ homepages) helps restaurants show up more prominently online and sell more.


    Savvy restaurants welcome celebrations as lucrative sales occasions

    Restaurants can spice up Mom’s life by letting her relax and receive the gifts of tasty meals, time savings and family festivities. Operators can simplify Mother’s Day celebrations by giving consumers a hassle-free dining experience so families can focus on connecting rather than cooking.

    For a business advantage, restaurant operators can apply digital marketing insights to boost their agility in responding to consumers’ needs and rivals’ moves.

    To stay agile and competitive as the food delivery market booms, leading restaurant chains and food delivery providers are collaborating with DataWeave to make data-driven pricing, menu and promotional decisions that fuel online sales.

    1 Retail Holiday and Seasonal Trends: Mother’s Day. NRF. 2021
    2 New Study Shows What Moms Really Want On Mother’s Day. US Foods. May 2020.
    3 Valinsky, Jordan. 10,000 of America’s restaurants have closed in the past three months. CNN. December 9, 2020.
    4 Contreras, Tricia. How the pandemic is shaping home cooking trends. SmartBrief. September 30, 2020.
    5 Lalley, Heather. Despite pandemic, Mother’s Day was huge for restaurants. Restaurant Business. May 18, 2020.

  • This Mother’s Day, Dazzle Moms With These Beauty & Fashion Trends

    This Mother’s Day, Dazzle Moms With These Beauty & Fashion Trends

    Show moms extra love this year. With Mother’s Day coming up fast, savvy beauty and fashion brands will use this special occasion to inspire pampering and gift giving to fuel their e-commerce sales growth.


    This year, beauty and fashion are poised to boom, as 40% of consumers plan to buy beauty products and 37% will buy new outfits for going out. 1 According to eMarketer, apparel and accessories e-commerce sales will grow nearly 19% this year due to pent-up demand for clothing, while health and beauty sales will rise 16%. 2

    “People will be happy to go out again …
    there will be a fiesta in makeup and in fragrances.”

    ~L’Oréal CEO and Chairman Jean-Paul Agon

    After beauty and apparel sales declined last year, brands now seize every opportunity to capitalize on the categories’ resurgence in 2021. To differentiate their goods, brands can use e-commerce marketing best practices to position their fashion and beauty items as spectacular gifts that moms will love.


    Aligning with the latest trends can help brands boost online growth.

    Hot trends dominating beauty and fashion

    This Mother’s Day, shoppers can delight moms with beauty bestsellers like:

    • Mask-friendly makeup: As we continue to wear masks over the short-term, cosmetics like false lashes, smudge-proof mascara and ultra-hypoallergenic eyeshadow will remain popular. 3
    • Fragrances: Online fragrance sales rose 45% year-over-year in 2020. Clean and organic beauty categories grew 56% with fragrance brands growing the most. 4
    • Purpose-led brands: Consumers crave companies that care. More online searches contain keywords like “ethical beauty” and “sustainable makeup” for products that help consumers look good and feel good. 5

    Online fashion is in vogue

    Before the pandemic, consumers bought less than one-third of their apparel or footwear online; last year, the proportion surged to an astounding 51%. In 2021, consumers will invest even more in their wardrobes, including trends like:

    • Comfort: Athleisure will remain in demand as many consumers still prefer comfortable clothing when they work from home. 7
    • Beloved staples: Classic pieces like jeans, dresses and simple yet elegant tops are making a comeback as consumers start to go out more. 8
    • Retro ‘80s: Ladies are ready to party like it’s 1984. Bright and metallic colors and sequins for occasionwear (and even NFL linebacker-inspired shoulderpads) are recreating a fun, indulgent ’80s vibe. 9

    Brands’ secret weapon for a competitive advantage

    For successful Mother’s Day campaigns, more fashion and beauty brands will use digital shelf analytics for marketing decisions that maximize their ROI and e-commerce sales.

    To ensure online shoppers discover Mother’s Day products with ease, brands are using Share of Search insights to measure their share of digital shelf. These DataWeave analytics tell brands which keywords perform best. Brands can also benchmark their search and navigation visibility against rivals’ rankings across e-commerce categories, websites and geographic regions.


    Using Content Audit insights tells brands how their content is performing. They can discover and fill content gaps so their products show up more prominently. Optimizing content (like keywords, product page titles, descriptions, ads and sponsored space) and images to align with the retailers’ search algorithms ensures a consistent brand experience across all online channels. Improving content helps brands connect to consumers with marketing that resonates and inspires them to buy.
    Brands also use

    Pricing and Promotions insights to measure the effectiveness of their online promotions and secure sales. Brands can stay competitive by ensuring their pricing and promotions are in line with rivals’ offers, such as identifying first movers and rivals with the deepest discounts across retailers and SKUs. Brands can even determine how imitating rivals’ pricing and promotional moves could impact revenue and sales volume.

    Help shoppers make Mom’s day

    Since Mother’s Day is almost here, beauty and fashion brands can apply these data insights to connect consumers with a variety of products moms will love. Digital shelf analytics from DataWeave can help brands deliver timely campaigns, improve their return on digital marketing spend and make effective marketing decisions to drive e-commerce sales.

    1 Howland, Daphne. Wells Fargo sees permanent behavior shifts from the pandemic. Retail Dive. March 29, 2021.
    2 Droesch, Blake. US Ecommerce by Category 2021. eMarketer. April 27, 2021.
    3 Wood, Dana. Is Makeup Dead? The Robin Report. April 18, 2021.
    4 Larson, Kristin. Fragrance Sales Pick Up As Consumers Reengage With The Outside World. Forbes. April 27, 2021.
    5 What Can Brands Learn About Sustainability From Green Beauty Consumers? Beauty Business Journal. June 15, 2020.
    6 Howland, Daphne. Wells Fargo sees permanent behavior shifts from the pandemic. Retail Dive. March 29, 2021.
    7 Ibid.
    8 Bhattarai, Abha. Americans are starting to buy real clothes again. The Washington Post. March 18, 2021.
    9 Warren, Liz. Loose Denim and Bold Occasionwear on Full Display for Fall 2021. Sourcing Journal. April 2, 2021.

  • Similarity matching keeps retailers competitive: Know your rivals

    Similarity matching keeps retailers competitive: Know your rivals

    Soaring e-commerce growth has made retail more crowded, complex and competitive. Now retailers face an urgent need to keep an eye on more rivals with potential substitute products to maximize their own e-commerce growth.

    Consider these recent figures, which illustrate online shoppers’ abundance of product choices:
     

    • 24% year-over-year increase in direct-to-consumer (DTC) brands in the U.S. alone was estimated for 2020 as more brands bypass retailers1
    • 55% of shoppers have purchased private label in the past year and many retailers are investing more in their own brands2
    • 110% average increase in small retailers’ 2020 online holiday sales, as more players launched new e-commerce shops during the pandemic3
    • 39% of U.S. consumers have changed brands, with the level of brand switching doubling in 2020 compared to 2019, especially among Gen Z and Millennial consumers, as loyalty declines4

    These statistics prove that in 2021 retailers need to navigate more online players and products. Now retailers need a new approach to stay on top of market trends to keep their e-commerce strategies competitive, profitable and attractive to discerning online shoppers. 


    Retailers reduce the risk of substitutes with similarity matching

    In response to online crowding, more leading retailers are turning to similarity matching. Similarity matching is a type of retail analytics that scour global e-commerce sites to find products that exactly match a specific item as well as products that closely match it. Similarity matching insights have grown in strategic significance because they increase retailers’ visibility into potential substitute products, so they can respond to all rivals’ moves with greater agility and efficiency to stay competitive.


    In terms of e-commerce applications, similarity matching helps retailers gather insights on potential substitute products so they can adjust their pricing and assortment strategies accordingly. Retailers can align their pricing with rivals’ pricing moves for similar items to protect their margins and maximize profitability. They can also make informed assortment decisions, including which product mix of bestsellers, unique items and private labels could optimize their online sales performance.

    Online shoppers search for products differently across different categories

    Consumer behavior plays a role, as online search habits differ across product categories, which influences the type of similarity matching retailers need. For example, categories like fashion, toys, home and kitchen work best with similarity matching based on text and images. In these highly-visual categories, consumers can quickly determine whether a product fits the design and aesthetic they are looking for. As a result, e-commerce product titles, descriptions and product images play a big role in consumers’ purchase decisions.

    By contrast, consumer electronics and furniture are categories in which consumers tend to seek specific product attributes, such as a certain level of resolution for their high-definition TV or a couch with particular dimensions so it fits their living room. For these types of products, consumer purchases are driven by product specifications, so similarity matching takes into account their specific needs as well as a degree of tolerance for exact or near-similar attributes across online competitors.

    Expect intense e-commerce rivalry in 2021

    As more consumers shop online, they are increasingly informed by online product comparison information. A wide variety of product choices means consumers can substitute similar goods with ease, especially if a particular item is out-of-stock. Perceived product differentiation, price sensitivity and private labels can also influence consumers’ purchase decisions.

    Across categories, e-commerce growth is outpacing total retail growth. When competition is this fierce, there is an increased risk that numerous and aggressive players will drive down profit margins. Leading retailers are now seizing opportunities to earn consumer loyalty. Using similarity matching helps retailers by offering in-demand products that consumers will actually buy and deliver exceptional online experiences to prevent shoppers from switching to rivals and their comparable products.

    Similarity matching lets you stay competitive

    As e-commerce traffic and rivalry increase, similarity matching helps retailers stand out and serve online shoppers more effectively.

    Retailers gain visibility into their entire competitive landscape to keep their e-commerce strategy responsive to shifts among consumers and rivals. By knowing the full scope of potential substitute products available online, retailers can keep their pricing and assortment strategies in line with rivals’ to reduce their risk of losing sales to rivals, and boost their top line, profitability and cost savings.

    The data insights give retailers the flexibility they need to align with online shoppers’ different needs across categories. As a result, retailers can use similarity matching to boost agility and gain a competitive advantage by adapting to online shoppers’ needs, winning their sales and fueling e-commerce growth.DataWeave’s similarity matching capability lets clients

    1 US Direct-to-Consumer Ecommerce Sales Will Rise to Nearly $18 Billion in 2020. eMarketer. April 2, 2020.

    2 Ochwat, Dan. Shopper study: Private brands purchased because they’re preferred. Store Brands. February 24, 2021
    3 Miranda, Leticia. Small businesses who pivoted to e-commerce saw record sales during Black Friday weekend. December 1, 2020.
    4 Charm, Tamara, Harrison Gillis, Anne Grimmelt, Grace Hua, Kelsey Robinson and Ramiro Sanchez Caballero. Survey: US consumer sentiment during the coronavirus crisis. McKinsey & Company. March 24, 2021.

  • How Brands Make Their Marketing Magnetic

    How Brands Make Their Marketing Magnetic

    E-commerce is getting crowded.

    The proliferation of informed shoppers, e-commerce sites, and competitors of all sizes has increased the complexity of – and lucrative opportunities in – brand management.

    Now more brands rely on data insights to uncover specific ways to make their digital marketing more arresting, effective and profitable. Many brands struggle with e-commerce profitability due, in part, to advertising expenses that often yield lackluster results.1

    Analytics are growing in retail significance, as 88% of retail and consumer goods marketers say data improves their marketing by allowing them to personalize touchpoints. Relevant marketing and great marketers helps brands connect with consumers. Let’s see why leading brands are adding data insights to their 2021 marketing strategies to fuel online sales growth.

    Brands discover how to get discovered

    Consumer goods brands no longer leave it up to chance that consumers will find them online. The digital migration of companies and consumers over the past year means more noise for brands to breakthrough.

    Now search is growing in importance to improve brands’ online product discovery. Here’s why:

    • 87% of shoppers begin their hunt in digital channels3
    • 17% rise in paid search in late 20204
    • 24% rise in paid social advertising during the same period5

    To grab consumers’ attention by being easier to see, more brands are turning to data insights to track their online visibility.

    Brands need to look for ways to mitigate the high costs of acquiring customers online6

    Brands use marketing analytics related to keywords and navigation searches help brands know exactly how much space on the digital shelf they occupy across different online platforms.

    These DataWeave’s Share of Search solutions help brands understand what percentage of the digital shelf they command through either keywords or navigation. These insights can help brands decide whether to boost their brand visibility using sponsored ads to ensure their products show up more prominently in online search results to boost brand reach and awareness on each channel. For instance, brands can tell whether consumers search for products using branded, generic or category-specific keywords to align their marketing accordingly.

    In addition, brands can see how their organic and sponsored results rank compared to their competitors to spot ways to improve their visibility rank and decrease customer acquisition costs.

    Content differentiates a brand’s digital shelf

    For a striking digital presence and enhanced discoverability, leading brands measure how effectively their content inspires online shoppers to choose them.

    Brands can improve their digital marketing results by using Content Audit insights to spot patterns among their top-performing campaigns. They can also benchmark their content with category bestsellers to discover how to optimize their online performance to grow sales volume and market share.


    Strategic advertising requires high-quality photography and data-driven content7

    Using these data insights from DataWeave helps brands determine how well their content (including product description pages and images) align with e-commerce algorithms and lead to online traffic, engagement and sales. Brands also adapt faster by adjusting underperforming campaigns to reduce costs and optimize their digital marketing spends.

    Brands can fill content gaps across online channels with enhanced product information that aligns content and images with brands’ product information management (PIM). Using analytics to deliver a consistent brand experience across all online channels can help brands build relationships with consumers and earn their trust.


    Alluring promotions help brands secure the sale

    As e-commerce evolves, brands have matured beyond Google AdWords and Facebook campaigns to offer targeted promotions across digital touchpoints, which increases marketing reach and complexity.

    To boost clarity, be in demand and drive sales across online platforms, more leading brands use data insights to measure the effectiveness of their digital Promotions. Promotional insights from DataWeave keep brands informed of trending categories and products to keep their online offerings relevant and timely. Brands can pinpoint exactly which products to promote and which e-commerce sites help them drive the most profitable results with compelling digital offers.

    Brands that respond quickly to their customers’ needs have the upper hand8

    Analytics also keep brands competitive and relevant by benchmarking their promotional strategies with their rivals’ and continuously monitoring rivals’ online moves. For instance, brands can track the promotions their competitors offer for similar products across different e-commerce sites. These competitive insights help brands quickly spot opportunities to optimize their online conversions with appealing promotions that reflect market trends.

    Better marketing decisions can help brands grow sales and share

    Data insights make brands more enticing by connecting the dots among their online visibility, content and promotions. Brands uncover ways to make smarter marketing decisions faster to improve their top line and decrease customer acquisition costs. DataWeave analytics also help brands stand out and improve product discovery, engagement and sales. As a result, brands save time and boost their agility with relevant marketing that resonates and inspires shoppers to keep coming back.

    1 Jansen, Caroline, Cara Salpini and Maria Monteros. 8 DTC trends to watch in 2021. Retail Dive. February 3, 2021
    2 Casna, Kathryn. Ecommerce Trends That Are Shaping the Way Businesses Sell Online. Salesforce. 2021.
    3 Casna, Kathryn. Ecommerce Trends That Are Shaping the Way Businesses Sell Online. Salesforce. 2021.
    4 The Future of eCommerce in 2021. Shopify Plus. 2021.
    5 The Future of eCommerce in 2021. Shopify Plus. 2021.
    6 Jansen, Caroline, Cara Salpini and Maria Monteros. 8 DTC trends to watch in 2021. Retail Dive. February 3, 2021.
    7 Glasheen, Jasmine. 2021 Forecast: Next Gens in a Brand-New World. The Robin Report. January 3, 2021.
    8 Monteros, Maria. Forrester: Few brands can anticipate and act on consumer needs. Retail Dive. February 10, 2021.

  • How Brands Boost Their E-Commerce Profitability

    How Brands Boost Their E-Commerce Profitability

    Brands that protect their bottom line will win online.

    As global e-commerce smashes sales records, more brands are now taking control over their online presence (“digital shelf”) to enhance their performance and profit margins.

    In the U.S., the increase in e-commerce penetration during the first half of 2020 was equivalent to that of the last decade.1 Last year also marked the first time in history that all retail sales gains came from e-commerce.2 E-commerce has lasting appeal, as two-thirds of consumers plan to continue to shop online after the pandemic.3

    “Brands need to continue to look for ways to
    mitigate the high costs of acquiring customers online.”     4

    To keep up as shopping migrates online, brands face bigger expenditures. In the second quarter of 2020, e-commerce costs grew much faster (up 60% year-over-year) than revenues (up 40%).5 Namely, brands face steep costs for customer acquisition and logistics, which erode their online profit margins.

    The bottom line for brands is they must sell online – profitably – to stay competitive. They urgently need new ways to drive online sales and incur fewer costs. Let’s see why brand leaders are using data insights to optimize their e-commerce decisions and profitability.

    Brands find new growth opportunities

    Over the past year, e-commerce has gotten more crowded. Now brands seek proven ways to differentiate their offerings and consistently deliver an alluring online experience. That’s because a recent study found 42% of consumers cite less trust in online shopping due to poor experiences, such as inconsistent pricing and out-of-stock merchandise.6 In response, these e-commerce best practices can help brands improve the customer journey and top line sales.

    To help consumers find their products online with ease, brands can use data insights for superior product discovery. Insights help brands know exactly which keywords shoppers search for to earn high visibility rankings among consumers’ online search results. Data analytics direct brands to the most relevant keywords, which they can use in marketing, including product descriptions, for effective online discovery.7


    Brands also face increased pressure to keep up with rivals’ real-time pricing changes across retailers’ e-commerce sites, online marketplaces and social media. Insights help brands price competitively across channels by monitoring and promptly adapting to competitors’ online pricing moves. Brands can even use data to ensure merchants consistently respect pricing policies.

    Data analytics also help brands measure their marketing effectiveness and popularity across e-commerce

    websites, and how they compare to their rivals. Brands can improve how they promote their products by using targeted digital content that resonates. For instance, they can publish unique content on each channel tailored to the platform’s unique algorithm and use data to discover patterns among their top performing campaigns. Also, brands can determine when to use their own social media channels or pay for sponsored ads to drive more sales.


    As we saw last March, in-stock merchandise is essential to maximize online sales. Data analytics help brands track their stock status to ensure products are available across all their digital channels for reliable service that sparks more sales. 

    Brands find new efficiencies

    Cost effectiveness is also vital and these e-commerce best practices help brands boost their online efficiencies.

    Brands use insights to pinpoint and keep sharing content that effectively resonates with and enages their target audience. They can use data insights to see where to allocate their marketing spend for online promotions and either revitalize or drop underperforming online promotions. Brands can also track whether their online promotions align with rivals’ promotions to stay competitive and agile.

    Likewise, measuring a brand’s popularity through consumer reviews reveals which underperforming products to downplay to conserve marketing resources for the specific products and bundles that perform best in their categories. For instance, PepsiCo’s and Kraft Heinz’s new online shops offer only large items or bundles for basket sizes large enough to offset shipment costs.8


    To reduce the high cost of product returns, brands can use data insights to prioritize bestselling products rather than items consumers are more likely to send back. Using clear, up-to-date content, including product descriptions with accurate dimensions, can also help online consumers know exactly what they’re buying to minimize returns.


    How brands and consumers profit

    When brands use insights to make better e-commerce decisions, they can compensate for ballooning expenses. Analytics help brands connect the dots among their online visibility, promotions, performance and reviews. These best practices can give brands an edge by uncovering how to be more aggressive with revenue-earning and cost-cutting opportunities. Brands find effective ways to acquire more online customers to improve their top line and offset e-commerce expenses with new efficiencies. Data-driven digital marketing decisions help brands improve their e-commerce effectiveness to stay profitable and competitive. 


    Meanwhile, consumers also win by having an inviting, smooth and reliable online shopping experience. They find the products they want with greater ease, and feel confident enough to buy based on information like a brand’s pricing, promotions and product availability.

    1 Arora, Arun, Hamza Khan. Sajal Kohli and Caroline Tufft. DTC e-commerce: How consumer brands can get it right. McKinsey & Company. November 30, 2020.
    2 Ali, Fareeha. US ecommerce grows 44.0% in 2020. Digital Commerce 360. January 29, 2021.
    3 Arora, Arun, Hamza Khan. Sajal Kohli and Caroline Tufft. DTC e-commerce: How consumer brands can get it right. McKinsey & Company. November 30, 2020.
    4 Jansen, Caroline, Cara Salpini and Maria Monteros. 8 DTC trends to watch in 2021. Retail Dive. February 3, 2021.
    5 Haber, John. Logistics Costs Challenge E-Commerce Profit Margins. Parcel Industry. October 9, 2020.
    6 O’Carroll, Derek. 5 Hidden Trends That Will Shape E-Commerce in 2021. Total Retail. February 4, 2020.
    7 Leong, Brandon. COVID-19 strategy: Use the power of your digital sell sheet. Digital Commerce 360. August 23, 2020.
    8 Arora, Arun, Hamza Khan. Sajal Kohli and Caroline Tufft. DTC e-commerce: How consumer brands can get it right. McKinsey & Company. November 30, 2020.

  • [INFOGRAPHIC] 2020: The Year the World Navigated Uncertainty Together

    [INFOGRAPHIC] 2020: The Year the World Navigated Uncertainty Together

    The start of 2020 brought with it the promise of global economic growth. Markets in the US were on a steady rise we also witnessed demand from brands and retailers in Europe and the Middle East. All seemed to be on track to make it a year of plenty.

    Out of nowhere, the end of the first quarter saw the world coming to a grinding halt. The world was held hostage by a global pandemic and the force with which we were hit, was unprecedented.

    From February to mid-May we saw things come to a sharp halt. We at DataWeave seized this intermittent downtime to bolster our product offerings.

    On the flip side, when the world did start opening May onwards, we saw completely new categories take center stage digitally. With new habits and trends taking shape, the pandemic single-handedly caused exceptional growth in the Food and Grocery Delivery intermediaries. Predictably, the rest of the world followed. Our existing customers saw the competition rise steeply with everyone coming online. We invested substantially in our Digital Shelf Analytics solutions after noticing that e-commerce was seeing a boom. 2020 saw brands making their online presence the new norm. This meant that small, medium and large enterprises had to now divert their spending to analytics and e-commerce. 

    It is interesting to note that the rise in the food and grocery delivery segment gave brands another channel to focus on vis a vis their presence. Brands that were available on these sites focused on how they could optimize their sales on these channels, which proved to be the front runners during the height of the pandemic. While the challenges and opportunities for both these segments overlapped and seemed similar, our solutions helped measure and optimize brand performance across all online channels. Some of the in-demand solutions and analytics we saw our customers use were; share of search, content audit, assortment and availability, pricing and promotions, and ratings and reviews. 

    There were mixed emotions in the market, with regard to the best use of marketing spends. Human resource and client cutbacks happened across the board. At DataWeave however, we had the pleasure of onboarding 25 new clients including retailers and brands ranging from food and grocery delivery, home improvement from across multiple geographies.

    Infographics

    Throughout the year, the work never ceased at DataWeave. The team showed incredible resilience while working remotely, making sure our deliverables were being taken care of, at all times. Due to the e-commerce boom and immense pressure from existing and new entrants in the digital space, our clients saw a need to gather more insights. With the given uptick, we are happy to report that our stellar 95%+ accuracy record for in-depth insights at scale, was maintained through the course of all the work done.

    Looking forward to the year 2021:

    In the US, the adoption of e-commerce accelerated as traditional brick and mortar stores shut down and pivoted. To put things into perspective, e-commerce adoption grew only by 4.3% from 2014 to 2019. In just three months in 2020, e-commerce adoption grew at 4.3%! Add to that, with approved vaccines making their way slowly to the public, we do anticipate the travel sector to open up and we look forward to working with new clients.

    Nike’s Chief Executive, John Donahoe recently said, ” We know that digital is the new normal. The consumer today is digitally grounded and simply will not revert back…the shift to online sales could be a permanent trend.” We could not agree more! With online sales here to stay, brand and retailers’ requirements to keep their competitive edge will only continue to grow. We at DataWeave, look forward to delivering the results they want in this new year, and for the years to come.

  • AI-Driven Mapping of Retail Taxonomies- Part 2

    AI-Driven Mapping of Retail Taxonomies- Part 2

    Mapping product taxonomies using Deep Learning

    In Part 1 we discussed the importance of Retail taxonomy and the applications of mapping retail taxonomies in Assortment Analytics, building Knowledge Graph, etc. Here, we will discuss how we approached the problem of mapping retail taxonomies across sources.

    We solved this problem by classifying every retail product to a standard DataWeave defined taxonomy so that products from different websites could be brought at the same level. Once these products are at the same level, mapping taxonomies becomes straightforward.

    We’ve built an AI-based solution that uses state-of-the-art algorithms to predict the correct DataWeave Taxonomy for a product from its textual information like Title, Taxonomy and Description. Our model predicts a standard 4 level (L1-L2-L3-L4) taxonomy for any given product. These Levels denote Category, Sub Category, Parent Product Type and Product Type respectively.

    Approach

    Conventional methods for taxonomy prediction are typically based on machine learning classification algorithms. Here, we need to provide textual data and the classifier will predict the entire taxonomy as a class.

    We used the classification approach as a baseline, but found a few inherent flaws in this:

    • A Classification model cannot understand the semantic relation between input text and output hierarchy. Which means, it cannot understand if there’s any relation between the textual input and the text present in the taxonomy. For a classifier, the output class is just a label encoded value
    • Since the taxonomy is a tree and each leaf node uniquely defines a path from the root to leaf, the classification algorithms effectively output an existing root-to-leaf path. However, it cannot predict new relationships in the tree structure
    • Let’s say, our training set has only the records for “Clothing, Shoes & Jewelry > Men > Clothing > Shorts” and  “Clothing, Shoes & Jewelry > Baby > Shoes > Boots”, Example:

    {‘title’: “Russell Athletic Men’s Cotton Baseline Short with Pockets – Black – XXX-Large”, 

    ‘dw_taxonomy’: “ Clothing, Shoes & Jewelry > Men > Clothing > Shorts”},

    {‘title’:” Surprise by Stride Rite Baby Boys Branly Faux-Leather Ankle Boots(Infant/Toddler) – Brown -”,

    ’dw_taxonomy:” Clothing, Shoes & Jewelry > Baby > Shoes > Boots”}

    Now, if a product with Title “Burt’s Bees Baby Baby Boys’ Terry Short” comes for prediction, then the classifier will never be able to predict the correct taxonomy. Although, it would have seen the data points of Shorts and Baby.

    E-commerce product taxonomy has a very long tail, i.e. there’s a huge imbalance in counts of data per taxonomy. Classification algorithms do not perform well for very long tail problems.

    Encoder-Decoder with Attention for Taxonomy Classification

    What is Encoder-Decoder?

    Encoder-Decoder is a classical Deep Learning architecture where there are two Deep Neural Nets, an Encoder and a Decoder linked with each other to generate desired outputs.

    The objective of an Encoder is to encode the required information from the input data and store it in a feature vector. In case of text input, the encoder is mostly an RNN or Transformer based architecture and for image input, it is mostly a CNN-based architecture. Once the encoded feature vector is created, the Decoder uses it to produce the required output. The Encoder and Decoder can be interfaced by another layer which is called Attention. The Role of Attention mechanism is to train the model to selectively focus on useful parts of the input data and hence, learn the alignment between them. This helps the model to cope effectively with long input sentences (when dealing with text) or complex portions of images (when input is an image).

    Instead of classification-based approaches, we use an Encoder-Decoder architecture and map the problem of taxonomy classification to the task of machine translation (MT) AKA, Seq2Seq. An MT system takes the text in one language as input and outputs its translation as a sequence of words in another language. In our case, the input maps to the textual description of a product, and the output maps to the sequence of categories and sub-categories in our taxonomy (e.g., Clothing, Shoes & Jewelry > Baby > Shoes > Boots). By framing taxonomy classification as an MT problem, we overcome a lot of limitations present in classical classification approaches.

    • This architecture has the capability to predict a taxonomy that is not even present in the training data.
      • Talking about the example we discussed earlier where a traditional classification model was not able to predict the taxonomy for “Baby Boys knit terry shorts – cat & jack gray 12 m”, this Encoder-decoder model easily predicts the correct taxonomy as “ Clothing, Shoes & Jewelry > Baby > Clothing > Shorts”
    • We achieved a much higher accuracy because the model understands the semantic relationship between the input and output text, as well as giving attention to the most relevant parts in the input, when generating the output
    Fig. Attention visualization for product title “South of France lavender fields Bar Soap”. It can be seen from the image that the attention weights of “soap” word is very high when predicting the output at different time-steps.

    We used pre-trained fasttext word embeddings to vectorize textual input, pass on to the GRU-RNN based encoder which processes the input sequentially, and generates the final encoded vector. The Decoder which is also a GRU-RNN takes this encoded input and generates the output sequentially. Along with the encoded vector, there is also an attention vector which is passed to the Decoder for the output at every time-step.

    We trained both the Classification model (Baseline) and the Encoder-Decoder model for the Fashion category and the Beauty & Personal Care category. 

    For Fashion, we trained the model with 170,000 data points and validated it on a 30k set. For Beauty Category, we trained the model on 88k data points and validated it on a 20k set. We were able to achieve 92% Seq2Seq accuracy in 1,240 classes for the Fashion category and 96% Seq2Seq accuracy in 343 classes for the Beauty Category, using the Encoder-Decoder approach.

    Summary and the Way Forward

    Since we moved to this approach, we have seen drastic improvements in the accuracy of our Assortment Intelligence accounts. But the road doesn’t end here. There are several challenges to be tackled and worked upon. We’re planning on making this process language agnostic by using cross-lingual embeddings, merging models from different categories and also using product Image to complement the text-based model with visual input via a Multi-Modal approach.

    References

    Don’t Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation by Maggie Yundi Li, Stanley Kok and Liling Tan

    SIGIR eCom’18 Data Challenge is organized by Rakuten Institute of Technology Boston (RIT-Boston)

    Massive Exploration of Neural Machine Translation Architectures by Denny Britz, Anna Goldie, Minh-Thang, and Luong Quoc Le

  • Mapping eCommerce Product Taxonomy with AI Pt. 1

    Mapping eCommerce Product Taxonomy with AI Pt. 1

    Product Taxonomy and its importance in retail

    Every product on a retail website is categorized in such a way that it denotes where the product belongs in the entire catalog. Generally, these categorizations follow a hierarchy that puts the product under some Category, Subcategory and Product Type (Ex. Clothing, Shoes & Jewelry > Men > Clothing > Shirts). We call this hierarchical eCommerce product categorization as Product Taxonomy. Categorizing products in a logical manner – in a way a shopper would find intuitive, helps in navigation when he or she is browsing an e-commerce website. 

    In addition, with a good category organization, a product lends itself for better searchability (for search engines) on e-commerce websites. Search engines work by looking up query terms in an index which points to products which contain those terms. Matches in various fields are ranked differently in relevance.

    For instance, a term that matches a word in the title, indicates greater relevance compared to one which matches the description. Additionally, terms that are exclusive to certain products, signal greater selectivity and hence contribute more to ranking. In light of this, the choice of words in fields indicating a product’s category affects the relevance of search results for a user query. This improves discoverability and as relevant results show up, it in turn improves the user experience. A good product taxonomy contributes to increased sales by helping shoppers find relevant products while browsing or searching. 

    Retail websites organize products into a taxonomy which they deem intuitive for their users, and fits the organization of their business units. Different retail websites could thus have taxonomies varying significantly from each other. Since we deal with millions of products across hundreds of websites on a daily basis, we often have to work with various taxonomies for the same product coming from different websites. 

    We are required to align these to a common standard taxonomy for our analyses. Standard taxonomies like Global Product Classification (GPC) taxonomies and Google Product taxonomies offer a standard way of representing a product. However, none of these taxonomies are complete and generic. Hence, we at DataWeave have come up with our own Standard Taxonomies for each category in e-commerce, which are generic enough to represent products on websites across different geographies.

    Having a standard taxonomy for each retail product is important for our Data Orchestration pipeline. A Standard Taxonomy helps in enriching the DataWeave Retail Knowledge Graph at scale.

    DataWeave’s Retail Knowledge Graph

    The information about products on most of the retail websites is unstructured and broken. We process this unstructured data, derive structured information from it and store it in a connected format in our Knowledge Graph. The Knowledge Graph is used in downstream applications like Attribute Tagging, Content Analysis, etc. The Knowledge Graph follows a standard hierarchy of 4 levels  (L1 > L2 > L3 > L4) for all the retail products.

    Mapping eCommerce retail taxonomies is not only a requirement for the Knowledge Graph, but has some direct business applications as well:

    Assortment Analytics

    • Mapping competitors’ products to their own taxonomies help retailers understand the exact gap in their assortment, regardless of how competitors are categorizing their products
    • Let’s say a retailer is interested in knowing the assortment of a product type, Scented Candles in their competitor’s catalog. Now, the retailer might have categorized it as Home & Kitchen > Home Decor > Scented Candle but the same product type could have been categorized as Fragrance > Home > Candles on a competitor’s website. Here, having an efficient and scalable mechanism to map product taxonomies provides accurate assortment analytics which retailers look for. Example:

    Health & Household > Health Care > Alternative Medicine > Aromatherapy > Candles

    Fragrance > Candles & Home Scents > Candles

    Automated Catalog Suggestion

    It is also used in Catalog Suggestion as a Service, where for any product we suggest the appropriate taxonomy it should follow on the website for a better browsing experience.

    Stay tuned to Part-2 to know how we are solving the  problem of mapping various retail taxonomies.

    Click here to know more about assortment analytics

  • Black Friday Prices Tempt Health & Beauty Shoppers

    Black Friday Prices Tempt Health & Beauty Shoppers

    Black Friday looked downright sultry with desirable discounts on health and beauty products.

    This year, health and beauty sales faced the threat of declining demand, as the pandemic keeps many consumers cooped up at home and in-store product testers no longer allowed. Yet consumers’ enduring desire to look and feel their best means this category will remain resilient. (Plus, we want to look smokin’ hot on Zoom.)

    That’s why we were curious to know how retail rivals, ranging from discounters to department stores, are battling it out to become bodacious beauty destinations to win the hearts, wallets and fake lashes of online shoppers.

    To calculate which retailers’ prices offered the broadest and most generous discounts, we examined health and beauty products’ pricing at Amazon, JC Penney, Macy’s, Neiman Marcus, Overstock, Nordstrom, Target and Walmart. We compared the pre-sale period (November 24-26) to the holiday sales period (Black Friday on November 27 through Cyber Monday on November 30) for a glimpse of retailers’ pricing strategies in this fiercely competitive category.

    BlackFriday_Health_Beauty_img1

    To gain insights into retailers’ competitive pricing strategies, we tracked three scenarios: whether prices decreased, increased or remained the same during the last week of November 2020. The vast majority of health and beauty products (91.0%) maintained the same prices during the pre-sale and sales periods. An astounding 99.7% of Target’s health and beauty prices stayed the same during the period.

    Amazon had the highest proportion of health and beauty products that offered a price decrease (18.1%), particularly on men’s fragrance, women’s fragrance and men’s hair care. Offering discounts on more items hints that Amazon wants to attract more health and beauty consumers, including men, by making more items affordable. Target offered the lowest proportion of health and beauty products with price decreases (0.8%).

    Amazon also had the greatest proportion of health and beauty products with a price increase (7.0%) with 15.3% of men’s fragrance earning a price hike.

    BlackFriday_Health_Beauty_img2

    On Black Friday, among health and beauty products with price decreases, Target gave the most generous average discount (37.6% vs. 7.2% for Overstock). However, Target’s discounts applied to only 12 products compared to 798 for Overstock.

    Common types of health and beauty products with the highest average discount on Black Friday have included face makeup, men’s fragrance, men’s hair care, and shampoo and conditioner.

    Among health and beauty products with price increases on Black Friday, Nordstrom had the highest average price hike (43.8% on one women’s fragrance) and Walmart offered the lowest (10.3% on 250 products).

    These findings suggest that Target was willing to create aggressive loss leaders in this category and Amazon wanted to boost health and beauty sales among male shoppers.

    Black Friday vs. Cyber Monday

    BlackFriday_Health_Beauty_img3

    This year, most retailers offered more additional discounts on health and beauty products on Cyber Monday than on Black Friday, possibly to prioritize clearing out their inventory before year-end. JC Penney and Macy’s were the exception. Overall, the top product types that received additional discounts included shave and hair removal, women’s fragrance and face makeup.

    On Cyber Monday, Amazon offered additional discounts on the greatest proportion of health and beauty products (21.1% vs. 2.1% for Target). Amazon focused on men’s hair care, shampoo and conditioner and face makeup.

    BlackFriday_Health_Beauty_img4

    Half the retailers (Amazon, JC Penney, Nordstrom and Overstock) offered deeper additional discounts on health & beauty on Cyber Monday than Black Friday, possibly to clear out their inventory before the end of the year. Cyber Monday discounts ranged from 35.0% for Nordstrom to 7.8% for Overstock.

    Meanwhile, both Neiman Marcus and Walmart offered the same levels of discounts on both Black Friday and Cyber Monday.

    Overall, the types of health and beauty products with the deepest discounts on both Black Friday and Cyber Monday were shampoo and conditioner, men’s hair care and face makeup.

    Additional discounts across products by “premiumness” level

    For almost all the retailers, the percentage of health and beauty products with additional discounts was higher on Cyber Monday than on Black Friday. Overstock had the highest proportion (20.2%), slightly more than Amazon (20.0%).

    JC Penney had a higher percentage of products with additional discounts on Black Friday. Neiman Marcus had the same percentage of products on both sales days.

    All the retailers except JC Penney and  Neiman Marcus allocated the greatest percentage of their additional discounts to health and beauty products at the high level of premium. The retailers may have wanted to appeal to upscale shoppers and make high premium goods more accessible to a broader audience of consumers.

    Half of the retailers (JC Penney, Nordstrom, Target and Walmart) offered deeper discounts on Black Friday than Cyber Monday.

    Target offered the most generous discounts on Black Friday with an average additional discount of 39.7%, which ranged from 50.1% on moderately premium health and beauty products to 28.6% for products at a high premium level. Target appeared to make more beauty items, including high premium items, affordable to more consumers to stay competitive as a beauty destination.

    Conversely, Amazon, Overstock and Macy’s were more generous with additional discounts on Cyber Monday. Among the high premium level of health and beauty products on Cyber Monday, Macy’s offered the deepest discounts (31.3%), edging out department store rival JC Penney (30.4%) in competing for upscale shoppers.

    Health & Beauty’s Ravishing Holiday Prices

    This year’s Black Friday and Cyber Monday pricing strategies showed retailers’ attempts to stand out, expand their market reach to stay competitive. Appealing to a broader audience included spanning upscale and value tiers, and wooing more male online shoppers to grow their top line and boost loyalty in an intense category amid a pandemic.

    Stay tuned for more Black Friday and Cyber Monday 2020 analysis to discover how retailers strategically price their products to win in leading e-commerce categories.


  • Who Won Black Friday’s Electronics Price War?

    Who Won Black Friday’s Electronics Price War?

    Electronics have never been hotter.

    This year’s COVID-19 pandemic created a seismic shift towards tech, directly affecting retailers’ Black Friday and Cyber Monday pricing strategies for electronics. Prime Day 2020’s new fall date also inevitably influenced pricing and purchasing patterns. If consumers pampered themselves with a 75-inch TV in October, what are the odds they’re in the market for another big-screen TV in late November?

    Consumer electronics are perennial holiday bestsellers because they make gift-giving easy, whether we buy them for others or for personal indulgence. Continuous innovation also means a comparatively shorter product lifecycle, making electronics an exciting, progressive retail category.

    To determine which retailers’ pricing strategies offered the most generous discounts on electronic products, we examined electronics pricing at Amazon, Best Buy, Overstock, Target and Walmart. We compared during the pre-sale period (November 24-26) to the holiday sales period (Black Friday on November 27 through Cyber Monday on November 30) for a glimpse of retailers’ pricing strategies to stay competitive in 2020.

    For competitive pricing insights, we tracked three scenarios before and during 2020’s traditional holiday sales season: whether prices decreased, increased or remained the same. Most strikingly, the overwhelming majority of electronics products (89.8%) maintained the same prices during the pre-sale and sales periods. For instance, Target kept a whopping 98.0% of its electronics prices the same during the period.

    Amazon had the greatest proportion of electronics products that offered a price decrease (11.7%), particularly on laptops, mobiles and wearable technology. These results also suggest Amazon wants to reach more consumers by making more electronics affordable with discounts. Target offered the lowest proportion of electronics with price decreases (2.5%).

    Overstock had the greatest proportion of electronics products that offered a price increase (10.7%) with 30.3% of TVs increasing in price. Best Buy offered the lowest proportion of electronics with price increases (1.2%).

    Among electronics products with price decreases on Black Friday, Best Buy offered the highest average discount (16.6%) and Amazon offered the lowest (10.2%). Among all the retailers, the types of electronics with the highest average discount included tablets, headphones, laptops and TVs.

    Among electronics products with price increases on Black Friday, Best Buy had the highest average price hike (30.2%) and Amazon offered the lowest (9.8%). That said, Best Buy increased the price of one laptop by 73.1% whereas Amazon increased the price of 44 laptops by an average of 4.2%.

    These findings show that Best Buy aggressively protected its market share in this competitive category by offering the most generous discounts.

    Black Friday vs. Cyber Monday

    Without exception, the retailers offered more additional discounts across the electronics category on Cyber Monday than on Black Friday. Retailers may have wanted to clear out their inventory to make room for new, innovative products in their assortments.

    Amazon had the greatest proportion of electronics with additional discounts on Cyber Monday (15.7%, which is more than double the 7.3% each for Overstock and Target). Amazon’s additional discounts focused on mobiles, laptops and wearable technology.

    Overall, the greatest proportion of additional discounts on electronics on Cyber Monday focused on laptops, desktops and USB flash drives.

    While most retailers offered deeper discounts on electronics on Cyber Monday than Black Friday, Overstock was the sole exception.

    On Cyber Monday, Target offered the most generous average additional discounts (19.6% vs. 10.2% for Amazon); however, Target’s discounts applied to 260 electronics products compared to 924 for Amazon.

    Overall, the types of electronics with the deepest discounts on Cyber Monday on electronics were USB flash drives, tablets and headphones.

    Additional discounts across products by “premiumness” level

    When we examine electronics’ additional discounts according to the products’ premium level, several patterns stand out.

    Most apparent is that every retailer offered a higher proportion of additional discounts on Cyber Monday compared to Black Friday, ranging from 15.9% for Amazon to 6.4% for Best Buy.

    With only one exception, Amazon offered the greatest proportion of additional discounts across all premium levels. Only Target offered a slightly higher proportion among low premium electronics (11.9% vs. 11.3% for Amazon). This approach could help Amazon make more electronics products more affordable to more consumers and boost its reach in this competitive category.

    Among electronic items at the high premium level, Amazon was most aggressive in allocating additional discounts (21.0% vs. 5.4% for Target), which could help the e-commerce giant earn top-of-mind status among affluent shoppers in the market for big-ticket electronics.

    Most retailers (Amazon, Best Buy and Walmart) offered deeper discounts on Cyber Monday than Black Friday. By contrast, Overstock and Target were more generous on Black Friday.

    Interestingly, Target’s average additional discount on Cyber Monday (18.8%) was still more generous than those of the other retailers.

    Among moderately premium items, Target’s average additional discount was 22.3%, more than double Amazon’s 10.3%. Target may have tried to make mid-market electronics more affordable to its core audience of value-seeking shoppers.

    Additional discounts across products by “popularity” level

    A review of retailers’ additional discounts by electronics’ popularity level reveals that most retailers allocated a bigger proportion of discounts on Cyber Monday than on Black Friday. Overstock was the exception. Again, clearing out 2020 inventory before year-end likely influenced retailers’ pricing strategies.

    Overall, on Cyber Monday retailers showed a direct relationship between additional discounts and electronics’ popularity levels. For instance, Amazon offered additional discounts on 21.7% of highly popular electronics and 15.3% on moderately popular electronics. Since Amazon strives to be “The Everything Store,” it makes sense to make more products more appealing and affordable to more consumers. Meanwhile, Target offered nearly double the proportion of additional discounts of less popular electronics than discount rival Walmart (12.5% vs. 6.7%) to tempt value-seekers with deals.

    Most retailers (Amazon, Best Buy, Target and Walmart) offered deeper discounts on Cyber Monday than Black Friday. Overstock was more generous on Black Friday.

    On Cyber Monday, Target’s average additional discount (21.8%) was the most generous of all the retailers, nearly double that of Amazon (11.1%). However, Target’s discounts applied to 259 electronics products. vs. 903 for Amazon.

    Both Amazon and Overstock gave their most generous discounts to less-popular electronics, possibly to clear out their inventory to make room for more popular or higher-margin items.

    Black Friday & Cyber Monday 2020 Electronics Pricing Strategies

    This year, the pandemic jolted consumers to focus on digital technology to stay connected to work, school and retail, which heightened demand for electronics.

    In response, retailers’ 2020 pricing strategies for Black Friday and Cyber Monday suggest a desire to extend their reach beyond their core audience to maximize their brand appeal and steal rivals’ market share.

    The Cyber Monday findings, in particular, suggest retailers decluttered their assortments to make space for the latest and highest-margin tech gadgets in time for Christmas.

    Click here for more Black Friday and Cyber Monday 2020 analysis for greater clarity on the evolving pricing positions of retail rivals across top e-commerce categories.


  • Black Friday Furniture Prices Inspire Home Makeovers

    Black Friday Furniture Prices Inspire Home Makeovers

    As most of us stay home for the holidays this year, retailers hope we’ll invest in our nest.

    The global pandemic ignited sales in the red-hot home furniture category, as our domestic comfort, functionality and aesthetics suddenly became urgent priorities in 2020. Now we’re investing more in domestic leisure, organizing and redecorating as our consumption habits shift.

    That’s why we wanted to know how retailers adapted their Black Friday and Cyber Monday furniture pricing strategies to meet consumers’ needs. For instance, did retailers give the best deals on low-commitment furniture like area rugs or on big-ticket items like dining room sets?

    To pinpoint which retailers offered the greatest proportion of discounts and the deepest discounts, we reviewed furniture at Amazon, Home Depot, JC Penney, Overstock, Target, Walmart and Wayfair. We compared the pre-sale period (November 24-26) to the holiday sales period (Black Friday on November 27 through Cyber Monday on November 30) to monitor retailers’ furniture pricing moves.

    Top product types by additional discount

    BlackFriday_homefurnishings

    To review retailers’ holiday pricing strategies in the furniture category, we tracked three scenarios: whether prices decreased, increased or remained the same during the last week of November 2020.

    The proportion of furniture items that maintained the same prices during the pre-sale and sales periods ranged widely, from 91.6% for Home Depot to only 22.6% for Wayfair.

    That’s because Wayfair had by far the highest proportion of furniture with a price decrease (77.2% on 4254 products vs. 7.1% on 342 products for Home Depot). Since Wayfair specializes in home décor, it makes sense for the retailer to aggressively distribute discounts across its furniture assortment.

    Among all retailers, the top types of furniture with discounts included bookcases, entertainment units, sofas and storage and cabinets. These findings suggest we are investing in domestic leisure, relaxation and organization.

    BlackFriday_homefurnishings_saleanalysis

    On Black Friday, JC Penney offered the most generous average discount (21.3% vs. 4.6% for Wayfair). This means that although Wayfair spread discounts across its furniture subcategories, the actual discounts were lower than rivals’.

    Types of furniture with the highest average discount on Black Friday at JC Penney included storage and cabinets, bookcases and rugs. These products tend to be affordable additions to our homes compared to bigger investments like a dining room set.

    Black Friday Vs. Cyber Monday

    CyberMonday_homefurnishings

    On Cyber Monday, Target offered additional discounts on the greatest proportion of furniture (27.4% vs. 7.8% for JC Penney). These findings suggest Target really wants to be a convenient option for shoppers, including their home furnishing needs.

    Among all retailers, the top types of discounted furniture included rugs, beds and entertainment units.

    Most retailers offered deeper additional discounts on furniture on Cyber Monday than Black Friday, possibly to maximize sales before the end of the year. Cyber Monday discounts for furniture ranged from 20.9% for JC Penney to 4.9% for Wayfair. Among all retailers, the top types of furniture that received discounts on Cyber Monday were rugs, storage and cabinets and dining table sets, which show that redecorating and organizing were in style this holiday season.

    Additional discounts across product “premiumness” levels

    BlackFridayVsCyberMonday_homefurnishings

    Nearly all retailers had a higher proportion of furniture with additional discounts on Cyber Monday than on Black Friday. Wayfair had the highest proportion (79.2% for both Cyber Monday and Black Friday vs. 9.3 for Home Depot). Wayfair’s use of discounts across all premium levels on both major sales days shows the retailer wants to extend its brand reach, own this category and earn top-of-mind status across diverse furniture shoppers at all price points.

    Only JC Penney had a higher percentage of furniture with additional discounts on Black Friday.

    Every retailer offered deeper discounts on Cyber Monday than Black Friday, likely to clear out 2020 merchandise.

    On Cyber Monday, JC Penney offered the most generous furniture discounts, with an average additional discount of 38.0%, which ranged from 35.1% at the low premium level to 40.9% at the moderately premium level. Aggressive discounts could set JC Penney apart among department stores and attract more low- to mid-market consumers with tempting furniture deals.

    Also, most retailers gave the deepest discounts on furniture at the high premium level, which can help to make upscale items accessible and affordable to a greater number of consumers.

    Additional discounts across product “popularity” levels

    CyberMondayVsBlackFriday_homefurnishings

    Almost all retailers offered a greater proportion of additional furniture discounts on Cyber Monday than on Black Friday, ranging from 67.6% for Wayfair to 7.8% for Home Depot.

    Across all levels of popularity for furniture, Wayfair dominated with discounts on the most diverse array of products to give more furniture shoppers an opportunity to save money.

    Meanwhile, Amazon and JC Penney offered the greatest proportion of discounts at the low level of popularity, possibly to declutter their furniture assortment to make room for in-demand products.

    Nearly every retailer offered deeper discounts on furniture on Cyber Monday than on Black Friday, with JC Penney being the most generous (39.1% vs. 5.3% for Wayfair).

    Home Depot and Wayfair prioritized discounts among less popular furniture, likely to clear them out and make more room in their assortments for items people really want.   

    Amazon and Walmart gave deeper discounts on highly popular furniture to battle it out over bestsellers.

    Holiday Furniture Pricing Inspires Us to Reimagine Our Space

    This year, more consumers decluttered their homes and more retailers decluttered their furniture assortment by clearing out 2020 merchandise with desirable deals on Black Friday and Cyber Monday.

    The findings of this pricing analysis hint at retailers’ competitive positioning. To own the furniture category, Wayfair aggressively allocated discounts across its assortment even if rivals gave deeper discounts. Target’s deep discounts helped to position the chain as a convenient option for shoppers’ cross-category needs, including furniture. JC Penney’s astonishingly deep discounts on Black Friday and Cyber Monday could help to liquidate inventory, whereas Amazon and Walmart battled over bestsellers.

    Click here for more Black Friday and Cyber Monday analysis to learn about retailers’ holiday pricing strategies during 2020’s e-commerce boom.

  • Black Friday Prices Wowed Fashionistas

    Black Friday Prices Wowed Fashionistas

    Retailers really wanted to dress us up this holiday season.

    This year’s Black Friday and Cyber Monday fashion pricing trends reflect how retailers have responded to the pandemic’s influence on apparel shopping to boost their resilience and competitiveness.

    For instance, since most consumers now cocoon at home, few of us are likely to splurge on fancy gowns or suits as holiday gifts for ourselves or others. That’s why we wanted to know which retailers doubled down on Black Friday fashion discounts and which ones used Cyber Monday discounts to make room for in-demand merchandise.

    To calculate which retailers’ prices offered the greatest proportion of discounts and the deepest discounts, we analyzed men’s and women’s fashions at Amazon, Bloomingdale’s, JC Penney, Macy’s, Neiman Marcus, Overstock, Nordstrom, Target and Walmart. We compared the pre-sale period (November 24-26) to the holiday sales period (Black Friday on November 27 through Cyber Monday on November 30) to gain insights into retailers’ pricing strategies in fashion.

    Top product types by additional discounts- Men’s fashion

    To review retailers’ holiday pricing strategies, we tracked three scenarios: whether prices decreased, increased or remained the same during the last week of November 2020.

    The overall proportion of men’s fashion items that maintained the same prices during the pre-sale and sales periods was 88.6%, ranging from 99.5% for JC Penney to 75.0% for Neiman Marcus.

    Neiman Marcus had the highest proportion of men’s fashions with a price decrease (25.0% vs. 1.4% for JC Penney). Top types of men’s fashions that had discounts were formal shoes, jackets and coats, and sports shoes. These findings seem to reflect how we rarely go out during the pandemic yet we’re exercising more.

    In addition, Amazon and Walmart were most active in offering discounts across all men’s fashion subcategories with Amazon offering more than double Walmart’s percentage of products discounted (15.9% vs. 7.1%).


    On Black Friday, JC Penney offered the most generous average discounts (35.6% vs. 9.4% for Overstock). While that contrast seems dramatic, it’s important to note JC Penney’s discounts applied to only 8 products compared to 929 for Overstock.

    Men’s fashions with the highest average discount on Black Friday included formal shoes, jackets and coats and jeans.

    Top product types by additional discounts- Women’s fashion

    For women’s fashions we also tracked whether prices decreased, increased or remained the same during the last week of November 2020. The vast majority of women’s fashions (89.3%) maintained the same prices during the pre-sale and sales periods. A whopping 99.3% of Target’s women’s fashion prices stayed the same.

    Neiman Marcus had the highest proportion of women’s fashions with a price decrease (33.4%), particularly on casual shoes, t-shirts and lingerie. JC Penney and Target offered the lowest proportion of price decreases on women’s fashions (1.9%).

    Similar to men’s fashions, Amazon and Walmart offered price discounts across all the women’s fashion subcategories with Amazon offering a higher proportion of products with discounts. (10.7% vs. 7.7% for Walmart)

    On Black Friday, JC Penney offered the most generous average discounts (45.0% vs. 12.2% for Overstock) yet JC Penney’s discounts applied to only 28 products compared to 1952 for Overstock.

    The types of women’s fashions with the highest average discount on Black Friday included tops, casual shoes and handbags. Perhaps women pampered themselves with a new purse and new tops to look chic on Zoom calls.

    Black Friday Vs Cyber Monday

    During this year’s holiday sales events, almost all retailers offered more additional discounts on men’s and women’s fashion on Cyber Monday than on Black Friday, possibly to sell off seasonal inventory before year-end. Nordstrom was the only exception, offering more discounts on Black Friday.

    On Cyber Monday, Target offered additional discounts on the greatest proportion of men’s fashions (63.3% vs. 10.6% for Walmart). Top types of men’s fashions with discounts included underwear, jeans, jackets and coats.

    Similarly, Target offered additional discounts on the greatest proportion of women’s fashions on Cyber Monday (79.4% vs. 3.2% for JC Penney). The most common types of discounted women’s fashions were dresses and jumpsuits, t-shirts and casual shoes.

    These findings suggest Target is aggressively pursuing value shoppers and positioning the chain as a convenient source for all the whole family’s apparel needs.

    Most retailers (Amazon, Nordstrom, Overstock, Target and Walmart) offered deeper additional discounts on men’s fashions on Cyber Monday than Black Friday, possibly to maximize year-end sales and clear out seasonal inventory. Cyber Monday discounts for men’s fashions ranged from 29.8% for Nordstrom to 11.0% for Overstock. Top types of men’s fashions that received Cyber Monday discounts included jackets and coats, formal shoes, sunglasses and t-shirts, which reflect how men are going out less.

    Conversely, most retailers (JC Penney, Macy’s, Neiman Marcus, Nordstrom and Walmart) offered deeper additional discounts on women’s fashions on Black Friday than Cyber Monday, possibly to entice women to get a jumpstart on the holiday sales weekend to maximize top line performance in this competitive category. Black Friday discounts for women’s fashions ranged from 45.0% for JC Penney to 12.2% for Overstock. Top types of women’s fashions with Black Friday discounts included swimwear, lingerie and t-shirts, which reflect seasonal merchandise.

    Additional discounts across products by “premiumness” level

    For almost every retailer, the percentage of fashions with additional discounts was higher on Cyber Monday than on Black Friday. Target had the highest proportion (62.7% vs. 5.7% for JC Penney). It appears Target really wants to win value-seeking apparel shoppers, by offering additional discounts on 93.3% of fashions at the low premium level (vs. 4.6% for Walmart).

    By contrast, Nordstrom had a higher percentage of fashions with additional discounts on Black Friday.

    Most retailers (Amazon, Bloomingdale’s, Neiman Marcus, Overstock, Target and Walmart) offered deeper discounts on Cyber Monday than Black Friday, likely make room for new seasonal merchandise.

    Neiman Marcus offered the most generous fashion discounts on Cyber Monday with an average additional discount of 30.1%, which ranged from 31.7% at the high premium level to 28.9% at the low premium level. This aggressive discounting could help Neiman Marcus stand out among department stores, and extend its reach and appeal by making fashions more affordable across price points.

    Conversely, JC Penney, Macy’s and Nordstrom offered deeper discounts on Black Friday. All three department stores were most generous at the low premium level for fashions, with JC Penney offering the deepest discounts (47.8%) to turn low premium fashions into irresistible Black Friday bargains.

    Additional discounts across products by “popularity” level

    Almost all retailers offered a greater proportion of additional fashion discounts on Cyber Monday than on Black Friday, ranging from 69.2% for Target to 5.2% for JC Penney, with a direct relationship between product popularity and additional discount percentage. Across all levels of popularity for fashions, Target was by far the most aggressive with discounts to appeal to the broadest variety of fashion shoppers.

    Only Nordstrom offered a higher proportion of additional discounts on fashions on Black Friday, focusing on both high and low levels of popularity.

    Most retailers (Amazon, Neiman Marcus, Overstock, Target and Walmart) offered deeper fashion discounts on Cyber Monday than on Black Friday, with both Neiman Marcus and Target being the most generous (28.8%). Amazon and Neiman Marcus were most generous with discounts among less popular items, while Overstock, Target and Walmart were most generous among moderately popular fashions.

    Conversely, JC Penney, Macy’s and Nordstrom offered more generous fashion discounts on Black Friday, with JC Penney being the most generous (39.2%). All three retailers offered the deepest discounts at the low level of popularity, possibly to make room for in-demand fashion items.

    2020’s Fashionable Holiday Prices

    As this year’s Black Friday and Cyber Monday fashion pricing results show, we prioritized comfort and basics over debonair formalwear. Since staying at home is in style, many retailers discounted dressier attire.

    In terms of competitive pricing strategies, Target’s aggressive discounts could boost the chain’s appeal among diverse fashion shoppers. Also, Neiman Marcus stood out among department stores by extending its reach and affordability across pricing tiers. 

    Click here for more Black Friday and Cyber Monday analysis to learn about retailers’ holiday pricing strategies during 2020’s e-commerce boom.


  • Country of Origin: E-retailers in India | DataWeave

    Country of Origin: E-retailers in India | DataWeave

    ‘Make in India’ is a headline you’re likely to have seen smeared across newspapers or as campaign rhetoric. E-retailers in India were mandated to display the country of origin against all the products starting 1st August, 2020. The rationale behind this move was to provide the customer the information which would aid the government to accelerate their plans on curbing imports.  

    To get an idea of the extent that this was followed, we looked at 29,000+ products on Amazon and 20,000+ products on Flipkart, across popular categories.

    What our data revealed (illustrated in the chart above) is that Flipkart had updated the country of origin information for 91% of the products while Amazon had updated for only 56%. In categories like Grocery/ Cooking Essentials and Personal Care, Flipkart updated this information across 100% of products that we looked at. 

    The chart above reveals interesting insights into the respective product mixes of Flipkart and Amazon. We narrowed our study to three categories that stood out; Electronics, Baby products and Men’s’ fashion. These are the categories we noticed that have the most number of products that are manufactured out of India. Apart from India, the largest manufacturer is China, where a lot of these products come from. Looking at this chart, we see that most of Flipkart’s products are manufactured in India, compared to its counterpart.

    To sum up, we noticed that Flipkart has updated 91% of its products while Amazon has updated only 56% of their products of the products we tracked. 

    With the heightened emphasis on Make in India and reducing imports, sellers importing from other countries might have to rethink how to replace the products they currently are sourcing with local products. This also provides an opportunity to the Indian manufacturers to produce popular products which are currently being imported.

    We’ll now have to wait and watch over the coming months to see how things unfold for these retailers and the sellers.

  • Amazon’s losing its pricing advantage this holiday season

    Amazon’s losing its pricing advantage this holiday season

    Amazon’s pricing advantage has declined in key categories, compared to last year as we enter 2020’s holiday season.

    The holidays are here and the retail industry is gearing up for the yearly stampede. In a report published by Bain & Company, in partnership with DataWeave, it was observed that, “When it comes to pricing, Amazon’s historical advantage is also deteriorating. The research shows that in October and November 2019, Amazon matched or beat competitors’ prices 81% of the time in the categories studied. By November of 2020, that rate dropped to 74%”. This was based on the four key categories where we had pricing data for Amazon and at least one other competitor.

    Amazon’s pricing advantage has declined in key categories

    Amazon_product_pricing

    Aggressive pricing, which was once Amazon’s forte, seems to be on a downward trend this year. All but one category saw an increase in the percentage of products where they beat the lowest price, ‘movies, music, video games’ – by a small margin of one percentage point.

    What could this shift be attributed to? The obvious would be the repercussions of COVID but there perhaps is more at work here. As observed last year, the behemoth that Amazon is, does not deter its competitors from constantly biting at the heels, with a steely determination to rope in market share. Everything from increased and specific customer demands, to government legislation, there are a lot of moving parts.

    One thing is for sure, this is surely just the beginning of the great e-commerce battle. For access to the full article that was published in the Retail Holiday Newsletter by Bain & Company and powered by DataWeave, click here.

  • How Essential Goods Have Shaped Retail Strategies

    How Essential Goods Have Shaped Retail Strategies

    The rapid evolution in essential goods is rattling retail. That’s because the COVID-19 pandemic has dramatically changed shopping habits and retail necessities, leading to unpredictable shifts in demand.

    Most notably, U.S. e-commerce has surged by an astonishing 45% year-over-year, as the pandemic accelerated online shopping by five years.[1] Since more consumers now work and learn from home, many pandemic-inspired habits will likely shape retail for years to come.[2]

    Now that the risk of the second wave lies ahead, it’s the ideal time for retailers to review pandemic bestsellers and patterns to adapt to shifts in shopping behavior.

    Pandemic’s bestsellers shape retail strategies

    2020’s unexpected consumption patterns give retailers a glimpse of how they can adapt and thrive. The best-selling essential goods during the pandemic have included:

    • Toilet paper: +734% year-over-year (YoY) growth in March[3]
    • Disposable gloves: +670% in March[4]
    • Fitness equipment: + 535% YoY in online sales for February to March[5]
    • Hand sanitizer: +470% YoY for the week ending March 7[6]
    • Yeast: +410% YoY for the four weeks ending April 11[7]
    • Puzzles: +370% YoY in the last two weeks of March
    • Pyjamas: + 143% in online sales between March and April[8]

    As such, retailers can ensure their assortments contain these types of popular cross-category items, which reflect overall themes of consumers’ needs for self-sufficiency, wellness and comfort.

    E-grocery is also soaring, as experts predict a 40% rise in U.S. online grocery sales in 2020 due to the pandemic.[9] Top categories bought by online grocery shoppers include:

    • Packaged non-fresh food (69%)
    • Toiletries, personal care and diapers (63%)
    • Household cleaning and paper products (61%)[10]

    In response to these trends, retailers can prioritize shelf-stable center store products and non-food consumer goods throughout the pandemic.

    How retailers boost agility, clarity and sales amid COVID-19 chaos

    Consumer panic led to pricing volatility for hard-to-find items like hand sanitizer, disinfectant wipes and masks.[11] To keep up with competitors’ online price fluctuations, more retailers use competitive analytics to adapt their own prices accordingly. Notably, McKinsey & Company cites data insights and price sensitivity as the top two disruptive trends the pandemic has turbocharged.[12]

    In March, shortages of toilet paper and flour led consumers to react with panic and hoarding that created urgent supply chain issues. To avoid out-of-stock items, more retailers now turn to data insights to identify potential disruptions. Up-to-date insights help retailers spot emerging market trends and adapt their assortment to stock in-demand items.

    Now that more consumers shop online, retailers are investing in digital promotions to boost sales. Data analytics help retailers quickly evaluate the effectiveness of their promotions, which can inspire consumers to fill their baskets. Nimbly adapting to competitors’ promotions is essential, as McKinsey cites rising competition for deals among the pandemic’s most disruptive retail trends.[13]

    Avoid empty shelves: The pandemic has motivated more retailers to rely on data insights to make fast, effective pricing and assortment decisions.

    As consumption habits evolve, high-level dashboards help retailers quickly spot inventory shortages to prevent out-of-stocks.

    To make their retail strategies pandemic-proof, leading retailers are collaborating with DataWeave to access accurate, actionable insights that boost online agility and sales. Applying DataWeave’s trusted data gives retailers clarity amid today’s chaotic market and shifting demand for essential goods, so they can make effective decisions fast. Insights also help retailers enhance the customer experience by supporting in-stock product assortments, competitive pricing and effective promotions that boost sales, trust and loyalty. To see how DataWeave helps retailers stay agile and competitive, visit dataweave.com.

    [1] Perez, Sarah. COVID-19 pandemic accelerated shift to e-commerce by 5 years, new report says. TechCrunch. August 24, 2020.

    [2] Gottlieb, David. 5 Strategic Imperatives for Retail’s New Normal. Total Retail. August 18, 2020.

    [3] Weiczner, Jen. The case of the missing toilet paper: How the coronavirus exposed U.S. supply chain flaws. Fortune. May 18, 2020.

    [4] Clement, J. COVID-19 impact on fastest growing e-commerce categories in the U.S. 2020. Statista. June 19, 2020.

    [5] Gibson, Kate. Coronavirus inspires fitness buying binge that tops New Year’s. CBS News. April 1, 2020.

    [6] Chasark, Krisann. Coronavirus impact: Hair dye becoming next high-demand item amid COVID-19 pandemic. ABC News. April 11, 2020.

    [7] Guynn, Jessica and Kelly Tyko. Dry yeast flew off shelves during coronavirus pantry stocking. Here’s when you can buy it again. USA Today. April 23, 2020

    [8] Thomas, Lauren. Comfort is en vogue during coronavirus: PJ sales surge 143%, pants sales fall 13%. CNBC. May 12, 2020.

    [9] Redman, Russell. Online grocery sales to grow 40% in 2020. Supermarket News. May 11, 2020.

    [10] Redman, Russell. Online grocery sales to grow 40% in 2020. Supermarket News. May 11, 2020.

    [11] Levenson, Michael. Price Gouging Complaints Surge Amid Coronavirus Pandemic. The New York Times. March 27, 2020.

    [12] Kopka, Udo, Eldon Little, Jessica Moulton, René Schmutzler, and Patrick Simon. What got us here won’t get us there: A new model for the consumer goods industry. McKinsey & Company. July 30, 2020.

    [13] Kopka, Udo, Eldon Little, Jessica Moulton, René Schmutzler, and Patrick Simon. What got us here won’t get us there: A new model for the consumer goods industry. McKinsey & Company. July 30, 2020.

  • How E-commerce Brands Build Customer Trust | DataWeave

    How E-commerce Brands Build Customer Trust | DataWeave

    Brands that build consumer trust will win big as online shopping explodes this year. As the COVID-19 pandemic propels more shoppers online, an astounding $5 trillion (30%) of annual global retail sales is up for grabs as the market shifts to e-commerce, according to Boston Consulting Group.[1]

    Notably, e-commerce has changed brands’ retail processes. Unlike brick-and-mortar stores, the digital shelf is where brands manage their company and products among online shoppers, influencing what they browse and buy.

    Rather than stocking merchandise through retailers’ physical stores, brands can now manage their online products by working with logistics experts like Fulfillment by Amazon. Instead of merchandising in stores with planograms and endcap displays, brands promote their digital assortment with targeted product content that resonates and keeps them coming back.[2] The evolution of retail can help brands save time and effort, and increase their agility and effectiveness.


    Gaining high visibility on the digital shelf can help brands boost their reach, brand awareness and sales. That’s why more brands are now investing in proven e-commerce best practices to increase consumer confidence by offering reliable products, relevant marketing and a smooth online experience. Now that the 2020 holiday sales season is underway, it’s the perfect time to see how leading brands compete in the increasingly crowded online market by demonstrating credibility and consistency.

    Market fragmentation increases brand complexity

    Selling across multiple online touchpoints means brands have more e-commerce websites and digital shelves to monitor to ensure compliance with brand guidelines to ensuring a consistent customer experience. To engage online shoppers, brands’ marketing strategies must now diligently manage their digital shelf across diverse online shopping arenas, including:

    • Direct-to-consumer (DTC) sites: More brand manufacturers are shortening the supply chain by bypassing retailers and selling directly to consumers, including sales through their own e-commerce websites.eMarketer predicts that U.S. DTC e-commerce sales will grow by 24% to reach nearly $18 billion in 2020.[3]

    • Retailers’ e-commerce sites: Brands are also migrating online because the retailers who sell their merchandise moved online this yearto adapt to the pandemic and survive shuttered storefronts. As of April 21, e-commerce grew 129% year-over-yearin U.S. and Canadian orders and U.S. e-commerce sales are on track to hit nearly $710 billion this year.[4] More than ever, sharing timely, accurate product information with retail partners is essential for success.

    • Online marketplaces: A growing number of brands are investing in digital advertising and content to stand out on popular, high-traffic online marketplaces. Global e-commerce leaders Amazon, eBay and China’s Alibaba and JD, are mostly search-based sites, as users know what they want and search for it, which makes product description pages and ads important marketing tools. Conversely, China’s Pinduoduo platform involves group-buying and interactive games to boost brand awareness and sales by entertaining online consumers and inspiring flattering word-of-mouth.[5] Among online marketplaces, digital content fuels brand discovery and sales.

    • Last-mile delivery channels: Brands can also sell in collaboration with their last-mile partners. Last-mile experts like Peapod, Instacart, Uber and Shipt offer online advertising opportunities for brands to reach new audiences. For instance, Instacart launched a self-serve advertising platform that lets brands promote their products in search results. Brands can choose the products to promote, set a budget and pay when users engage with those products.[6]

    • Social media: To reach and influence consumers where they already spend their time, more brands are investing in social media promotions and even embracing social commerce innovation. Social media matters to brands’ marketing effectiveness, as 52% of online brand discovery happens on social feeds.[7] Also, 92% of Instagram users say they’ve followed a brand, visited their website or made a purchase after seeing a product on Instagram.[8]

    Brand promotions have evolved beyond Google AdWords and Facebook campaigns. Now promotions include digital content and ads across all of these digital touchpoints, which increases the scope of brand marketing efforts to reach online consumers.

    How brands transform digital shelf complexity into clarity

    To earn online shoppers’ trust across e-commerce arenas, more leading companies are turning to a common solution: data.


    Too often, online shoppers abandon their cart due to concerns that they will unwittingly buy inauthentic products from fraudulent sellers. To protect their brands, manufacturers use data insights to pinpoint and prevent unauthorized sellers, counterfeit products and minimum advertised price (MAP) violations to demonstrate authenticity, accountability and price parity.

    “Digital is the new normal”
    ~ Nike CEO John Donahoe    [9]

    To invigorate underperforming online promotions, brands rely on analytics to connect the dots among their online promotions, marketing performance and share of voice. Insights on advertising, keywords and consumer reviews help brands make better marketing decisions faster. These insights help brands stand out from competitors and build relationships with shoppers by ensuring their promotions resonate and drive more sales online.

    To overcome low online traffic and sales, more brands apply data insights to improve their digital presence, visibility and sell-through rates. Brand analytics measure their popularity on e-commerce websites and track their stock status to improve accessibility, optimize digital shelf velocity and deliver a reliable customer experience that builds trust.

    Watch over your brand: To stay competitive and earn consumer trust, more brands now rely on data insights to make fast, effective decisions that enhance their reputation and boost online sales.

    As e-commerce explodes, more leading brands are collaborating with DataWeave for actionable brand analytics to protect their digital shelves, decrease complexity and boost consumer trust. These accurate, trusted insights help brands gain clarity to make smarter e-commerce decisions faster. Making data-driven brand management, promotional and digital marketing decisions helps brands prove their authenticity, improve marketing effectiveness and boost online sales. To see how DataWeave helps brands stand out, sell more and stay competitive, visit www.dataweave.com.

    [1] Taylor, Lauren, Chris Biggs, Ben Eppler, Henry Fovargue and Gaby Barrios.  How Retailers Can Capture $5 Trillion of Shifting Demand. Boston Consulting Group. August 31, 2020.

    [2] Gibbons, David. Ecommerce and content: How retailers have shifted strategies during the COVID-19 pandemic. Digital Commerce 360. August 18, 2020.

    [3] US Direct-to-Consumer Ecommerce Sales Will Rise to Nearly $18 Billion in 2020. eMarketer. April 1, 2020.

    [4] Wertz, Jia. 3 Emerging E-Commerce Growth Trends To Leverage In 2020. Forbes. August 1, 2020.

    [5] Lee, Emma. The incredible rise of Pinduoduo, China’s newest force in e-commerce. TechCrunch. July 26, 2018.

    [6] Goyal, Vivek. Browsing e-commerce: An untapped $250B+ opportunity. Medium. September 27, 2020.

    [7] Cooper, Paige. 43 Social Media Advertising Statistics that Matter to Marketers in 2020. Hootsuite. April 23, 2020.

    [8] Cooper, Paige. 43 Social Media Advertising Statistics that Matter to Marketers in 2020. Hootsuite. April 23, 2020.

    [9] Grill-Goodman, Jamie. ‘Digital is the New Normal,’ Nike CEO Says. RIS News. September 23, 2020.

  • Amazon Great Indian Festival Vs Big Billion Day- Who offered better discounts?

    Amazon Great Indian Festival Vs Big Billion Day- Who offered better discounts?

    The Great Indian Festival finally arrived and it coincided with Flipkart’s Big Billion Day Sale. The pandemic has pushed consumers to shop online and both, the Great Indian Festival and the Big Billion Day sales had been eagerly anticipated. Flipkart’s sale lasted between 16-21 October, while Amazon’s (in India) took started on 17th October.

    It is claimed that Amazon and Flipkart have hit $3.5 billion in sales in just four days. On the last day of its Sale, Flipkart claimed to have achieved 10 times growth as compared to last year’s Big Billion Day sale. Clearly, the sales have surpassed all the forecasts made for this year’s sale. We at DataWeave took a closer look to analyze the discounts that were offered across popular categories, to see if customers really had access to better deals and discounts. 

    Our Methodology:

    We looked at the top 500 products across categories like Fashion – men and women, electronics, Amazon devices, baby products, grocery and personal care. The pricing offered on these products across the sale period was compared with the pre-sale price, to understand the trend in discounts across the popular categories and brands.

    The Verdict:

    We segmented the products we were tracking into the following:

    Type 1: Products were either priced the same or were discounted over the sale compared to pre-sale 

    Type 2: Products were either priced the same or witnessed price increase during the sale compared to pre-sale 

    Type 3: Products which saw both price increase and decrease during sale compared to pre-sale

    Type 4: Products whose price continued to be the same even during the sale 

    flipkart_big_billion_day_2020_chart_1

    Flipkart clearly provided the better deals to customers for the categories we looked at during their Big Billion Day sale compared to Amazon. Flipkart discounted 54% of its products during the sale period compared to Amazon, and 26% of the products were discounted. 

    It is also interesting to note that in addition to offering more discounted products, Flipkart also offered additional discounts than Amazon.

    Amazon offered 13.2% additional discount and most of this average discounting can be attributed to a 33.8% discount on Amazon devices. It also ended up increasing the pricing for 16% of the products during the sale period, while Flipkart hiked the pricing for 6% products. 56% of products on Amazon continued being sold at the same price even during the sale. 

    Additional discounts across product premiumness levels

    Premiumness was based on the actual price of a product before the sale event. This was divided into low, medium and high premiumness levels, with high indicating higher selling prices.

    flipkart_big_billion_day_2020_chart_2
    big_billion_day_great_indian_sale_2020

    In Amazon devices, baby products, electronics and grocery-cooking essentials, Amazon showed a direct relationship between its additional discounts and the level of premiumness. While Flipkart did not seem to follow a particular pattern with respect to product premiumness. 

    Flipkart offered the highest discounts for premium products in the Fashion category (for both men and women) compared to the rest. 

    Top brands by additional discounts:

    We looked at popular brands across categories to arrive at brands that were being sold at the maximum discount. These brands appeared at least twenty times in the top 500 ranks we considered.

    amazon_great_indian_sale_2020_electronics

    Acer, Philips, Samsung, Lenovo, Bajaj, Asus which were common brands across both Flipkart and Amazon in the electronics category, were being sold at much deeper discounts on Flipkart (almost double), compared to that on Amazon.

    Avita was extremely popular under the laptop sub-category on Flipkart and was observed to be discounted the highest during the sale.

    In Fashion, Titan was the most discounted brand with 53.9% additional discount but only 4% and 2% of the products offered discounting in mens’ and womens’ fashion respectively. Reebok in mens’ fashion and Fastrack, Sonata and Puma in womens’ wear on Flipkart, had discounts across almost all the products. 

    amazon_great_indian_sale_2020_baby_care
    flipkart_big_billion_day_2020_baby_care

    In the baby care category, Hasbro gaming on Flipkart had the highest additional discount followed by Funskool. Both the brands had more than 85% of their products discounted. 

    Johnson’s, which was common on both Amazon and Flipkart, was offered at higher discounts on Amazon compared to Flipkart. However, only 31% products were discounted vs 72% on Flipkart.

    Most Visible Brands

    We looked at the top 200 ranks across each sub-category to narrow down on the most visible brands across the sale period.

    amazon_great_indian_sale_top_brands
    flipkart_big_billion_day_2020_top_brands

    Across all categories and their sub-categories, the sub-category laptop had distinct brands that hold the majority of the products. This is observed both in Amazon and Flipkart where brands like Lenovo, HP, Asus hold more than 33% share of the first 200 products. 

    “Mobile” category was dominated by brands like Redmi and Boat on Amazon, and Realme and OPPO on Flipkart. These brands occur at least 24% of the time in the top 200 ranks.

    Who Won?

    There are many ways to look at this. To begin with, the combined sales of Flipkart and Amazon during the festive season in India accounted for more than 90% of the e-commerce industry’s gross sales. That amounts to a 55% year-on-year growth. Delving further, we see that Flipkart was far more aggressive with their offerings.

    They discounted 56.8% additional products at an overall discount of 15%. On the other hand, Amazon retained their typical cautious approach to discounting, with only 28.4% of the products, at an overall discount of 12.8%.

    If we adopt a more macro view of the sales, we have to take into account that this year is somewhat of an anomaly. Given the social distancing norms and other SOPs governing the common man, more people have been ushered into the world of online shopping. The penetration extended far into the Tier 2 and Tier 3 cities as well, thus potentially benefiting Flipkart, owing to their interior reach.

    Going by the numbers, Flipkart seems to have taken this round without a doubt. As we observed though, there are many ways to look at this and what seems to stand out from these two giants, is the consumer. At the end of the day, it’s the consumer that in spite of these strange times, has shopped more than before, indicating that the situation is getting back to a semblance of normalcy.

    So Flipkart’s got the sales numbers but the consumer got deeper discounts on more products. As the old adage goes, ‘consumer is king’.

  • Prime Day 2020: Home categories fuel retail rivalry & desirable discounts

    Prime Day 2020: Home categories fuel retail rivalry & desirable discounts

    According to our preliminary analysis of Prime Day 2020, Amazon’s rivals offered more generous discounts within Home categories to stay competitive as more consumers invest in their homes this year.

    This year the COVID-19 pandemic has transformed consumers into homebodies who increasingly work, learn and shop from home. This year also marks the first time Prime Day took place in the Fall, jumpstarting the holiday sales season.

    At DataWeave, we wanted to know whether Prime Day 2020 lived up to the hype and how Amazon’s deals compared to other retailers’ discounts. Our analysis examines products across four popular Home categories: Bed & Bath, Furniture, Kitchen and Pet Care.

    Our Methodology

    We tracked the pricing of several leading retailers (Home Depot, Target, Walmart and Amazon) selling the Home categories of Bed & Bath, Furniture, Kitchen and Pet Care to assess their pricing and assortment strategies during this annual sales event. Our analysis focused on additional discounts offered during the sale to estimate the true value that the sale represented to consumers. Our calculations compared product prices on Prime Day versus the prices prior to the sale. The sample consisted of up to the top 750 ranked products across 16 popular product types for the home.

    The Verdict 

    Overall, Amazon reported the lowest price reduction in all Home categories (12.4%), compared to Target (22.1%), Home Depot (16.5%), and Walmart (15.1%). Yet Amazon reported the second-highest percentage of additionally discounted products (9.6% vs. 11.0% for Target).

    After Prime Day ended, certain retailers’ Home assortments saw more significant price increases than others. For instance, 88% of Target’s 760 products in Bed & Bath, Furniture, Kitchen and Pet Care received a price increase during the post-sale period, compared to 47% of Walmart’s 1005 products. Walmart’s everyday low price strategy helps to explain the difference between the two big box retailers.

    These results suggest that Prime Day 2020 may boost Amazon’s marketing and PR engagement yet its rivals offered the most generous deals in Home categories. As home-related categories’ sales soared during the pandemic, Amazon’s competitors offered deep discounts to stand out online and grow their market share. As such, consumers may want to embrace the habit of comparing multiple retailers’ websites to discover the best Prime Day deals in Home categories.

    Top product types by additional discount

    In Bed & Bath, Target offered the biggest average additional discount (27.4%) and Amazon offered the lowest (13.3%). Bed sheets and pillowcases were a popular product category for additional discounts across all four retailers, with Target offering the best average additional discount at 31.3%. Other popular product types among rival retailers included blankets, comforters and bathroom furniture.


    In Furniture, Home Depot (20.5%) offered the biggest overall additional discount, closely followed by and Target (19.2%). Living room furniture was a popular subcategory for all four retailers, with Home Depot offering the highest additional discount (29.1%). Other popular product types included furniture for the bedroom, home office, kitchen and dining room.

    In the Kitchen category, Target offered the biggest average additional discount for small appliances (21.8%), a subcategory in which all four retailers offered discounts. Within the large appliance subcategory, Walmart’s additional discounts were nearly triple Amazon’s (15.7% vs. 5.6%).

    Within the Pet Care category, Target offered the biggest average additional discount (18.5%). Cat food was a popular product category, with Target offering the best average additional discount (50.0%). Other popular product types across all four retailers included dog collars, leashes and dry dog food.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into low, medium and high premiumness levels, with high indicating higher selling prices.

    In Bed & Bath, most retailers showed an inverse relationship between their additional discounts and the products’ level of premiumness. Target offered the biggest additional discounts across all levels of premiumness, more than double Amazon’s discounts (27.2% vs. 12.3%). Target’s bold discounting strategy shows a commitment to protecting its competitive position across the entire Bed & Bath category.

    By far, Amazon offered the greatest percentage of additional discounts in Bed & Bath compared to its rivals across all levels of premiumness. Comparatively pervasive discounts help the e-commerce giant offer a greater variety of appealing deals within this category.

    In Furniture, most retailers showed a direct relationship between their additional discounts and the level of premiumness. Notably, Home Depot offered massive additional discounts at the high premium level, nearly triple Amazon and Walmart (34.5% vs. 12.7%). This move suggests Home Depot is serious about winning the business of upscale consumers in the Furniture category.

    Target differentiated its assortment by discounting by far the greatest portion of its Furniture at all premiumness levels (22.4%) and Home Depot discounted the least (4.4%). Amazon and Walmart distributed the greatest portion of their additional discounts to the moderate level of premiumness. Target’s strategy tries to attract all Furniture shoppers while Amazon and Walmart try to make their mid-market offerings affordable to more consumers.

    Across all levels of premiumness for Kitchen products, Target offered the biggest additional discounts, including almost double Amazon’s discounts at the medium level (22.5% vs. 13.4%). Target’s aggressive discounting shows a desire to be more competitive by attracting consumers at all levels of the Kitchen category.

    In the Kitchen category, most retailers offered a direct relationship between the proportion of additional discounts and the level of premiumness, yet Home Depot showed an inverse relationship. Amazon’s proportion of additional discounts across all levels of premiumness nearly tripled Home Depot’s (10.1% vs. 3.7%). This discount strategy shows Amazon’s willingness to offer shoppers deals across a broader variety of Kitchen items.

    In Pet Care, Walmart offered the highest overall additional discounts (16.2%), which could fortify its low-cost leadership position for pet lovers at all price points.


    While Target offered the greatest overall percentage of additional discounts in Pet Care, Amazon applied more discounts to the higher end of the premium spectrum and Target focused on the lower end.

    Additional discounts across visibility levels

    In Bed & Bath, Target offered the highest overall additional discounts across all levels of visibility (27.3%) and Amazon offered the lowest (12.4%). Amazon focused its additional discounts on the most visible Bed & Bath products to help online shoppers discover those items with ease and make them appealing enough to add to their cart.


    Amazon offered the lowest additional discounts in the Furniture category across all levels of product visibility. Yet, among the Furniture category’s most visible items, Amazon offered its highest additional discounts. Home Depot’s additional discounts approach was the most aggressive except among the lowest product visibility levels. Home Depot’s discount strategy shows a desire to compete for Furniture’s most visible items.

    In the Kitchen category, Home Depot consistently offered the lowest additional discounts among products at the higher visibility levels. Conversely, Target was the most aggressive in this category, offering additional discounts of up to 43.2% at moderate levels of visibility and double Home Depot’s discounts (26.3% vs. 13.4%) among the most visible items. Amazon may feel confident that men already choose Amazon for their apparel needs.

    In Pet Care, the retailers generally offered the most additional discounts for items in the middle of the visibility spectrum. Walmart offered the most aggressive additional discounts among the most visible Pet Care items, more than double Target’s discounts (13.5% vs. 6.5%).

    Overall, Prime Day 2020 offered an ideal time for Amazon to attract homebound consumers to invest in domestic products, yet its rivals offer much higher additional discounts in Bed & Bath, Furniture, Kitchen and Pet Care. How about other categories? Watch this space for more insights!

  • How Prime Day 2020 Deals Influenced Retail Pricing Strategies

    How Prime Day 2020 Deals Influenced Retail Pricing Strategies

    Our preliminary analysis reveals that Prime Day 2020 motivated Amazon’s rivals to offer deeper discounts in key categories to try to make their merchandise more magnetic and lure consumers away from the e-commerce giant.

    This year’s Prime Day is momentous, as the COVID-19 pandemic has encouraged more consumers to make online shopping a more regular habit. It also marks the first time Prime Day took place in the strategically significant final quarter of the year, kicking off the holiday sales season.

    At DataWeave, we wanted to know whether Prime Day 2020 lived up to the hype and how Amazon’s deals compared to other retailers’ discounts. Our analysis examines products across three popular categories: electronics, beauty and fashion.

    Our Methodology

    We tracked the pricing of several leading retailers (Best Buy, Target, Walmart and Amazon) selling consumer electronics, beauty and fashion to assess their pricing and assortment strategies during this annual sales event.

    Our analysis focused on additional discounts offered during the sale to estimate the true value that the sale represented to consumers. Our calculations compared product prices on Prime Day versus the prices prior to the sale. The sample consisted of up to the top 750 ranked products across 21 popular product types in consumer electronics, beauty and fashion.

    The Verdict

    Overall, Amazon reported the lowest price reduction in the Electronics, Beauty and Fashion categories (13.4%), compared to Best Buy (22.5%), Target (21.7%) and Walmart (16.3%). Yet Amazon reported the second-highest percentage of additionally discounted products (12.0% vs. 15.7% for Target).

    After Prime Day ended, certain assortments reflected more significant price increases than others. For instance, 97% of Target’s 158 products in Electronics, Beauty and Fashion had a price increase during the post-sale period, compared to 49% of Walmart’s 986 products. This discrepancy makes sense given Walmart’s everyday low price strategy.


    These results suggest that although Prime Day generates tremendous media buzz for Amazon, the most generous deals come from its rivals. To stand out and lure shoppers away from Amazon, competitors offered comparatively deeper discounts, especially in categories in which they want to grow their market share. This means online shoppers would be wise to compare prices across retailers’ websites to find the best cross-category deals on Prime Day.

    Top product types by additional discount

    In Electronics, Best Buy offered the biggest average additional discount (22.4%) and Amazon offered the lowest (9.4%). Tablets were a popular product category among Amazon, Best Buy and Walmart, with Best Buy offering the best average additional discount at 19.1%. Other popular product types among rival retailers included TVs, desktops and laptops.


    In Beauty, Target (13.2%) and Walmart (13.1%) almost tied for the biggest overall additional discount. Makeup was a popular beauty subcategory, with Walmart offering the highest additional discount at 19.7%. Other popular product types included hair care, skin care and fragrance.

    In Men’s Fashion, Target offered the biggest average additional discount of 28.1%. Suits and blazers were a popular fashion subcategory, in which Target offered the highest average additional discount at 50.0%. Other popular product types included T-shirts and tank tops, shirts and jeans.


    Within the Women’s Fashion category, Walmart offered the biggest average additional discount of 20.5%. Tops and tees were a popular product category across all three fashion rivals, with Walmart offering the best average additional discount at 23.6%. Other popular product types included dresses, jumpsuits and jeans.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into low, medium and high premiumness levels, with high indicating higher selling prices.


    In Electronics, Amazon showed a direct relationship between its additional discounts and the level of premiumness; Best Buy and Walmart showed an inverse relationship. Best Buy offered the biggest additional discounts across all levels of premiumness, nearly triple Amazon’s discounts (20.7% vs. 7.0% ) at the low end of the premium spectrum, and more than double Amazon’s discounts (18.5% vs. 7.3%) at the moderate level. Best Buy’s discounting strategy show it’s serious about protecting its competitive position in electronics.

    Best Buy and Walmart offered the most additional discounts at the high end of the premiumness spectrum, making both retailers more competitive in the high-ticket electronics category. By contrast, Amazon offered nearly double the additional discounts of its rivals within the low segment, which helps to protect its margins while making products even more affordable and appealing.


    In Beauty, Amazon and Walmart offered their biggest additional discounts at the low premium level, possibly to position those products as loss leaders. Meanwhile Target nearly doubled and tripled its rivals’ additional discounts at the high premium level (30.0% vs. 16.0% for Walmart and 11.0% for Amazon) to stand out in this intensely competitive category.

    Amazon stood out by discounting the greatest portion of its Beauty offerings at all premiumness levels and Target discounted the least. Amazon and Walmart showed a direct relationship between their distribution of additional discounts and the beauty products’ premiumness level.


    Across all levels of premiumness for Men’s Fashion, Target offered the biggest additional discounts, including more than triple Amazon’s discounts at the high end (38.4% vs. 12.4%). Target’s aggressive discounting shows a desire to be more competitive within the most premium segment of Men’s Fashion.

    Amazon’s additional discounts accounted for the greatest percentage of its Men’s Fashions across all levels of premiumness, nearly triple Target’s overall average (15.4% vs. 5.3%). This approach shows Amazon’s willingness to give shoppers deals across a broader variety of Men’s Fashion items.

    In Women’s Fashion, Target’s and Walmart’s overall additional discounts were comparable, and Amazon’s discounts were consistently the lowest among all levels of premiumness. Walmart offered its most generous discounts at the low and medium level of premiumness, which could reinforce its low-cost leadership image.

    While Amazon and Target offered a comparable overall percentage of additional discounts in Women’s Fashions, Amazon applied more discounts to the higher end of the premium spectrum and Target focused on the lower end.

    Additional discounts across visibility levels

    In Electronics, Amazon offered the lowest average additional discounts across all levels of visibility. Among the most visible electronics, Amazon and Best Buy gave the most visible electronics higher additional discounts to make those items more alluring to help consumers find the items fast and add them to their online baskets.

    Among the Beauty category’s most visible items, Amazon and Target offered their highest additional discounts. Yet Target was most aggressive in beauty, offering a 30% additional discount at the most visible end of the spectrum as well as at the least visible. This discount strategy shows Target wants to compete in Beauty, spreading its generosity beyond an exclusive focus on highly visible items.

    In Men’s Fashion, Amazon consistently offered the lowest additional discounts at all visibility levels. Target was the most aggressive in this category, offering additional discounts of 50% at moderate levels of visibility and 34.5% among the most visible items. Amazon may feel confident that men already choose Amazon for their apparel needs.

    In Women’s Fashion, the retailers generally offered the most additional discounts for items at the higher end of the visibility spectrum. Walmart offered the most aggressive additional discounts among the most visible items in Women’s Fashion to try to boost its market share in this category.

    Overall, while Prime Day is an effective way for Amazon to boost brand engagement, its rivals overwhelmingly offer higher additional discounts in Electronics, Beauty and Fashion. How about other categories like the booming Home space? Watch this space for more insights!

  • Food Delivery Boom Fuels Competition Among Restaurants

    Food Delivery Boom Fuels Competition Among Restaurants

    This year, homebound consumers crave the convenience of food delivery.
    Growing 20% since 2015, restaurant delivery has sparked intense rivalry to reach consumers’ homes. Although the pandemic led to $165 billion in lost sales industry-wide between March and July, experts predict online food delivery sales will reach $220 billion by 2023, accounting for 40% of total restaurant sales.[1,2]

    This massive market opportunity makes food delivery an urgent priority for restaurants to stay competitive and solvent during the pandemic. This year nearly one in six U.S. restaurants have closed either permanently or long-term.[3]

    Also, 40% of U.S. operators say they will likely be out of business within six months if economic conditions persist and 60% of Canadian restaurants could close permanently by November.[4,5]

    COVID-19 compounds market complexity

    Powerful market trends are rattling restaurants. During the pandemic, nearly 70% of operators have added third-party delivery to lift sales.[6]

    This year, third-party delivery from food delivery apps like Uber Eats, Grubhub and DoorDash will grow 21% over 2018.[7] The global market for cloud kitchens (also called ghost kitchens or virtual kitchens), commercial kitchens intended for delivery-only orders, will grow from $650 million in 2018 to $2.6 billion by 2026.[8]


    To avoid the need to rely on delivery partners, many chains invest in their own last-mile delivery capability to serve their fleet of restaurants.
    E-grocery sales are poised to surge 40% in 2020 and meal kits have boomeranged back into popularity, nearly doubling 2019 sales.[9, 10]

    Consumers demand speed to keep their food fast, fresh and hot. Prompt service matters, as one survey found when consumers face a food delivery issue, 93% want it resolved within 10 minutes.[11]

    The recession and job losses mean more consumers now need affordable food options. Meanwhile, restaurants are investing more in technology to modernize operations for efficient omnichannel service.

    How restaurants are adapting to 2020’s disruption

    Restaurant prices have risen during the pandemic to cover operating costs. Third-party delivery fees have led 41% of consumers to prefer to order food by contacting the restaurant directly (vs. 16% for third-party delivery).[12] To optimize pricing competitiveness, more restaurants now compare their delivery fees and offerings with rivals’ to spot and correct gaps, and keep their prices affordable.

    To streamline operational processes and costs during the pandemic, 28% of restaurants shrank their menus.[13]

    For clarity on which items to keep, operators now use data insights on restaurant listings and menu items down to the ZIP code level. This information also helps them decide whether to adapt to consumers’ diverse tastes, including vegan, gluten-free and organic, for competitive local assortments.

    Outperform rivals: Restaurant operators seek proof of their brand visibility on food delivery apps’ homepages.

    Restaurants have discovered consumers welcome reasons to celebrate at home this year. One chain’s weekly virtual happy hours on Facebook Live drew 80,000 participants and a $40,000 sales increase from delivery and takeout orders.[14]

    More restaurants now compare their promotional strategies with rivals’ to evaluate marketing performance, including homepage discoverability and visibility ranking, to ensure consumers find their brand online with ease.

    Delivery speed and precision also matter. A survey found 70% of consumers had food delivery order complaints, including late delivery (50%), incorrect order (37%) and cold or stale food (36%).[15] Using accurate geographic data can help restaurants improve speed and the customer experience.

    To gain a competitive advantage in today’s booming food delivery market, a growing number of leading chains and food delivery providers are collaborating with DataWeave to access actionable insights to make better strategic and operational decisions faster. Using trusted insights to make data-driven pricing, menu and promotional decisions help restaurants save time, reduce risk and gain clarity in today’s evolving market.

    Applying DataWeave’s accurate, up-to-date information also helps restaurants deliver affordability, convenience and variety to remain responsive to consumers and agile among competitors. To see how DataWeave helps restaurants stay relevant and competitive, contact us today.

    [1] Rogers, Kate. Winter is coming, bringing a new challenge to already-struggling restaurants. CNBC. September 14, 2020.
    [2] Zahava Dalin-Kaptzan. Food Delivery: Industry Trends for 2020 and beyond. Bringg. April 30, 2020.
    [3] Klein, Danny. 100,000 Restaurant Closures Expected in 2020. QSR. September 14, 2020.
    [4] Rogers, Kate. Winter is coming, bringing a new challenge to already-struggling restaurants. CNBC. September 14, 2020.
    [5] Charlebois, Sylvain. Don’t Want to Save the Restaurant Industry? Fine, but Use it to Save the Canadian Economy. Retail Insider. September 11, 2020.

    [6] Rogers, Kate. Winter is coming, bringing a new challenge to already-struggling restaurants. CNBC. September 14, 2020.
    [7] US Food Delivery App Usage Will Approach 40 Million Users in 2019. eMarketer. July 2, 2020.
    [8] Levy, Ari. Virtual Kitchen, founded by ex-Uber execs to help restaurants with delivery, raises $20 million. CNBC. Sept. 8 2020
    [9] Redman, Russell. Online grocery sales to grow 40% in 2020. Supermarket News. May 11, 2020.
    [10] De Leon, Riley. How the coronavirus pandemic delivery surge created a lifeline for Blue Apron meal kits. CNBC. May 22, 2020.
    [11] Guszkowski, Joe. Delivery services have room to improve, consumers say. Restaurant Business Online. Sept. 1, 2020.
    [12] Guszkowski, Joe. Consumers’ desire to order directly from restaurants is a big opportunity. Restaurant Business Online. Aug. 27, 2020.
    [13] Romeo, Peter. Best practices for weathering a second COVID wave. Restaurant Business Online. Aug. 28, 2020.
    [14] Ibid. 
    [15] Guszkowski, Joe. Delivery services have room to improve, consumers say. Restaurant Business Online. Sept. 1, 2020.

  • Introducing the CPG Brand Monitor by DataWeave

    Introducing the CPG Brand Monitor by DataWeave

    As DataWeave continues to engage with brands and manufacturers of all sizes, a consistent theme keeps emerging, “click and collect tracking”. Right now, brands rely on manual-store checks or waiting upwards of two weeks for a retailer to report sales data, which reveals low sales because a product is out of stock. In addition, there are always questions about the local price of your products compared to top competitors in the category. This is where DataWeave’s CPG Brand Monitor solution can help. 

    Click here for a quick tour of our dashboard.

    What we cover?

    On a daily basis, we track over 13,000 variant level SKUs across 100 stores, via seven of the top grocery retailers. We have selected the largest grocers in each region of the US, to allow for the widest coverage. These grocers include Albertsons/ Safeway in the west, HEB in Texas, Kroger in the upper mid-west, Wegmans in the Mid-Atlantic and Publix in the Southeast. 

    How does it work?

    In the application, you will see the list of all the SKUs we’re covering, with filters on the left side of the page to help with navigation. You can sort by Brand, Category, Store/ City, State, etc. After the filters are applied, the SKU list will be displayed based on these filters.  On the right side of the screen, you will see all the product level details including a 7-day price history, individual store level pricing/ stock availability and exportable charts and graphs. 

    How do I get access?

    Simply access the CPG Brand Monitor page, fill in your credentials via “Start Free Trial” and your login will be sent directly to your inbox. No commitments or phone calls are needed to test out the data. After a few days, our team will be in touch to make sure you understand how to navigate the tool and take you through our subscription options.   

    What else do we offer?

    DataWeave also offers a full Digital Shelf Analytics suite that covers Share of Voice (keyword, navigation and banner audits), Content Audit/ Optimization, Ratings/ Review Sentiment Analysis, Promotional Analysis, and much more. 

  • JioMart Launches Online Grocery Store

    JioMart Launches Online Grocery Store

    JioMart, the online channel for Reliance Retail Limited, launched in December 2019 as a contender in the e-grocery segment. Currently in India, this segment is being dominated by bigbasket, Amazon, Flipkart Supermart, Grofers, etc. After less than a year and from their initial launch in Mumbai, they now have their presence in 205 cities across India.

    According to their recent press release, they claim to be clocking over 250,000 daily orders, compared to bigbasket’s 220,000 and Amazon’s 150,000. To get an understanding of this rapid penetration, we had a look at the PIN codes that JioMart serves, spanning the country.

    The map below represents the percentage of PIN codes that are being served by JioMart’s online grocery in each state:

    **Disclaimer -Map for representation purposes only

    While states like Chandigarh, Delhi and Punjab in the North are covered extensively, JioMart has a stronger distribution in the Southern states.

    The image below shows the top ten states in India where JioMart’s online grocery has the highest presence:

    They’re yet to launch in 14 more states but it’s interesting to note that in this limited time, they’ve managed to cover 14% of the PIN codes in the country and all this, in the midst of lockdowns.

    Assortment

    To get an idea of the assortment in their range, we analyzed select PIN codes across three tiers of cities in India. The parameters we looked at were categories, brands and discounts to get an understanding of how JioMart is stacking up against its competitors. The cities we examined were:

    • Tier 1 – Bangalore, Delhi, Kolkata, Mumbai
    • Tier 2 – Ahmedabad, Jaipur, Kochi, Visakhapatnam
    • Tier 3 – Mohali, Mysore, Nagpur, Siliguri

    In its range, they offer eight broad categories, of which, we focussed on the four that offer the highest selection of products: home care, personal care, snacks & branded food and staples.

    The table below represents the average selection of products offered across each tier.

    Overview of discounts offered and the private label split

    Out of the assortment we looked at in the three tiers, we noticed that an average of 18% of the products are JioMart’s private labels. What stood out further is that private labels accounted for 48% in the Staples category and 24% in Personal Care. We noticed this trend (increase in the private label) when we did an analysis of Amazon.

    When it comes to discounts, we noticed that a near-total 91% of the products listed are being sold at a discount. Out of this, the highest discounts were witnessed in the Home Care and Staples categories.

    The brands with the highest number of products listed were Good Life, Reliance, Amul, Gillette and items sold loosely. All these accounted for 14% of the assortment. Out of these, Good Life, Reliance and the loose items are JioMart’s private labels.

    Competitor analysis

    To get an idea of where JioMart stands with relation to its competitors, we focussed on food and essentials in the Tier 1 cities. The table below highlights the number of product offerings in each category:

    It’s clear that in these categories (food and essentials), JioMart has the least number of products on discount. There’s no doubt that bigbasket is miles ahead in its product range/ assortment.

    To get a better idea of the discounting patterns, we analyzed the same categories to get a count of the number of products being discounted, as well as the average discount being offered. 

    We noticed that JioMart bookended our analysis – the least average discount, across the most number of products. Grofers offered the highest average discount of 23% with Flipkart Supermart and bigbasket closely behind. Lastly, bigbasket had the least number of products on discount with a little over 53%.

    Conclusion

    JioMart launched during a tumultuous and unprecedented time; the COVID-19 pandemic and the subsequent nation-wide lockdowns. Given this trial by fire, they managed to make an impact in this highly competitive space. Their expansion plans of tying up with mom and pop stores to fortify their penetration, had to take a back seat due to the ongoing situation but is sure to resume once conditions improve. This set-back did not however deter JioMart from attracting strategic investments from Facebook, Google and 12 other investors  in a span of 3 months. 

    In a study by Goldman Sachs, it found that India’s e-commerce business is expected to grow at a compound annual growth rate of 27% by 2024, resulting in a $99 billion market share. What’s even more shocking is that 50% of this market will be captured by Reliance Industries. It, therefore, stands to reason that all we’ve seen and heard of so far, is merely the tip of the iceberg and there’s surely more to come in the near future.

  • Market Intelligence Platform with Kenshoo

    Market Intelligence Platform with Kenshoo

    We’re thrilled to announce that we have teamed up with Kenshoo to offer an integrated marketing solution that combines DataWeave’s digital shelf analytics and commerce intelligence platform with Kenshoo’s ad automation platform. This in turn, provides better recommendations on promotions to retailers and consumer brands.

    As e-commerce surges, consumer brands can now promote their products through retail-intelligent advertising. Product discoverability, content audit, and availability across large marketplaces can be critical to a brand’s success. Using DataWeave’s digital shelf solutions, Kenshoo now can offer marketers greater visibility into a brand’s performance.

    Even large retailers and agencies can use our commerce intelligence platform to improve their price positioning, address category assortment gaps, and more.  

    Through this partnership, Kenshoo – a global leader in marketing technology, can help its significant base of consumer brands and retailers invest their marketing dollars intelligently and in a timely manner.

    At DataWeave, we have constantly strived to bring in a holistic approach to help our customers optimize their online sales channels. This partnership furthers our resolve in this direction. As we collectively strive to adjust to a post-COVID-19 world, we are observing an acceleration towards digital commerce. This acceleration and change in consumer behavior is going to be a lasting change, creating significant growth opportunities for both DataWeave and Kenshoo.

    With this partnership, we look forward to helping our customers make timely, intelligent, and data-driven decisions to grow their business.

  • Amazon Triples Down on its Private-label Product Portfolio

    Amazon Triples Down on its Private-label Product Portfolio

    Among Amazon’s most prominent and decisive steps in achieving retail dominance over the last few years has been its focus on expanding its private label portfolio.

    The most recent collaborative report between DataWeave and Coresight Research determines that Amazon’s private label assortment in early 2020 has grown three-fold over the previous two years, most of which is in categories outside of apparel and accessories.

    In addition, the report covers various facets of Amazon’s private label penetration and strategy. These include the size of Amazon’s private label portfolio, the distribution of private label products by category, the product ratings and number of reviews, the average price points across products and brands, and more.

    Our detailed and proprietary Amazon private label dataset includes information on over 20,000 products and 111 brands.

    Some of our key findings are:

    • Amazon’s private-label offering spans 22,617 products across 111 identified private labels.
    • Around half of the private-label products are in clothing, footwear and accessories, which is lower than the three-quarters found in our similar research from June 2018, indicating Amazon’s push into a broader range of categories.
    • The average Amazon private-label product generates a customer rating of 4.3 out of 5, representing positive customer feedback overall.

    Amazon’s Private-Label Offering Spans 22,617 Products across 111 Identified Private Labels

    The number of private-label products—22,617—is more than triple the total of 6,825 from June 2018 (see our previous report). The number of private-label brands also increased substantially (up 50% versus June 2018), indicating that the e-commerce giant has stepped up its private-label strategy.

    Around Half of Private-Label Products Are in Clothing, Footwear and Accessories

    Just over half of Amazon’s private-label products are in “clothing, footwear and accessories,” versus almost three-quarters when we undertook similar research in June 2018, indicating Amazon’s push into a broader range of categories. Other categories that feature more than 1,000 private-label products include “home and kitchen,” “grocery and gourmet food” and “tools and home improvement.”

    Source: DataWeave/Coresight

    The Average Amazon Private-Label Product Generates a Customer Rating of 4.3 out of 5

    We examined feedback provided by Amazon’s private-label customers: Customer satisfaction can be measured by the average star rating that customers have left in reviews. We chart both average star rating and average number of customer reviews per product in the graph below.

    The average Amazon private-label product generates a customer rating of 4.3 stars out of 5, suggesting overall solid customer satisfaction levels.

    Source: DataWeave/Coresight

    The full report is available for Coresight’s premium subscribers. It includes further details of categories and subcategories that suggest longer-term implications—including how Amazon targets a niche customer base through specific category labels but appeals to broader consumer needs by offering multicategory labels.

    To access DataWeave’s proprietary database on Amazon’s private label brands and products, reach out to us today!

  • How Apache Airflow Optimizes Complex Workflows in DataWeave’s Technology Platform

    How Apache Airflow Optimizes Complex Workflows in DataWeave’s Technology Platform

    As successful businesses grow, they add a large number of people, customers, tools, technologies, etc. and roll out processes to manage the ever-increasingly complex landscape. Automation ensures that these processes are run in a smooth, efficient, swift, accurate, and cost-effective manner. To this end, Workflow Management Systems (WMS) aid businesses in rolling out an automated platform that manages and optimizes business processes at large scale.

    While workflow management, in itself, is a fairly intricate undertaking, the eventual improvements in productivity and effectiveness far outweigh the effort and costs.

    At DataWeave, on a normal day, we collect, process and generate business insights on terabytes of data for our retail and brand customers. Our core data pipeline ensures consistent data availability for all downstream processes including our proprietary AI/ ML layer. While the data pipeline itself is generic and serves standard workflows, there has been a steady surge in customer-specific use case complexities and the variety of product offerings over the last few years.

    A few months ago, we recognized the need for an orchestration engine. This engine would serve to manage the vast volumes of data received from customers, capture data from competitor websites (which can range in complexity and from 2 to 130+ in number), run the required data transformations, execute the product matching algorithm through our AI systems, process the output through a layer of human verification, generate actionable business insights, feed the insights to reports and dashboards, and more. In addition, this engine would be required to help us manage the diverse customer use cases in a consistent way.

    As a result, we launched a hunt for a suitable WMS. We needed the system to satisfy several criteria:

    • Ability to manage our complex pipeline, which has several integrations and tech dependencies
    • Extendable system that enables us to operate with multiple types of databases, internal apps, utilities, and APIs
    • Plug and play interfaces to execute custom scripts, and QA options at each step
    • Operates with all cloud services
    • Addresses the needs of both ‘Batch’ and ‘Near Real Time’ processes
    • Generates meaningful feedback and stats at every step of the workflow
    • Helps us get away with numerous crontabs, which are hard to manage
    • Execute workflows repeatedly in a consistent and precise manner
    • Ability to combine multiple complex workflows and conditional branching of workflows
    • Provides integrations with our internal project tracking and messaging tools such as, Slack and Jira, for immediate visibility and escalations
    • A fallback mechanism at each step, in case of any subsystem failures.
    • Fits within our existing landscape and doesn’t mandate significant alterations
    • Should support autoscaling since we have varying workloads (the system should scale the worker nodes on-demand)

    On evaluating several WMS providers, we zeroed in on Apache Airflow. Airflow satisfies most of our needs mentioned above, and we’ve already onboarded tens of enterprise customer workflows onto the platform.

    In the following sections, we will cover our Apache Airflow implementation and some of the best practices associated with it.

    DataWeave’s Implementation

    Components

    Broker: A 3 node Rabbit-MQ cluster for high availability. There are 2 separate queues maintained, one for SubDags and one for tasks, as SubDags are very lightweight processes. While they occupy a worker slot, they don’t do any meaningful work apart from waiting for their tasks to complete.

    Meta-DB: MetaDB is one of the most crucial components of Airflow. We use RDS-MySQL for the managed database.

    Controller: The controller consists of the scheduler, web server, file server, and the canary dag. This is hosted in a public subnet.

    Scheduler and Webserver: The scheduler and webserver are part of the standard airflow services.

    File Server: Nginx is used as a file server to serve airflow logs and application logs.

    Canary DAG: The canary DAG mimics the actual load on our workers. It runs every 30 minutes and checks the health of the scheduler and the workers. If the task is not queued at all or has spent more time in the queued state than expected, then either the scheduler or the worker is not functioning as expected. This will trigger an alert.

    Workers: The workers are placed in a private subnet. A general-purpose AWS machine with two types of workers is configured, one for sub-DAGs and one for tasks. The workers are placed in an EC2-Autoscaling group and the size of the group will either grow or shrink depending on the current tasks that are executed.

    Autoscaling of workers

    Increasing the group size: A lambda is triggered in a periodic interval and it checks the length of the RMQ queue. The lambda also knows the current number of workers in the current fleet of workers. Along with that, we also log the average run time of tasks in the DAG. Based on these parameters, we either increase or decrease the group size of the cluster.

    Reducing the group size: When we decrease the number of workers, it also means any of the workers can be taken down and the worker needs to be able to handle it. This is done through termination hooks. We follow an aggressive scale-up policy and a conservative scale-down policy.

    File System: We use EFS (Elastic File System) of AWS as the file system that is shared between the workers and the controller. EFS is a managed NAS that can be mounted on multiple services. By using EFS, we have ensured that all the logs are present in one file system and these logs are accessible from the file server present in the controller. We have put in place a lifecycle policy on EFS to archive data older than 7 days.

    Interfaces: To scale up the computing platform when required, we have a bunch of hooks, libraries, and operators to interact with external systems like Slack, EMR, Jira, S3, Qubole, Athena, and DynamoDB. Standard interfaces like Jira and Slack also help in onboarding the L2 support team. The L2 support relies on Jira and Slack notifications to monitor the DAG progress.

    Deployment

    Deployment in an airflow system is fairly challenging and involves multi-stage deployments.

    Challenges:

    • If we first deploy the controller and if there are any changes in the DAG, the corresponding tasks may not be present in workers. This may lead to a failure.
    • We have to make blue-green deployments as we cannot deploy on the workers where tasks may still be running. Once the worker deployments are done, the controller deployment takes place. If it fails for any reason, both the deployments will be rolled back.

    We use an AWS code-deploy to perform these activities.

    Staging and Development

    For development, we use a docker container from Puckel-Airflow. We have made certain modifications to change the user_id and also to run multiple docker containers on the same system. This will help us to test all the new functionality at a DAG level.

    The staging environment is exactly like the development environment, wherein we have isolated our entire setup in separate VPCs, IAM policies, S3-Buckets, Athena DBs, Meta-DBs, etc. This is done to ensure the staging environment doesn’t interfere with our production systems. The staging setup is also used to test the infra-level changes like autoscaling policy, SLAs, etc.

    In Summary

    Following the deployment of Apache Airflow, we have onboarded several enterprise customers across our product suite and seen up to a 4X improvement in productivity, consistency and efficiency. We have also built a sufficient set of common libraries, connectors, and validation rules over time, which takes care of most of our custom, customer-specific needs. This has enabled us to roll out our solutions much faster and with better ROI.
    As Airflow has been integrated to our communications and project tracking systems, we now have much faster and better visibility on current statuses, issues with sub processes, and duration-based automation processes for escalations.
    At the heart of all the benefits we’ve derived is the fact that we have now achieved much higher consistency in processing large volumes of diverse data, which is one of DataWeave’s key differentiators.
    In subsequent blog posts, we will dive deeper into specific areas of this architecture to provide more details. Stay tuned!

  • Coronavirus Outbreak: Impact on E-Commerce Retailers and Consumer Brands

    Coronavirus Outbreak: Impact on E-Commerce Retailers and Consumer Brands

    The Coronavirus, otherwise known as COVID-19, has made landfall on U.S. shores. At the time of writing this article, there are over 230 confirmed cases in the country and 12 deaths. The growing unease about the virus, which has quickly accumulated 95,000+ confirmed cases globally, has, among other things, adversely affected businesses and stock markets the world over.

    In the wake of this outbreak, U.S. based retailers and brands would be prudent to brace themselves and plan ahead to minimize disruptions as much as possible.

    Businesses and consumers in China, the global epicenter of the epidemic, have been dealing with these challenges over the last couple of months. It’s likely that some of the trends observed in China would be mimicked in the U.S. as well, something that domestic retailers and brands would do well to study and prepare for.

    The Inadvertent E-commerce Wave

    When the outbreak happened in China, it caused an uptick in e-commerce adoption as shoppers were reluctant to step out of their homes and instead, opted to shop for their goods online.

    Reports indicate that Chinese online retailer JD.com’s online grocery sales grew 215% YoY over a 10-day period between late January and early February. Similarly, Carrefour’s vegetable deliveries grew by 600% YoY during the Lunar New Year period. Online sales of Dettol, a disinfectant produced by Reckitt Benckiser, rose 643% YoY between 10 February and 13 February on China’s Suning.com.

    In Singapore, another region affected by the virus more recently than in China, Lazada’s grocery arm, RedMart, and Supermarket chain, NTUC FairPrice, both reported an unprecedented surge in demand, which tested their delivery capabilities to the limit.

    This bump in online sales isn’t just restricted to grocery, but other categories as well. Jean-Paul Agon, CEO of L’Oréal, recently said that online sales of the brand’s beauty products increased in China in February.

    Given such a consistent shift in shopping behavior across coronavirus-affected regions, it’s logical to expect that a similar trend would be followed in the U.S. – in fact, it might already be underway.

    A recent survey by Coresight Research indicated that 27.5% of U.S. respondents are avoiding public areas at least to some extent, and 58% plan to if the outbreak worsens. Of those who have altered their routines, more than 40% say they are “avoiding or limiting visits to shopping centers/ malls” and more than 30% are avoiding stores in general. The survey also found consumers will likely begin to avoid restaurants, movie theaters, sporting events and other entertainment venues.

    Therefore, it’s essential for U.S. retailers and brands to swiftly energize their e-commerce readiness and be fully prepared to cater to the circumstances-induced shift in shopping behavior, inclined toward online.

    A Logistical Nightmare

    The most obvious area of impact for retailers and brands is in their supply chain and order fulfilment operations.

    A large portion of consumer product manufacturers rely to some extent on China, and the potential impact of the virus on supply chain processes is inescapable. Chinese factories have been operating at partial capacity, impacting supply chains globally. This has largely affected highly popular e-commerce categories like consumer electronics, fashion and furniture.

    Shares in the U.S. of furniture e-commerce retailer, Wayfair, fell as much as 26% toward the end of February, according to a Bloomberg report. The is particularly revealing, as the online retailer reportedly relies on China for half of its merchandise.

    Retailers struggling to cope with this stress in their supply chain systems would do well to warn their customers beforehand about delays in deliveries, like AliExpress has just done.

    For categories like CPG, as consumers increasingly shop online, retailers that offer Buy Online Pick Up In Store (BOPIS), should expect a surge in its adoption, and reinforce their online infrastructure and in-store operations to cater to the rising demand.

    In addition to disruptions in the supply chain, several other mission-critical areas are likely to get affected too.

    Keeping Up With The Online Surge

    As with any event of this magnitude, the business implications reach far and wide. The following are a few areas that we’ve identified as critical, based on our experience working with retailers and brands. Being aware of and focusing on these issues are likely to alleviate some of the issues faced by consumers today.

    Fair pricing: There have been several reports of price gouging on e-commerce platforms. Examples include 2-ounce Purell bottles being sold for $400 and face masks for up to $20. While these prices have mostly been set by third party merchants, brands are likely to face the flak from consumers. A recent Bloomberg article reported that online retailers still rely partly on employees to manually monitor these items. This approach has obvious limitations, such as products quickly reappearing on the website after being de-listed. Brands and e-commerce platforms will need to explore automated ways of controlling their online pricing practices at large scale.

    3P merchant and counterfeit management: Often, unauthorized third-party merchants selling an original manufacturer’s goods are the ones who unreasonably inflate prices. These merchants tend to test the markets on online marketplaces with their pricing, which adversely affects the brand image of the manufacturer. Further still, they sometimes list counterfeit or fake goods that make incorrect or extravagant claims. Brands will need to swiftly identify and de-list these merchants from online marketplaces.

    Ensuring stock availability: During times like these, it’s a common sight to see empty aisles at supermarkets selling items like canned food, water, paper products and personal care products. Consumers will benefit from brands monitoring their stock availability at stores, which will help them better align their supply chain operations to the rapidly changing demand patterns across the U.S. map. This way, efforts can be more targeted at regions with severe shortages.

    Content compliance: Helium 10, a technology provider for Amazon sellers, reported that since 26 February, 90% of searches on Amazon are coronavirus related, and searches for hand sanitizers spiked to 1.5 million searches in February compared to 90,000 in November. As a result, to arrest exploitative practices, some online marketplaces have announced policy guidelines on product content claiming health benefits. Words like ‘Coronavirus‘, ‘COVID-19‘, ‘Virus‘ and ‘epidemic’ are, in fact, prohibited.  Amazon has already de-listed several merchants claiming fraudulent cures. Ebay has gone as far as to ban all new listings for face masks, hand sanitizers, and disinfecting wipes, due to regulatory restrictions. In this context, retailers and brands will benefit from deploying tracking mechanisms that quickly identify offenders.

    The areas of business presented above are by no means a comprehensive list for retailers and brands to rely on during this time. Still, these are critical impact areas for them to address, even as huge efforts are made toward managing highly stressed supply chains.

    DataWeave Offers Support

    The coronavirus outbreak is likely to get worse before it gets better. As we enter unchartered territories, DataWeave is offering to contribute in small ways, pro bono, by leveraging our expert talent and competitive intelligence technology platform, to address some of the challenges faced by retailers and brands.

    We’re announcing a limited-time, no-cost offer to detect and report on price gouging, the presence of unauthorized third-party merchants, as well as stock availability across U.S. ZIP-codes. This offer will be valid for 4-6 weeks (timeline will be flexible based on how the outbreak develops) and limited to monitoring the top 10 U.S. online marketplaces, as well as critical product categories such as medicinal and hygiene-related products, emergency food items, survival-related products, fuel, etc.

    Reach out to us for further details.

  • [INFOGRAPHIC] 2019 at DataWeave: Blazing New Trails

    [INFOGRAPHIC] 2019 at DataWeave: Blazing New Trails

    As another year comes to a close, we look back at 2019 with fond memories and look forward to the exciting new prospects of 2020. Take a trip with us as we highlight some of DataWeave’s milestones of the last twelve months.

    Over the course of the year, DataWeave’s success has gone hand in hand with the evolution of retail and e-commerce, reinforcing the relevance of our technology platform.

    Our rapid growth in the North American market is a reflection of how intense competition in the region is triggering the need for accurate, timely, and actionable competitive and market insights, as well as other avenues for retailers and brands to gain a competitive edge.

    Last year, we saw a resurgence of big-box (omnichannel) retailers as they adopted innovative approaches to play to their strengths (their offline stores). Offering buy online, pick up in store (BOPIS) or click-and-collect options, rolling out price match guarantee programs, and expanding their partnerships with delivery services like Instacart, enabled these retailers to leverage the best of both the online and offline worlds to compete with e-commerce firms.

    Amazon continues to dominate e-commerce with a daunting 38% share in the US. Still, the partnerships between brands and Amazon are increasingly being tested. Nike and Ikea recently joined the likes of Swatch and Birkenstock to sever ties with the retail behemoth. This seemingly growing trend is largely due to counterfeits continuing to leak through the system.

    Brands that used to de-prioritize their focus on their eCommerce channel (as it often was only a small portion of their revenues) have come to realize that consumers use large marketplaces like Amazon not just to shop for products but also to perform product research. As a result, how these brands are represented and sold online impact their offline sales. And with the onset of BOPIS and click-and-collect initiatives, brands can now analyze this correlation even at a hyperlocal (ZIP-code) level.

    Large marketplaces, for their part, have started taking advantage of the increasingly brand-agnostic shopping behavior of consumers by launching ad-platforms for brands and manufacturers, enabling them to boost their visibility online.

    Due to such sweeping transformations to the market landscape, brands and retailers are increasingly looking more toward intelligent tech-based solutions to help them gain a competitive edge.

    In order to effectively serve the growing need for competitive and market insights, we’ve pushed our platform to its limits and beyond. It’s our constant endeavor to innovate and improve. This is evident with the launch of a host of new features on our product suite, especially Brand Analytics – designed to enable consumer brands to protect their brand equity and optimize e-commerce performance.

    One of the key factors that enabled us to achieve all the milestones we did is the aggressive hiring of some of the most skilled talent in the tech industry. Our team grew by 44% in 2019, giving us additional confidence to raise the bar on our capabilities and offer 95% accuracy in our data and insights to our customers consistently.

    We’re encouraged by the fact that we’ve more than doubled as a business, year-over-year, for the past several years, without depending solely on growing the team, but also by consolidating our technology stack, optimizing our processes, and scaling our products.

    Here’s a sneak peek into our performance in 2019:

    2020 Vision

    The upcoming year promises to be an exciting one for the retail industry and the consumer brand space at large. We plan to be at the helm and increase our footprint all around. There’s a strong focus to expand our US team and consequently, the business. While we continue to strengthen our roots in India, we will look toward other mature markets like the UK, Germany and the Middle East as well.

    On other fronts, we’re gathering steam on new partnership engagements – consulting firms, ad tech firms, marketing agencies and complementary technologies. We will also expand our foray into the travel and delivery services verticals.

    With our diversifying portfolio, we haven’t lost sight of one of the most important aspects of any successful company – its employees. We will continue to keep our employees engaged, motivated, and satisfied by providing vertical and horizontal career growth opportunities, conducting personalized training programs, organizing hackathons, fostering cross-team collaboration and learning, and encouraging everyone to periodically blow off some steam at company retreats and the ferociously fought in-house sports tournaments.

    Here’s to a stellar 2020 of empowered retailers and brands. We wish them well as they navigate the dense competitive landscape, knowing that they have an ally in their corner with DataWeave.

  • Black Friday 2019 Pricing for Online Furniture

    Black Friday 2019 Pricing for Online Furniture

    For today’s shoppers, instant gratification is the need of the hour. It’s, therefore, no surprise that furniture e-retail has been picking up steam over the last decade. What was once a norm to physically touch and feel before making a purchase, is now just a few clicks away. Retailers have bridged the gap by making the purchase process as seamless as possible – easy finance options, hassle-free returns and variety.

    While several factors play a role in driving consumers to shop furniture goods online, price is the primary motivator, as is the case with most popular product categories sold online.

    During Black Friday 2019, DataWeave performed an analysis on a sample of 23,000+ products across six of the top furniture retailers – Amazon, Home Depot, JCPenney, Target, Walmart and Wayfair. Ten product types were covered in the furniture category (such as Beds, Bookcases, Mattresses, Sofas, etc.) and the analysis focused on the top 500 ranked products of each product type.

    To get an accurate depiction of the additional markdowns during the sale, we took the mode of the prices for the preceding week and compared them with that during the sale.

    Additional markdowns

    Target (25%) and Home Depot (21%) marked down their prices most aggressively during the sale.  JCPenney and Wayfair stood out for offering additional markdowns on the highest portions of their ranges (67% and 46% respectively), even though the average markdown percentage was fairly conservative. Amazon and Walmart were steady as usual, with additional markdowns of 8% and 10% on 15% and 17% of their assortment, respectively.

    Premiumness

    To further understand the furniture pricing strategies of these retailers, we categorized their products into buckets of how expensive or cheap the product is (High, Medium, and Low in terms of price), relative to the rest of the products hosted by the retailer, and studied how the additional markdowns varied across these buckets. Where the MRP was not displayed, the most expensive price of the product during the holiday period prior to Black Friday was considered to define the “premiumness” of the product.

    Two patterns clearly stand out from this analysis. Most of the retailers remained relatively equitable between their premium categories with nothing significant to report in terms of varying markdowns. Home Depot and and JCPenney are the only exceptions here, but not by much.  The other interesting insight is that the percentage of marked-down products had a near unanimous pattern of the high level being the most covered, followed by the medium and then low.

    Therefore, while there wasn’t a significant variation in the average markdown across premiumness levels, a larger portion of the high-premium goods were consistently offered at a discount across all retailers.

    Popularity

    Much like our premiumness categorization, we investigated products based on their popularity levels as well. We’ve defined popularity using a combination of the average review rating and the number of reviews for each product, condensed to a scale of low, medium and high.

    We observe slightly different furniture pricing strategies adopted by retailers across popularity levels. While Home Depot, Amazon, and Wayfair chose to provide higher markdowns on their more popular products, Target, JCPenney, Walmart chose to provide higher markdowns on their least popular products. In addition, a larger portion of the least popular products were consistently offered on discount by almost all retailers.

    In combination with our findings across premiumness levels, we can surmise that part of the strategy of most retailers was to liquidate their stock of expensive and unpopular products during the sale.

    Price Change Activity

    As part of our analysis, we also tracked the level of pricing activity across retailers over the last week of November, in terms of the number of price changes made as well as the average price variation for each retailer.

    In general, we can see that Amazon and Walmart  consistently made several price changes through the week, though the average magnitudes of these price changes were relatively low. This echoes the pattern we’ve observed through our analysis of other product categories during the sale event, as well.

    Also, we see an almost coordinated increase in the number of price changes and the average magnitude across the 27th and 28th of November. This is likely an attempt by the retailers to get a head start on Black Friday deals.

    An unusual and interesting pattern was observed with Wayfair, which started out the week with the most changes at 2500. It then tanked the next day and hovered around 500 till the 28th, only to spike to 2500 again. All these changes though, had their variation in and around 5%.

    In summary, its interesting to observe how different retailers approached the much-anticipated holiday season sale events differently. As one might expect, there are significant variations among retailers in the aggressiveness of discounting activity as they approached Black Friday, and on Black Friday itself. Contrasting pricing strategies for popular and premium goods were also observed.

    If you would like to learn more about the pricing of top U.S. retailers across other product categories like consumer electronics, fashion, and beauty & health, check out our series of articles on Black Friday 2019.

  • Health & Beauty on Black Friday: Analyzing Pricing Strategies of Top U.S. Retailers

    Health & Beauty on Black Friday: Analyzing Pricing Strategies of Top U.S. Retailers

    We’ve come a long way from face paint and medicinal herbs to multi-billion dollar industries revolving around health and beauty. Customers are getting increasingly bombarded with variety that promises something for everyone. In fact, a recent DataWeave study identified Health & Beauty as one of the most popular CPG categories in the U.S., both in terms of assortment strength and brand concentration. As with most other categories, pricing activity around Health & Beauty is especially abuzz when Thanksgiving weekend comes around.

    As part of our series of articles analyzing the pricing of leading retailers across categories on Black Friday, the DataWeave team performed an analysis on a sample of 14,000+ products across six top retailers – Amazon, JC Penney, Macy’s, Nordstrom, Target and Walmart. Seven product types were covered across the category, such as Fragrance, Hair Care, Makeup, etc. and the analysis focused on the top 500 ranked products of each product type.

    Additional markdowns

    For this analysis, we considered the mode of the prices for the week before and compared it with that during the sale. This painted a picture of the additional markdowns for the duration of the sale.

    Similar to our prior coverage of the Fashion category during Black Friday, Macy’s had the broadest reach in terms of the marked down products at 25.6%. The average percentage of the markdowns was 22% and was only eclipsed by JC Penney with an average of 34.7%, though this was only offered on 3% of its range. At the other end of the spectrum, Amazon and Walmart had the lowest markdowns at 8.9% and 8.4% respectively but were among the top three in products covered (18% & 12%). Target and Nordstrom offered mid-range markdowns across the board but on a rather conservative selection of products of 5% and 3%, respectively.

    Additional markdowns by product types

    When we delved further into the product types, we noticed that a majority of the retailers heavily marked down makeup, shampoo & conditioner and men’s hair care products. The table illustrates the top three discounted categories for each retailer we analyzed.

    Premiumness

    We categorized the products across retailers into buckets of how expensive or cheap a product is, relative to the rest of the products hosted by the retailer in the respective product type. Where the MRP was not displayed, the most expensive price of the product during the holiday period prior to Black Friday was considered for this categorization. We then tagged products as High, Medium and Low in terms of product premiumness, with High referring to the more expensive products.

    In line with previous trends, Macy’s had the highest markdown on its high level products at 32.8%. It also had the widest coverage for the category at 20%. Amazon, Macy’s, Target, Walmart followed the expected approach of providing higher markdowns on the more premium products, and also on a higher portion of these products. This would be consistent with their goal of providing attractive offers on premium goods while also protecting their margins.

    JC Penney and Nordstrom were exceptions here, with JC Penney providing higher markdowns on its cheaper goods, while Nordstrom focused its markdowns on the medium bucket.  That being said, it should be reiterated that the portion of products with markdowns for both thee retailers was relatively small.

    Popularity

    Similar to categorizing the products at levels of product premiumness, we categorized them into levels of popularity as well. Here, popularity is defined using a combination of the average review rating and number of reviews obtained for each product.

    Interestingly, no consistent pattern has emerged that indicates a strategic focus on factoring product popularity into their pricing strategies for Black Friday.

    Macy’s, JC Penney, and Nordstrom chose to provide higher markdowns on their highly popular products, of which only JC Penney and Macy’s chose to also markdown a higher portion of their highly popular products. It was just as common though to see retailers (including Amazon) marking down the prices of their least popular products. This is likely an attempt by the retailers to liquidate their excessive stock of less popular products during the sale.

    Price Change Activity

    As documented quite often in recent years, the Black Friday sale is no longer limited only to a single day, but attractive offers are often seen right through November, especially over the last week of the month. We tracked the level of pricing activity across retailers over the last week of November, in terms of number of price changes as well as the average price variation for each retailer.

     

    In typical fashion, we observed that Amazon had the most number of pricing changes by a large margin, peaking at 2500 for the set of products tracked. The next in line was Walmart a long way down at 618 changes on the 27th. Even after the multiple changes, their average price change variation remained at the lower end of the scale – in and around 10%.

    The rest of the retailers exercised fewer price changes, with the slight exception of Macy’s in the days leading up to Black Friday. However, the changes almost ceased from the day before only to marginally rise on the 29th.

    While all the retailers tended to follow a predictable pattern of decreasing variation on the 28th and sharply increasing it the next day, Nordstrom and Walmart did the exact opposite, having likely chosen to jump the gun in offering discounts during Black Friday.

    Conclusion

    To conclude, we deduced that Macy’s had relatively higher markdowns on more of its products than the rest. JC Penney, Nordstrom and Target offered high markdowns on the face of it but on a very small section of products. Unsurprisingly, Amazon and Walmart stayed true to their past patterns and remained conservative in their additional markdowns during the sale but generous in their reach.

    Have a look here at our other observations regarding the Black Friday sale and stay tuned for more insights from our analysis of other product categories!

  • Fashion on Black Friday: Decoding Pricing Strategies of Top U.S. Retailers

    Fashion on Black Friday: Decoding Pricing Strategies of Top U.S. Retailers

    Over the last few Thanksgiving Weekend sales, fashion, what was a category once typically reserved for offline purchases, has evolved into a regularly marked down and popular category as shoppers get more comfortable making these purchases online. This can be credited to the ease of purchase that retailers offer – trials, returns, etc. combined with the desire for shoppers to refresh their wardrobe for the new year ahead.

    At DataWeave, we performed an analysis on a sample of 40,000+ products across six of the top fashion retailers – Amazon, Bloomingdale’s, Macy’s, Nordstrom, Target and Walmart. . Twenty product types were covered across both men’s and women’s fashion and the analysis focused on the top 500 ranked products of each product type.

    Additional Markdowns

    For the sake of this analysis, we compared the prices during the sale with the mode of the prices the week before. This gave us a clear picture of the additional markdowns during the sale period and therefore, the additional value to shoppers.

    Dominating the fashion space, Nordstrom and Macy’s came in hot with the most aggressive discounts on the largest share of their product range, 36% and 27% respectively, on more than a quarter of their range. In the women’s lineup, Target offered a 36% markdown, compared to ~22% for its men’s lineup. Across both categories though, this was only on 1% of Target’s range. In what seems to be an expected trend, Amazon and Walmart remained relatively conservative with their additional markdowns, as they tend to be competitively priced even during non-sale periods.

    Drilling down into the product types, we noticed that very aggressive markdowns were being offered on t-shirts and skirts (over 40%). Swimwear, hosiery, handbags, and sunglasses were other product types that were featured with attractive prices across websites.

    Product Premiumness

    We categorized the products across retailers into buckets of how expensive or cheap the product is, relative to the rest of the products hosted by the retailer. Where the MRP was not displayed, the most expensive price of the product during the holiday period prior to Black Friday was considered. We then bucketed products in categories of High, Medium and Low of product premiumness, with High containing the more expensive products by price.

    Amazon, Bloomingdale’s, Macy’s and Nordstrom chose to markdown the more expensive products in their range higher than the rest of their assortment. This aligns well with what one would expect retailers to do as shoppers tend to expect attractive offers on the more expensive range of products. Also, with the more expensive products, retailers and brands likely have more room to be flexible with margins. Amazon shows a consistent strategy here, having provided higher markdowns on a relatively higher portion of its most premium products and vice versa. This trend can only otherwise partially be seen with Macy’s.

    Walmart though, chose to go the other route and provided higher markdowns on its least premium products. This might have been a targeted effort to maintain their perception among shoppers as a destination for affordable goods. Though it’s important to note here that these markdowns were seen only on a small set of cheap goods – just over 5%.

    Price Change Activity

    Walmart, Nordstrom and to an extent, Bloomingdale’s, had an almost consistent number of price changes throughout the week. Nordstrom recorded the most significant dip in the magnitude of the markdowns over time.

    Amazon and to a smaller degree, Macy’s, had more price changes during the week. However, Amazon’s average price variation remained among the lowest whereas Macy’s clocked in the highest by Black Friday at just under 40%.

    Across the board, the price changes dipped on the 28th and then rose again on the 29th. This can be seen as a conscious effort to have more aggressive activity on Black Friday.

    In summary, fashion-first retailers like Macy’s and Bloomingdale’s went all-in during the sale, while Nordstrom, a multi-category retailer, stood out for its aggressive focus on fashion.

    Amazon and Walmart continued to operate within the competitive space that they’ve carved out for themselves as the leading retailers in the US. We observed a similar trend even in the other product categories we’ve analyzed for the sale. Check out our analysis of the Electronics category during Black Friday here.

  • Black Friday Sale: Breaking Down Pricing Strategies in Consumer Electronics

    Black Friday Sale: Breaking Down Pricing Strategies in Consumer Electronics

    Online holiday shopping (Nov-Dec) in the US for 2019 is projected to be $143.7B, a 14.1% increase from 2018. This sets a rather exciting stage for retail giants in the battle to claim market share. Interesting patterns emerge as each one tries to out-smart the other. Black Friday, in particular, is when most of the activity was expected to be concentrated.

    Inevitably, consumer electronics had strong representation, according to research by Coresight. As traffic steers more towards online shopping, there’s an increased sense of comfort in purchasing big ticket items on an ecommerce platform. There are multiple reasons why electronics lead the race during the holiday season – easy to gift, personal indulgence, comparatively shorter shelf life and well, because who among us can really resist a gadget on sale.

    In line with expectations during the season, there’s been a slew of generous discounts across the board. According to prior trends, Amazon was on course to be the lowest priced. In order to assess this, we decided to study a sample of 1000 products on Amazon and match them against its competitors like Walmart, Target, Best Buy and New Egg. Doing this gave us an accurate picture of the comparative pricing across retailers during this season, right up to Black Friday.

    Competitive Pricing Analysis

    There is a commonly held assumption that Amazon is the lowest priced retailer in most cases. How true is that? Here are our findings:

    We tracked the split across three scenarios during the holiday period – Amazon being exclusively the lowest priced, Amazon sharing the lowest priced spot and Amazon not being the lowest priced.

    Clearly, Amazon monopolized the share of lowest priced products during the entire period – with its share of lowest priced products ranging between 86% and 60%. The dip from 86% to 60% was immediate on the 27th, as Amazon’s competitors caught up. In general, Amazon’s share of lowest priced products fell from 76% to 62% on Black Friday, as its competitors launched their most aggressive promotional campaigns for the holiday season. As shown in the next chart below, a large portion of this can be attributed to Target’s pricing activity.

    Relative Price Index

    From 21 November until Black Friday, we calculated the price index across retailers, which indicates the relative pricing levels each day for the set of matched products – the lower the price index, the lower the average relative price.

    Unsurprisingly, Amazon has been consistently the lowest priced by a fair margin. A few rungs down, New Egg and Fry’s have been going head-to-head with their price positions. Target on the other hand, underwent a spike in relative pricing from 26-28 November. To sum up, in order of lowest pricing, it’s Amazon, Best Buy, Walmart, New Egg, Fry’s and Target.

    Additional Markdowns

    While the insights above were unearthed by comparing the products of retailers against a sample of 1000 Amazon products, we went further and performed a separate analysis on a different sample of 15,000+ products across retailers, which focussed on the top 500 ranked products of each product type for Amazon, Best Buy, Target and Walmart. The product types considered include Digital Cameras, DSLRs, Headphones, Laptops, Mobile Phones, Refrigerators, Tablets, Televisions, USB Flash Drives and Wearables.

    Here, we compared the prices during the sale with the mode of the prices of the same retailer the week before. This put into perspective the level of additional markdowns during the sale period, enabling us to better understand the additional value to shoppers during the sale period (since discounts are often offered during non-sale periods too).

    Looking at opposite ends of the spectrum, we find Amazon with the least drastic markdowns during the sale as it tends to consistently have lower prices across the board. At the other end, there’s Best Buy and Target with the most aggressive markdowns; Target taking the lead, 25.5% on 35% of its products, which is also consistent with the activity we observed in the previous sample of matched products.

    Going further, we’ve broken down the markdown activity by the top product types for each retailer. Across the board, we observe attractive discounts on Headphones, USB Flash Drives and Mobile Phones.

    Price Change Activity

    With the proliferation of pricing intelligence tools (often driven by algorithms), dynamic pricing is a commonly observed behavior among retailers. We analyzed this trend during the holiday period to identify the retailers that are most aggressive in their price change activity. The following charts reveal the number of price changes performed by retailers in our sample as well as the average price variation during this holiday period.

    Amazon made several price changes during the week but with a relatively low magnitude, since it was the lowest priced anyway through the week. The only other player with similar activity was Walmart. Target and Best Buy had significantly fewer price changes but when they did make the changes, the magnitude was much larger. Their focus was solely on a smaller, select set of products where they went all in.

    In conclusion

    As the years advance, the duration of holiday sales is no longer restricted to the actual holiday, but the days preceding and following them as well. With more and more people getting increasingly comfortable with online shopping (14.1% increase from 2018), buying habits are evolving too. Big retailers are cashing in on this and driving their pricing strategies to keep up with the evolution.

    One of the clear cut findings from our research is that there are two primary paths they take: smaller additional markdowns over a longer period and larger additional markdowns over a shorter period. Whichever path they choose, retailers need to be on top of the game with valuable insights, that give them a competitive edge. For accurate and large scale competitive intelligence, reach out to us.

  • Amazon on course for an aggressive Black Friday

    Amazon on course for an aggressive Black Friday

    The holidays are around the corner and that much awaited holiday cheer, has now become directly proportional to the arrival of an Amazon package. According to a new report, in partnership with Bain & Company, DataWeave has observed that early in November, Amazon had the lowest price 30%-50% of the time and matched the lowest price 35%-60% of the remaining cases, based on an analysis performed on a sample of over 16,000 products across 10 websites and 5 product categories.

    Aggressive pricing strategies have been Amazon’s modus operandi for a while now and it’s not about to change this season. In the build up to the Black Friday promotions this year, they even slashed their prices of the rarely discounted Apple products, such as the iPad Pro. This sets the tone for what shoppers can expect as the holiday season comes upon us.

    Results of a recent survey, published as part of the Bain report, revealed that ‘value for money’ was the primary concern that influence purchasing decisions, across categories. In the same breath, the respondents went on to say that they perceive Amazon as a ‘value leader’, sans womens’ clothing and pet supplies.

    Although this season might continue to see Amazon rake in the most market share, competitors are not far behind. There’s heavy investment from the likes of Walmart and others in order to negate the effects of the undercut. If these competitive responses become louder, the dent on customer perception could begin to tilt to more neutral ground.

    Stay tuned as we follow this pattern during the season and release our findings over the next few weeks.

    For access to the full article that was published in the Retail Holiday Newsletter by Bain & Company and powered by DataWeave, click here.

  • 3 Common Problems Brands Face in eCommerce | DataWeave

    3 Common Problems Brands Face in eCommerce | DataWeave

    Over the last three years, I have helped deploy eCommerce analytics solutions for several brands and manufacturers globally. During this time, I have conversed with day-to-day users up through C-Suite executives of some of the world’s most successful brands, while also working with the founders of startup brands who were simply trying to find their place in the world of commerce.

    As I look back on my time to date, I have noticed a few themes emerge from my diverse client conversations with brands, which are indicative of an ecosystem that’s only now coming to terms with retailers and consumers moving online. Here are three fundamental problems I’ve seen brands often run into as they adapt to the world of eCommerce:

    1. “We have no idea what we are doing”

    My favorite part about being an analytics solution provider is the introduction session with a new client. I always entered these conversations with a few key questions:

    – What are your top three eCommerce initiatives for the next 12 months?

    – How does your team and other internal resources align with these initiatives

    – How do you envision using this type of tool to help you succeed with your goals? What made you choose ours?

    Early in my career, what always amazed me was that these enormous brands – wildly successful brands – entered into a partnership without a clear plan to execute. Many would fumble through what I thought were very basic questions. After a few of these conversations, I came to the realization that most brands have a limited understanding of what they are doing in eCommerce.

    How could this be possible?

    I remember a conversation with a large CPG brand executive. He said, “Keep in mind, most of the people doing these jobs are from a bricks-and-mortar world. They don’t have eCommerce experience because no one does. It is too new. We don’t have the resources to hire more people because eCommerce makes up less than 1% of our total revenue.”

    As an industry, brands are collectively making it up as they go. Few admit it, but the industry is growing and evolving so fast, the best that some do is hold on for the ride (while taking a few calculated chances along the way).

    2. “We measure success poorly”

    I have noticed that, with time, many brands are starting to get a better grasp on how to operate online, though there is still a long way to go for many. The best evidence for this improvement is the growth in the number of job posts for eCommerce-focused roles, new vendors popping up in this space, and industry centers of excellence being developed. As more people choose eCommerce roles, the biggest challenge that I see is the lack of effective measurement and training processes.

    Often, the issue is that many brands take a long-standing, loyal executive and assign them as the eCommerce leader. When this person is not forward thinking, analytical or open to trying things a new way, brands fail. The reason startup brands are winning online is because they are entering the eCommerce game with an open and fresh perspective. Forcing old ways into eCommerce will surely lead to failure.

    I have worked with many brands that have developed eCommerce centers of excellence and have shared best practices on how to measure teams and success. The most painful to deal with were the organizations that brought their bricks-and-mortar measurements into the eCommerce world. The data used to measure success was the wrong data. The KPIs were set in a way that people would surely fail.

    In my opinion, the best measurements for success are sales growth (not share growth), digital shelf KPIs (search and content first), and a subjective measure on maturity in the industry. The best first step is to have someone lead the team who understands how to measure success and execute in a cutting-edge and evolving environment.

    3. “We sign up with either too many or the wrong service providers”

    The final observation is one that is costing many teams a lot of money. Many brands start to move into eCommerce based on their old team structures. Each team has a separate eCommerce objective, budget, and set of tools to execute with.

    Then, when the centralized eCommerce team (Center of Excellence) gets established, they will likely find many teams working with many tools. Sometimes, they see many teams signed up, via separate contracts, with the same tool. Worse still – it’s often the wrong type of tool.

    As brands evaluate tools, they need to ask questions such as:

    • Does this vendor provide global coverage, so that we can establish a global way of thinking and executing (with the ability to customize for local consumption when required)?
    • Does this vendor have the backbone (people and technology) to scale with my business?
    • (The best question, in my opinion) Does this vendor have people who are willing to listen and understand my business, or are they simply people who want to sell me a cookie-cutter solution?

    In my experience, I have seen brands spending way too much time, effort, and money on vendors who do not check the boxes listed above.

    Summing up…

    As I look back over my time serving brands in the eCommerce analytics space, I have seen an industry morph and transform time and time again. I have seen companies shift, re-shift, panic and pivot.

    If you’re a brand, my encouragement to your team is to hit the pause button. Ask the right questions. Evaluate your goals, your team structure, and your vendor partners. If the strategy, execution, people, and measurement, are not aligned, come up with a plan to get them back on track. Be willing to learn a new way to do business.

    Pause. Reset. Measure.

  • Flaunt Your Deep-Tech Prowess at Bootstrap Paradox Hackathon Hosted by Blume Ventures

    Flaunt Your Deep-Tech Prowess at Bootstrap Paradox Hackathon Hosted by Blume Ventures

    When DataWeave was founded in 2011, we set out to democratize data by enabling businesses to leverage public Web data to solve mission-critical business problems. Eight years on, we have done just that, and grown to deliver AI-powered competitive intelligence and digital shelf analytics to several global retailers and brands, which include the likes of Adidas, QVC, Overstock, Sauder, Dorel, and more.

    As the company has grown, so has our team, which is now 140+ members strong. We’re still constantly on the lookout for smart, open, and driven folks to join us and contribute to our success.

    And so, we’re excited to partner with Skillenza and Blume Ventures to co-host the Bootstrap Paradox Hackathon, where we are eager to engage with the developer community and contribute in our own way back to the startup ecosystem.

    The event will be conducted as an offline product building competition, with a duration of 24 hours on August 3-4, 2019 at the Microsoft India office in Bengaluru. It will provide a platform for developers and coders to interact with and solve challenges thrown up by DataWeave and other Blume portfolio companies, such as Dunzo, Unacademy, Milkbasket, Mechmocha, and Locus.

     

     

    Taking up DataWeave’s challenge during this Hackathon will give you a sneak peek into what our team works on daily. It’s no surprise that we have “At DataWeave, it’s a Hackathon every day!” plastered on our walls. After all, it’s not just all about intense work, but also a lot of fun and frolic.

    The problems that we deal with are as exciting as they are hard. Some of our key accomplishments in technology include:

    • Matching products across e-commerce websites at massive scale and at high levels of accuracy and coverage
    • Using Computer Vision to detect product attributes in fashion such as a color, sleeve length, collar type, etc. by analyzing catalog images
    • Aggregating data from complex web environments, including mobile apps, and across 25+ international languages

    One of our more recent innovations has been in optimizing e-commerce product discovery engines, which dramatically improves shopper experience and purchase conversion rates. During the Bootstrap Paradox Hackathon, coders will get a chance to build a similar engine, with guidance and assistance from DataWeave’s technology leaders.

    Data sets containing product information like title, description, image URL, price, category etc. will be provided, and coders will need to clean up the data, extract information on relevant product attributes and features, and index them, in the process of building the product discovery engine.

    For more details on the challenge, register here on the Skillenza platform.

    As a sweetener, the event also promises everyone a chance to win over 10 lakhs in prize money.

    Simply put, if you love code, this is the place to be this weekend. See you there!

  • Prime Day 2019 Fashion: Were the Deals as Attractive as the Merchandise?

    Prime Day 2019 Fashion: Were the Deals as Attractive as the Merchandise?

    Target and Walmart offered more appealing discounts than Amazon during Prime Day 2019.

    Statista estimates that e-commerce fashion accounted for approximately 20.4% of overall fashion retail sales in the United States in 2018, which amounted to about $103 billion in absolute terms. According to Internet Retailer, apparel is the largest and among the most competitive retail categories in e-commerce. Moreover, as a share of total apparel and accessories sales, online apparel sales is growing at a faster rate than US e-commerce as a whole.

    Given the high-growth and competitive nature of the category, we at DataWeave were interested to find out how high the stakes got during the fifth annual Prime Day earlier this month.

    Our Methodology

    Since Prime Day is no longer necessarily an Amazon event (since competing websites often offer attractive discounts as well), we tracked the pricing of several leading retailers selling fashion apparel, footwear, and accessories to assess their pricing and product strategies during the sale event. Our analysis was focused on additional discounts offered during the sale to estimate the true value that the sale represented to its customers. We calculated this by comparing product prices on Prime Day versus the same prices prior to the sale.

    Our sample consisted of 20 product types across women’s as well as men’s fashion categories. While we did monitor exclusive fashion retailers Macy’s, Bloomingdales, Nordstrom, and Neiman Marcus, we did not find them offering any additional discounts – an interesting insight all on its own since they’ve clearly chosen not to compete with Amazon during the two days of the Prime Day sale. We therefore restricted the rest of our study to Amazon, Target, and Walmart – the latter two of which interestingly offered immensely aggressive discounts in their apparel categories.

    The Verdict

    Despite owning the day at least in name, Amazon was found to offer the lowest additional discounts among the retailers studied. Target and Walmart, on the other hand, ensured that they didn’t lose out on market share this Prime Day by offering substantially high discounts of their own. While Target was the most aggressive with a steep average markdown of 26.5%, Amazon closed out the bottom at 8.4%.

    Walmart and Target didn’t seem particularly focused on compensating their sharp discounts with price increases in other products – their focus seems to have been solely only on offering timely discounts during the sale. Amazon, on the other hand, marked up just about as many products as it marked down, with the markup margin being close to double that of the markdown in an effort to protect margins during the sale.

    Top product types by additional discount

    Target and Walmart both offered aggressive discounts across their top product categories. Walmart ended up with a marginally higher overall average additional discounts on product types like Shirts, T-shirts, and Tops.

    Interestingly, though Amazon offered moderate discounts across its top categories (Lingerie, Swimwear, and Underwear), the volume of marked down products was very limited.

    Additional discounts across popularity levels

    We determined popularity using a combination of average review rating and number of reviews, and the resulting scores were categorized as low, moderate, and high.

    When it came to discounting popular products, there were clear differences in strategy among all the three retailers. Amazon, which interestingly had close to 60% of its products in the low popularity bucket, chose to offer the highest discounts in the same category – indicating an effort to clear its stock of unpopular products. Target and Walmart, on the other hand, focused their discounts on moderate rated products.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into four percentile blocks, with higher percentile blocks indicating higher selling prices.

    As found in the electronics and furniture categories that were analyzed previously, most of the discounting activity was focused on the lower end of the premium spectrum with a view to protect margin – despite a largely equitable distribution of discounted products across percentile ranges (with the exception of Target, which had a discounted assortment heavily dominated by its least premium products).

    This indicates a clear strategy to protect margins, while still maintaining the perception of promoting attractive offers to draw traffic. Target and Walmart both offered substantial additional discounts of close to 30% on their least premium products, while at 12%, Amazon offered less than half that discount.

    Additional discounts across visibility levels

    Given the fairly large number of SKUs across the fashion category in general, the discounts across visibility levels understandably didn’t vary much when compared to the more pronounced fluctuations observed in the electronics and furniture categories. This is also largely because consumers tend to explore lower ranked products more so in the fashion category than in other categories.

    Across product categories, we’re seeing lower-than-expected additional discounts on Amazon this Prime Day, coupled with more aggressive pricing activity by Amazon’s competitors. While this puts more pressure on Amazon, this also is a strong validation of Prime Day as a key annual sale event on the US shopper’s calendar.

    Curious to know how Amazon and its competitors performed in other product categories this Prime Day? Watch this space for more!

  • Online Furniture Pricing Strategies on 2019 Prime Day

    Online Furniture Pricing Strategies on 2019 Prime Day

    Just as with electronics, other retailers actually offered far better discounts than Amazon during Prime Day 2019.

    Online furniture sales have risen significantly since the 2000s, driven largely by a growing array of products, and even more so by the convenience of avoiding travel and crowded stores. According to Statista, online furniture and homeware sales were estimated to reach approximately $190 billion in 2018, with China and the United States accounting for over $60 billion in revenue each.

    Thus, furniture has quickly become a key product category during sale events globally – and Prime Day was no different. At DataWeave, we got down to figuring out exactly how plum those deals were this year.

    Our Methodology

    We tracked the pricing of several leading retailers selling home and furniture products to assess their pricing and product strategies during the sale events. Our analysis was focused on additional discounts offered during the sale to estimate the true value that the sale represented to its customers. We calculated this by comparing product prices on Prime Day versus the same prices prior to the sale. Our sample consisted of the top 1,000 ranked products across 10 popular product types, including beds, dining table sets, sofas, entertainment units, and coffee tables – analyzed for five retailers (Amazon, Home Depot, Target, Walmart, and Wayfair).

    The Verdict

    As we found in the electronics category, there were surprising price spikes in this category too – with Target reporting an average increase as high as 14.7%, and Amazon clocking a still moderately high 9.4%. Target also reported the highest distribution of products with price markups. Home Depot indicated the lowest price increase at 4.6%.

    When it came to additional discounts, Amazon fell short of expectations – at 4.7%, it offered the lowest average among its competitors. Target, on the other hand, was extremely aggressive both in terms of additional discounts and volume of discounted products.

    To conclude, all the retailers observed seemed to be keeping a close watch on their margins by countering price reductions with nearly equivalent surges elsewhere in their assortment.

    While there was no single product type that was found to be popular across all five retailers, it was clear that Target was again the most aggressive at offering discounts. It also had among the largest product ranges on discount.

    Amazon chose to follow a very moderate route both in terms of average discount and discounted product volume.

    Additional discounts across popularity levels

    We determined popularity using a combination of average review rating and number of reviews, and the resulting scores were categorized as low, moderate, and high.

    There doesn’t seem to have been much of a focus on low-popularity products in terms of additional discounts. Most of the attention was focused on products with moderate popularity, since there isn’t much of a need to be aggressive on price for highly popular products, and products with lower popularity aren’t really worth promoting.

    The only retailer that offered a higher discount on its most popular products was Home Depot. Walmart, too, seemed reluctant to let go of the opportunity to capitalize on popularity – it chose to offer the same discount on moderately as well as highly popular products.

    Interestingly, Walmart seems to have a disproportionately large share of products in its low popularity category – something it should possibly evaluate in the future in terms of brand quality, products, and service.

    The percentage distribution of products mostly indicated a linear relationship, with the highest distribution usually being offered for highly popular products. The exception was Wayfair, which offered a much larger array in its moderately popular category.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into four percentile blocks, with higher percentile blocks indicating higher selling prices.

    Most of the discounting activity seems to have occurred in the lower end of the premium spectrum, with a view to protect margin – despite a largely healthy distribution of products across percentile ranges. This indicates a clear strategy to protect margins, while also promoting attractive offers to draw traffic.

    However, there are a couple of exceptions – Target was consistent throughout the “premiumness” spectrum, resulting in the highest overall discounting activity. Home Depot too was aggressive, but selectively so – it chose attractive pricing for the lower and higher ends of its assortment.

    As expected, many retailers showed higher discounting activity in the higher ranks of their listing pages. As usual, though, there are a few exceptions here too. Home Depot and Wayfair indicated unusual patterns – perhaps relying on search results as opposed to organic listing page results. On the other hand, Target again indicated a consistent pattern, with mostly similar discounts across visibility levels.

    Overall, across all parameters analyzed, both the Electronics and Furniture categories have been treated quite similarly in terms of pricing activity by most retailers. Is Prime Day really all about its marketing hype, or will it live up to its promise in at least one segment? Stay with us to find out as we follow through with our series of articles analyzing various product categories on this year’s Prime Day.

  • A Study of Deals on Amazon Prime Day 2019 | DataWeave

    A Study of Deals on Amazon Prime Day 2019 | DataWeave

    Our preliminary analysis reveals that Prime Day 2019 had other retailers offering better deals than Amazon in many cases.

    As Prime Day extended into an additional day this year, Amazon seems to be hitting the right note with its customers, going by the revenue it’s raking in. This year, the longest Prime Day event ever witnessed a sales increase of 72% – overtaking Black Friday and Cyber Monday combined.

    At DataWeave, we were curious to find out how prime these deals were, and if in fact other retailers were offering better discounts. We started with the electronics category, which remains among the most popular categories year on year.

    Our Methodology

    We tracked the pricing of several leading retailers selling consumer electronics to assess their pricing and product strategies during the sale event. Our analysis was focused on additional discounts offered during the sale to estimate the true value that the sale represented to its customers. We calculated this by comparing product prices on Prime Day versus the prices prior to the sale. Our sample consisted of up to the top 1,000 ranked products across 10 popular product types in consumer electronics on Amazon, Best Buy, Target, and Walmart.

    The Verdict

     

    What we found most surprising was that across retailers, some portions of the assortment underwent price increases as well. While Amazon indicated the lowest increase at 9.1%, Best Buy indicated an increase as high as 27.1%. However, Amazon reported the highest percentage of products (6.9%) that showed a price increase.

    Equally surprising was that Amazon reported the lowest price reduction at 6.3% – Walmart, Target, and Best Buy in fact reduced their prices by much larger margins than Amazon did. A point to note here, however, is that Amazon did report the highest percentage of additionally discounted products – with Best Buy coming in at a close second.

    This goes to show that Prime Day, for all its hype, does not in truth offer the best deals to Amazon shoppers. This, of course, is expected based on the competitors’ perspective of wanting to avoid losing market share. As a result, shoppers would be well advised to compare prices across websites to find the best deal.

    Top product types by additional discount

     

    USB flash drives were a popular product category across all four retailers analyzed, with Best Buy offering the best average additional discount at 40.7%. Other popular product types ranged from the usual personal devices such as mobile phones, tablets, and smartwatches to home appliances such as refrigerators and TVs.

    Additional discounts across popularity levels

    We determined popularity using a combination of average review rating and number of reviews, and the resulting scores were categorized as low, moderate, and high.

    Interestingly, discounts were not found to be directly proportional to popularity. Except Walmart, all the retailers tended to offer the best discounts on products that enjoyed moderate popularity. This makes sense, since there isn’t a strong need to be aggressive on price for highly popular products in any case. On the other hand, products with lower popularity aren’t really worth promoting. Walmart, which was the exception, reported a higher discount on low- and high-popularity products than it did on moderately popular products.

    The percentage distribution of products did mostly show a directly proportional relationship, with the highest distribution usually being offered for highly popular products. The exception in this case was Best Buy, which evidenced a much higher distribution in its moderately popular goods.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into four percentile blocks, with higher percentile blocks indicating higher selling prices.

    In general, all retailers were found to have slightly higher additional discounts in the lower end of the “premiumness” spectrum. This is still a smart move, as it enables sellers to save on margin while still promoting attractive discount percentages. Interestingly, Amazon offered the lowest additional discount – a flat 5% – across all categories, despite offering more or less competitive product distributions compared to other retailers.

    Additional discounts across visibility levels

    Here, too, the lower end of the spectrum mostly witnessed higher additional discounts. This tactic actually offers double benefits – one, the most attractive discounts are offered in the higher realms of visibility, thus effectively enticing consumers to buy these products, and two, it helps build a low price perception (despite this not holding good as one delves deeper into the higher ranks). Again, it’s interesting to note that Amazon didn’t offer the highest discounts here either – in fact, it mostly offered the lowest additional discounts.

    All in all, it seems that Prime Day isn’t all it’s hyped up to be, at least not in the Electronics segment. How about other categories? Watch this space for more insights!

  • The Importance of Pricing Parity for Brands

    The Importance of Pricing Parity for Brands

    With bricks-and-mortar stores steadily increasing their online presence, the balancing act of pricing online and in-store is now more important and complex than ever. Companies spend years building brands and brand equity. Yet, a misplaced or poorly executed pricing strategy to handle both online and offline pricing can erode that equity with consumers very quickly.

    This problem is not new. It first started when Clubs like Costco and Sam’s started popping up in the 80’s. Suddenly, brands had to figure out a way to balance Club and Grocery pricing while taking advantage of a new, fast-growing channel. The biggest difference between now and then is that consumers now can check prices within seconds on their phone.

    So, how do you avoid losing your brand equity while ensuring price parity across online and offline channels?

    The key areas to consider are:

    1. Product Mix

    Do you have a broad enough mix of product sizes and case configurations for each channel? To maximize your sales and minimize your price disruption, reviewing your supply chain and product mix to ensure you are able to deliver value to both online and offline retailers is critical. Each channel is looking for ways to improve and maximize your brand sales. If you do not give them the right size and case configuration to enable them to increase margins, you will end up relying disproportionately on trade spend (dollars a brand spends with a retailer to promote products) to do so, or find your product on page 212 of every search.

    Examples of this strategy can be seen with companies offering only “bundled” items such as 12 cans or a large case on online marketplaces, while other retailers offer individual cans for purchase. This allows your online partners to make up margin by shipping a full case and not going through the process of breaking down a case and shipping single units. Also, this allows bricks-and-mortar retailers to have a sharper price point to lure consumers into the store. This strategy has played out well for many brands as they dealt with the rise of Club stores and can be played successfully in e-commerce as well, benefiting all parties.

    2. Price Lists

    Do you have harmonized price lists that do not favor one channel over another? If you do not, you are likely subsidizing the higher list cost in a channel with trade spend, which is highly inefficient. A single price list that provides an adequate price slope between the various sizes across your product range will maximize your ability to manage both channel pricing and brand equity.

    The single largest mistake brands tend to make is thinking that offering “net price” price lists to online marketplaces will benefit them while they use trade dollars in bricks-and-mortar stores to cater to EDLP (Everyday low price) customers. This approach is quite inefficient in many ways, and consumes valuable time and resources that can otherwise be better utilized. Having a single price list with the same price offered to all retailers allows for a more manageable and equitable pricing environment. It also enables a more profitable distribution of trade spend across the most effective areas to invest in for each retailer.

    I have worked with two brands in the past – one that managed two separate price lists and one that we implemented as a single-standard. While the one with the single price list saw sales grow and trade spend remain constant, the other saw trade spend double in just two years as it got caught in a scenario of always having to placate one side of the equation or the other.

    3. Trade Spend

    Today’s brands need to focus on a balanced trade spend strategy to address each channel’s unique needs. Using trade spend with online retailers can be tricky, as the channel is usually assumed to be the lowest priced anyway. Still, it can be used to drive traffic and offset supply chain costs, in order to ensure sufficient margins for the retailer, which will keep you off the CRAP (Can’t Realize A Profit) lists. Meanwhile, as JC Penny quickly learned when it made the disastrous shift to EDLP, consumers still want in-store discounts and sales.

    The best approach I have worked with is to set a single dead net price inclusive of all trade. For example, if your product’s standard list cost is $6.80 and you have a dead net price for promotions (or EDLP) of $5.40, then all retailers – online and bricks-and-mortar – are on equal footing. The only variance in the price for consumers will be the margin each operator chooses to take. This approach is not without issues, as you have to apply all elements of trade spend (such as ad fees, etc.) to the promotional unit costs to ensure you are truly capturing the dead net cost of the retailer.

    Still, the advantage of utilizing this approach is that when a retailer complains about the price another is offering to consumers, the conversation turns to margins being taken and not the cost of the product. At the very least, this approach provides a common ground on which to have a constructive conversation with all retailers.

    So why does this all matter so much to a brand?

    The road to selling online is littered with disaster and missed expectations for sales. Most manufacturers that jumped to online sales without considering pricing quickly learned that abandoning one channel for another does not lead to increased sales. Conversely, we have seen a few brands go from online only to in-store as well. These brands seem to have learned from the others’ mistakes and rarely will you find price variances between the online and offline channels. Instead, you tend to see these brands growing, as online consumers start experiencing the brand in-store.

    A Business To Community study by Larisa Bedgood in 2019 showed that “lower price” was second to only “convenience” for why consumers shop online, while 51% of consumers said that the biggest drawback to shopping online was not being able to touch and feel the product. Brands that are able to bridge the gap and provide consumers with the convenience of online while also showing up well in-store at the right price point will be able to break out of the stagnate 1-2% (if they are lucky) growth most CPG companies are experiencing. If online selling is growing 40-50% a year, why are these companies only managing brand declines and flat growth? I believe it is mainly due to the lack of a proper pricing parity strategy for the two channels along with a lack of actionable e-commerce data.

    Brands that do not focus on all three areas listed above often find themselves in a constant churn of conversations with retailers on all sides, which will typically lead to either online marketplaces or bricks-and-mortar stores deprioritizing the brand in promotions or search. Finding and setting a level playing field will allow for a balanced trade spend and growth for brands on both platforms, while also enabling a brand to break out of the net 1% growth that is plaguing a lot of CPG brands today.

    Outside of deploying basic pricing principles for your brand, I would also suggest early and strong investments in data, systems and people to monitor your brand’s health and pricing. Many brands jumped online without any way to monitor the consumer conversation around the brand or the pricing of the brand online. Not having the tools and resources in place to do this can lead to a quick and long-lasting erosion of brand equity and sales. Most, if not all, large manufacturers have subscribed to POS data for years and fully understand how to analyze this data. But the world has shifted. If your organization has not invested in digital shelf analytics, you may be driving blind and unaware that your brand is losing equity, which equals losing consumers and sales.

    Using a combination of pricing principles and e-commerce data mining tools will help you maintain price parity and brand relevance, while keeping you from becoming the last brand of choice for consumers, regardless of where they shop.

  • Compete Profitably in Retail: Leveraging AI-Powered Competitive Intelligence at Massive Scale

    Compete Profitably in Retail: Leveraging AI-Powered Competitive Intelligence at Massive Scale

    AI is everywhere. Any retailer worth his salt knows that in today’s hyper-competitive environment, you can’t win just by fighting hard – you have to do it by fighting smart. The solution? Retailers are turning to AI in droves.

    The problem is that many organizations regard AI as a black box of sorts – where you can throw all your data (the digital era’s blessing that feels like a curse) in at one end and have miraculously meaningful output appearing out the other. The reality of how AI works, however, is a lot more complex. It takes a lot of work to make AI work for you – and then to derive value out of it.

    Image Source: https://xkcd.com/1838

    Following the advent of the digital era, businesses across industries, particularly retail, were left grappling with massive amounts of internal data. To make things worse, this data was unstructured and siloed, making it difficult to process effectively. Yet, businesses learned to leverage simple analytics to extract relevant data and insights to affect smarter decisions.

    But just as that happened, the e-commerce revolution stirred things up again. As businesses of all shapes, sizes, and types moved online, they suddenly became a whole lot more vulnerable to other players’ movements than they were just about a decade ago, when buyers rarely visited more than one store before they made a purchase. In other words, retailers are now operating in entire ecosystems – with consumers evaluating a number of retailers before making a purchase, and a disproportionate number of players vying for the same consumer mindshare and share of wallet.

    Thus, external data from the web – the largest source of data known to man at present – is becoming critical to business’ ability to compete profitably in the market.

    Competing profitably in the digital era: Can AI help?

    As organizations across industries and geographies increasingly realized that their business decisions were affected by what’s happening around them (such as competitors’ pricing and merchandize decisions), they started shifting away from their excessive obsession with internal data, and began to look for ways to gather external data, integrate it with their internal data, and process it all in entirety to derive wholesome, meaningful insights.

    Simply put, harnessing external data consistently and on a large scale is the only way for businesses to gain a sustainable competitive advantage in the retail market. And the only way to practically accomplish that is with the help of AI. Many global giants are already doing this – they’re analyzing loads of external data every minute to take smarter decisions.

    That said, though, what you need to know is that all this data, while publicly available and therefore accessible, is massive, unstructured, noisy, scattered, dynamic, and incomplete. There’s no algorithm in the world that can start working on it overnight to churn out valuable insights. AI can only be effective if enormous amounts of training data is constantly fed back into it, coaxing it to get better and more astute each time. However, given the scarcity of readily available training datasets, limited and unreliable access to domain-specific data, and the inconsistent nature of the data itself, a majority of AI initiatives have ended up in a “garbage in, garbage out” loop that they can’t break out of.

    What you need is the perfect storm

    At DataWeave, we understand the challenge of blindly dealing with data at such a daunting scale. We get that what you need is a practical way to apply AI to the abundant web data out there and generate specific, relevant, and actionable insights that enable you to make the right decisions at the right time. That’s why we’ve developed a system that runs on a human-aided-machine-intelligence driven virtuous loop, ensuring better, sharper outcomes each time.

    Our technology platform includes four modules:

    1. Data aggregation: Here, we capture public web data at scale – whatever format, size, or shape it’s in – by deploying a variety of techniques.

    2. AI-driven analytics: Since the gathered data is extremely raw, it’s cleaned, curated, and normalized to remove the noise and prepare it for the AI layer, which then analyzes the data and generates insights.

    3. Human-supervised feedback: Though AI is getting smarter with time, we see that it’s still far from human cognitive capabilities – so we’ve introduced a human in the loop to validate the AI-generated insights, and use this as training data that gets fed back to the AI layer. Essentially, we use human intelligence to make AI smarter.

    4. Data-driven decision-making: Once the data has been analyzed and the insights generated, they can either be used as it to drive decision-making, or then integrated with internal data for decision-making at a higher level.

    With intelligent, data-backed decision-making capabilities, you can outperform your competitors

    Understandably, pricing is one of the most popular applications of data analytics in retail. For instance, a leading, US-based online furniture retailer approached us with the mission-critical challenge of pricing products just right to maximize sell-through rates as well as gross margin in a cost-effective and sustainable manner. We matched about 2.5 million SKUs across 75 competitor websites using AI and captured pricing, discounts, and stock status data every day. As a result, we were able to affect an up to 30% average increase in the sales of the products tracked, and up to a 3x increase in their gross margin.

    DataWeave’s powerful AI-driven platform is essentially an engine that can help you aggregate and process external data at scale and in near-real time to manage unavoidably high competition and margin pressures by enabling much sharper business decisions than before. The potential applications for the resulting insights are diverse – ranging from pricing, merchandize optimization, determination of customer perception, brand governance, and business performance analysis.

    If you’d like to learn more about our unique approach to AI-driven competitive intelligence in retail, reach out to us for a demo today!

  • 6 Smart Pricing Strategies for eCommerce Success

    6 Smart Pricing Strategies for eCommerce Success

    Over the last decade, the proliferation of e-commerce and the consequent surge in competitiveness among retailers has brought focus to one of the most critical drivers of success in online retail: pricing. According to McKinsey, an average 1% increase in price can translate into an 8.7% increase in operating profits (with the assumption that there’s no loss of volume). Yet, the company estimates that up to 30% of pricing decisions fail to provide the best price – every year. That’s a potential impact of millions in lost revenue for most modern-day retailers, a fact only made worse by the irony that in today’s times of automation and big data, there’s no shortage of intelligence to facilitate the best decision-making.

    What you need is the ability to gather and rationalize all the data out there – of competitor prices, price perceptions, market dynamics, buyer behavior, etc. – in good time to price your products just right for maximum margin and revenue. The best part? Effective product pricing contributes significantly toward fostering a great customer experience, too.

    Once you have your intel in place, there are plenty of eCommerce pricing strategies to choose from – it’s only a matter of identifying the metrics that matter the most to your business goals. That said, there are several models that have gained widespread popularity and acceptance over the years, like the following six:

    1) Introductory pricing

    This is a common marketing strategy used in the e-commerce space, where you draw consumer focus to a newly launched product or service, or the fact that you’re a new entrant in a market. There are two ways to do this – one is to start with steep discounts (particularly during sale events, and often in partnership with the consumer brand) with the aim of winning over more market share. At the other end is the strategy of setting relatively high initial prices. This works best for “exclusive offer” or “limited edition” opportunities; for instance, the opportunity to be the first to own the latest iPhone model.

    2) Cost-linked pricing

    In this method, you calculate how much it costs to sell a product and add a pre-determined margin to the final cost. In the world of online retail, product cost amounts to a lot more than the mere sum of manufacturing costs. For instance, it includes the procurement, labor, software, sales and marketing, shipping, and overhead costs that contribute to the total cost of housing it as long as it’s unsold. Therefore, all these costs need to be factored when determining the final product price. While the advantages of this model are its simplicity and the promise of guaranteed returns for each product sold, the flip side is that it doesn’t factor in the competitive landscape. The trick, therefore, lies in finding the balance between higher margin and sell-through rates, particularly given the aggressively competitive nature of online retail.

    3) Competitive pricing

    Today’s digitally savvy customers are forever comparing prices across several websites in the quest for the lowest prices. In fact, price is among the most critical factors that influences purchase decisions across products as well as categories. The competitive eCommerce pricing strategy, therefore, determines product price based on how the same products are priced by various competitors. While this model allows you to modify prices as frequently as necessary to drive efficient pricing and maximize revenue and margin, the complexity lies in ensuring consistent access to competitor prices, particularly in today’s highly dynamic e-commerce environment. DataWeave’s Pricing Intelligence platform helps eCommerce businesses overcome this challenge by helping them identify price improvement opportunities based on timely competitive intelligence at a massive scale.

    4) Dynamic pricing

    This model takes into account competitor prices, demand, and inventory levels, which are set up as triggers for automated pricing rules. While this results in sustained competitiveness, it requires a price optimization model that determines the optimal price in real-time response to fluctuations in demand and competitive prices – all the time ensuring alignment with your business goals. In other words, this model allows you to ensure consistently competitive yet optimized prices, thus acquiring and retaining a competitive edge in the market.

    5) Price perception management

    The company most famous for following this strategy is Amazon. The retail giant frequently identifies its most popular products and offers its largest discounts on them, often undercutting competitors. In other words, in this model, you “invest” in customer acquisition through excessively aggressive discounts on a select group of products – following which, you can cross-sell or up-sell other higher-priced products. Thus, you boost your perceived value to customers. Another way to drive a positive perception is to display discounted products at higher ranks on featured listings. For instance, in a recent study that we conducted, we found that 9 out of 10 leading US retailers’ top 50 ranked products (in each category) were significantly cheaper than the rest of their products.

    6) Bundle pricing

    The principle for this model is simple. You sell a number of the same products (or a range of complementary ones) for a combined, economical price. This is different from customers adding products individually to their cart as it works on the consumer psyche, which is more likely to favor a purchase that offers considerable perceived value. Thus, not only are you offering enhanced value to your customers (and in turn improving overall customer experience), you’re also actually increasing sales. Bundle pricing works best for products that are likely to involve repeat purchases (such as batteries, cereal boxes, or socks), and also for those that may need accessories (for instance, a food processor with various attachments). However, for bundle pricing to be effective, it’s also important to understand how your competitors are bundling their products.

    Granted, it isn’t easy to identify the perfect pricing strategy for you. As customers increasingly engage with you at every stage of their decision-making process and market dynamics become exceedingly complex, pricing as a function has to keep pace. As a retailer, your objective is to unearth the actionable insights hidden in your big data and leverage the resulting opportunities to drive the maximum possible revenue and margin – without getting lost in the flood.

  • Retailers Adopt Aggressive Private Label Pricing Strategies in CPG

    Retailers Adopt Aggressive Private Label Pricing Strategies in CPG

    Nine out of 10 leading retailers price their private label products lower than the average prices of their respective categories, reveals the latest DataWeave study, drafted in collaboration with SunTrust Robinson Humphrey The study reveals that an increasing number of retailers are viewing private label brands as a way to ensure sustained profitability.

    “As the CPG space reels under intense competition, a number of retailers are doubling down on private labels to capture valuable additional margin. For instance, Kroger, Walmart, and Amazon Fresh have a higher degree of private label penetration than the other retailers we analyzed,” said Karthik Bettadapura, Co-founder & CEO at DataWeave. “Our study unveils several such key insights covering product assortment & distribution patterns, price perception, and private label dynamics, revealing a clear snapshot of the disruptive transformations sweeping across the US CPG landscape.”

    Other key findings from the report, which tracked and analyzed 450,000 products across 10 leading retailers and 10 ZIP codes each, include the following:

    • Product assortment is emerging as a driver that’s as critical as pricing when it comes to customer retention. Target, H-E-B, and Kroger have a head start here, offering the largest product assortments among the retailers analyzed.
    • A sharp assortment strategy customized to local tastes and preferences is key to sustaining and enhancing customer satisfaction. Albertsons, Walmart, and Amazon Fresh lead here, revealing a higher focus on localized assortments.
    • “Home” and “Beauty & Personal Care” categories lead the distribution of private label products across retailers. The focus on these categories echoes a similar focus among national brands as well. These categories have the highest overall brand concentration, with around 4,000 brands each.

    To download the entire report, click here.

  • 2018 at DataWeave: A Year of Prolific Success and Growth

    2018 at DataWeave: A Year of Prolific Success and Growth

    As we enter 2019, in the backdrop of DataWeave’s unprecedented growth and success, we decided to take a breath and look back at some of the highlights of our progress over the last 12 months.

    DataWeave’s growth through the year has been complemented and influenced by the evolution of the retail sector, reinforcing the relevance of our technology platform.

    Amazon continued to dominate the online retail landscape, now commanding a staggering 49% of US e-commerce. At the same time, several large retailers have taken sure-footed strides toward establishing a stronger e-commerce presence, which places them head to head against the Seattle-based retail behemoth. As a result, competitive intelligence is no longer a “good-to-have” but is fundamental to the survival and growth of both traditional and new-age retailers, enabling them to devise smarter, data-driven competitive strategies.

    Consumer brands are continuing to figure out the dynamics of selling on online marketplaces, which happens to give them valuable access to a vast base of shoppers while simultaneously restricting their ability to influence the brand experience. In their quest to sell more through the e-commerce channel, while trying to safeguard the brand experience and loyalty, consumer brands have turned increasingly toward e-commerce performance platforms to augment their decision-making process.

    These trends have reinforced our confidence in our technology platform, which aggregates and analyzes data from the Web at massive scale to deliver actionable competitive insights, as we’re well poised to address the evolving challenges presented to retailers and brands today.

    In 2019, there are no signs of slowing down for DataWeave.

    We will continue to execute strongly in high-growth regions, and especially in the US, which has, in a span of two years, become the largest revenue generating region for DataWeave. We will also build a stronger footing in Europe, with specific focus on the UK market.

    With time, our historical repository of data increases in volume and granularity, which enables us to better serve the maturing space of Alternative Data. We have already witnessed highly encouraging inbound interest over the last year, and we expect this interest to rise significantly moving forward.

    With great success, comes the need for great people. In 2019, we will aggressively expand our team across functions, organization levels, and regions. As always, DataWeave is on the lookout for people who flourish in a competitive environment and can propel us to the next stage of growth.

    Our technology platform never ceases to impress in its ability to aggregate and analyze billions of data points accurately each day. As our pipeline swells and we onboard bigger and more diverse customers, the platform will consistently be pushed to its limits, driving further innovations and improved efficiency.

    Over the following 12 months, on the strength of all the lessons learnt and successes achieved in 2018, we look forward to another challenging year of empowering retailers and consumer brands to compete profitably in the new world order.

    Watch this space for more on DataWeave through the year!

  • Thanksgiving Weekend Sale: How Top US Consumer Brands Fared

    Thanksgiving Weekend Sale: How Top US Consumer Brands Fared

    Online retailers in the US have enjoyed an impressive turnover during 2018’s Thanksgiving weekend sale. Over the last few weeks, DataWeave has published deep-dive reports on the performance of top US retailers in fashion and consumer electronics during this period, detailing their discounting and product strategies across several product types.

    In continuation of our series of articles on the Thanksgiving weekend sale, this article focuses specifically on the top brands across all retailers analyzed.

    Read Also:

    A Study of Fashion Retail Pricing Across Thanksgiving, Black Friday and Cyber Monday 2018

    How Consumer Electronics Was Priced Across Thanksgiving, Black Friday and Cyber Monday 2018

    While a lot of attention from the media and analysts during these sale events is often focused on the strategies and performance of retailers, the festive sale period is equally vital for consumer brands. Both established brands and new entrants across all categories compete aggressively to gain market share during a period that accounts for a substantial portion of annual sales turnover.

    For brands, the two primary drivers of conversion specific to sale events are competitive pricing and prominent brand visibility. At DataWeave, we went about analyzing which brands came out on top across retailers and categories during the Thanksgiving weekend sale, based on these two factors.

    Our Methodology

    We tracked the pricing of 6 leading fashion retailers and 5 major consumer electronics retailers to study the pricing strategies of brands during the sale events. Our analysis focused on additional discounts offered during the sale period to evaluate the true value of the sale event to customers. To calculate this effect, we compared the pricing of products on Thanksgiving Day, Black Friday and Cyber Monday to the pricing of products prior to the sale commencing. We considered the Top 500 ranked products on 11 product types across Men’s and Women’s Fashion and 11 popular consumer electronics products for this analysis.

    Consumer Electronics Brands

    In digital cameras, Canon’s traditional role as a discount leader was on show, featuring on both Best Buy (14%) and Target (20%), the two most aggressive price discounters in consumer electronics. Nikon took Canon’s place in DSLR cameras, for Best Buy (13%), New Egg (10%) and Walmart (4%), albeit at a comparatively low additional discount point.

    Razor benefited from Amazon’s strategy of promoting its lower-priced products, promoting a modest 9% additional discount but across its entire range of laptop products. The competitiveness of this category between brands is shown by Samsung’s decision to give an additional 53% discount across 36% of its product line at Best Buy.

    The strategic approach brands take with different retailers was illustrated by HP’s 30% additional discount on 31% of its products at Target while over at Walmart, HP had a dire a 4% additional discount on a mere 13% of its products. A similar strategy was employed by LG with its televisions. On Amazon, its TVs had a 10% additional discount applied to 46% of its products, while at New Egg that translated to 25% and 8% respectively.

    Among the fast emerging wearables category, under-pressure Chinese firm Huawei dropped an aggressive 46% additional discount on 100% of its product range at Best Buy. By comparison, the next highest in this category was Marc Jacobs at Target with 33% and 40% respectively.

    Most Visible Brands Across Product Types

    In our analysis, brand visibility is represented in terms of both the number of products for each brand, as well as the average rank of all its products (“lower” the rank value, higher is the visibility).

    The influence an online retailer exerted on a brand’s average ranking is illustrated by Canon’s digital cameras. On Amazon, its 296 products had an average ranking of 272, while on Best Buy it was 30 and 48, 73 and 212 on New Egg and 20 and 69 on Walmart. For all these retailers, Canon was the most visible brand in digital cameras, despite such variation.

    It was a similar story on laptops, with HP’s Amazon ranking of 298 based on 166 products, contrasting with a Target ranking of 14 on 18 products and Walmart ranking of 21 on 20 products.

    These patterns appear to play out in TVs too, with Samsung’s Amazon average ranking of 292 based on 150 products contrasting with Walmart average ranking of 10 across 7 products.

    Unsurprisingly, across our analysis of additional discounts and brand visibility, the top brands are well known and recognizable brands in each product type, with very few new entrants breaking out from the pack. This story, though, takes a turn in the following analysis on visibility growth.

    Brands With Highest Growth in Visibility

    To perform this analysis, we developed an index for the visibility of a brand based on the number of products available per brand as well as the average rank of those products. We then compared this score for each brand between before and during the sale period, and subsequently calculated the percentage growth.

    The list of brands that showed the highest growth in visibility for each product type is an interesting mix of well established and newer brands. The usual suspects included the likes of Philips, Fitbit, Sony, Kodak, Nikon, etc. The presence of brands like Apple, Google, and Bose is surprising as they would be expected to command strong visibility even before the sale. Some of the newer brands include Rha, Westinghouse, Garmin, Lanruo, and more.

    Some brands showed a dramatic increase in visibility. Examples include Bose on Walmart (698%), HTC on New Egg (657%), Galanz on Amazon (657%), and Jlab on Target (608%).

    Kodak’s digital cameras (2% growth) on Best Buy took the honors for the lowest increase in visibility, just ahead of HP laptops (3%) on Walmart, Nostalgia Electrics refrigerators (4%) and Belkin Tablets (7%) both on sale at Target. These numbers indicate a relatively static assortment for the respective retailers and product types.

    Fashion Brands

    Moving over to the Fashion category, we observed significantly more aggressive discounting activity, as expected. Parent’s Choice T-shirts recorded the highest additional discount (80%) applied to the widest product range (Walmart 91%). Similarly, Fruit of the Loom saw Amazon promote a 78% additional discount applied across 20% of its products.

    In shoes, Macy’s promoted a 60% additional discount on 50% of Kenneth Cole’s product range. In watches, Amazon featured a 57% additional discount on 50% of Kate Spade New Year branded products. Meanwhile, in sunglasses, Ray Ban in Bloomingdale’s enjoyed a 20% additional discount spread across a whopping 95% of its products, compared to just a 14% additional discount applied to a mere 10% of Ray Ban products in New Egg.

    In stark contrast to what was observed in Electronics, the Fashion category saw fewer large brands dominate the discounting landscape across categories. This isn’t surprising given how the Fashion category tends to be cluttered with a plethora of brands, while the Electronics category usually consists of a leaner set of popular brands in each product type.

    Most Visible Brands Across Product Types

    In casual shoes, Nike’s ranking of 264 on 93 and Converse’s ranking of 239 on 89 products contrasted with Vision Street Wear’s ranking of 8 on 9 products and Time And Tru’s 15 ranking on 14 products.

    Another point of contrast was Micheal Kors (Handbags) cross-retailer platform performance - its average ranking of 184 on 102 products on Macy’s while its average ranking on New Egg was 20 across 12 products. Still, it appears the brand discounted heavily in New Egg to compensate for its relatively low visibility on the website.

    Ray Ban recorded a category high ranking of 209 based on 321 products on Macy’s. By comparison, Ray Ban had a ranking of 17 on 34 products at New Egg. Over at Amazon, Ray Ban managed a creditable 189 ranking on 124 products and a 163 ranking on 120 products at Bloomingdale’s.

    Brands With Highest Growth in Visibility

    Compared to the Electronics category, Fashion consists of certain brands that skyrocketed in their visibility. Examples include Next Level T-shirts (Amazon 2,000%), Michael Kors Watches (Walmart 1,424%), Dakota Watches (Target 751%) and Adidas sports shoes (Amazon 516%).

    Bloomingdale’s delivered amazing visibility growth for key brands, with Burberry (527%), Reiss (500%), The Kooples (%00%), Tory Burch (500%), J Brand (475%), and Adidas (300%) all enjoying strong visibility growth.

    At the other end of the visibility growth spectrum, the growth rates of Lucky shirts (New Egg, 11%), Micheal Kors (New Egg, 20%) Dickies jeans (Target, 22%), Tasso Elba shirts (Macy’s, 23%), and Puma Casual Shoes (Target, 25%) indicate a relatively more static assortment in their respective product types.

    Depth Of Product Range And Discounting Strategy Matters

    Across the three sales, DataWeave identified several different additional discounting and product assortment strategies by both the retailers and the brands.

    While retailers are increasingly discounting the lower priced products to shape price perceptions among shoppers (take a bow Amazon), what are the implications for brands? Firstly, a thin product range is going to make achieving visibility more challenging. Secondly, brand strategies across online retailing platforms will need to be more clearly defined and executed. Thirdly, those brands that treated Thanksgiving, Black Friday and Cyber Monday as discrete events are going to have to rethink their approach as these lines increasingly blur with time.

    If you’re interested to learn more about how DataWeave aggregates and analyzes data from online sources as massive scale, as well as how we provide competitive intelligence to retailers and consumer brands, visit our website!

  • Consumer Electronics Prices During the Holidays

    Consumer Electronics Prices During the Holidays

    Consumer electronics has always been one of the most popular product categories for consumers during the Thanksgiving weekend sale each year.

    Shoppers often hold off on making expensive purchases in electronics in anticipation of great discounts during these sale events. While Cyber Monday is traditionally the key day for offers in electronics, recent trends, triggered by the growth of eCommerce, lean toward offering attractive prices across the entire sale weekend.

    Studies indicate that in 2018, the average value of an online transaction hit $97. This compares with $91 in 2017 and $87 in 2016, continuing the trend of a steadily increasing transaction value over the past two years. This year, the scene was set for a massive Cyber Monday as Black Friday purchases of electronics reached $6.22 billion, up 23.6 percent from last year according to Adobe Analytics.

    At DataWeave, we recently analyzed and published a blog post on the Thanksgiving weekend sale for the Fashion vertical.

    (Read here: A Study of Fashion Retail Pricing Across Thanksgiving, Black Friday and Cyber Monday 2018)

    As part of the same project, we scrutinized the consumer electronics vertical just as keenly across top electronics retailers in the US by monitoring prices across the weekend.

    Our Methodology

    We tracked the pricing of the 5 leading retailers selling consumer electronics to assess their pricing and product strategies during the sale events. Our analysis focused on additional discounts offered during the sale to evaluate the true value the sale event represented to customers. To calculate this effect, we compared the pricing of products on Thanksgiving Day, Black Friday and Cyber Monday to the pricing of products prior to the sale commencing. We considered the Top 500 ranked products on 11 popular product types in carrying out this analysis.

    Key Findings

    In contrast to the Fashion category, the consistency in the discounting strategy for all retailers across the three sale days in the Consumer Electronics category was striking. The only exception was Walmart, which opted somewhat curiously to roll back its discounts on Cyber Monday. All other retailers held similar additional discounts levels on a fairly similar set of products through the sale weekend.

    Target and Best Buy led the electronics discount charge at 22% and 21% for 18% and 17% of their assortment, respectively.

    While Amazon discounted the highest number of products at 29% of its range, it continued its recent strategy of not discounting steeply. In fact, Amazon was among the lowest in terms of additional discounts. The other end of the spectrum, Walmart provided a 28% additional discount on the first two sale days, offered only on a modest range of products (4% and 1%).

    Headphones and USB Drives proved popular lead product types for discounting by all retailers. Other product types making the cut included Refrigerators (Target), Laptops (Walmart), and Wearable Technology (Newegg).

    Amazon’s discounting strategy appears to be informed significantly by product visibility. The highest ranked products were far more aggressively discounted, and the discounts reduced progressively as we move to less visible products. This supports previous evidence illuminating Amazon’s strategy to develop a low price perception. We saw a similar trend emerging from Best Buy and Newegg as well.

    This discounting approach is in stark contrast to the behavior we witnessed in our earlier analysis of the Fashion category, where we found little correlation between visibility and discounts. However, given the higher price points and greater price elasticity in the Electronics category, we were not surprised to see this level of strategic clarity. Interestingly, our analysis of Target’s discounting behavior showed an opposite trend as Target opted to load up discounts on its less visible products.

    Walmart was excluded from this part of our study due to the very low number of common products before and during the sale that we could analyze.

    Another stable trend which emerged during our analysis of the sale weekend is the consistency with which lower priced products are offered at higher additional discounts relative to the more premium, higher priced products in the retailers’ product type. This trend largely held across retailers. Customer perceptions of low prices can be built by heavily discounting products at the lower end of the premium spectrum, while retailers can harvest their critical margin on their higher value goods.

    Diving Deeper Into Amazon

    Amazon announced a few days ago that it had its biggest shopping day in the company’s history on Cyber Monday. In its announcement, the company also stated the five shopping days starting with Thanksgiving and continuing through to Cyber Monday shattered records as US consumers bought millions of more products over the five-day sales compared with the same sales period last year.

    When the product popularity was evaluated and compared with additional discounts, we see higher discounts for better-reviewed products on Thanksgiving and Black Friday. Cyber Monday was an exception where discounts were distributed more smoothly across the three popularity bands.

    As with what we witnessed in the Fashion category, we detected higher additional discounts in Amazon’s Electronics private label brands (17%) relative to the average discount for other brands (7%).

    Profitability is back in the spotlight

    Electronics continued to be a key focus eCommerce retailers during their pivotal sales events in 2018. We are seeing signs of a shift to eCommerce and an accelerating emergence of a “Black November” and a “Cyber Post-Thanksgiving Weekend” impacting on sales results for the beginning of the holiday season.

    This year, there was a more concerted and strategic approach by retailers to maximize margin in the high-value end of the Electronics Category while still discounting the more popular and lower priced products. As expected, both Target and Best Buy featured prominently with their heavy discounting, while both Amazon and Newegg appeared to be executing a more nuanced discounting strategy. This rather reserved approach to the sale and careful focus on profitability is backed up by recent reports of Amazon’s shift in approach to housing low margin products.

    As was the case with the Fashion category, we saw the importance of Cyber Monday for Electronics sales being eroded and spread across the entire weekend, on the backdrop of a larger trend of attractive offers encompassing much of November and December.

    If you would like to know more about how DataWeave aggregates data from online sources to deliver actionable insights to retailers and consumer brands, check out our website!

  • A Study of Fashion Retail Pricing Across Thanksgiving, Black Friday and Cyber Monday 2018

    A Study of Fashion Retail Pricing Across Thanksgiving, Black Friday and Cyber Monday 2018

    The biggest holiday sale event of the western retail calendar — the Thanksgiving weekend sale, which includes Thanksgiving Day, Black Friday, and Cyber Monday — came and went a few weeks ago and made a huge splash along the way. While the sale event, especially Black Friday, is traditionally an offline sale event, modern online retailers too step up to offer products at attractive prices through this period.

    Online retail sales numbers grew at an impressive clip based on stats reported by Adobe Analytics. Thanksgiving Day sale itself generated $3.7 billion in sales, up 28 percent from a year ago. Black Friday delivered a record $6.22 billion in online sales — a substantial leap of 23.6 percent year on year. Cyber Monday sales online generated a new record of $7.9 billion, up nearly 18 percent from last year.

    Spending on fashion specifically was up 5.4 percent over the 2018 Black Friday weekend, the best growth seen since 2011, according to consulting firm Customer Growth Partners. Apparel retailers now book nearly a quarter of their annual sales during these holiday sales — a measure of just how important these annual sales have become to the online retailer’s commercial performance.

    As a provider of Competitive Intelligence as a Service to retailers and consumer brands, DataWeave consistently monitors and captures pricing and assortment information from leading retailer websites during sale events to study their product and pricing strategies — and we’ve done the same for this year’s Thanksgiving weekend sale as well.

    Our Methodology

    We tracked the pricing of 6 leading fashion retailers to study their pricing and product strategies during the sale events. Our analysis focused on additional discounts offered during the sale to evaluate the true value of the sale event to customers. To calculate this effect, we compared the pricing of products on Thanksgiving Day, Black Friday and Cyber Monday to the pricing of products prior to the sale commencing. We considered the Top 500 ranked products on 15 product types across Men’s and Women’s Fashion for this analysis.

    Key Findings in Men’s Fashion

    Macy’s and Bloomingdale’s featured prominently among the top discounting retailers. This is unsurprising, given their focus on Fashion. Macy’s, in particular, additionally discounted just over half its fashion assortment over the three days. This was an order of magnitude greater than its nearest competitor Amazon at 29 percent.

    Target and Walmart too discounted aggressively on Thanksgiving and Black Friday. Target exceeded Macy’s by 2 percentage points. However, Target and Walmart rolled back their discounts on Cyber Monday effectively halving them.

    Walmart’s discount strategy displayed significant variation across the 3 days of sale. On Black Friday, Walmart led the retailing pack with its 46 percent discount only to roll back to 15% on Cyber Monday. The fluctuations in these discounts reflect significant variation and churn in Walmart’s Top 500 ranked products across the three days of sales.

    As we have seen in previous sales, Amazon was a model of consistency in its discount strategy across the three days, maintaining a healthy 15% — 16% on roughly a third of its assortment. Strikingly, Newegg elected not to compete too aggressively in Fashion this year, adopting high single digit discounts on a similar percentage of its products.

    Across all six retailers, Shirts, Jeans, and T-shirts proved to be the most popular product types in terms of additional discounts although accessories such as sunglasses (Newegg) and watches (Macy’s) broke up apparel’s dominance.

    Did additional discounts vary by price range?

    We also studied the variation of discounts across ranges of product “premiumness”. We generated a percentile scale based on price ranges of products from before the sale, and studied the additional discounts offered for products in these price range buckets during the sale. A percentile score or 1 is the cheapest product and 100 is the most expensive product. All of these metrics were calculated first at a product type level and then aggregated at an overall level for each retailer.

    Amazon and Target display a clear strategy to additionally discount their more affordable range of products – those in the 1–20 cluster.

    Bloomingdale’s showed a less structured strategic approach. Its additional discounts were largely spread evenly across levels. Its product churn among the Top 500 items during the sale focused on its more expensive products as indicated by its score of 0 for the 81–100 percentile bracket.

    Macy’s opted to discount even more evenly across the board than Bloomingdale’s. It’s likely Macy’s relied on a different lever to drive discounts strategically. Walmart’s pricing approach was markedly uneven and all over the board from a strategic perspective.

    Key Findings in Women’s Fashion

    One of the most interesting patterns to emerge from these sale events was the marked difference in discounting strategy adopted for Women’s Fashion compared to Men’s Fashion. Both Amazon and Macy’s discounted their Women’s Fashion line up far less aggressively than their Men’s Fashion products. Their discounts also applied to a smaller set of products.

    Bloomingdale’s Women’s Fashion discounting was similarly marginally less aggressive than its approach to its Men’s Fashion. Only Target’s pricing remained consistent across its Men’s and Women’s Fashion products. However, Newegg’s strategy of not engaging too aggressively in Men’s Fashion this year carried over to its treatment of Women’s Fashion.

    The top product types additionally discounted were also not unexpectedly different between the Men’s and Women’s Fashion products. Skirts, Shoes, and Tops emerged as the favorite product types to discount, although no two retailers had the same discounting emphasis.

    As with Women’s Fashion, Amazon and Target discounted their less expensive products more consistently. However, in Women’s Fashion, they were joined by Walmart and to a lesser degree, Newegg.

    This showed evidence of a strategy to retail the less expensive products at more attractive price points to generate the price perception of being low-priced. Meanwhile, they continued to harvest comparatively more margin through their more expensive products. This was a more nuanced approach to margin management than what we saw in Men’s Fashion.

    Does product visibility correlate with discounts?

    One working hypothesis is that products discounted heavily tend to have higher visibility to drive the perception of lower price. However, the results of our analysis appear counter-intuitive.

    Amazon’s additional discounts in Men’s Fashion appear relatively uniform across all product cohorts. In fact, Amazon’s peaked additional discounts with the 200–400 cohort.

    Similar trends surfaced with other retailers. Newegg additionally discounted its longer tail products, while Walmart additionally discounted its Top 50 products at only 16% compared to an average of around 23% for other cohorts in its Top 500.

    A closer look at Amazon.com

    (Read Also: Amazon’s US Fashion and Apparel Product Assortment Evolves)

    We extracted data on Amazon’s reviews and ratings to investigate its discounting strategy across ranges of product popularity — a measure that’s defined using a combination of average review rating and number of reviews. We compiled a measure of all products that were rated as High, Medium, and Low cohorts and evaluated Amazon’s discounting strategy in each cohort.

    In Men’s Fashion, Amazon aggressively discounted its Medium and Low rated products on Thanksgiving, only to switch its strategy the next day on Black Friday. This tactical switch was presumably intended to showcase Amazon’s well-reviewed products at attractive prices on Black Friday — a larger sale event.

    By Cyber Monday, Amazon’s Medium reviewed products were back enjoying more aggressive discount levels, albeit the discount variance across all three cohorts was minor.

    Amazon’s discounting strategy for its Men’s Fashion products was in stark contrast to its strategy in Women’s Fashion. Here, Amazon additionally discounted its High and Medium reviewed products on Thanksgiving. While there was no specific discernible pattern on Black Friday, Amazon’s discounting was most consistent across its three popularity cohorts on Cyber Monday.

    We also looked at Amazon’s discounting activity across its private label products relative to other brands. Unsurprisingly, Amazon discounted its private label fashion products at an aggressive 30%, while the other brands benefited from, on average across all days and all categories, an additional 15% discount.

    Online drives shifting tides in holiday sale events

    While traditionally the holiday shopping season sees a peak around Black Friday and Christmas, retailers are increasingly seeing the demand spread across the entirety of the sale season of November and December. As a result, retailers need to stay on their toes to drive increased sales and gain market share over an extended period of time.

    Certainly, in 2018, we witnessed a more focused approach to mine margins in Women’s Fashion while still discounting aggressively. As expected, both Macy’s and Bloomingdale’s featured prominently in the discounting stakes while both Amazon and Target appeared to implement a more nuanced approach to juggling a reputation for low prices and driving increased margin.

    If you’re curious about how DataWeave aggregates data from eCommerce data at massive to deliver actionable insights to retailers and consumer brands, check us out on our website!

  • Decoding Alibaba’s Singles Day Sales

    Decoding Alibaba’s Singles Day Sales

    An average of $11.7 million per second was the rate at which Alibaba clocked $1 billion in sales during the first 85 seconds of Singles’ Day. As Alibaba’s annual sale event continues to grow in scale, referring to it as a global retail phenomenon is an understatement. Alibaba closed the day having shipped 1.04 billion express packages based on sales of merchandize worth 213.5 billion yuan ($30.67 billion).

    This performance shredded any lingering concerns analysts may have harbored about the prospects of this year’s sale, given the international backdrop of the ongoing trade skirmish between the US and China.

    Along with attractive discounts across a range of product categories, Singles’ Day also promised an integrated experience fusing entertainment, digital and shopping, in stark contrast to other large global sale events like Black Friday, which focus predominantly on discounts.

    At DataWeave, we set out to investigate if all the hype resulted in actual price benefits to the shoppers and how the various categories and brands performed in terms of sales during the event. To do this, we leveraged our proprietary data aggregation and analysis platform to capture a range of diverse data points on Tmall Global, covering unit sales (reported by the website) and pricing associated with Tmall Global’s major categories over the Singles’ Day period.

    Our Methodology

    We captured 5 separate snapshots of data from Tmall.com during the period between October 25 and November 14, encompassing over 15,000 unique products each time, across 15 product categories.

    To calculate the average discount rate, we considered the percentage difference between the maximum retail price and the available price of each product. We also looked at the additional discount rate, for which we compared the available price during Singles’ Day to the available price from before the sale. This metric reflects the truest value to the shopper during Singles’ Day in terms of price.

    Our AI-powered technology platform is also capable of capturing prices embedded in an image. For example, the offer price of ¥4198 was extracted accurately from the accompanying image by our algorithms and attributed as the available price while ¥100 from the same image was ignored.

    This technology was employed across hundreds of products using DataWeave’s proprietary Computer Vision technology.

    Domestic Appliances and Digital/Computer Categories Powered Turnover

    The Domestic Appliances and Digital/Computer categories dominated the Singles Day Sale in terms of absolute sales turnover. This isn’t surprising, since the average order value for these categories are typically much higher compared to the other categories analyzed.

    What clearly stands out in the above infographic is that the two largest categories in terms of sales turnover had average additional discounts of only 2 per cent and 0 per cent — a rather surprising insight. In general, with the exceptions of Women’s skincare, Men’s skincare, and Women’s bags (11 per cent, 10 per cent, and 9 per cent respectively), all other categories saw low additional discounts during Singles’ Day.

    However, the absolute discounts across the board were consistently high, with only Luggage (6 per cent), Digital/Computer (9 per cent) and Women’s wear (12 per cent) staying significantly below the 20 per cent mark. In fact, eight categories enjoyed absolute discounts greater than 30 per cent.

    Among common categories between Men and Women, the Men clocked more sales in Men’s wear, shoes, and bags. Only skincare proved to be an exception, where Women’s skincare generated twice the turnover of their Men’s equivalent.

    The Infants category was another intriguing sector to emerge during the sale. Both Diapers (38 per cent) and Infant’s Formula (25 per cent) were substantially discounted, despite only receiving low additional discounts of 2 per cent and 0 per cent respectively – indicating aggressive pricing strategies in this category even during non-sale time periods.

    The biggest takeaway from our analysis is the lack of any correlation between sales turnover and additional discounts, or even the absolute discounts.

    International Brands Make Gains

    International brands continue to penetrate the Chinese market showing up amongst the Top 5 brands of 13 of the 16 categories on sale.

    In the Diaper category, Pampers delivered nearly twice the sales turnover of its next biggest competitor. As expected, Apple and Huawei battled it out for honors in the Digital/Computer category although Xiaomi enjoyed pleasing results, nearly matching Huawei’s sales to go with its sales leadership of the Domestic Appliances category. Local brands, though, swept the Domestic Appliances, Furniture and Women’s Wear categories.

    The challenge posed by Chinese brands was illustrated by Nike’s spot in the second place in the highly competitive Men’s Shoes category after Anta.

    International brands topped only five of the 16 categories and Top 3 positions in ten categories. Still, there’s a growing presence of international brands in China’s eCommerce.

    Gillette won handsomely over its competition in the Personal Care category while Skechers enjoyed a similar result in Women’s Shoes, racking up nearly twice the retail sales of its nearest competitor. Another category dominated by international brands was the Women’s Cosmetics category where international brands accounted for 4 of the Top 5 brands.

    Similarly, Samsonite’s acquisition of American Tourister gave it two top 5 brands in the Luggage category. Other global brands to make the cut during the Singles’ Day sale included L’Oréal, Canada’s Hershel, Playboy, South Korea’s Innisfree and Japan’s Uniqlo.

    It’s Not All About Price On Singles’ Day

    The dramatic rise in shopping during Singles’ Day is not driven solely by price reductions. Alibaba’s commitment to its “New Retail” strategic model has led the Chinese giant to channel its impressive resources to focus on bringing together the online elements of its business with the more traditional offline aspects of its retail distribution. This is combined with entertainment to create a larger story based around the shopper’s overall “experience” rather than just driving “attractive prices” as a short-term retail hook.

    Alibaba is betting big on erasing the line between online and offline and its futuristic vision of structuring retail around the way people actually want to shop. Based on the consistently impressive results of Singles’ Day year after year, “New Retail” has a promising future.

    If you wish to know more about how DataWeave aggregates data from online sources to provide actionable insights to retailers and consumer brands, check out our website!

  • CEO Speak: Serving the US Market, Hiring the Right Talent, And More

    CEO Speak: Serving the US Market, Hiring the Right Talent, And More

    Recently, Karthik Bettadapura, Co-founder & CEO at DataWeave, was interviewed by Vishal Krishna, Business Editor at YourStory, in the Bay Area, California. They discussed DataWeave’s focus on the US market, challenges that retailers face today, DataWeave’s technology platform and hiring practices, and more.

    The following is a transcript of the interview.

    (The transcript has been edited for clarity and brevity)

    Vishal Krishna (VK): You left India to come and conquer America, why is that?

    Karthik Bettadapura (KB) : Just a bit of history — we started in 2011 and product development and research was based in Bangalore, and still is. At the end of the first 5 years, we realized that we built great technology, but we were not able to scale beyond a certain point [in India]. If we had to build a growing business, we had to look at other markets as well.

    VK: Quickly, can you tell me what DataWeave does?

    KB: We provide Competitive Intelligence to retailers and customer brands. We work with some of the largest brands and retailers out there and we provide them with analyses to compete profitably.

    VK: You said you had marque clients in India, yet you didn’t want to stay there because you wouldn’t have scaled beyond a particular point. Why is that?

    KB :The ticket size in India is still on the lower side. If you must build a sustainable business, you need access to a much larger customer base and we found that in the US.

    VK: Let’s start from the basics. What are a few things that a startup should decide to do when coming to America?

    KB: A few things:

    • A good understanding of the market
    • Learn fast about the market
    • Build a team here, or a have a team here already doing some work initially
    • Consider how your team back in India will go about doing things in your absence
    • The last one is about your own personal journey. I was so used to walking into an office and interacting with people. You come here, and you are all alone!

    VK: It’s a lonely journey. Doors don’t open all that easily and you’ve got to hustle. Why?

    KB: For people here, you are an unknown entity. Why should they be trusting someone who does not have enough customers here or has not raised money here? We had two US-based customers when we came in. It’s an uphill task to ensure that customers trust you.

    VK: Who was the first customer you personally met here and why was that meeting so important?

    KB: The first customer I met here was a large, big box retailer, and the meeting was primarily focused around why they should trust us — how can they know that we would survive and serve them, as well as how we are better than some of the other guys out there.

    VK: Can you tell us what DataWeave does for US retailers?

    KB: For retailers, we provide competitive intelligence, primarily around pricing optimization and assortment analytics. In the US, a lot of retailers are shutting shop and filing for bankruptcy.

    VK: Yeah, we saw Sears go through something like that.

    KB: The reasons fall broadly into 3 categories:

    • They failed to compete profitably with a lot of these new age businesses.
    • The new age retailers offer superior customer experience. They have figured out a better assortment/product strategy.
    • The third one is ‘Price’ — price is such an important feature.
      What we do is help these retailers optimize their strategies around pricing, assortment and promotions, eventually enabling them to compete profitably.

    VK: Typically, customers pay you on the outcome, pricing, license or subscription?

    KB: It’s a subscription-based model. There is a one-time setup fee and an ongoing subscription fee.

    VK: So you plug into their data management system?

    KB: Yes, but we can also have our product sit independently. Sitting out of their internal systems is a benefit for us as we don’t have to get into the entire loop of integrations into their internal systems right from Day 1. We prove our product works and then we integrate with their systems.

    VK: How do you integrate? Is the CIO your target?

    KB: No, we don’t sell to the CIO world. We sell to analytics, pricing, and merchandising teams.

    VK: Can pricing alone give retailers a competitive edge?

    KB: Yes, pricing is a big lever that retailers use. For example, last holiday season’s sale, Amazon and Walmart made 120 million price changes in just 2–3 days.

    VK: So they change the prices so dynamically to compete with each other. Is this price war coming to India?

    KB: It is happening in India already.

    VK: How much data can DataWeave’s infrastructure ingest?

    KB: We are a global platform — we have customers across the globe, not just the US or India. So, on a daily basis, we process data on around 120 million products.

    VK: Talk a little bit on R&D quickly. Do you have your marketing team in the US?

    KB: We have marketing teams in the US and India.

    VK: And the engineering team?

    KB: The engineering team is in Bangalore.

    VK: For people who want to work in your company, what kind of talent are you looking for?

    KB: We look at 4 broad talent areas:

    • One is in the world of data acquisition, which addresses issues like how data can be aggregated from thousands of websites and millions of pages on an ongoing basis, and how this data can be stored.
    • The second area is on what kind of insights can be generated using this data. This could be done using text analytics, image analytics, and other technologies. This includes process optimization, in terms of building efficient and scalable systems.
    • The third area is on how well the data can be represented if we have a customer who wants 60–70 million data points to be consumed on a weekly basis.
    • And the last area is on data modeling — what kind of insights can we eventually give to the customer? And, when I say insights, I mean specific actions.

    VK: You want people who can handle massive scale and for that they should be good at linear regression.

    KB: We value people who write good code. We primarily work in Python, and we use a lot of optimization techniques in the middle of the stack to help us scale.

    VK: Would you do something for supermarkets?

    KB: Absolutely. The largest offline supermarket in India is our customer.

    VK: So what can you do for supermarkets?

    KB: Offline retailers across the world are facing something that’s called showrooming. This is when a shopper walks up to a store, looks at and feels a product, then searches online to see it’s available at a better price. So we have retailers who are wary of this phenomenon. We also have retailers who are wary of diminishing customer loyalty. So they have to constantly ensure that they are priced better in the market and are not losing customers because of [online] pricing.

    VK: How powerful are your algorithms?

    KB: There is a dedicated team that works on our algorithms. These fall into several buckets. One is pure data scale algorithms — how do you build systems which ensure that you are able to efficiently query them in real time and get the desired output. The second one is — how do you keep improving your machine learning algorithms. For example, computer vision algorithms, text analytics algorithm, etc. The third — how do you keep experimenting effectively.

    VK: What role can an MBA degree holder play in DataWeave?

    KB: We have people who hold MBA degrees and are working in customer success, delivery management, marketing, and sales.

    VK: Do you spend time in training?

    KB: You do have some lead time if you are a fresher, but if you are a lateral hire, its expected that you keep the ball rolling. They should be able to learn and learn fast — learning is more important than knowing. So, we give a lot of importance to people who can learn and pick up things quickly – about our product, handling customer objections, etc.

    *

    Watch the whole video here or check out DataWeave’s website to know more about how we use data engineering and artificial intelligence to enable retailers and brands to compete profitably in the age of eCommerce.

  • Evolution of Amazon’s US Product Assortment

    Evolution of Amazon’s US Product Assortment

    As with many other product categories, Amazon has made a significant incursion in Apparel — a key battleground category in retail today. Recently, DataWeave once more collaborated with Coresight Research, a retail-focused research firm to publish an in-depth report revealing insights on Amazon’s approach to its US fashion offerings.

    Since our initial collaborative report in February this year, we have witnessed some seismic shifts in the category at both the brand and the product-type level.

    Research Methodology

    We aggregated our analytical data on more than 1 million women’s and men’s clothing products listed on Amazon.com in two stages:

    Firstly, we identified all brands included in the Top 500 featured product listings for each product subcategory in both the Women’s Clothing and Men’s Clothing sections featured on Amazon Fashion (e.g., the top 500 product listings for women’s tops and tees, the top 500 product listings for men’s activewear, etc.). We believe these Top 500 products reflect around 95 percent of all Amazon.com’s clothing sales. This represents 2,782 unique brands.

    We then aggregated the data on all product listings within the Women’s Clothing and Men’s Clothing sections for each of those 2,782 brands. This generated a total of 1.12 million individually listed products. This expansive list forms the basis for our highlights of the report.

    Third-Party Seller Listings Are Rising Sharply

    We identified a total of 1.12 million products across men’s and women’s clothing — a significant increase of 27.3 percent in the seven months between February and September 2018. The drivers of this sharp spike are third-party seller listings. In contrast, the report indicates only a 2.2 percent rise in first-party listings over the same period, compared to a 30.5 percent jump in third-party listings.

    In addition, Amazon has listed just 11.1 percent of all clothing products for sale, with third-party sellers offering the remaining 88.9 percent — an indication of the strength of Amazon’s open marketplace platform.

    A Major Brand Shift On Amazon Fashion Is Underway

    In just over six short months, major brand shifts on Amazon Fashion have taken place. The number of Nike listings has plummeted by 46 percent, driven by a slump in third-party listings following Amazon’s new partnership with Nike — a story recently covered by Quartz. Limited growth in Nike clothing first-party listings failed to compensate for this decline.

    Gildan’s spike in total product listings appears to be fueled by increased first-party listings off a low base. Calvin Klein’s 2017 agreement to supply Amazon with products appears to be driving the Calvin Klein brand’s double-digit uptick in first-party listings on Amazon Fashion.

    Aéropostale’s decline appears to be entirely driven by a drop in its third-party listings. The brand itself is not listed as a seller on Amazon.com.

    Amazon Is Rebalancing Its Apparel Portfolio and Switching Its Focus from Sportswear To Suits

    As its Fashion footprint rapidly matures, Amazon now appears to be rebalancing its portfolio with strong growth being shown in listings for formal categories such as suits and away from sportswear. We recorded a 98.6 percent increase in listings of women’s suits and blazers complemented by a 52.2 percent rise in men’s suit and sports coat listings between February and September 2018.

    Generic “Non-Brands” Are Surging On Top 25 Brands List

    Over the past six months, low-price generic brands have made major inroads into Amazon’s listings. Four unknown “brands” captured the top positions on the list of brands offered on Amazon Fashion. The WSPLYSPJY, Cruiize and Comfy brands appear to be shipped directly to customers from China.

    Source: Coresight/DataWeave (Amazon Fashion: Top 25 Brands’ Number of Listings, February 2018 vs. September 2018)

     

    Source: Coresight/DataWeave (Amazon Fashion: Top 25 Brands’ Number of Listings, February 2018 vs. September 2018)

    WSPLYSPJY alone accounts for fully 8.6 percent of Amazon men’s and women’s clothing listings. Cruiize accounts for a further 3.2 percent of listings while Comfy chips in another 3.1 percent.

    Amazon Appears To be Executing A Strategic Pivot

    Amazon’s fashion offering is fast maturing. We saw substantial growth in the number of listings for more formal categories. The realignment in third-party listings by Nike together with increased first-party listings for Calvin Klein and Gildan appear to be driven by alliances with Amazon.

    Simultaneously, ultralow-price generic clothing items delivered on order from China have inundated the “Most-Listed Products” rankings. Third parties now represent nearly 90 percent of Amazon Fashion’s offering.

    While Amazon Fashion shoppers enjoy a wider choice than they did even six months ago, we believe a stronger emphasis on first-party listings would grow the products eligible for Prime delivery. This tactic could strengthen Amazon Fashion’s long-term appeal as a shopping destination.

    If you’re interested in DataWeave’s technology, and how we aggregate data from online sources to provide unique and comprehensive insights on eCommerce products and pricing, check us out on our website!

  • Inside India’s eCommerce Battle: Attractive Offers Usher In The Festive Season

    Inside India’s eCommerce Battle: Attractive Offers Usher In The Festive Season

    It’s festival season in India again and shoppers took advantage of aggressive cutthroat competition between Indian online retailers to drive sales to unprecedented highs.

    All the major Indian eCommerce websites including, Amazon, Flipkart, Myntra, and Shopclues opted to go head to head by holding their first sale event this season over 4 to 5 days starting on the 10th of October. Still, as industry reports indicate, one retailer came out on top during this event — an insight supported by our analysis as well.

    A New Battleground

    The highlight this year was seeing how the announcement of global retail colossus Walmart’s acquisition of Flipkart would impact the sale events. The acquisition was the most influential development in India’s eCommerce sector, and it has transported a decades-long U.S. rivalry between Amazon and Walmart to Indian soil. As a result, this year’s sale event held out the promise of more attractive pricing and vast product selection for India’s consumers than ever before.

    Industry analysts estimate that the sale generated a cumulative Rs 15,000 crore in sales over the spread of the five sale days, a whopping outcome. In 2018, this translated into around a 64 per cent year-on-year growth outcome compared to the USD 1.4 billion (around Rs 10,325 crore) generated by the 2017 sales.

    The DataWeave Analysis

    At DataWeave, we analyzed the performance of each of the major eCommerce platforms including Amazon, Flipkart, Myntra, Paytm, and Shopclues. For each eCommerce website, we aggregated data on the Top 500 ranked products for over 40 product types spread across 6 product categories (Electronics, Men’s & Women’s Fashion, Furniture, Haircare, Skincare).

    We focused our analysis on only the additional discounts offered during the sale and compared them to prices prior to the sale, to reflect the true value of the sale to India’s shoppers.

     

    The battle of the discounts was led primarily by Flipkart and Amazon. Flipkart’s average additional discounts by category actually exceeded Amazon’s in three out of six categories, and it discounted more products that Amazon across all categories.

    Clearly, the focus for all e-tailers was skewed towards the main battlegrounds of Electronics and Fashion, compared to mainstream FMCG categories such as Hair and Skin Care. However, this is not surprising given FMCG functions on rather skinny margins.

    Across retailers, the Men’s and Women’s Fashion categories were the most aggressively discounted, attracting both the highest additional discounts and the highest percentage of products with additional discounts.

    The Furniture category too was an interesting battleground between Amazon and Flipkart, attracting attractive discounts on a wide range of products, particularly in Flipkart’s case.

    Prospective shoppers in search of relatively more expensive clothing products on discount during the sale would have established Myntra as their ideal destination, as it carried more premium products on discount during the sale, relative to all its competitors. For shoppers in search of an electronics bargain though, they would have done well to opt for Flipkart.

    Shoppers may have found some interesting deals on Paytm Mall too, especially in Men’s Fashion, while Shopclues largely held itself back from any dramatic price reductions.

    While Myntra capitalized on its niche though aggressive discounting in the Fashion category, most of the discounting action revolved unsurprisingly around Amazon and Flipkart. To drill down for a more complete understanding of just how the Amazon and Flipkart discounted their products, we conducted a more detailed follow-on analysis.

    We normalized additional discounts and popularity using a scale of 1 to 10 and plotted each product on a chart to analyze its distribution characteristics. Popularity was calculated as a combination of the average review rating and the number of reviews posted. Products with a popularity score of zero, as well as zero additional discounts were excluded from this analysis.

     

    The most obvious insight yield through this analysis is how Flipkart elected to distribute its additional discounts across a larger range of discount percentages. By contrast, Amazon went all in on the more limited range of products it decided to provide additional discounts on. This is a strategy we have seen Amazon adopt previously.

    One other intriguing insight is Flipkart’s decision to go for a much higher distribution of products falling below a popularity score of 0.5 compared to Amazon. Amazon’s strategy resulted in more of its discounted products having a higher popularity score, relative to Flipkart, albeit only by a comparatively minor amount. However, a shopper’s chances of buying a popular, positively reviewed product at a lower price were higher on Amazon than Flipkart during this sale.

    Achieving a Consistent Competitive Edge

    Flipkart claims to have recorded a 70 per cent plus share of entire Indian e-commerce market in the 4 day-BBD’18 sales. Flipkart further claimed to have cornered an 85 per cent share in the online Fashion category together with a 75 per cent share in the Electrical category’s large appliances during the sale. This includes a contribution by Flipkart’s subsidiary Myntra.

    As these numbers reflect, Amazon still has some way to go to entrench itself in the Fashion category of the Indian market. However, Amazon appears content to continue its surgical discounting philosophy.

    Overall, this year witnessed an impressive participation by Tier II and Tier III Indian city consumers — a sign of things to come in Indian online retail.

    With increasing competitive pressure, retailers simply cannot adopt discounting and product selection strategies in isolation and be successful. Having access to up to date insights on competitors’ products dynamically during the day is emerging as key to ensuring they’re able to sustain their lowest priced strategy for appropriate products. These insights are also proving critical in identifying gaps in their product assortment, which can hamper customer conversion and retention.

    During sale events, modern retailers need to rely on highly granular competitive insights on an hourly basis (or even more frequently) to inform their pricing and product strategies to ensure they consistently maintain a competitive edge for the consumer’s wallet. And while access to reliable competitive intelligence is critical, true value can only be derived when it gets integrated with a retailer’s core business and decision-making processes, such as assortment management, promotions planning, pricing strategies, etc.

    DataWeave’s Competitive Intelligence as a Service helps global retailers do just this by providing timely, accurate, and actionable competitive pricing and product insights, at massive scale. Check out our website to find out more!

  • Evaluating the Influence of Learning Models

    Evaluating the Influence of Learning Models

    Natt Fry, a renowned thought leader in the world of retail and analytics, published recently an article expounding the value and potential of learning models influencing business decision-making across industries over the next few years.

    He quotes a Wall Street Journal article (paywall) published by Steven A. Cohen and Matthew W. Granade who claim that, “while software ate the world the past 7 years, learning models will ‘eat the world’ in the next 7 years.”

    The article defines a learning model as a “decision framework in which the logic is derived by algorithm from data. Once created, a model can learn from its successes and failures with speed and sophistication that humans usually cannot match.”

    Narrowing this down to the world of retail, Natt states, “if we believe that learning models are the future, then retailers will need to rapidly transform from human-learning models to automated-learning models.”

    This, of course, comes with several challenges, one of which is the scarcity of easily consumable data for supervised learning algorithms to get trained on. This scarcity often results in a garbage-in-garbage-out situation and limits the ability of AI systems to improve in accuracy over time, or to generate meaningful output on a consistent basis.

    Enabling Retailers Become More Model-Driven
    As a provider of Competitive Intelligence as a Service to retailers and consumer brands, DataWeave uses highly trained AI models to harness and analyze massive volumes of Web data consistently.

    Far too often, we’ve seen traditional retailers rely disproportionately on internal data (such as POS data, inventory data, traffic data, etc.) to inform their decision-making process. This isn’t a surprise, as internal data is readily accessible and likely to be well structured.

    However, if retailers can harness external data at scale (from the Web — the largest and richest source of information, ever), and use it to generate model-driven insights, they can achieve a uniquely holistic perspective to business decision-making. Also, due simply to the sheer vastness of Web data, it serves as a never-ending source of training data for existing models.

    DataWeave’s AI-based model to leverage Web data

     

    Web data is typically massive, noisy, unstructured, and constantly changing. Therefore, at DataWeave, we’ve designed a proprietary data aggregation platform that is capable of capturing millions of data points from complex Web and mobile app environments each day.

    We then apply AI/ML techniques to process the data into a form that can be easily interpreted and acted on. The human-in-the-loop is an additional layer to this stack which ensures a minimum threshold of output accuracy. Simultaneously, this approach feeds information on human-driven decisions back to the algorithm, thereby rendering it more and more accurate with time.

    Businesses derive the greatest value when external model-based competitive and market insights are blended with internal data and systems to generate optimized recommendations. For example, our retail customers combine competitor pricing insights provided by our platform with their internal sales and inventory data to develop algorithmic price optimization systems that maximize revenue and margin for millions of products.

    This way, DataWeave enables retailers and consumer brands to utilize a unique model-based decision framework, something that will soon be fundamental (if not already) to business decision-making across industry verticals and global regions.

    As AI-based technologies become more pervasive in retail, it’s only a matter of time before they’re considered merely table stakes. As summarized by Natt, “going forward, retailers will be valued on the completeness of the data they create and have access to.”

    If you would like to learn more about how we use AI to empower retailers and consumer brands to compete profitably, check out our website!

    Read Natt’s article in full below:

    Steven A. Cohen and Matthew W. Granade published a very interesting article in the Wall Street Journal on August 19, 2018 — https://www.wsj.com/articles/models-will-run-the-world-1534716720

    Their premise is that while software ate the world (Mark Andreessen essay in 2011, “Why Software is Eating the World”) the past 7 years, learning models will “eat the world” in the next 7 years.

    A learning model is a decision framework in which the logic is derived by algorithm from data. Once created, a model can learn from its successes and failures with speed and sophistication that humans usually cannot match.

    The authors believe a new, more powerful, business opportunity has evolved from software. It is where companies structure their business processes to put continuously learning models at their center.

    Amazon, Alibaba, and Tencent are great examples of companies that widely use learning models to outperform their competitors.

    The implications of a model-driven world are significant for retailers.

    Incumbents can have an advantage in a model-driven world as they already have troves of data.

    Going forward retailers will be valued on the completeness of the data they create and have access to.

    Retailers currently rely on the experience and expertise of their people to make good decisions (what to buy, how much to buy, where to put it, etc.).

    If we believe that learning models are the future then retailers will need to rapidly transform from human-learning models to automated-learning models, creating two significant challenges.

    First, retailers have difficulty in finding and retaining top learning-model talent (data scientists).

    Second, migrating from human-based learning models to machine-based learning models will create significant cultural and change management issues.

    Overcoming these issues is possible, just as many retailers have overcome the issues presented by the digital age. The difference is, that while the digital age has developed over a 20 year period, the learning-model age will develop over the next 7 years. The effort and pace of change will need to be much greater.

  • Prime Day Sale: Unraveling the Highs and Lows of Amazon’s Flagship Event

    Prime Day Sale: Unraveling the Highs and Lows of Amazon’s Flagship Event

    Another year, another round of media frenzy, and another set of records broken.

    In only three years, Amazon’s Prime Day has evolved into one of the landmark sale events of the shopper’s calendar. Reports indicate that this year’s sale made a major splash, raking in over $4.2 billion in sales — a 33% increase compared to last year. Also, the retail behemoth shipped over 100 million products during the 36-hour sale. Amazon stated that they “welcomed more new Prime members on July 16 than on any other previous day in Prime history.”

    The much talked about website outage added some spice and drama to the proceedings during the first hour. However, this was fixed quickly.

    This year is also the first Prime Day with Whole Foods, Amazon’s most expensive acquisition, giving US shoppers unprecedented incentives to shop at the physical stores of the grocery retailer.

    However, Prime Day is not just about the US, but a truly global event. In India, as part of its promotions for Prime Day, Amazon leveraged VR to have people experience the products in their true form factor at select malls.

    At DataWeave, our proprietary data aggregation and analysis platform enabled us to keep an eye on the pricing and discounts of products during the sale. We tracked Amazon.com, Amazon.co.uk, and Amazon.in before (14th July) and during the sale (16th July) and monitored several product types in Electronics, Men’s Fashion, Women’s Fashion and Furniture categories. We captured information on the price, brand, rank on the category page, whether Prime was offered or not, etc. and analyzed the top 200 ranks in each product type listing page. To best indicate the additional value to shoppers during the sale, we focused our analysis only on additional discounts on products between the 14th and 16th of July.

    Scrutinizing the data yielded some rather interesting insights:

    Amazon UK was more aggressive with its discounts than the US and India across most categories, with Furniture being the only exception (highest discounts in the US).

    In the US, Women’s Fashion observed the steepest discounts (12%), though there were discounts available on a larger number of Men’s Fashion products (5% additional discount on 20% of products).

    While disparity between discounts on Prime products vs non-Prime was quite evident, it was surprisingly low for many categories. In fact, the Electronics category in the UK and the Furniture category in India witnessed sharper discounts for non-Prime products than Prime.

    Top categories by additional discount include Women’s Handbags, Sports Shoes, and Pendrives in the US, Sunglasses and Tablets in the UK, and Women’s Tops, Men’s Jeans, Women’s Sunglasses, and Refrigerators in India. Top brands include Nike, Amazon Essentials, Sandisk, and 1home in the US, Oakley, Toshiba, Belledorm, and rfiver in the UK, and Adidas, Sony, UCB, and Red Tape in India.

    As indicated in the following infographic, some of the most discoverable brands during the sale include Canon, Apple, Nike and Casio in the US, Sandisk, Amazon, Levi’s, and Ray Ban in the UK, and Nikon, UCB, Whirlpool, and HP in India. Discoverability here is measured as a combination of the number of the brand’s products in the top 100 ranks and the average rank of all products of the brand. Also in the infographic, is a set of products with high additional discounts during the sale.

     

    Amazon’s competitors though aren’t ones that simply roll with the punches.

    Flipkart, Amazon’s largest competitor in India (recently acquired by Walmart), announced its own Big Shopping Days sale between July 16 and July 19. On Prime Day, the company joined in with some attractive offers:

    • 8%, 10%, and 7% additional discounts on 11%, 29%, and 16% of Electronics, Men’s Fashion, and Women’s Fashion categories, respectively.
    • 35% off on Perfect Homes 3-seater Sofa
    • 27% additional discount on Acer Predator Helios Gaming Laptop
    • 25% additional discount on Sandisk 16GB Pen Drive

    Propelling the Amazon Flywheel

    While Amazon clearly benefits in the short-term with this sale, the long-term effect of feeding its famous flywheel is evident as well.

    Amazon’s flywheel is a framework through which the company looks to build a self-feeding platform that accelerates growth over time. Attractive discounts and a broad selection of products improves customer experience, which increases traffic to the website, which attracts more merchants on its platform, who in turn broaden the selection of available products.

    Sale events like Prime Day create the sort of hype needed to draw a lot of traffic to Amazon’s website, generating momentum that has a compounding effect on Amazon’s growth. Not surprisingly, more than half of the people surveyed in the US by Cowen last December said they lived in a household with at least one Prime subscription.

    As Amazon’s stock traded at an all time high following Prime Day, it’s only a matter of time before the company becomes the world’s first trillion dollar company.

    Check us out, if you’re interested in learning more about our technology and how we provide Competitive Intelligence as a Service to retailers and consumer brands.

  • How to Win the Coveted Amazon Buy Box | DataWeave

    How to Win the Coveted Amazon Buy Box | DataWeave

    Did you know that over 80% of purchases on Amazon.com is via the buy box?

    While Amazon is all the rage today, raking in 43% of all eCommerce dollars, thousands of merchants on the online marketplace look to seize every opportunity to attract shoppers and drive sales each day. And for these merchants, getting on the buy box is more than half the battle won.

    Recently, Forbes.com published our study of how online merchants can plot their strategy to win the buy box. In this article, we’ll explore some of the key takeaways from this study.

    What is the Amazon buy box?

    The buy box is the section on the right side of Amazon’s product page, where shoppers can add items for purchase to their cart. Since multiple merchants often offer the same product, they compete to win the buy box spot on the product page, which is where customers typically begin the purchasing process — a huge competitive advantage.

    How can merchants win the buy box spot?

    At DataWeave, we aggregate and analyze billions of data points from the Web to deliver Competitive Intelligence as a Service to retailers and consumer brands. Using our proprietary technology platform, we aggregated data for a large sample of products in the mobile phones, clothing, shoes and jewelry categories on Amazon and collected information on all merchants (over 700 in number) selling these product over a period of 10 days.

    We looked closely at several factors that could possibly impact the choice for the buy box:

    • Was Amazon a merchant or not?
    • The effective price (list price + shipping charges — offer/cashback amount) — after all, a common assumption is that the lowest priced merchant has the best chance of winning.
    • Were Prime benefits offered?
    • The quality of review ratings
    • The stock status
    • The number of products offered by a merchant

    We parsed through the data to unearth some interesting insights and found that some factors influenced the move to the buy box spot more than others.

    We see that when Amazon is a merchant, it’s twice as likely to win the buy box compared to other merchants. Further analysis revealed that for around 95% of instances where Amazon was a merchant but was NOT the in the buy box, Amazon was selling at a price 20% greater than the minimum price.

    When the effective price is the lowest, relative to other merchants, the chances of the merchant winning the buy box increased 2.5-fold. Essentially, for the set of merchants who were the lowest priced for each product, only 26% of them won the buy box.

    Merchants who provided Prime benefits to shoppers were 3.5 times more likely to win compared to other merchants. And lastly, if the percentage of positive reviews for a merchant are decreasing over time, the merchant is 5X less likely to win. All other factors analyzed failed to yield statistically significant results.

    Interestingly, no single factor played an overwhelming role in influencing the buy box criteria. So, with the help of statistical modelling, which considers and weighs all factors, we better understood the relationship between all factors, and traced a path for merchants to win the buy box.

    The Cheat Sheet

    While it isn’t quite possible to develop a fool proof framework, the following flowchart can act as a fairly useful guide.

     

    Clearly, the path to the buy box is not a straightforward one.

    If Amazon itself is a merchant for a product, chances of other merchants winning the buy box are low (35%). However, if a merchant is looking to compete with Amazon for the buy box spot, offering Prime benefits is key (82% probability). Without offering Prime, chances of winning the buy box are almost negligible, even if the merchant is the lowest priced. It’s interesting to note that when Amazon does occupy the buy box spot, it’s the lowest priced in 79% of the cases.

    When Amazon is not a merchant for a product, and competition is only between third-party merchants, offering Prime benefits is still the most influential factor (78%). When Prime isn’t offered, the price is the primary determinant of the buy box merchant (86%).

    Evidently, reducing the price is not always the best course of action. It appears that offering Prime benefits has the biggest impact on a merchant’s chances of winning the buy box, across various scenarios.

    However, it’s important to keep in mind that moving up the “merchant ladder” is a gradual process, based on how merchants perform consistently over time.

    If you’re interested to learn more about DataWeave’s technology, and how we help retailers and consumer brands optimize their online strategies, visit our website!

  • Clearance Sale Analysis: Retailing Woes Stagger H&M and Toys “R” Us

    Clearance Sale Analysis: Retailing Woes Stagger H&M and Toys “R” Us

    Confidence amongst retailing analysts was rocked last month by two successive announcements.

    H&M’s most recent quarterly report, which revealed it had accumulated over $4.3 billion in unsold inventory, shocked retail analysts. In an era of on-the-fly inventory replenishment where stocks are closely matched to sales, a spike in unsold inventory is a strong indicator of trouble ahead. The news left analysts questioning H&M’s competitiveness in the fiercely contested global apparel category, where ever-changing consumer preferences demand agility in managing inventory levels.

    In the other major announcement, Toys “R” Us officially closed its doors to shoppers. The retailer’s losses continued to pile up and the chain groaned under a mountain of debt, leaving it little choice but to close down. “The stark reality is that the (chain is) projected to run out of cash in the U.S. in May,” it said in its bankruptcy filing.

    While the emergence of the online shopping phenomenon hasn’t helped Toys “R” Us, its ongoing afflictions largely reflect strategic missteps that predated the online shopping boom. In a category where the shopping experience is all, the retailer failed to adapt to changing consumer expectations. The warehouse context which shaped the retailing did little to promote toys sales or communicate the sheer breadth of inventory carried by Toys “R” Us.

    So, as Toys “R” Us begins to wind down its operations, the company has shuttered its online store and is channeling customers to its remaining physical retail outlets. However, prior to the closure, shoppers enjoyed some amazing bargains during their clearance sale.

    H&M’s problems appear less terminal. Its management claim to have implemented a strategy to slash its accumulated inventory and reign in its aggressive store expansion strategy.

    At DataWeave, we leveraged our proprietary data aggregation and analysis platform to analyze the clearance sales of both H&M and Toys “R” Us. We tracked the pricing, product categories, discounts, review ratings, stock status and more between 29-Mar and 3-Apr.

    The Toys “R” Us Sale

     

    Although the dolls and stuffed animals category carried the most products, its average discount was along the mid-range point for the sale at 28 percent. Games & Puzzles and Action Figures and NERF were the most heavily discounted categories at 40 percent and 36 percent respectively.

    As anticipated, products with lower review ratings were sold at slightly higher discounts. However, even exclusive products were sold at comparatively high discounts. Not surprising, given this was effectively a clearance sale.

    Hasbro, Mattel, and Spin Master were the highest represented brands during the sale, while for their part, Kid’s Furniture and Outdoor Play had fewer products participating in the sale. Other popular brands such as Fisher-Price and LEGO had a presence during the sale but offered fewer products.

    Zuru was the most aggressive in offering discounts with Spin Master the least aggressive. The remaining brands offered discounts of between 30 and 36 percent.

    Reports suggest that last year, toymakers Mattel and Hasbro each sold around $1 billion worth of their toys at Walmart, more than the volume they achieved selling through Toys “R” Us. Strategically, these leading brands seem to have their bases covered even though Toys “R” Us is closing down.

    Interestingly, some products were seen to go out of stock during the sale week, only to be replenished a day later, as illustrated in the above infographic.

    The H&M Sale

    Overall, H&M’s clearance sale was more aggressive in Women’s Apparel with three times more products on offer than for Men’s Apparel. However, there wasn’t much difference between the two in terms of the discounts on offer which hovered around the 45 percent range. Women’s Tops, Cardigan’s and Sweaters offered discounts on the most products during the sale period.

    Little difference was observed tactically, between how the different product categories, were handled.

    We saw a significant movement of products in Women’s apparel during the week, with over 330 newly added products and close to 200 products that were effectively churned. This pattern indicates H&M achieved a faster shelf velocity for this category than for Men’s, possibly due to a more aggressive approach to the selection of items on sale.

    Customer focus is key

    Reports indicate that despite a series of widespread and aggressive markdowns as shown in the analysis above, H&M is struggling to sell off its mountain of accumulated merchandise. Changing consumer tastes and increasing competition seem to have taken their toll on the once agile Swedish retailer. If it is going to weather this storm, H&M needs to revisit its fast fashion approach to assortment and inventory management. The retailer would also appear to need to improve its demand forecasting expertise.

    The bankruptcy filing by Toys “R” Us presents yet another lesson for eCommerce and bricks-and-mortar retailers alike, to address evolving consumer expectations and focus closely on the customer experience aspect of their business, which are supported by appropriate pricing and product assortment strategies.

    At DataWeave, our technology platform enables retailers to do just that, through comprehensive and timely insights on competitive pricing, promotions, and product assortment. Check out our website to find out more!

     

  • Recognize Product Attributes with AI-Powered Image Analytics

    Recognize Product Attributes with AI-Powered Image Analytics

    Anna is a fashionista and a merchandise manager at a large fast-fashion retailer. As part of her job, she regularly browses through the Web for the most popular designs and trends in contemporary fashion, so she can augment her product assortment with fresh and fast-moving products.

    She spots a picture on social media of a fashion blogger sporting a mustard colored, full-sleeved, woolen coat, a yellow sweatshirt, purple polyester leggings, and a pair of pink sneakers with laces. She finds that the picture has garnered several thousand “likes” and several hundred “shares”. She also sees that a few other online fashion influencers have blogged about similar styles in coats and shoes being in vogue.

    Anna thinks it’s a good idea to house a selection of similar clothing and accessories for the next few weeks, before the trend dies down.

    But, she is in a bit of a pickle.

    Different brands represent their catalog differently. Some have only minimalistic text-based product categorization, while others are more detailed. The ones that are detailed don’t categorize products in a way that helps her narrow down her consideration set. Product images, too, lack standardization as each brand has its own visual merchandising norms and practices.

    Poring through thousands of products across hundreds of brands, looking for similar products is time-consuming and debilitating for Anna, restricting her ability to spend time on higher-value activities. Luckily, at DataWeave, we’ve come across several merchandise managers facing challenges like hers, and we can help.

    AI-powered product attribute tagging in fashion

    DataWeave’s AI-powered, purpose-built Fashion Tagger automatically assigns labels to attributes of fashion products at great granularity. For example, on processing the image of the blogger described earlier, our algorithm generated the following output.

    Original Image Source: Rockpaperdresses.dk

    Vision beyond the obvious

    Training machines is hard. While modern computers can “see” as well as any human, the difference lies in their lack of ability to perceive or interpret what they see.

    This can be compared to a philistine at a modern art gallery. While he or she could quite easily identify the colors and shapes in the paintings, additional instructions would be needed on how the painting can be interpreted, evaluated, and appreciated.

    While machines haven’t gotten that far yet, our image analytics platform is highly advanced, capable of identifying and interpreting complex patterns and attributes in images of clothing and fashion accessories. Our machines recognize various fashion attributes by processing both image- and associated text-based information available for a product.

    Here’s how it’s done:

    • With a single glance of its surroundings, the human eye can identify and localize each object within its field of view. We train our machines to mimic this capability using neural-network-based object detection and segmentation. As a result, our system is sensitive to varied backgrounds, human poses, skin exposure levels, and more, which are quite common for images in fashion retail.
    • The image is then converted to 0s and 1s, and fed into our home-brewed convolutional neural network trained on millions of images with several variations. These images were acquired from diverse sources on the Web, such as user-generated content (UGC), social media, fashion shows, and hundreds of eCommerce websites around the world.
    • If present, text-based information associated with images, like product title, metadata, and product descriptions are used to enhance the accuracy of the output and leverage non-visual cues for the product, like the type of fabric. Natural-language processing, normalization and several other text processing techniques are applied here. In these scenarios, the text and image pipelines are merged based on assigned weightages and priorities to generate the final list of product attributes.

    The Technology Pipeline

    Our Fashion Tagger can process most clothing types in fashion retail, including casual wear, sportswear, footwear, bags, sunglasses and other accessories. The complete catalog of clothing types we support is indicated in the image below.

    Product Types Processed and Classified by DataWeave

    One product, several solutions

    Across the globe, our customers in fast-fashion wield our technology every day to compare their product assortment against their competitors. Our SaaS-based portal provides highly granular product-attribute-wise comparisons and tracking of competitors’ products, enabling our customers to spot assortment gaps of in-demand and trending products, as well as to better capitalize on the strengths in their assortment.

     

    Some other popular use cases include:

    • Similar product recommendations: This intelligent product recommendation engine can help retailers identify and recommend to their shoppers, products with similar attributes to the one they’re looking at, which can potentially help drive higher sales. For example, they can recommend alternatives to out-of-stock products, so customers don’t bounce off their website easily.
    • Ensemble recommendations: Our proprietary machine-learning based algorithms analyze images on credible fashion blogs and websites to learn the trendiest combinations of products worn by online influencers, helping retailers recommend complementary products and drive more value. Combining this with insights on customer behavior can generate personalized ensemble recommendations. It’s almost like providing a personal stylist for shoppers!
    • Diverse styling options: The same outfit can often be worn in several different ways, and shoppers typically like to experiment with unconventional modes of styling. Our technology helps retailers create “lookbooks” that provide real world examples of multiple ways a particular piece of clothing can be worn, adding another layer to the customer’s shopping experience.
    • Search by image: Shoppers can search for products similar to ones worn by celebrities and other influencers through an option to “Search by Image”, which is possible due to our technology’s ability to automatically identify product attributes and find similar matches.
    • Fast-fashion trend analysis: Retailers can study emerging trends in fashion and host them in their product assortment before anyone else.

    The devil is in the details

    DataWeave’s Fashion Tagger guarantees very high levels of accuracy. Our unique human-in-the-loop approach combines the power of machine-learning-based algorithms with human intelligence to accurately differentiate between similar product attributes, such as between boat, scoop and round necks in T-shirts.

    This system is a closed feedback loop, in which a large amount of ground-truth (manually verified) data is generated by in-house teams, which power the algorithms. In this way, the machine-generated output gets more and more accurate with time, which goes a long way in our ability to swiftly deliver insights at massive scale.

    In summary, DataWeave’s Image Analytics platform is driven by: enormous amount of training data + algorithms + infrastructure + humans-in-loop.

    If you’re intrigued by DataWeave’s technology and wish to know more about how we help fashion retailers compete more effectively, check us out on our website!

     

  • Study of Brand Inconsistency in Furniture eCommerce

    Study of Brand Inconsistency in Furniture eCommerce

    From initially lagging well behind early high-penetration categories such as consumer electronics, books, and apparel, furniture is now emerging as a key growth category.

    Online furniture purchases are growing at a rapid clip, estimated to currently be around 14 percent rate annually and is anticipated to reach 7.6 percent of total category sales in 2018.

    Savvy furniture brands are becoming increasingly aware of this shift in consumer shopping patterns and are taking steps to embrace the importance of creating a seamless online customer experience consistent across all eCommerce websites.

    Selling furniture online remains logistically complex. It requires the disciplined coordination across an ecosystem teeming with bricks and mortar stores, salespeople, warehouses merchants, and a network of delivery systems.

    All this complexity poses challenges for brands looking to deliver a consistent brand experience for consumers across multiple eCommerce websites.

    One frequent outcome of this complex ecosystem is the emergence of white labeling.

    The Invasion of White Labeling in the Furniture Category

    A white label product is one that is manufactured by one company only to be bundled and sold by other online merchants using different brand names. The end product is positioned as having been manufactured by the brand marketer.

    These white label products are frequently sold at a significant discount, compared to more mainstream name brands in the category.

    Electronics brands have often been victims of this phenomenon. Typical electronic white label products now commonplace range from radios and DVD players to computer mice and keyboards, through to TV remote controls.

    Increasingly, the furniture vertical is no longer a stranger to white label packaging and marketing as well.

    At DataWeave, using our proprietary data aggregation and analysis platform, we analyzed a range of factors of the furniture vertical, specifically the emerging phenomenon of white labeling.

    Our analysis spanned a sample set of over 20,000 products that we tracked across the websites of two of our eCommerce customers (whom we don’t wish to name) that have a large assortment of furniture products. Let’s call these eCommerce companies Retailer A and Retailer B.

    We identified white labeled products as being those that featured the exact same image between the two retailers but were sold under different brand names. Here, our AI-powered advanced image analytics platform matched the images of various products at an accuracy of more than 95%.

    The following infographic summarizes our analysis.

    Clearly, not only is white labeling quite prevalent here, but in almost every instance, we identified price variation. Some of the white labeled products were sold by lesser-known brands with significantly lower price points. This pricing strategy could potentially damage the customer experience for well-established consumer brand franchises in several ways.

    The shopper sees through the branding exercise where the same product is repackaged and presented as having been “produced” by a different brand, potentially eroding brand loyalty.

    As some 71 percent of the products studied were identified as white labeled products, this exposes the category as a whole to this risk.

    The shopper may be confused by the price difference as well, undermining the brand’s carefully constructed pricing perception. The average spread of 21 percent between competing white labeled products is potentially a major source of consumer dissonance and confusion.

    A Closer Look at Pricing

    While the inconsistent experience potentially created by widespread white labeling is almost characteristic of the furniture vertical, other eCommerce areas such as pricing and promotion have also been demonstrated as being key influencers of the shopping experience.

    Today, brands have little control over how their products are priced on eCommerce websites and are susceptible to pricing decisions taken by either the merchant selling the product or retailers themselves. Here, price change decisions have little to do with providing a consistent brand experience, as it’s not really a priority for merchants and retailers.

    In a hyper-competitive retail environment, retailers often discount heavily or change prices frequently to drive sales and margins. The following infographic summarizes the differences in pricing approaches between the two retailers we analyzed.

    Both retailers demonstrated quite divergent approaches in their pricing strategies. The key point of difference appeared to be Retailer B’s discount execution, which proved more aggressive than Retailer A’s, routinely exceeding the latter by five percent or more.

    This discounting strategy is focused on the 40+ percentile (by price, with 100 percentile being the most expensive product), and above price bands, while both retailers displaying similar strategies to their Top 20 and Top 20 to 40 percentile ranges.

    We also observe how Retailer B is more inclined to offer higher discounts on products with higher review ratings, compared to Retailer B’s strategy — a play on developing a “low price” perception among shopper.

    The Consumer Experience Matters

    Today, consumers expect a truly seamless shopping experience right across a brand’s entire integrated retail community, regardless of whether it is physical or digital. Consumers have evolved beyond being merely time poor and have emerged as a group of impatient shoppers, unforgiving of inconsistencies in their experience.

    With retail evolving to embrace multiple consumer touch points with a brand, the practice of white labelling represents a dangerous source of potential confusion and disillusionment. This raises the degree of difficulty involved in converting website visitors into buyers. Further, inconsistent pricing between eCommerce websites, due to dissimilar pricing strategies adopted by each website, only compounds the problem for furniture brands.

    Technologies like DataWeave’s Competitive Intelligence as a Service, that can provide furniture brands with timely insights on white labelled products, unauthorized merchants, and price disparity between ecommerce websites, can assist furniture brands in their efforts to better manage their online channel.

    Visit our website to find out more on how we help consumer brands protect their brand equity and optimize the experience delivered to their customers on eCommerce websites!

     

  • Amazon’s Fashion & Apparel Product Assortment | DataWeave

    Amazon’s Fashion & Apparel Product Assortment | DataWeave

    Apparel remains one of the key battleground categories in retail today, and like in most other product categories, Amazon has made significant in-roads here. Beyond expanding the range of product offerings and brands in its marketplace, Amazon has also launched several private label brands in this vertical and looked to drive more sales as a first-party seller.

    Recently, DataWeave collaborated with Coresight Research, formerly known as Fung Global Retail & Technology, a retail-focused research arm of Li & Fung Group, to publish an in-depth report revealing Amazon’s strategic approach to product assortment in its fashion and apparel category.

    In this blog post, we’ll summarize some interesting insights into Amazon’s strategy from the report. For an in-depth and detailed view, check out the original article at — “Amazon Apparel: Who Is Selling What? An Exclusive Analysis of Nearly 1 Million Clothing Listings on Amazon Fashion”

    Research Methodology

    Our analysis focused on several critical areas, including the presence of Amazon’s private label, the demarcation between Amazon as a seller and its third-party sellers and the top brands and categories in women and men’s apparel.

    We aggregated data from Amazon.com in two stages:

    Firstly, we identified brands with a meaningful presence in Amazon’s clothing offering by identifying all brands included in the top 500 ranks of featured product listings for each product type in the Women’s Clothing and Men’s Clothing sections on Amazon (e.g., the Top 500 product listings for women’s tops and tees, the Top 500 product listings for men’s activewear, and so on.). This generated a total of 2,798 unique brands.

    Secondly, we aggregated our data on all product listings within the Women’s Clothing and Men’s Clothing sections for each of the 2,798 brands identified previously. This returned a total of 881,269 individually listed products. This extensive list forms the basis for the highlights in Coresight’s report.

    Coresight’s Analysis — Some Interesting Insights

    Strategically, Amazon remains heavily reliant on its third-party sellers in the clothing category. In total, just 13.7 percent of women’s and men’s clothing products featured on Amazon Fashion are listed for sale by Amazon itself (first-party sales), while third-party sellers account for 86.3 percent of listings.

    In womenswear, third-party sellers account for 85.7 percent of listings, while in menswear, they account for 87.1 percent of listings. Moreover, Amazon appears to be focusing its first-party clothing inventory on the higher-value categories. Clearly, the retailer’s reliance on third-party sellers underscores its opportunity to grow its sales of apparel volumes by bringing more of its current inventory in-house.

    The analysis found 834 Amazon private-label products on Amazon website, equivalent to 0.1 percent of all clothing available through Amazon Fashion. The company’s private labels appear to be clustered tightly in specific clothing categories.

    Womenswear brand Lark & Ro is by far the biggest of Amazon’s apparel private labels, as measured by the number of items.

    Nike is the most-listed brand on Amazon Fashion, with 16,764 listed products spanning womenswear and menswear. Lower-price brands such as Gildan and Hanes also rank very highly in terms of the number of products listed.

    Value-positioned brands that have traditionally focused on wholesaling to retailers, such as Gildan and Hanes, also rank very highly in terms of the number of products listed.

    What is clear is that currently, Amazon’s clothing listings are highly diluted, with no one major brand dominating the listings.

    Interestingly, casualwear and activewear clearly lead Amazon’s category rankings. Women’s tops and tees are the most heavily listed clothing category on Amazon Fashion, with 138,001 products listed.

    Men’s shirts, which includes a large number of casual shirts together with polo shirts and some T-shirts, comes in second, with 109,043 products listed. Echoing the prominence of the global Nike and Adidas brands on the Amazon website, activewear has achieved a centre of gravity status as a category, accounting for 76,930 men’s activewear products and 51,992 women’s activewear products listed on the site.

    Several Opportunities for Growth

    Amazon Fashion remains heavily dependent on third-party sellers. It’s a fair assumption that more first-party listings would attract greater numbers of shoppers, especially Amazon Prime members. Amazon’s private-label ranges represent another potential lever for growth.

    Also, the 30 most-listed brands on Amazon Fashion comprise 30 percent of all clothing products listed on the website, while just 189 brands have more than 1,000 products each listed on the website.

    This data indicates the presence of major growth opportunities across the board, be it Amazon private label brands, Amazon as a seller, and for several mid-range clothing brands.

    If you’re interested in DataWeave’s technology, and how we aggregate data from the Web to provide unique and comprehensive insights on eCommerce products and pricing, check us out on our website!

  • What Retailers Can Learn from the Lowe’s Board Announcement

    What Retailers Can Learn from the Lowe’s Board Announcement

    Last Friday, Reuters published, “Home Improvement chain Lowe’s said it has nominated two independent board members and plans to add a third following “constructive” talks with hedge fund D.E. Shaw Group, which has taken an activist stake.”

    It was reported that D.E. Shaw Group had utilized available external data to identify quantifiable opportunities to grow sales by several billion dollars and to reduce costs significantly.

    A question that comes immediately to mind is, “Why didn’t Lowe’s utilize this same available external data themselves?”

    Is it because Lowe’s and many other retailers spend their time focusing on internally generated data, rather than looking at available external data, or better yet, combining available external data with their internal data?

    There are huge opportunities to drive incremental sales, margins and profits through leveraging external data, like competitive intelligence data produced by firms like DataWeave.

    There are huge opportunities to drive incremental store sales, margins, and profits through leveraging digital data to drive better store specific assortments, prices and promotions by providing relevant local digital data to store executives using solutions by firms like Radius8.

    I expect to see more Lowe’s-like announcements in the near future as investment firms realize there are very substantial, untapped financial opportunities within retail.

  • Dataweave – CherryPy vs Sanic: Which Python API Framework is Faster?

    Dataweave – CherryPy vs Sanic: Which Python API Framework is Faster?

    Rest APIs play a crucial role in the exchange of data between internal systems of an enterprise, or when connecting with external services.

    When an organization relies on APIs to deliver a service to its clients, the APIs’ performance is crucial, and can make or break the success of the service. It is, therefore, essential to consider and choose an appropriate API framework during the design phase of development. Benefits of choosing the right API framework include the ability to deploy applications at scale, ensuring agility of performance, and future-proofing front-end technologies.

    At DataWeave, we provide Competitive Intelligence as a Service to retailers and consumer brands by aggregating Web data at scale and distilling them to produce actionable competitive insights. To this end, our proprietary data aggregation and analysis platform captures and compiles over a hundred million data points from the Web each day. Sure enough, our platform relies on APIs to deliver data and insights to our customers, as well as for communication between internal subsystems.

    Some Python REST API frameworks we use are:

    • Tornado — which supports asynchronous requests
    • CherryPy — which is multi-threaded
    • Flask-Gunicorn — which enables easy worker management

    It is essential to evaluate API frameworks depending on the demands of your tech platforms and your objectives. At DataWeave, we assess them based on their speed and their ability to support high concurrency. So far, we’ve been using CherryPy, a widely used framework, which has served us well.

    CherryPy

    An easy to use API framework, Cherrypy does not require complex customizations, runs out of the box, and supports concurrency. At DataWeave, we rely on CherryPy to access configurations, serve data to and from different datastores, and deliver customized insights to our customers. So far, this framework has displayed very impressive performance.

    However, a couple of months ago, we were in the process of migrating to python 3 (from python 2), opening doors to a new API framework written exclusively for python 3 — Sanic.

    Sanic

    Sanic uses the same framework that libuv uses, and hence is a good contender for being fast.

    (Libuv is an asynchronous event handler, and one of the reasons for its agility is its ability to handle asynchronous events through callbacks. More info on libuv can be found here)

    In fact, Sanic is reported to be one of the fastest API frameworks in the world today, and uses the same event handler framework as nodejs, which is known to serve fast APIs. More information on Sanic can be found here.

    So we asked ourselves, should we move from CherryPy to Sanic?

    Before jumping on the hype bandwagon, we looked to first benchmark Sanic with CherryPy.

    CherryPy vs Sanic

    Objective

    Benchmark CherryPy and Sanic to process 500 concurrent requests, at a rate of 3500 requests per second.

    Test Setup

    Machine configuration: 4 VCPUs/ 8GB RAM.
Network Cloud: GCE
Number of Cherrypy/Sanic APIs: 3 (inserting data into 3 topics of a Kafka cluster)
Testing tool : apache benchmarking (ab)
Payload size: All requests are POST requests with 2.1KB of payload.

    API Details

    Sanic: In Async mode
Cherrypy: 10 concurrent threads in each API — a total of 30 concurrent threads
Concurrency: Tested APIs at various concurrency levels. The concurrency varied between 10 and 500
Number of requests: 1,00,000

    Results

    Requests Completion: A lower mean and a lower spread indicate better performance

     

    Observation

    When the concurrency is as low as 10, there is not much difference between the performance of the two API frameworks. However, as the concurrency increases, Sanic’s performance becomes more predictable, and the API framework functions with lower response times.

    Requests / Second: Higher values indicate faster performance

    Sanic clearly achieves higher requests/second because:

    • Sanic is running in Async mode
    • The mean response time for Sanic is much lower, compared to CherryPy

    Failures: Lower values indicate better reliability

    Number of non-2xx responses increased for CherryPy with increase in concurrency. In contrast, number of failed requests in Sanic were below 10, even at high concurrency values.

    Conclusion

    Sanic clearly outperformed CherryPy, and was much faster, while supporting higher concurrency and requests per second, and displaying significantly lower failure rates.

    Following these results, we transitioned to Sanic for ingesting high volume data into our datastores, and started seeing much faster and reliable performance. We now aggregate much larger volumes of data from the Web, at faster rates.

    Of course, as mentioned earlier in the article, it is important to evaluate your API framework based on the nuances of your setup and its relevant objectives. In our setup, Sanic definitely seems to perform better than CherryPy.

    What do you think? Let me know your thoughts in the comments section below.

    If you’re curious to know more about DataWeave’s technology platform, check out our website, and if you wish to join our team, check out our jobs page!

     

  • Boxing Day Sale: How UK’s Top Retailers and Brands Fared

    Boxing Day Sale: How UK’s Top Retailers and Brands Fared

    Following a successful Black Friday in November, the United Kingdom geared up for the 2017 Christmas season in December. Analysts estimate the total splurge in December at about £45 billion, beating last December’s record of £43 billion.

    Online sales hit £1.03billion, passing the £1billion threshold for the first time and up 7.9 percent on 2016’s £954million, according to the Centre for Retail Research. The rise of online shopping together with the timing of Christmas in 2017 meant shopper footfall in physical stores was lower than in previous years as people increasingly moved to shopping online.

    Total shopper numbers were 4.5 percent down on the previous year, according to research group Springboard, which may reflect the growing strength and reliability of online’s product range and delivery responsiveness.

    Major online retailers though continued to pull out the big discount guns across categories in an effort to attract online shoppers on Boxing Day, the biggest sale event in December.

    At DataWeave, we focused our proprietary data aggregation and analysis platform to analyze the top 500 ranked products in over 20 product categories across electronics and fashion retailers in the UK. Our analysis included several top UK retailers, which include Amazon, Argos, Currys, Tesco, Asos, Marks & Spencer, and Topshop.

    The discounts in the infographic below indicate the magnitude of reduction in prices during the sale (26th Dec), compared to before the sale (19th Dec), in order to best represent the additional value derived from the sale for shoppers.

     

    Boxing Day Sale Highlights

    In electronics, while Amazon offered discounts on the most number of products, Argos was aggressive in the average size of its additional discounts.

    Surprisingly, Amazon appeared to be much more conservative in the Men’s Fashion category with an average additional discount of 13.8 percent, spanning 341 products. Here, Asos deployed the most aggressive combination of high average additional discounts (36.9 percent) on a large number of products (165).

    Marks & Spencer focused their targeted discounts (43.1 percent) on a tight set of Men’s Fashion products (45), while interestingly, the story almost reverses in Women’s Fashion, where both M&S (43.1 percent, 281 products) and Topshop (40.5 percent, 226 products) were aggressive in what turned out to be a critical battleground category.

    Leading brands weren’t left out of the discounting action either, with the largest discount on offer going to Ruche (48.9 percent on 33.3 percent) women’s tops, closely followed by M S Collection (41.9 percent on 32.3 percent) handbags and Asos’ (37.5 percent on 21.2 percent) men’s jeans.

    Most Discoverable Brands

    We also analysed the most discoverable brands in each product type. This was measured as a combination of the number of the brand’s products present in the Top 500 ranks of a product type, as well as the average rank (lower the number, higher is the discoverability).

    It was no surprise that Canon DSLR cameras were highly discoverable on Amazon with 90 products, along with an average ranking of 93.2, while 34 Asus laptops recorded an average ranking of 85.2. At Argos, 57 Acer laptops recorded an average ranking of 73.4 while 50 LG televisions delivered an average ranking of 124.1.

    Other highly discoverable brands included MS Collection in Marks & Spencer, Apple iPhones and Tablets on Curry’s and Tesco.

    The Online Retail March Continues

    If we look at sales results across the world, from the United Kingdom to the United States, to Asia in countries such as India, Singapore and Indonesia through to Australia, online retail is aggressively cannibalizing traditional bricks and mortar in-store retail sales. Online retail’s demonstrated superiority in exploiting competitive intelligence and a sophisticated suite of analytics that accompany digital transactions, is surfacing in its agile discounting strategies, and its ability to continuously refresh product lines during key sales periods.

    This Boxing Day in the UK, fashion proved to reveal divergent discounting strategies between retailers, while only marginal differences in approach were visible in electronics — both high volume categories around Christmas season.

    Overall, December 2017 in UK marked a strong validation of online retail’s influence and we can expect a continuation of it’s ability to harness discounting with extensive product offerings, in order to lure shoppers away from in-store.

    If you’re interested in DataWeave technology, and how we deliver Competitive Intelligence as a Service to retailers and consumer brands, check out our website!

     

  • [INFOGRAPHIC] 2017 at DataWeave: A Year in Retrospect

    [INFOGRAPHIC] 2017 at DataWeave: A Year in Retrospect

    And that’s a wrap! Another exciting year done and dusted, in which DataWeave continued to execute strongly through accelerated revenue growth, new customer wins, and expansion to heretofore unchartered regions.

    Through the year, we engaged with retailers and consumer brands of all types and sizes, and our belief that actionable competitive insights will increasingly play a defining role in driving profitable growth in retail was reinforced. Competition was stiff, and more times than not, we came out on top due to our ability to process huge data-sets, and the unmatched accuracy of our insights.

    Encouragingly, the emerging vertical of Alternative Data gained greater maturity, as adoption of non-traditional data sources from the Web by Asset Managers picked up steam.

    Our extensive focus on the North American market yielded impressive results, and we’ve only just scratched the surface.

    Other regions and verticals continued to contribute significantly, helping us close out the year with record sale volumes.

    As we wind ourselves up again for another marathon year in 2018, we look back at some of our achievements across the board, including customer impact, technology leadership, and team contribution and growth:

    Moving into 2018, we have a lot to look forward to.

    We’ll roll out a new and improved version of our SaaS-based data visualization platform, built with greater focus on actionability and customizability for our customers. Feedback from early beta tests have already been promising.

    As our team size swells, we’ll be on the lookout for passionate problem solvers, who thrive in a hyper-competitive environment, to join us and contribute to the next stage of our growth journey.

    Across verticals, we are well on our way to digging our heels into the North American market. 2018 will also see us gain a more solid footing in the Alternative Data space.

    With eCommerce adoption showing no sign of slowing down, demand in retail for competitive intelligence solutions is set to soar, and our proprietary data aggregation and analysis platform is up to the challenge of catering to this growing need.

    Stay tuned for more from DataWeave in 2018!

  • Myntra Leads End of Year Promotions in Fashion

    Myntra Leads End of Year Promotions in Fashion

    Following three back-to-back mega-sale events leading up to Diwali, India’s eCommerce companies once again opened the discount floodgates heralding Christmas and New Year. This time around, Fashion was the battleground category of focus for Indian e-retailers.

    Myntra launched its End of Reason Sale held between 22nd and 25th December. eCommerce behemoth Amazon too announced its own grand Amazon Fashion Wardrobe Refresh Sale on the same days, while Flipkart hit the market with its End of Year Bonanza held on the 24th and 25th of December. Paytm and Snapdeal held sale events as well, starting 23rd December. All competing sale events promised consumers up to 80 percent discounts across a range of products, especially in Fashion.

    At DataWeave, we analyzed and reported on the competing pricing strategies of Amazon, Flipkart, Myntra, Paytm, and Snapdeal. In the following infographic, we look specifically only at additional discounts offered on the top 500 ranked products of over 15 product types during the sale, compared to those before the sale events went live.

    Myntra Gets Aggressive

    Myntra elected to discount over 84 percent of its Top 500 ranked Fashion products encompassing each product category, with an average additional discount percentage of over 25 percent offered during the sale.

    A prime example of this discounting approach was the sports shoe segment, which received an aggressive additional discount of 28 percent on over 93 percent of the Top 500 ranked sports shoes. Similarly, Myntra’s additional discounts ranged from between 22 percent and 25 percent across most product types, including T-shirts, Shirts, Handbags, Jeans, Skirts, Sunglasses, and Watches. The fashion e-retailer’s private label brands enjoyed attractive reductions in prices, which include Hrx and Roadster, along with other brands like Red Tape, Nike, and Puma.

    Amazon Discounts To A Different Beat

    Amazon discounted 35 percent of its Top 500 ranked Fashion products in each product type, with an average additional discount percentage of 12.5 percent during the sale. Given Amazon’s track record of dynamic pricing, this was relatively conservative.

    Overall, additional discounts on Amazon ranged between 4 percent and 16 percent across all product types in Fashion. Top brands discounted on Amazon included Adidas, Fastrack, Hush Puppies and Ray-Ban.

    Flipkart Joins The Party

    Flipkart too joined the End of Year discount action with several attractively positioned offers, exceeding those featured on Amazon. Flipkart discounted over 65 percent of its Top 500 ranked Fashion products in each product type, with an average additional discount percentage of over 14 percent during the sale.

    Additional discounts promoted on Flipkart ranged between 8 percent and 22 percent across all Fashion product types, while some of the top discounting brands included Dkny, Metronaut and United Colors of Benetton.

    Conspicuously, other Indian e-retailers like Paytm and Snapdeal chose not to join in the price war. Snapdeal, especially, has consistently offered only moderate additional discounts during recent sale events, choosing to focus more on other areas of improving the user experience for their shoppers.

    Strategic Focus On Profitability

    In contrast to the profit-sapping Diwali sale season, characterized by steep discounts across all product categories, this end of year sale was more concentrated, largely honing in on Fashion. From a strategic and shareholder perspective, limiting the discounting action to Fashion insulated the retailers’ bottom line from another major profit hit.

    Myntra determinedly reaffirmed its leadership status in the Fashion category, with its highly aggressive discounting strategy. This was well received by shoppers, who spent a staggering ₹5 crore in only the first five minutes of the sale.

    Flipkart opted to double down this time around with attractive offers on its own eCommerce platform as well. The e-retailer, currently locked in a battle with Amazon for leadership in India’s eCommerce sector, had acquired Myntra in 2014 in a bid to strengthen its position in the fashion category.

    Amazon, intriguingly, opted for a more conservative approach to its end of year sale than we are used to witnessing from the eCommerce giant. As we enter the new year, and kickstart yet another cycle of aggressive e-retail promotions in India, there will be ample opportunities to see if this is evidence of a rethink in Amazon’s approach to pricing in India.

    If you’d like to know more about DataWeave’s technology, and how we provide Competitive Intelligence as a Service to retailers and consumer brands, check out our website!

     

  • Tracing Lazada’s Pricing Across the Month-Long Online Revolution Sale

    Tracing Lazada’s Pricing Across the Month-Long Online Revolution Sale

    Commencing on the 11th of November and ending just a few days ago on the 12th of December, Southeast Asia’s biggest sale event, the Lazada Online Revolution sale rewrote the record books.

    This mega-shopping event is held simultaneously across six Southeast Asian countries, spanning Singapore, Malaysia, Thailand, Indonesia, the Philippines and Vietnam and was bookended by its two biggest sale days, on 11.11 and 12.12.

    In an earlier blog post, we published a highly detailed analysis of the sale on 11.11, using DataWeave’s proprietary data aggregation and analysis platform. This post zoomed in on the pricing and product strategies of Lazada and its competitors in Singapore and Indonesia.

    On the 12th December, Southeast Asian shoppers shattered all retailing expectations by reportedly spending a record-breaking $250 million. This was double both this year’s 11.11 sale and last year’s 12.12 sale. According to Forbes, the 12.12 sales became such a hit that Indonesia even designated the day to be its National Online Shopping Day, or Harlbonas.

    At the end of the sale event on 12th December, DataWeave assimilated all the data we collated throughout the Online Revolution sale and examined pricing trends across the entire span of four weeks, exploring each retailer’s strategy by brand, by category, and by product type.

    We aggregated pricing information on the Top 500 ranked products of over 20 product types featured on each website (Lazada, ListQoo10, and Blibli), spread across the critical Electronics and Fashion categories, covering over 120,000 products in total.

    Online Revolution — Singapore

    Interestingly, one of the trends that became immediately apparent, was the relatively stable track of the average absolute discounts in Electronics, Men’s Fashion, and Women’s Fashion. No significant spikes or drops were evident throughout the duration of the entire sale season.

    Similarly, the number of discounted products remained relatively stable. However, in Electronics, there was a conspicuous dip in the number of discounted products, which occurred on the 21st of November. Aside from this anomaly, even the number of products discounted remained relatively stable. The other interesting phenomenon was an uptick in the number of discounted products on the 15th of December, after the Online Revolution sale — something counterintuitive.

     

    When we explored the behaviour of the average MRP of discounted products, we noticed a sharp dip on the 21st of November. Clearly, prices were increased specifically on higher-priced electronic products.

    Comparing these numbers with those of ListQoo10’s, who were forced to adopt a more aggressive stance on pricing to stay competitive through this period, we once again see a very consistent discount percentage throughout this period. The average discount in men’s fashion, however, showed a slight upward trend during this period.

    ListQoo10’s number of discounted products in Electronics dipped as well on the 21st of November, demonstrating the retailer’s ability to dynamically react to competitor strategies. This can be evidence of a robust market monitoring system.

    Returning to Lazada, DataWeave identified several product types displaying a significant variation in average discounts through this period. These included men’s shorts, women’s shoes, men’s Jeans, Laptops, DSLR Cameras, and women’s T-shirts.

     

    Once again, our analysis pointed to substantial competitive activity around the 21st of November, together with a second significant dip in discounts on men’s shirts in the period around 5th December. Discounts on women’s shoes, by contrast, proved to be a roller coaster throughout the entire sale period.

    Some of the brands with high variation through this period were Lenovo Laptops, Levi’s T-shirts, Adidas Women’s Shoes, Seiko Watches, and Sony Phones.

     

    Discounting activity by these brands appeared to be all over the place during this period, without any discernible pattern or structure. While Sony predictably lowered discounts on its phones after 12.12, Levi’s increased its discounts in the same period

    Online Revolution — Indonesia

    Moving on to Indonesia, we once again witnessed a similar approach to average discounting by category as we saw emerge in Singapore. At a category level, the retailer evidently opted for trading within a narrow discount band across the sale period rather than attempting to inject an overly dynamic discounting approach into their sale execution.

     

    This is not to say there were not some surprises in store with the number of discounted products in Indonesia. In electronics, there was a noticeable dip in the number of discounted products just ahead of the 12.12 sale. The number of discounted products then surprisingly surged after the 12.12 sale, in combination with a slight reduction in average discount percentage during the period.

    In comparing Blilbi, Lazada’s main competitor in Indonesia, we see a fairly consistent discounting level throughout the sale period, although markedly lower than those rolled out by Lazada across its three core categories.

    This approach held true even for the number of discounted products. Blibli seems to have been content to take a backseat to Lazada during the heavily promoted Online Revolution sale period, rather than attempting to compete aggressively in any single category.

    It will be interesting to see if Blilbi is content to repeat this strategy in 2018 as it effectively surrenders the discounting high ground to Lazada during the peak sales period. While this strategy may yet be proven to have paid off in terms of profitability, it may have undesirable consequences for Blilbi’s brand and share performance in the longer term.

    Returning our focus to Lazada in Indonesia, some product types showed major variation through the sale period, specifically DSLR Cameras, which dipped significantly approximately a week out from the 12.12 sale. However, compared to Singapore, Indonesian discounts by product types appeared relatively more stable, except a few dips prior to 12.12.

    Three distinct discounting strategies appears to have been adopted by participating brands. Some, such as Electrolux (Refrigerators), opted for a comparatively stable discounting approach. Others, like Apple, increased prices through the sale period, while Alienware, reduced prices through the sale period.

    In particular, Apple’s pricing approach to its iPhones was surprising, given its strong partnership with Lazada during this Online Revolution sale. Yet another example where the marketing hype failed to translate into an aggressive discounting strategy.

    More Talk Than Walk

    For Lazada, the Online Revolution sale proved to be a triumph, effectively extending its record-breaking streak with USD 250 million in sales on 12.12 alone. However, on parsing through the pricing across the entire month of the sale, there is clearly no dramatic increase in discounts either on 11.11 or on 12.12 — some anomalies notwithstanding.

    This goes to show that much of the sales is driven by hype, more than the additional value of discounts. To be fair, 11.11 and 12.12 hosted discounts on some of the more premium products in the assortment, while discounts on most of the mid-range products remained consistent. While some competitors like ListQoo10 chose to stay competitive, so as not to lose out significantly on their customer base and market share, others like Blilbli chose to sit and watch, and pick up on what’s left after the sale.

    This year’s Online Revolution has set the bar high for South East Asian retail, and going by how the event has grown over the last few years, few would be surprised if we witness another record braking sale in 2018.

    If you’re interested in DataWeave’s technology, and how we provide Competitive Intelligence as a Service to retailers and consumer brands, check us out on our website!

     

  • Consumer Packaged Goods Join The Black Friday Blitz

    Consumer Packaged Goods Join The Black Friday Blitz

    While the Thanksgiving weekend sale, which includes Black Friday and Cyber Monday, is famous for attractive offers across all consumer categories, it remains better known for its discounts on Electronics and Fashion. Consumer goods, traditionally, have evaded much the hype.

    This year, notwithstanding notoriously slim margins, consumer goods and grocery retailers and brands joined Electronics and Fashion in offering sharp discounts on select products in an attempt to carve out increased market share.

    In the past, discounts on consumer packed products have been to drive increased store traffic during the holiday season. Increasingly, however, Thanksgiving has emerged as a viable opportunity for grocers to recruit online shoppers as well and build out their franchise.

    Online Grocers Make Their Move

    Faced with the holiday rush, large numbers of shoppers are proving to be relaxed about trusting the retailer to bag up and deliver their holiday feasts and treats. Grocers themselves have taken the strategic decision to boost their online shopping presence this year.

    They geared up to support their new holiday presence with aggressive price cuts designed to cut through the holiday sales clutter and make direct appeals to a newly-in-play online shopper pool. So transparent was this commercial decision, that many retailers experienced sharp drops in their share prices as industry analysts anticipated the retailers’ new discount-driven strategy.

    Tracking The Numbers

    At DataWeave, using our proprietary data aggregation and analysis platform, we have been tracking, through November, pricing and product information of the Top 1,000 ranked consumer goods products in over 10 product types featured on Amazon Prime, Walmart, Target, Costco, Kroger, Safeway, and Whole Foods, across up to six zip codes each, distributed across the country.

    DataWeave’s major focus was to compare the three main days of the Thanksgiving weekend; Thanksgiving Day, Black Friday, and Cyber Monday. We performed an in-depth analysis of discounts offered across product types and brands, together with how aggressively dynamic retailers were in both their pricing strategy and in the products they displayed.

    In analyzing this major sale event, we observed an extensive range of products enjoying high absolute discounts, but with no additional discounts during the sale, i.e. prices remained unchanged between the period prior to the sale and during each day of the sale, even though high discounts were advertised. The following infographic highlights some of the products where this phenomenon was observed.

    As a result, we focused our analysis only on the additional discounts offered on each day of the sale, compared to the period prior to the sale (we considered 11.21), in order to accurately illustrate the true value shoppers enjoyed during these sale days.

    The following infographic reveals some interesting highlights from our analysis, including the level of additional discounts offered to shoppers, the top brands featured, and the number of dynamic price changes implemented during the sale. All prices analysed are in USD, and all discount percentages represent average values across all zip codes, analyzed for individual retailers.

    In contrast to Amazon Prime, Costco, and Kroger who opted to run with deep discounts on a limited range of products, retailers such as Target and Walmart chose to offer only marginally higher additional discounts but across a large number of products. Others like Safeway adopted a safer approach, combining low discounts on a modest range of products.

    Overall, our analysis discovered little variation in discounts offered across each of the three sale days, with the only enduring trend being a marginally higher discount percentage implemented on Cyber Monday across all retailers.

    Categories significantly discounted across retailers included Personal Care, Deli, Dairy & Eggs, and Babycare products. Stove Top, Martinelli, Colgate, Dove and Hillshire Farm emerged as the leading brands to adopt a more aggressive discount approach.

    While most of the products offered across each of the three peak holiday sale days were comparatively constant (few new products featured amongst the Top 500 ranks), there were a number of conspicuous exceptions. Amazon Prime (19 percent on Cyber Monday), Whole Foods (15 percent on Thanksgiving), and Kroger (12 percent and 11 percent on the first two days of sale respectively), elected to refresh a significant portion of their Top 500 ranked product assortment.

    Across the entire Thanksgiving week, we saw Target, Amazon Prime, and Kroger all highly active in changing prices to stay competitive. Our analysis of these retailers showed more than 1.6 price changes for each price-changed product. While these were implemented on roughly 20 percent of their assortment, itself a significant proportion, the average price variation for each of these retailers was also on the higher side of expectations. In contrast, the other retailers adopted a far more conservative approach to dynamic pricing.

    Consumer Goods Walk The Discount Talk

    In a year when Amazon acquired Whole Foods to forever merge the dynamics of offline and online grocery retail, aggressive discounting by several retailers in specific product categories, combined with high visibility brands, has carved out a new profile for CPG retail.

    Grocers are eyeing a future where online shopping becomes a prime feature of their retail franchise. Amazon for its part demonstrated its prowess in discounting strategy, and its ability to implement a dynamic pricing strategy in tandem with a refreshed Top 500 product assortment.

    Other retailers are not far behind, as the use of market and competitive intelligence technologies pick up steam across the board. In today’s digital economy, data can be the biggest competitive advantage for a retailer, and retail technology providers like DataWeave have upped their game to deliver highly unique and sophisticated data and insights to meet this demand.

    Visit our website, if you’re interested in DataWeave and how we provide zip-code level Competitive Intelligence as a Service to retailers and consumer brands.

  • Thanksgiving, Black Friday and Cyber Monday Parade Discounts in Fashion

    Thanksgiving, Black Friday and Cyber Monday Parade Discounts in Fashion

    Fashion has always been one of the great engines of retail, and two of its iconic sale events are Thanksgiving and Black Friday. While Black Friday was traditionally an in-store shopping event, a large number of shoppers have migrated online taking much of the sales action with them.

    Despite shoppers typically liking to be able to touch and feel fashion and apparel products prior to purchasing them, the convenience of online shopping combined with time-poor shoppers returning to work after their Thanksgiving break has triggered changes to consumer behavior. Today, the retail narrative has shifted to focus on online, with this year’s Thanksgiving weekend turnover up 6.8 percent from last year.

    At DataWeave, using our proprietary data aggregation and analysis platform, we have been tracking the pricing and product information of the Top 500 ranked Fashion products across 15 product types on Amazon, Walmart, Target, Bloomingdales, JC Penney, Macy’s, Neiman Marcus, and Nordstrom.

    Our primary focus was to compare the three key days of the Thanksgiving weekend: Thanksgiving Day, Black Friday, and Cyber Monday. We performed an in-depth analysis of discounts offered across product types and brands, together with how dynamic retailers were in both their pricing strategy and products displayed.

    (Read also: Thanksgiving vs Black Friday vs Cyber Monday: The Electronics Price War Heats Up)

    In analyzing these monster sale events, we observed a range of products sneaking through to enjoy high absolute discounts, but offer no additional discounts during the sale, i.e. prices remained unchanged between before the sale and during each day of the sale, even though high discounts were advertised. The following infographic highlights some of the products where this phenomenon was observed.

     

    Having identified the aggressive use of high but unchanged absolute discounts among the retailers during the sale, we focused our analysis on the additional discounts offered on each of the days of the sale, compared to before the sale (we considered 11.21), in order to more accurately reflect the true value these sale events deliver to American shoppers.

    The following infographic provides some interesting insights from our analysis along several perspectives, including additional discounts offered, top brands, quality of product assortment, number of price changes, and more. All indicated prices are in USD.

     

    Our analysis illustrated how aggressive Target was in its strategy for discounting fashion, compared to most other retailers, especially on Thanksgiving and Black Friday. Interestingly, while Macy’s offered reasonably attractive discounts across all product types, it chose to offer them on a much larger product set than any other retailer.

    Overall, the level of discounts, together with the number of products they were offered on, shows no dramatic change for each retailer over the three-day sale period.

    With Neiman Marcus however, we observed a unique pattern. Sharp discounts were offered on Thanksgiving and Black Friday, which were subsequently rolled back completely on Cyber Monday. This represents a clear holiday pricing and discount strategy, albeit conducted on a comparatively compact and highly targeted set of products.

    Other sales discounting phenomena we observed include major discounts on Sunglasses, Shoes, Skirts, and T-shirts across all retailers, clearly representing battleground categories, while some of the top brands offering attractive discounts include Ray Ban, Oakley, Levi’s and Nike.

    Another relatively constant factor across each of the sale days was the average selling price of respective retailers. This parameter indicates how premium each retailer’s product mix is, providing another perspective on each retailer’s customer segment targeting strategy.

    As expected, Target, Walmart and JC Penney housed the more affordable set of products (average selling prices of $25, $31, and $45 respectively). At the other end of the premium spectrum, Neiman Marcus — home to luxury brands and products — adopted a more premium product assortment (average selling price between $820 and $914).

    In fashion, presenting a fresh assortment consistently is key to customer retention, and Amazon leads the pack in this regard, with a product churn rate of 50% in the top 100 ranks each day. Contrast that with Walmart and Target, who follow a more traditional approach, with a largely static set of options to choose from in its top ranks.

    Most of the retailers we analysed implemented several price changes to large percentages of their product sets. Macy’s and Walmart were at the forefront of this dynamic pricing activity. While Bloomingdales too made over 1,300 price changes, the average magnitude of these changes proved to be very high, at 206 percent.

    Fashion Fast-Forwards Its Online Sales

    While the memories of frantic shoppers tussling over fashion and apparel items on Black Friday still linger, they are fast receding as online fashion sales turnover goes from strength to strength. Shoppers are firmly placing long, winding queues in their rearview mirror and embracing the digital shopping cart more with each passing year, as spotlighted this Thanksgiving sale weekend.

    Sunglasses, Shoes, Skirts, and T-shirts emerged as key battleground categories for retailers over the weekend, while individual retailers displayed diverse approaches to capturing and retaining market share with their target demographic — quite assuredly while using modern retail technologies that help develop and execute on competitive strategies.

    As retailers move into the Christmas sales phase it will be fascinating to discover how they are evolving their ability to dynamically change pricing, refresh product categories and focus their shopper promotions.

    Visit our website, if you’re interested in DataWeave’s technology and how we provide Competitive Intelligence as a Service to retailers and consumer brands.

     

  • [INFOGRAPHIC] Thanksgiving vs Black Friday vs Cyber Monday: The Electronics Price War Heats Up

    [INFOGRAPHIC] Thanksgiving vs Black Friday vs Cyber Monday: The Electronics Price War Heats Up

    Alibaba may have raked in some $25 billion on Singles’ Day in the largest one-day sales turnover ever. In the Western world, however, Black Friday remains an economic force.

    This Black Friday, American shoppers spent a record $5 billion online in just 24 hours, representing a 16.9 percent increase in dollars spent online compared with last year.

    The sale period, though, comprises of Thanksgiving Day and Cyber Monday as well — each generating over a billion and half dollars in online sales this year.

    Cyber Monday has especially been a popular day for buying online, as people head back to work after the long weekend, making a physical visit to the stores to pick up deals less manageable during the day.

    However, the idea of the Thanksgiving weekend as a single shopping event was laid to rest this year.

    It’s Now Black November

    Online sales from November 1st through the 22nd totalled almost $30.4 billion this year, driven by deals available throughout the month on eCommerce platforms. In fact, every single day in November so far saw over $1 billion in online sales, creating a new paradigm for both shoppers and retailers, in stark contrast to the brick-and-mortal retail driven Black Friday sale events of the past.

    Several online retailers began offering attractive discounts from the beginning of November, specifically on “Black Friday Deals” pages of their websites.

    At DataWeave, using our proprietary data aggregation and analysis platform, we have been tracking, through November, pricing and product information of the Top 500 ranked Electronics products across 10 products types on Amazon, Walmart, Target, Best Buy, and New Egg.

    (Read also: Black Friday Sales Season: How US Retailers Are Gearing Up)

    We also took a few snapshots of the products and discounts offered on the “Black Friday Deals” pages of Amazon and Walmart. We saw both websites offering deep absolute discounts in Electronics (40.1 percent on Amazon, 30.4 percent on Walmart) on over 400 products each day.

     

    Moreover, these discounts weren’t restricted to static product sets. 73.2 percent (Amazon) and 30.6 percent (Walmart) churn of products was observed on these pages each day, providing shoppers with a steady stream of attractive discounts on new products every day.

    Our major focus, though, was to compare the three main sale days of the Thanksgiving weekend. We performed an in-depth analysis of discounts offered across product types and brands, as well as how dynamic retailers were in both the pricing and products displayed — all of these, across Thanksgiving (11.23), Black Friday (11.24) and Cyber Monday (11.27).

    We looked specifically only at additional discounts offered on each of the days of the sale, compared to before the sale (represented by products and its prices on 11.21).

    Overall, we discovered that the level of discounts, together with the number of products they were offered on, does not change dramatically across all 3 days. Some exceptions include –

    • Higher number of additionally discounted products on Amazon and Walmart on Cyber Monday
    • Lower additional discounts offered by Best Buy on Cyber Monday
    • Lower number of products additionally discounted on New Egg on Thanksgiving and Black Friday.

    Discounting strategies across most retailers converged on significant discounts on Pendrives, Smartwatches, DSLR Cameras, and Mobile Phones, while some of the top brands that offered attractive discounts include Apple, Fossil, Canon, Nikon, Sandisk, and HP — across a range of product types.

    While the average selling price (indicative of how premium the product mix is) for each retailer did not change significantly across each of the featured sale days, there was some variation at a product type level, with Laptops and Digital Cameras displaying some variation in average assortment value across Target, Walmart, and New Egg.

    Perhaps the most interesting insight provided by the analysis is just how different each retailer is in its approach to changing its prices. Over the entire week (11.21 to 11.27), Amazon made over 3,600 price changes on over 50 percent of its consistently-top-ranked products. Compare that to Target’s 289 price changes on 30 percent of its products.

    While the average magnitude of price change on Amazon is 27 percent, Best Buy has been far more aggressive with the magnitude of its price adjustments (47 percent), even if it has implemented fewer price changes. Amazon clearly leads the industry here, with its continual focus on employing advanced retail technologies that enable automated, optimized price changes designed to ensure its products are competitively priced.

    How Strategic Is Retail Pricing?

    Another aspect DataWeave explored was whether e-retailers sometimes increase their prices in the lead-up to a sale, only to reduce them during the sale, enabling them to advertise larger discounts. We did observe that all e-retailers effectively increased their prices on a discrete and small set of products prior to their sale. For the purposes of our analysis, price increases before the sale was calculated as an increase in price between 11.14 and 11.21.

     

    Highlights of our analysis include the discovery that Best Buy increased its prices in Electronics significantly on a small selection (3.5 percent) of its product range prior to the sale, only to reduce those prices immediately during the Thanksgiving weekend sale.

    While Amazon proved not to be as aggressive in the magnitude of this activity as Best Buy, this phenomenon was observed across a larger portion of Amazon’s assortment (6.7 percent)

    Online is Now More Important Than Ever

    While the legend and aura of past Black Friday sale events, complete with long overnight queues and highly publicized stampedes, is ebbing away, in lock-step with the dwindling numbers of store footfall this year (down 2 percent), the Thanksgiving sale season is set for a new transformation, following the growing number of shoppers preferring to shop online.

    A survey by the National Retail Federation found that 59 percent of shoppers plan to shop online this year, marking the first time that online has emerged as the most popular choice for America’s shoppers.

    With an extended sales season to offer discounts, and moving into Christmas, it has become increasingly important for retailers to monitor and react dynamically to their competitors’ pricing, product and promotional activities. Without the ability to track, react, and tweak in real time, retailers risk having their competitive position eroded, dramatically impacting both sales and retail margins.

    Leading eCommerce retailers such as Amazon, and evolving retailers like Walmart have embedded these systems into their overarching strategy and operations, while others are condemned to play catch up.

    As this fascinating cycle of the sale season ends, and retailers crunch their numbers to assess their comparative performance, sights are now set on Christmas to extend this sale extravaganza.

    Visit our website, if you’re interested in DataWeave’s technology and how we provide Competitive Intelligence as a Service to retailers and consumer brands.

     

  • Walmart’s Online Pricing Analysis | DataWeave

    Walmart’s Online Pricing Analysis | DataWeave

    In an increasingly competitive retail landscape and facing intense margin pressures, improving the profitability of online commerce is a growing area of focus for all retailers.

    When Amazon acquired Whole Foods in August, several media outlets and analysts speculated whether there would be a slashing of prices across the board. Instead, Amazon lowered prices only on those items that it knew would drive increased traffic to the stores, resulting in a 25% increase in footfall the first 30 days after the acquisition closed.

    (Read also: Amazon’s Whole Foods Pricing Strategy Revealed)

    Disrupting the Status Quo

    Walmart has now announced a shift in its online pricing to draw more shoppers to purchase from its brick-and-mortar stores and save on shipping costs.

    Sarah Nassauer wrote an interesting article for the Wall Street Journal recently, outlining Walmart’s online pricing strategy and its approach to pricing its products differently between its online and offline stores.

    Sarah reports, “Walmart wants to charge customers more to buy some products online than in stores, part of the company’s efforts to boost profits and drive store traffic as it competes with Amazon.”

    What’s interesting is Walmart’s move to display the lower offline store prices on its website for some grocery products, nudging shoppers to drive down to the nearest Walmart store.

    Again, Walmart did not raise prices for all items but only a few, select food and household items, “including boxes of Kraft Macaroni & Cheese, Colgate toothbrushes and bags of Purina dog food, according to people familiar with the matter and comparisons between online and in-store prices.”

    The article goes on to state that, “[T]he move is unusual for Walmart, which has long honed an ‘everyday low price’ message and has worked to keep online prices at least as low as shoppers find in its 4,700 U.S. stores. Walmart e-commerce workers responsible for product sales have been instructed to boost profits along with sales, according to the people familiar with the situation, and are ‘no longer obligated to follow store pricing.’”

    This move indicates a greater focus on online-to-offline (O2O) strategies by the world’s largest retailer in an effort to cut down on the crippling costs of transport operations and logistics. According to a cost analysis by consultants Spend Management Experts, “A $1.28 box of Kraft Macaroni & Cheese could cost a big retailer around $10 to ship from Chicago to Atlanta, depending on how remote the buyer’s address is . . . A smaller retailer would likely pay about double.”

    With this news, the days of providing the same price online and in stores are over, setting a precedent and reflecting important differences in costs and competitor capabilities.

    But how did Walmart know which items to focus on for lowering (or raising) prices?

    Cutting-Edge Competitive Intelligence Solutions

    Did Walmart pick items at random or guess? Not likely. With recent enhancements in competitive intelligence and data analysis solutions, the era of guesswork, gut-fuelled decisions, and manual number crunching is over.

    In today’s digital economy, actionable competitive intelligence has become a critical component in the transformation of retail. Retailers like Amazon and Walmart use competitive insights to identify categories and items that show the greatest potential for increased shopper interest, sales, and profits, to adjust their prices.

    Competitive intelligence providers like DataWeave provide unique, AI-driven, competitive insights and business recommendations by harnessing and analyzing competitive data from the Web.

    When retailers link these competitive insights and data to their internal pricing and inventory systems, they create a powerful engine that marries internal and external forces to produce highly accurate assortment, pricing, and promotion recommendations, all in near real-time.

    As retailers like Walmart experiment with their pricing and merchandizing across channels, they have come to rely on modern retail technology solutions that continue to evolve to help them reduce operational complexities and yield higher ROI.

  • Alibaba’s Singles Day Sale: Decoding the World’s Biggest Shopping Festival

    Alibaba’s Singles Day Sale: Decoding the World’s Biggest Shopping Festival

    $17.5 million every 60 seconds.

    That’s the volume of sales Alibaba generated on 11.11, or Singles Day. This mammoth event, decisively the world’s biggest shopping day, dwarfed last years’ Black Friday and Cyber Monday combined.

    This year, the anticipation around Singles Day was all-pervasive, and the sale was widely expected to break all records, as more than 60,000 global brands queued up to participate. By the end of the day, sales topped $25.3 billion, while shattering last year’s record by lunchtime.

    It’s an astonishing feat of retailing, eight years in the making. When Alibaba first started 11.11 in 2009, they set out strategically to try and convert shopping into a sport, infusing it with a strong element of entertainment. “Retail as entertainment” is a unique central theme for 11.11 and this year Alibaba leveraged its media and eCommerce platforms in concert to create an entirely immersive experience for viewers and consumers alike.

    From a technology perspective, the “See Now, Buy Now” fashion show and the pre-sale gala seamlessly merged offline and online shopping so viewers tuning in to both shows can watch them while simultaneously shopping via their phones or saving the items for a later date.

    The eCommerce giant also collaborated with roughly 50 shopping malls in China to set up pop-up shops, eventually extending its shopper reach to span 12 cities.

    Of course, attractive discounts on its eCommerce platforms were on offer as well.

    Deciphering Taobao.com

    At DataWeave, we have been analyzing the major sale events of several eCommerce companies from around the world. During Singles Day, when we trained our data aggregation and analysis platform on Taobao.com (Alibaba’s B2C eCommerce arm), and its competitors JD.com and Amazon.ch, our technology platform and analysts had to overcome two primary challenges:

    1. All text on these websites were in Chinese

    All information — names of products, brands, and categories — were displayed in Chinese. However, our technology platform is truly language agnostic, capable of processing data drawn from websites featuring all international languages. Several of our customers have benefited strategically from this unique capability.

    2.  Discounted prices were embedded in images on Taobao.com

    While it’s normal for sale prices to be represented in text on a website (relatively easy to capture by our advanced data aggregation system), Taobao chose to display these prices as part of its product images — like the one shown in the adjacent image.

    However, our technology stack comprises of an AI-powered, state-of-the-art image processing and analytics platform, which quickly extracted the selling prices embedded in the images at very high accuracy.

    We analyzed the Top 150 ranked products of over 20 product types , spread across Electronics, Men’s Fashion, and Women’s Fashion, representing over 25,000 products in total, each day, between 8.11 and 12.11.

    In the following infographic, we analyze the absolute discounts offered by Taobao on 11.11, compared to 8.11 (based on pricing information extracted from the product images using our image analytics platform), together with an insight into the level of premium products included in their mix for each product type, between the two days of comparison.

    Unexpectedly, we noticed that each day, ALL the products in the Top 150 ranks differed from the previous day — a highly unique insight into Taobao’s unique assortment strategy.

    Counter-intuitively, absolute discounts across all categories were considerably higher on 8.11 than on 11.11, even if it were for a marginally fewer number of products. The number of discounted Electronics products on sale rose on 11.11 compared to 8.11 (124 versus 102 respectively), while there was little movement in the number of discounted Men’s Fashion(55 versus 57) and Women’s Fashion (35 verses 27) products.

    Taobao targeted the mobile phone and tablets segment with aggressive discounts (21.0 percent and 18.2 percent respectively), compared to the average Electronics discount level of 7.7 percent.

    Interestingly, the average selling price drifted up for Electronics on 11.11 compared to 8.11 (¥4040 versus ¥3330). Men’s Fashion dropped to ¥584 from ¥604 while prices for Women’s Fashion was stable.

    It’s clear that even with all the fanfare, Singles Day didn’t produce the level of discounts that one might have expected, indicating that purchases were driven as much by the hype surrounding the event as anything else.

    How did Alibaba’s Competitors Fare?

    While Taobao was widely expected to offer discounts during Alibaba’s major sale event, we looked at how its competitors JD.com and Amazon.ch reacted to Taobao’s strategy.

    As over 80 percent of top-ranked products were consistently present in the Top 150 ranks of each product type on these websites, we analyzed the additional discounts offered during 11.11, compared to prices on 8.11.

    Broadly speaking, both Amazon.ch and JD.com appear to have elected not to go head to head with Taobao on specific segments. JD.com’s discount strategy was spearheaded by Sports Shoes (22.1 percent) and Refrigerators (14.8 percent) while Amazon.ch featured TVs (15.3 percent) and Mobile Phones (10.2 percent).

    The average additional discounts offered by Amazon.ch and JD.com in Electronics (8.4 percent) was slightly above Taobao’s overall absolute discount (7.7 percent). TCL was aggressive with its pricing on both websites, offering over 20% discount on almost its entire assortment.

    Surprisingly, JD.com swamped Amazon.ch’s number of additionally discounted products, across all three featured categories although this may be partially explained by Amazon.ch electing to adopt a significantly more premium price position in both Men’s and Women’s Fashions compared to JD.com, while remaining roughly line ball on Electronics.

    Jack Ma’s “New Retail”

    Interestingly, JD.com wasn’t far behind Taobao in terms of sales, clocking up $20 billion in revenue, and sparking an interesting public debate between the two eCommerce giants extolling their respective performances.

    Singles Day is one of the pillars of Jack Ma’s vision of a “New Retail” represented by the merging of entertainment and consumption. Ma’s vision sees the boundary between offline and online commerce disappearing as the focus shifts dramatically to fulfilling the personalized needs of individual customers.

    Hence, Alibaba’s Global Shopping Festival should be understood as not just a one-day event that produces massive revenue, but as a demonstrable tour de force of Alibaba’s vision for the future of retail. One thing is certain — as competition heats up between Chinese retailers, we can be prepared for another Singles Day shoot-out sale next year that one-ups the staggering sales volumes this year.

    If you’re intrigued by DataWeave’s technology, check out our website to learn more about how we provide Competitive Intelligence as a Service to retailers and consumer brands globally.

     

  • Under the Microscope: Lazada’s 11.11 Online Revolution Sale

    Under the Microscope: Lazada’s 11.11 Online Revolution Sale

    Lazada’s signature event, Online Revolution, is a month-long sale extravaganza that commenced with a Mega Sale on 11 November, and culminates in an End-Of-Year sale on 12 December. The shopping event is held across six southeast Asian countries — Singapore, Malaysia, Thailand, Indonesia, the Philippines and Vietnam — making it the region’s biggest retail event.

    Lazada Group’s chief executive officer Maximilian Bittner observed, “We aim to provide Southeast Asia’s rapidly growing middle-class the access to a wide range of products with deals and discounts that were previously available only abroad or in the capital cities.”

    On 11.11, the first Mega Sale, shoppers took advantage of great deals, ordering 6.5 million items (nearly doubling last year’s tally), resulting in sales of US$123m, annihilating last year’s takings by a whopping 191 percent.

    At DataWeave, our proprietary data aggregation and analysis platform enabled us to seamlessly analyze and compare Lazada’s discounts during 11.11 with those of its competitors. We focussed specifically on two markets — Singapore and Indonesia. While the sale itself is Lazada’s, we looked at its immediate competitors as well, to study how competitively they position themselves during Lazada’s sale.

    For our analysis, we aggregated pricing information on the Top 500 ranked products of over 20 product types on each website, spread across Electronics and Fashion, covering over 120,000 products in total.

    11.11 — Singapore

    In our analysis, we scrutinized the additional discounts offered by Lazada, ListQoo10, and Zalora during the sale period, compared to prices leading up to the sale. As today’s shoppers often encounter deep discounts on several products even on normal days, our analysis of additional discounts offered during the sale more accurately reflects the true value of the sale event to shoppers.

    In the following infographic, all prices are in Singapore Dollars, and additional discounts are the percentage reduction in price on 11.11 compared to 10.11.

    Lazada’s discounting strategy was more focused on Fashion rather than Electronics. However, Lazada didn’t have it all its own way with Zalora providing comparably high discounts, enabling it to compete effectively, especially in Women’s Fashion (16.2 percent on 406 products).

    Zalora actually exceeded Lazada in the number of additionally discounted products on offer (Zalora 406, Lazada 347). ListQoo10 did not match either Lazada or Zalora’s level of discounting.

    While Lazada held a more premium, high-value product mix in Electronics compared to ListQoo10, it chose to target the more affordable segment in Fashion, with both ListQoo10 and Zalora displaying a higher average selling price in each category.

    Interestingly, Lazada refreshed very few of its Top 500 products during the sale, limiting new options to choose from for its shoppers. On the other hand, Zalora refreshed 22.5 and 22.8 percent of its products in men’s and women’s fashion respectively.

    11.11 — Indonesia

    Using a similar methodology to our Singapore analysis, we analyzed Lazada’s promotions against Blibli and Zalora, three of the top eCommerce websites in the region. In the following infographic, all currencies are in Indonesian Rupiah.

    As with its Singapore strategy, Lazada targeted Fashion as the lead category for discounts in Indonesia. It offered steep discounts in both Men’s and Women’s Fashion (around 18 percent in each) across a large number of products (550 and 776 respectively). While Zalora matched and occasionally exceeded the discounts offered by Lazada, it did so across a significantly smaller range of additionally discounted products.

    Surprisingly, Electronics were de-emphasised in Indonesia (4.1 percent compared to 9 percent in Singapore).

    Compared to the market leaders Lazada and Zalora, Blibli struggled to be competitive from both an absolute discount level and a product assortment perspective.

    Like in Singapore, Lazada looked to be targeting the affordable value end of the product mix spectrum across all categories, and introduced very few new products in its Top 500 ranks.

    Zalora had a healthier churn rate of 14.6 percent and 18.1 percent in Men’s and Women’s Fashion, compared to Lazada’s 9.1 percent (Electronics), 10.7 percent (Men’s Fashion) and 10.8 percent (Women’s Fashion).

    It’s Not Just About Discounts

    Lazada’s ‘Fashion First’ targeting strategy creates an effective tie-in to its broader model of surfing the convergence wave between entertainment and eCommerce, something unique to southeast Asia.

    Together with sumptuously attractive discounts, major sale events in South East Asia are fast becoming characterized by entertainment. By launching Southeast Asia’s first star-studded eCommerce TV show, Lazada continues to be the region’s eCommerce innovator, following in the footsteps of its pioneering parent company, Alibaba.

    While time will tell how effective Lazada’s strategy ultimately proves to be, together with Alibaba, it has set up a fascinating and uniquely Asian retail sale model. No doubt another milestone will be set on 12.12 when the Online Revolution Mega Sale returns with even greater deals. At DataWeave, we’ll be sure to analyze that sale as well and bring you all its highlights.

  • Black Friday Sales Season: How US Retailers Are Gearing Up

    Black Friday Sales Season: How US Retailers Are Gearing Up

    In today’s rapidly evolving online and mobile worlds, few things encapsulate the competitive nature of the online retail battlefield like the Black Friday sales season. With this year’s Black Friday and Cyber Monday sale events just around the corner, 2017 promises another titanic tussle between contenders.

    The holiday shopping season commences on Black Friday, November 24, and continues through much of December. Anticipating the sales season, many retailers are already offering discounts on several key categories and anchor products, providing a sneak peek into what we can expect towards the end of the month.

    While traditionally, Black Friday sales were dominated by brick and mortar retail stores, with the odd shopper stampede not unheard of, retail dynamics have changed in the recent past. Online sales now consume a larger proportion of Black Friday spending, and for the first time, consumers are expected to spend more online in the 2017 holiday season than in-store.

    In anticipation of this mammoth sale event, we at DataWeave trained our proprietary data aggregation and analysis platform on several major US retailers to understand the competitive market environment before the sales kick off.

    Between the 15th and 29th of October, we tracked the prices of the top 200 ranked products each day in the Electronics and Fashion categories across several major retailers. For Electronics, we analyzed Amazon, Walmart, Best Buy, and New Egg, while Amazon, Walmart, Bloomingdales, Nordstrom, Neiman Marcus, New Egg, and JC Penney provided our insights into the pivotal Fashion category. Product types analyzed include mobile phones, tablets, televisions, wearables techs, digital cameras, DSLRs, irons, USB drives, and refrigerators in Electronics, and T-shirts, shirts, shoes, jeans, sunglasses, watches, skirts, and handbags in Fashion.

    Automated Competitive Pricing Is the New Norm

    With the accelerated evolution of online commerce, retailers have increasingly harnessed the power of competitive data to drive changes on the go to their pricing, product assortment, and promotional strategy. During sale events, however, these numbers spike significantly. Amazon famously made 80 million price changes each day during 2014’s Christmas Season sale. Similarly, even on normal days some retailers have adopted the tactics of changing their product pricing more frequently than others, in their quest to stay competitive and build their desired price perception amongst shoppers.

    In our analysis of price changes, we considered the set of products that ranked consistently in the Top 200 from the 20th to the 25th of October. We identified the number of price changes together with the number of products affected by price changes that were implemented by the retailers.

    As anticipated, Amazon led the way with 508 price changes on 236 products in the Electronics category during the period compared to Walmart’s 413. By comparison, New Egg’s 95 price changes trailed the field by a significant margin and illustrate the tactical advantage Amazon’s dynamic pricing technology confers. However, the price variation (8.0%) of Amazon’s was also the lowest of the four retailers included in the study, showing that Amazon makes short, sharp tweaks to its pricing at a higher frequency than its competitors.

    By comparison, the Fashion category demonstrated a much lower level of price changes than Electronics, albeit with significantly higher price variations. Walmart leads the pack, adopting an order of magnitude greater number of price changes across a significantly larger number of products compared to the majority of its competitors.

    Product Mix Suited to Target Market Segments

    While competitive pricing is one strategy for attracting new customers and retaining existing ones, the selection of products featured in a retailer’s inventory is just as important. Ensuring a disciplined product assortment, which caters exclusively to a retailer’s target market segments is key. While some retailers such as Walmart choose to house a more affordable range of products, Neiman Marcus and Bloomingdales target the more premium segment of shoppers.

    It is clear from the data that Walmart has aligned its pricing strategy to support its affordability pitch to its shopper base, while Neiman Marcus and Nordstrom use pricing to juggle the demands of a more premium inventory with perceptions of price competitiveness.

    Product Movement In The Top 200

    Much of a retailer’s sales performance comes down to how effectively it maintains the optimal mix of reassuring bestsellers complemented by attractive new arrivals. Sound product assortment clearly provides shoppers with a variety of options each time they visit the retailer’s website. To achieve this balance, retailers typically employ their own, unique algorithm that ranks products in their listings based on several factors, including price range, discount offered, review ratings, popularity and promotions by brands.

    To study this, we evaluated the average percentage of products that were replaced in the Top 200 ranks for each product type of each website.

    Amazon has clearly adopted a strategy of offering new options to its shoppers each day, with an average of 60% new products in the Top 200 ranks of the Fashion category. Contrast that with Walmart which appears to be more conservative in its approach to churning its Top 200 products. In the case of Neiman Marcus however, the reason for the lower volume of product pricing movements in its Top 200 ranks may be due to the relatively high value of its premium product assortment, which imposes the internal constraints of having a smaller pool of new products to choose from.

    Online-First, This Black Friday Sale Season

    Amazon continues to demonstrate its dominance as a pacesetter in US retail, largely due to its progressive online pricing and merchandising strategies. These embrace the power of big data in its approach to online retail.

    Research shows online is consistently outperforming in-store along critical customer satisfaction dimensions spanning: product quality, selection and/or variety, availability of hard-to-find and unique products, ease of searching and delivery options.

    According to global consultancy Deloitte, for the first time ever, American shoppers will purchase more online than they buy offline in the 2017 holiday shopping season — 51 percent, up from 47 percent in 2016. With Black Friday looming in the next few weeks, it will be interesting to see how US retailers push to seize a larger piece of this growing pie.

    Check out our website to learn more about how DataWeave provides Competitive Intelligence as a Service to retailers and consumer brands globally.

  • Our Analysis of Diwali Season Sales

    Our Analysis of Diwali Season Sales

    As the battle of the Indian eCommerce heavyweights continues to accelerate, we have witnessed three separate sale events compressed into the last four weeks of this festive season. Flipkart has come out with all guns blazing following its multi-billion-dollar funding round, leaving Amazon with little choice but to follow suit with its own aggressive promotions. At this stage of a highly competitive eCommerce cycle, market share is a prize worth its weight in gold and neither Flipkart nor Amazon are prepared to blink first.

    At DataWeave, our proprietary data aggregation and analysis platform enables us to seamlessly analyze these sale events, focusing on multiple dimensions, including website, category, sub-category, brand, prices, discounts, and more. Over the past six weeks, we have been consistently monitoring the prices of the top 200 ranked products spread over sub-categories spanning electronics, fashion, and furniture. In total, we amassed data on over 65,000 products during this period.

    The first of these pivotal sale events was held between the 20th and 24th September, which we earlier analyzed in detail. Another major sale soon followed, contested by Amazon, Flipkart and Myntra for varying periods between the 4th and 9th of October. Lastly, was the Diwali season sale held by Amazon, Flipkart, and Myntra between the 14th and 18th of October, joined by Jabong between the 12th and 15th of October.

    In analyzing these significant sale events for all eCommerce websites, we observed an extensive range of products enjoying high absolute discounts, but with no additional discounts during the sale, i.e. prices remained unchanged between the day before the sale and the first day of the sale. The following infographic highlights some of the sub-categories and products where this phenomenon was more pronounced during the recently concluded Diwali season sale. Here, discount percentages are average absolute discounts of products with unchanged discounts during the sale.

    Having identified the aggressive use of high but unchanged absolute discounts amongst eCommerce heavyweights during the sale, we focused our analysis on the additional discounts offered during the sale, to more accurately reflect the value these sale events deliver to Indian consumers.

    Several categories, sub-categories and brands emerged as enjoying substantial additional discounts. The following infographic details our analysis:

    Amazon and Flipkart continue to stand toe to toe on discounts in Electronics, although Amazon offered discounts across a greater number of products. Flipkart adopted a more premium brand assortment in the Electronics category with an average MRP of INR 30,442 for additionally discounted products.

    What stands out in our analysis is Amazon’s consistently aggressive discounting in fashion compared to Flipkart. As anticipated, Jabong and Myntra continued to offer attractive discounts in a large number of fashion products, seeking to maintain their grip in their niche. Furniture, too, is a category where Amazon out-discounted Flipkart, albeit through a less premium assortment mix (average MRP of INR 23,580 compared to Flipkart’s INR 34,304).

    Several big brands elected to dig deep into their pockets during the sales to offer very high discounts. These included attractive discounts from Redmi, Asus, and Acer in Electronics, and W, Wrangler, Levi’s, Puma, Fossil, and Ray Ban in Fashion.

    Which Sale Delivered Greater Value For Consumers?

    Since DataWeave has extensive data on both the pre-Diwali sale (held between 4th and 9th of October), and the Diwali season sale (held between 12th and 18th October), we compared prices to identify which of the sale events offered more attractive discounts across categories, sub-categories and products.

    While the discount levels were generally consistent across most sub-categories, only varying by a few percentage points, we identified several sub-categories and products that displayed a large variation in the absolute level of discount offered.

    As the infographic above shows, Amazon identified women’s formal shoes as a key category in its discounting strategy, which saw its level of discounting triple during the Diwali sale. By comparison, Flipkart doubled its discount in men’s jeans, and Myntra tripled its discounts on Men’s shirts and sunglasses.

    Similarly, during the Diwali sale Amazon, Flipkart and Myntra all offered selected products with an aggressive 40% to 50% discount level.

    Interestingly, Amazon, Flipkart and Myntra all elected to reduce the level of discounts offered on specific products as well. One of the biggest discount moves was Amazon’s reduction on iPhone 6s from 34% to only 4%. Flipkart recorded a similar price move on Adidas originals Stan Smith sneakers (30% to 5%) and Canon EOS 200D DSLR cameras (20% to 8%).

    Market Share Reigns Supreme

    Based on our analysis of the festive season sales, Flipkart’s aggressive approach powered by its multi-billion-dollar funding round enabled it to stave off Amazon’s discounting strategy in the annual eCommerce festive season sales this year, increasing its lead over Amazon India in a market where the total sales is believed to have surged by up to 40 percent over 2016’s sales.

    Based on several reports, Flipkart’s share of total festive season sales appears to have increased from 45 percent in 2016 to 50 percent this year, capturing much of the market up for grabs from a now relegated Snapdeal. Amazon’s market share during a festive sales period that stretched over a month is estimated to have remained steady at 35 percent, though the company reported it saw a 50 percent share in other metrics such as order volume and active customers.

    The key question for both industry analysts and consumers alike is, how much deeper are retailers willing to go in their quest to capture market share at the expense of operating margins?

    If you’re interested in DataWeave’s data aggregation and analysis platform, and how we provide Competitive Intelligence as a Service to retailers and brands, visit our website!

  • Top 5 Drivers of Successful eCommerce | DataWeave

    Top 5 Drivers of Successful eCommerce | DataWeave

    Retail has undergone a dramatic transformation over the last decade. Once dominant retailers are today being given a run for their money amid a gradual decline in mall traffic and sharply growing consumer preference for shopping online.

    Surfing this online retail wave is Internet behemoth Amazon, which is raking in 43% of all new eCommerce dollars, leaving other retailers floundering in its wake.

    As it unfolded, this transformation has unleashed changes across many areas of retail, a phenomenon that’s been well documented by industry commentators in the media. Some of these shifts include:

    Customer preferences: Customers today are spoilt for choice, both in terms of being able to quickly and easily compare product prices across websites, as well as consistently driving the demand for new and unique products from retailers.

    Hyper-personalization: With shoppers increasingly relying on mobile apps, highly personalized shopping experiences are becoming the new normal.

    Delivery: e-Retailers are competing on faster home deliveries, stretching themselves to guarantee same day delivery, or even (as in the case of hyper-local grocery retailers) within a few hours. Drones, anyone?

    Payment Modes: Even the more tactical aspects of retail, like payment modes, have been forced to evolve. Starting with cash-on-delivery, this trend quickly spread to embrace card payments and digital wallets. These initiatives have posed significant technological and security challenges for retailers.

    As with a forced move in chess, traditional retailers have had to evolve and embrace changes like the ones listed above, in order to survive the incredibly cutthroat world of modern retail. Similar challenges exist for up-and-coming eCommerce companies as well.

    However, many pundits and retailers alike often forget that doing even simple, time-tested things correctly can go a long way in forging an effective competitive position, helping win both market share and customer affections. While digital transformation has altered how these strategies were routinely executed, the fundamentals remain as relevant today as they ever were.

    1. Smarter Pricing

    With 80 percent of first-time shoppers comparing products prior to buying, the need for an eCommerce website to offer competitive pricing has become a mandatory cost-of-entry capability. While dynamic pricing poses a challenge for e-retailers to stay competitive, it also presents them with an opportunity to track their competitors’ pricing and exploit that information to optimize their own pricing.

    However, e-retailers today are frequently forced to perform millions of price-changes every day in the eternal quest to either offer the lowest price or entrench a calculated premium price perception among shoppers.

    For instance, as far back as Christmas season 2014, Amazon is estimated to have made a total of 80 million price changes per day. Similarly, today’s hyper-local grocery retailers offer differentiated and targeted prices for shoppers living in specific zip codes.

    To achieve price controls on this level of scale demands sophisticated automated tracking of competitor pricing to facilitate timely, data-driven dynamic pricing decisions. This has, today, become a table stakes requirement.

    2. Variety and Depth of Product Range

    If customers cannot find what they are looking for on a website, all other aspects of how an eCommerce operator optimizes their retail strategy falls by the wayside.

    A website’s success remains dependent largely on it being able to cater effectively to the needs, wants and desires of its target audience. Simply put, a website offering a mammoth product range may still end up failing compared to a small niche website with a limited but highly targeted assortment that understands closely its customer’s sweet spot.

    However, with millions of products on offer online all day every day, gathering and harvesting deep insights into a competitor’s assortment mix can appear daunting. Include dynamically changing product assortments and different product taxonomies into the standard research mix, and many who lack access to automated competitive intelligence systems find themselves struggling to find the expertise required to gather and summarize this information in an actionable form.

    3. Customer Centricity

    Today, customers demand to be heard. As competitive pricing becomes an expected cost of doing business, retailers will need to place greater support resources and more effective processes to resolve customer problems and complaints in a timely fashion at the heart of their customer service model.

    Following the online social revolution, 9 out of 10 retail customers now expect a consistent response across all social media channels.

    Successful companies like Zappos, Best Buy and Amazon have been quick to understand this significant shift in customer preferences. These retailers have demonstrated their willingness to go the extra mile by establishing a robust, scalable omni-channel support structure.

    The level of this commitment can be seen in Amazon’s recent vision statement announcement, “Amazon today boasts of one of the most responsive omni-channel customer support and Zappos takes pride in sending a personalized response to customer queries. We seek to become Earth’s most customer centric company.” This aggressive customer centric sentiment drives a stake in the ground for all competing eCommerce companies’ to match via their customer service strategy.

    4. Superior Customer Experience

    While bricks and mortar retail stores continue to attract customers by enabling shoppers to touch, feel and test items before they purchase, online and omni-channel retailers have channelized their efforts into increasingly refining their web user experience.

    Several studies reveal it takes only a couple of seconds for a website visitor to decide whether to stay on or leave a website. Aspects such as visual design, ease of use, content attractiveness, website loading time and pervasive calls to action (CTA) are a few of the key user experience parameters that influence visitors to stay on a website.

    eCommerce sites such as Zara, Graze, Asos, and Amazon offer attractively organized and clutter-free designs, which are visually engaging and easy to navigate. While these design elements help them keep their customers engaged, it’s their disciplined focus on content that stimulates visitor conversions.

    Detailed product descriptions and high-quality images are helping these eCommerce sites educate their customers about their products while simultaneously boosting their website’s SEO ranking, helping it attract and engage still more online visitors.

    Complementing the online retailing revolution are substantial efforts by omni-channel retailers to optimize O2O (online to offline) strategies designed to bring together the best of both worlds — the discoverability of online, with the touch-and-feel of an offline environment.

    5. Optimized Promotional Strategies

    With so many options for a shopper to choose from in an increasingly cluttered and competitive online retailing environment, attracting new customers and entrenching customer loyalty is an ongoing challenge. Strategic online promotions are emerging as an effective technique in solving the customer recruitment and retention dilemma. Online promotions if executed effectively are doing wonders for generating inbound website traffic.

    However, for online promotions to be effective, it is critical for e-retailers to understand their competitor’s strategy if they are going to be able to sustain their competitiveness. Key questions to answer in this context are, what brands are they promoting more than others? For how long? At what frequency?

    Keeping a keen eye on and reacting to competitors’ promotions is a key aspect to designing effective online promotions. Being able to exploit this competitive intelligence not only boosts their own sales volumes but erodes that of their competitors as well.

    Competitive Intelligence As A Service

    Having understood the far-reaching impact of these evergreen drivers of eCommerce success, we at DataWeave work with omni-channel and online retailers to provide Competitive Intelligence as a Service and help them evaluate and optimize their strategic approach across the eCommerce landscape.

    If you’re interested in DataWeave’s solutions and would like to learn more about how we help retailers and brands optimize their retail strategies, visit our website!

  • Festive Season Sale: Who’s Winning the Great Indian eCommerce Battle?

    Festive Season Sale: Who’s Winning the Great Indian eCommerce Battle?

    In the lead up to October’s Diwali celebrations, almost all major Indian e-retailers had announced mammoth sale events for last week. Resuming the epic battle of India’s online shopping carts during festival seasons, Flipkart, together with Jabong and Myntra, kicked off their five-day-long “Big Billion Days” sales on September 20, while Amazon India‘s “Great Indian Festival” launched the next day.

    The stakes were high as Amazon and Flipkart are more evenly matched this year than ever before, making predicting an eventual winner of these dueling discounters a lot tougher than in previous years.

    At DataWeave, our proprietary data aggregation and analysis platform enabled us to easily assess which e-retailer offered better deals and discounts. Over the last two weeks, we have been consistently monitoring the prices of the top 200 ranked products in Amazon, Flipkart, Myntra, and Jabong, across several sub-categories of Electronics, Men’s Fashion, and Women’s Fashion, encompassing over 35,000 products in total.

    Divergent Discount Strategies

    In our analysis, we bring focus to the additional discounts offered by competing e-retailers during the sale, compared to prices before the sale. This is key, as today’s shoppers often encounter deep discounts on several products even on normal days, which could potentially dampen the value suggested by the large discounts advertised during the sale.

    Based on our analysis, Flipkart clearly adopted a more aggressive pricing strategy this year, establishing a lead over Amazon in average discount percentage for Electronics and Women’s Fashion. Moreover, Flipkart launched additional discounts on a larger number of products across categories. Amazon, though, offered 6.9 percent additional discounts on smartphones compared to Flipkart (6.2 percent), led by 10.7 percent discount on Apple and 7.7 percent discount on Redmi smartphones.

    Flipkart has already reported a doubling of revenue from the sale (which includes sales volumes of Myntra and Jabong) compared to last year, and claimed it accounted for 70 percent of eCommerce sales during these five days — beating Amazon by a considerable margin. Amazon, for its part, reported a “2.5X growth in smartphone sales, 4X increase in large appliances and 7X in fashion sales.”

    The difference in discounting strategies between Amazon and Flipkart is starkly illustrated by their respective highest discounts. Flipkart led the way with a 65.5 percent discount on Vero Moda skirts, a 65 percent discount on Tommy Hilfiger skirts, and 50 percent off Calvin Klein sunglasses.

    By contrast, Amazon’s greatest discounts were an 83.4 percent discount on Redfoot formal shoes, 45.5 percent on Motorola Tablets, a 40 percent on US Polo T-shirts, and a 25.1 percent discount on Puma sports shoes.

    Also, Flipkart hosted a more premium range of products in its assortment compared to Amazon, evidenced by a higher average MRP for its discounted products. Surprisingly, Amazon’s spread of discounted products has the least average MRP in Electronics and Women’s Fashion, compared to all other competitors.

    New Products Break Through the Top 200

    What’s fascinating in this battle of the e-retail giants is the correlation we uncovered between prices and rank. During the sale, as prices dropped on hundreds of products across the board, newer products successfully broke through into the Top 200 ranks for each sub-category. New products in the top 200 ranks had higher discount levels than the ones they replaced.

    This trend was especially pronounced in fashion, where we observed an almost complete overhaul of products filling the Top 200 during the sale period, led by sports shoes in Amazon, Men’s shirts in Flipkart, and Men’s formal shirts in Jabong.

    What About Pre-Sale Prices?

    Another angle we explored was whether (like most of us suspect) e-retailers increase their prices before a sale, only to reduce them during the sale, so they can advertise higher discounts. We observed that all e-retailers did increase their prices for an albeit small set of products before the sale.

    While the number of products where the prices increased for each website prior to the sales is small, it is interesting to observe that certain brands choose to perform the oldest trick in the retail book even today — raising prices to accentuate the degree of discount during the sale period, something shoppers need to keep an eye out for.

    A Sign of Things to Come?

    Based on our analysis, Flipkart has recognized the threat from Amazon and has approached this year’s “Big Billion Days” sale aggressively. It has dug deep into its freshly funded pockets, and offered better discounts for a larger set of products across most categories, in its attempt to lock down a greater market share in the burgeoning Indian eCommerce space.

    Amazon, though, has continued to maintain a firm grip on the Indian consumer, having achieved tremendous growth in specific categories during the sale.

    What’ll be interesting now is to see how these pricing strategies impact company revenues and margins, and how this will shape the soon-to-follow Diwali sales in mid-October.

    If you’re intrigued by DataWeave’s data aggregation and analysis technology, and would like to learn more about how we help retailers and brands build and maintain a competitive edge, please visit our website.

     

  • Analysis of Target’s Discount Strategy

    Analysis of Target’s Discount Strategy

    Earlier this year, we witnessed Amazon and Walmart going head to head in a CPG goods price war of fluctuating intensity that soon rippled out to embrace the entire grocery industry.

    This further intensified with Amazon’s takeover of Whole Foods and the Whole Foods’ subsequent announcement hinting at significant discounts toward the end of August.

    (Read Also: Amazon’s Whole Foods Pricing Strategy Revealed)

    Soon, Target announced it was lowering prices on literally “thousands of items.” As Mark Tritton, Target executive vice president and the chief merchandising officer put it, “We want our guests to feel a sense of satisfaction every time they shop at Target.”

    To drive home the seriousness of their intent, Target nominated grocery staples such as cereal, paper towels, milk, eggs, baby formula, razors and bath tissue and vowed to, “eliminate more than two-thirds of their price.”

    At DataWeave, we focused our proprietary data aggregation and analysis platform on Target’s reported price reduction. Our team acquired data on the prices of over 160,000 products listed by Target across 12 zip-codes selected at random. The platform then took two snapshots. Firstly, between 23rd August and 30th August which included the Whole Foods’ price reduction (to study any possible reactions on price) and, secondly, between the 6th September and 13th September, which included Target’s discount strategy announcement.

    Of the categories Target identified as priorities for its discount strategy, only baby products, cereals, and Milk & Eggs displayed significant price drops. This price discounting effect varies, however, across brands in each category. In cereals, while KIND (30.4%) and Purely Elizabeth (24%) displayed high discounts, Apple Jacks, Corn Pops, and Krave more surprisingly increased their prices by up to 25% each.

    Similarly, in the Milk & Eggs category, Price’s (13.6%) and Coffee-Mate (10%) exemplified hefty discounts, while Moon Cheese and Challenge Butter increased their prices by 33% and 48% respectively in the same time period. By comparison, Razors and Paper Towels showed no price changes whatsoever across the review period.

    Interestingly, we observed greater price-change activity coinciding with the time of the Whole Foods’ announcement (between 23rd and 30th of August) than the later time period. Once again, however, no definite price discounting pattern emerged from the study, indeed the team found discount rates fluctuated significantly across categories.

    Looking across the spectrum of CPG categories pricing, we saw significant, sustained variation across both categories and zip-codes.

    Beauty products showed a 2 percent discount on average although this varied by zip-code, fluctuating between a 7 percent discount and an actual 10 percent price increase. F&B showed a 2 percent price increase, which jumped to 10 percent in some zip-codes. Personal care displayed a 2.5 percent increase on average, varying anywhere between an 8 percent discount and a 10 percent price increase. Baby products surprisingly recorded a 4 percent price increase on average during the study.

    So, What Does This All Mean?

    Based on our analysis, Target’s pricing strategy appears to be a combination of very closely concentrated discounting, complemented by selective price increases. Is discounting more a perception than a reality at this stage of the CPG cycle?

    Aggressive price discounting has never been a decisive factor in successfully building Target’s consumer franchise. However, given the current trading environment and the continued pressure applied by competitive omni-channel strategies, which has seen a host of new entrants elbowing their way into the market, we anticipate price will continue to play a prominent role in retailing.

    We suspect, based on evidence we gathered, that price discounts are more a highly targeted weapon in the fight for market share than a broadsword slashing of prices across the board. As Target’s CEO Brian Cornell noted during an earnings call, the company experienced “a meaningful increase in the percent of our business done at regular price and a meaningful decline in the percent on promotion.”

    If you’re interested in DataWeave’s data aggregation and analysis technology, and would like to learn more about how we help retailers and brands build and maintain a competitive edge, visit our website.

  • Amazon’s Whole Foods Pricing Strategy Analysis | DataWeave

    Amazon’s Whole Foods Pricing Strategy Analysis | DataWeave

    Amazon.com, America’s retail behemoth, dominated headlines in August when it completed its acquisition of Whole Foods in early August 2017. Having officially taken control of the up-market grocer, which focuses on premium quality produce, market observers and consumers alike are eagerly awaiting Amazon’s pricing strategy analysis.

    At the heart of Amazon.com’s seemingly unstoppable growth trajectory is the company’s ability to understand consumers, complemented by deep insights into buying cycles and purchase decisions and preferences. It also helps that Amazon.com boasts one of the planet’s mightiest marketing and publicity machines.

    Is Amazon.com About To Launch A Grocery Price War?

    Reports of Amazon.com dropping Whole Foods prices by up to 43 percent quickly made splashes across the news media. Given Jeff Bezos has been quoted in the past as saying, “your margin is our opportunity”, an aggressive promotional campaign to achieve dominance for its new Whole Foods acquisition was anticipated by some commentators.

    These sentiments ignited fears of a profit-sapping price war, immediately hit stock prices in the cutthroat grocery industry, which survives on famously thin margins. Memories of Amazon.com’s impact on US department store profitability quickly surfaced with analysts pointing to Walmart’s revenue/market share plunge from 26 percent in 2005 to just 11 percent in 2016 when the sector came under sustained pressure from Amazon.com.

    How Deep Are Amazon.com’s Price Cuts Really?

    At DataWeave, a Competitive Intelligence as a Service provider for retailers and brands, we put Amazon.com’s actual Whole Foods discounts under the microscope. The resulting careful analysis of price discounts revealed quite a different story to the one initially featured in the media. Scrutiny by our proprietary data aggregation and analysis platform showed the drop in retail grocery prices was minimal to almost negligible, depending on the category.

    In delivering near-real-time competitive insights to retailers and brands, we acquire and compile large volumes of data from the Web on an ongoing basis. A key differentiator is our ability to aggregate data down to a zip-code level.

    Our analysis of Amazon.com’s reported drop in prices was based on data acquired for 13 zip-codes distributed across the country and selected at random. Our platform compared market prices by zip code valid between 23rd August and 30th August.

    Each zip code indicated the overall average discount offered varied between 0.20 percent and -0.20 percent. When the discounts at a category-level were separated out, the discounts available to customers per category varied between -6.8 percent (an actual price increase) and 6.1 percent.

    Moving on to the “Fill the Grill” category, discounts again were modest, varying between -5.6 percent (another price increase) and 6.1 percent across the zip codes analyzed.

    This aligns with Amazon.com’s recognized preference for basing its strategy on competing on breadth and depth of product assortment rather than pure pricing discounts at the checkout.

    Some Sunshine For Foodies

    There was some good news for shoppers looking for higher discounts. Amongst those products attracting a higher discount were:

    • Belton Farm Oak Smoked Cheddar Cheese: 50 percent
    • Beemster Premium Dutch Cheese: 50 percent
    • Heritage Store Black Castor Oil: 50 percent
    • Organic French Lentils: 45 percent
    • Vibrant Health Pro Matcha Protein: 40 percent
    • Hass Avocado: 50 percent (confined to one zip-code).

    Final Word

    Amazon.com’s marketing engine is renowned for skillfully nurturing consumer price perceptions of the giant retail website as being the lowest priced retailer. We kept a keen eye on Amazon’s pricing these past weeks, and unearthed a carefully conceived and executed Whole Foods pricing campaign, which is yet another example of their market shaping expertise at work.

    If you’re intrigued by DataWeave’s technology and would like to learn more about how we help retailers and brands build and maintain a competitive edge, please visit our website!

  • The Role of Competitive Intelligence in Modern Retail

    The Role of Competitive Intelligence in Modern Retail

    When retailers today look to compete in the cutthroat world of online commerce, they face several challenges unique to the nature of modern retail. It is now significantly harder for retailers to benchmark their pricing, assortment, and promotions against their competition, as the online world is highly dynamic and significantly more complex than before.

    Trends like the growing adoption of mobile shopping apps, the rising influence of customer reviews in buying behavior, hyperlocal e-commerce websites differentiating themselves by fulfilling deliveries in a matter of hours — the list goes on — have only added to this complexity.

    However, this complexity also presents an opportunity for retailers to incorporate layers of external competitive information into their merchandising strategies to deliver more value to customers and personalize their experience.

    Vipul Mathur, Chief Branding and Merchandising Officer at Aditya Birla Online Fashion, recently published an article highlighting some of the areas in which Competitive Intelligence providers like DataWeave can strategically influence modern merchandising.

    “The consumer is often driven by the aesthetics of a product, more so in the fashion and lifestyle industries than others. Hence, the choices of buyers are hard to interpret. However, innovative modern technologies are helping us understand these decisions,” says Vipul.

    He provides an example of how using AI-based tools (like DataWeave’s) to unearth the sentiments behind thousands of online reviews can help retailers better channel and message their online promotions.

    “Deciphering the consumers’ comments and converting them into tangible insights is incredible proof of the refinement possible with data analysis tools. It’s like knowing that consumers are delighted by the quality of the soles of a pair of Adidas running shoes. Using this, marketing communication can be modified to highlight this specific product feature,” explains Vipul.

    And it’s not just merchandising. This data can percolate across multiple functions in retail, enabling greater efficiency in operations. “If we have data on the best-selling styles across websites, including other attributes like pricing, region/locality (through pin-code mapping), and possibly even rate of sales, it’s up to our supply-chain systems to ensure that the supply is in accordance with demand.”

    DataWeave’s Retail Intelligence offers global retailers and e-commerce websites with these benefits and more. Our AI-powered technology platform aggregates and analyzes vast volumes of online competitive data and presents them in an easily consumable and actionable form, aiding quick, data-driven merchandising decisions.

    “DataWeave, our partner, has helped us refine our merchandising decisions, saving cost and creating value,” sums up Vipul.

    Read the entire article here, and if you’re intrigued by what DataWeave can do for retail businesses and wish to learn more, visit our website!

     

  • Video: Using Product Images to Achieve Over 90% Accuracy in Matching E-Commerce Products

    Video: Using Product Images to Achieve Over 90% Accuracy in Matching E-Commerce Products

    Matching images is hard!

    Images, intrinsically, are complex forms of information, with varying backgrounds, orientations, and noise. Developing a reliable system that achieves human-like accuracy in identifying, interpreting, and comparing images, without investing in expensive resources, is no mean task.

    For DataWeave, however, the ability to accurately match images is fundamental to the value we provide to retailers and consumer brands.

    Why Match Images?

    Our customers rely on us for timely and actionable insights on their competitors’ pricing, assortment, promotions, etc. compared to their own. To enable this, we need to identify and match products across multiple websites, at very large scale.

    One might hope to easily match products using just the product titles and descriptions on websites. However, therein lies the rub. Text-based fields are typically unstructured, and lack consistency or standardization across websites (especially for fashion products). In the following example, the same Adidas jacket is listed as “Tiro Warm-Up Jacket, Big Boys (8–20)” on Macy’s and “Youth Soccer Tiro 15 Training Jacket” on Amazon.

    Hence, instead of using text-based information, we considered using deep-learning techniques to match the images of products listed on e-commerce websites. This, though, requires massive GPU resources and training data fed into the deep-learning model — an expensive proposition.

    The solution we arrived upon, was to complement our image-matching system with the text-based information available in product titles and descriptions. Analyzing this combination of both text- and image-based information enabled us to efficiently match products at greater than 90% accuracy.

    How We Did It

    A couple of weeks ago, I gave a talk at Fifth Elephant, one of India’s renowned data science conferences. In the talk, I demonstrated DataWeave’s innovation of augmenting the NLP capabilities of Solr (a popular text search engine) with deep-learning features to match images with high accuracy.

    Check out the video of the presentation for a detailed account of the system we built:

    Human-Aided Machine Intelligence

    All products matched with the seed product are tagged with a corresponding confidence score. When this score crosses a certain threshold, it’s presumed to be a direct match. The ones that are part of a lower range of confidence scores are quickly examined manually for possible direct matches.

    The outcome, therefore, is that our technology narrows down the consideration set of possible product matches from a theoretical upper limit of millions of products, to only a few tens of products, which are then manually checked. This unique approach has two distinct advantages:

    • The human-in-the-loop enables us to achieve greater than 90% accuracy in matching millions of products — a key differentiator.
    • Information on all manually matched products is continually fed to the deep-learning model, which is used as training data, further enhancing the accuracy of the product matching mechanism. As a result, both our accuracy and delivery time keep improving with time.

    As the world of online commerce continues to evolve and becomes more competitive, retailers and consumer brands need the ability to make quick proactive and reactive decisions, if they are to stay competitive. By building an automated self-improving system that matches products quickly and accurately, DataWeave enables just that.

    Find out more about how retailers and consumer brands use DataWeave to better understand their competitive environment, optimize customer experience, and drive profitable growth.

  • Was Amazon’s Prime Day Sale Really That Big a Deal?

    Was Amazon’s Prime Day Sale Really That Big a Deal?

    Hint: Only in some product categories

    Amazon’s Prime Day sale, the first-of-its-kind in India, made a conspicuous splash across the media a couple of weeks ago, with several stories of the sale’s dramatic success doing the rounds. For 30 hours spread over 10th and 11th of July, the online retail giant rolled out deals as frequently as every five minutes, exclusively for Amazon Prime subscribers. And online shoppers lapped it up.

    According to Amazon India, more customers signed up for Prime on the day of the sale and in the week leading up to it, than on any other month since Prime’s launch in India last year. To boot, Prime subscribers shopped three-times more during the sale compared to other days.

    The discounts offered on several products were quite frequently in the range of 60–70% and beyond, with some products reaching absurd discount levels of up to 85%. However, for a retailer as competitively priced as Amazon, what’s interesting to explore is how much additional discount was offered during the sale. After all, even on normal days, Amazon discounts aggressively on its top 20% selling SKUs, in order to reinforce the commonly held perception that the company is the lowest priced retailer around.

    More Than Meets the Eye

    At DataWeave, our AI-based technology platform aggregates and analyzes publicly accessible data on the Web, at large scale, to deliver insights on competitors to retailers and consumer brands. We collected pricing and discount information for the Electronics and Fashion categories on Amazon during the sale, and compared it to numbers from before the sale. Thus, we evaluated just how much additional value Prime subscribers could’ve potentially drawn from this sale.

    We performed a similar analysis on Flipkart as well, to examine how competing e-commerce websites react to big-ticket sale events.

    The infographic below lists out some of the more interesting bits of our analysis.

    Unsurprisingly, Amazon strengthened its grip in the electronics category by offering, on average, 3.9% higher discount than Flipkart, even with a higher-value assortment mix. Subsequently, Amazon reported a 5X increase in sales of smartphones and an 8X increase in sales of televisions during Prime Day.

    While Apple discounted its phones by 8.5% during the sale, Sanyo was among the top discounting brands (10%) in Televisions, with the company reporting a 4X jump in television sales. TCL offered 20% additional discount, the highest for televisions.

    What stands out from this analysis, though, is that Flipkart beat Amazon on price definitively in the fashion for women category, by extending 6.8% more discount than Amazon on a significantly higher-value assortment mix.

    It’s not uncommon to see e-commerce companies lowering their prices across the board to take advantage of the hype surrounding a competing e-commerce website’s promotional activity. Clearly, it’s a good idea for shoppers to always compare prices across websites before buying any product online.

    The New Age of Retail

    That shoppers today can easily compare products and prices across different e-commerce websites has brought about greater competition among online retailers. With the consequent margin pressure, comes the need for retailers to be able to react to price changes by their competitors in near-real-time.

    And it’s no mean task. Amazon has been found to effect over 80 million price changes a day during holiday season, and retailer-driven sale events like the Prime Day Sale are here to stay. Consequently, retailers look to Competitive Intelligence providers like DataWeave for easily consumable competitive information that enables them to react effectively and compete profitably.

    DataWeave’s AI-powered technology platform aggregates, compiles, and presents millions of data points to provide e-commerce companies with actionable competitive insights. With our solutions, retailers can effect profitable price changes, implement high-value assortment expansion, and proactively monitor and respond to promotional campaigns by competitors.

    Find what we do interesting? Visit our website to find out more about how modern retailers benefit from using DataWeave’s Competitive Intelligence as a Service.

  • Implement a Machine-Learning Product Classification System

    Implement a Machine-Learning Product Classification System

    For online retailers, price competitiveness and a broad assortment of products are key to acquiring new customers, and driving customer retention. To achieve these, they need timely, in-depth information on the pricing and product assortment of competing retailers. However, in the dynamic world of online retail, price changes occur frequently, and products are constantly added, removed, and running out of stock, which impede easy access to harnessing competitive information.

    At DataWeave, we address this challenge by providing retailers with competitive pricing and assortment intelligence, i.e. information on their pricing and assortment, in comparison to their competition’s.

    The Need for Product Classification

    On acquiring online product and pricing data across websites using our proprietary data acquisition platform, we are tasked with representing this information in an easily consumable form. For example, retailers need product and pricing information along multiple dimensions, such as — the product categories, types, etc. in which they are the most and least price competitive, or the strengths and weaknesses of their assortment for each category, product type, etc.

    Therefore, there is a need to classify the products in our database in an automated manner. However, this process can be quite complex, since in online retail, every website has its own hierarchy of classifying products. For example, while “Electronics” may be a top-level category on one website, another may have “Home Electronics”, “Computers and Accessories”, etc. as top-level categories. Some websites may even have overlaps between categories, such as “Kitchen and Furniture” and “Kitchen and Home Appliances”.

    Addressing this lack of standardization in online retail categories is one of the fundamental building blocks of delivering information that is easily consumable and actionable.

    We, therefore, built a machine-learning product classification system that can predict a normalized category name for a product, given an unstructured textual representation. For example:

    • Input: “Men’s Wool Blend Sweater Charcoal Twist and Navy and Cream Small”
    • Output: “Clothing”
    • Input: “Nisi 58 mm Ultra Violet UV Filter”
    • Output: “Cameras and Accessories”

    To classify categories, we first created a set of categories that was inclusive of variations in product titles found across different websites. Then, we moved on to building a classifier based on supervised learning.

    What is Supervised Learning?

    Supervised learning is a type of machine learning in which we “train” a product classification system by providing it with labelled data. To classify products, we can use product information, along with the associated category as label, to train a machine learning model. This model “learns” how to classify new, but similar products into the categories we train it with.

    To understand how product information can be used to train the model, we identified what data points about products we can use, and the challenges associated with using it.

    For example, this is what a product’s record looks like in our database:

    {
“title”: “Apple MacBook Pro Retina Display 13.3” 128 GB SSD 8 GB RAM”,
“website”: “Amazon”,
“meta”: “Electronics > Computer and Accessories > Laptops > Macbooks”,
“price”: “83000”
}

    Here, “title” is unstructured text for a product. The hierarchical classification of the product on the given website is shown by “meta”.

    This product’s “title” can be represented in a structured format as:

    {
“Brand”: “Apple”,
“Screen Size”: “13.3 inches”,
“Screen Type”: “Retina Display”,
“RAM”: “8 GB”,
“Storage”: “128 GB SSD”
}

    In this structured object, “Brand”, “Screen Size”, “Screen Type” and so on are referred to as “attributes”. Their associated items are referred to as “values”.

    Challenges of Working with Text

    Lack of uniformity in product titles across websites –

    In the example shown above, the given structured object is only one way of structuring the given unstructured text (title). The product title would likely change for every website it’s represented on. What’s worse, some websites lack any form of structured representation. Also, attributes and values may have different representations on different websites — ‘RAM’ may be referred to as ‘Memory’.

    Absence of complete product information –

    Not all websites provide complete product information in the title. Even when structured information is provided, the level of detail may vary across websites.

    Since these challenges are substantial, we chose to use unstructured titles of products as training inputs for supervised learning.

    Pre-processing and Vectorisation of Training Data

    Pre-processing of titles can be done as follows:

    • Lowercasing
    • Removing special characters
    • Removing stop words (like ‘and’, ‘by’, ‘for’, etc.)
    • Generating unigram and bigram tokens
    • We represented the title as a vector using the Bag of Words model, with unigram and bigram tokens.

    The Algorithm

    We used Support Vector Machine (SVM) and compared the results with Naive Bayes Classifiers, Decision Trees and Random Forest.

    Training Data Generation

    The total number of product data we’ve acquired runs into the hundreds of millions, and every category has a different number of products. For example, we may have 40 million products in “Clothing” category but only 2 million products in the “Sports and Fitness” category. We used a stratified sampling technique to ensure that we got a subset of the data that captures the maximum variation in the entire data.

    For each category, we included data from most websites that contained products of that category. Within each website, we included data from all subcategories and product types. The size of the data-set we used is about 10 million, sourced from 40 websites. We then divided our labelled data-set into two parts: training data-set and testing data-set.

    Evaluating the Model

    After training with the training dataset, we tested this machine-learning classification system using the testing dataset to find the accuracy of the model.

    Clearly, SVM generated the best accuracy compared to the other classifiers.

    Performance Statistics

    • System Specifications: 8-Core system (Intel(R) Xeon(R) CPU E3–1231 v3 @ 3.40GHz) with 32 GB RAM
    • Training Time: 90 minutes (approximately)
    • Prediction Time: Approximately 6 minutes to classify 1 million product titles. This is equivalent to about 3000 titles per second.

    Example Inputs and Outputs from the SVM Model (with Decision Values)

    • Input: “Washing Machine Top Load”

    Output: {“Home Appliances”: 1.45, “Home and Living”: 0.60, “Tools and Hardware”: 0.54}

    • Input: “Nisi 58 mm Ultra Violet UV Filter”

    Output: {“Cameras and Accessories”: 1.46, “Eyewear”: 1.14, “Home and Living”: 1.12}

    • Input: “NETGEAR AirCard AC778AT Around Town Mobile Internet — Mobile hot”

    Output: {“Computers and Accessories”: 0.82, “Books”: 0.61, “Toys”: 0.27}

    • Input: “Nike Sports Tee”

    Output: {“Sports and Fitness”: 1.63, “Footwear”: 0.63, “Toys and Baby Products”: 0.59}

    Largely, most of the outputs were accurate, which is no mean feat. Some incorrect outputs were those of fairly similar categories. For example, “Home and Living” was predicted for products that should have ideally been part of “Home Appliances”. Other incorrect predictions occurred when the input was ambiguous.

    There were also scenarios where the output decision values of the top two categories were quite close (as shown in the third example above), especially when the input was vague. In the last example above, the product should have been classified as “Clothing”, but got classified as “Sports and Fitness” instead, which is not entirely incorrect.

    Delivering Value with Competitive Intelligence

    The category classifier elucidated in this article is only the first element of a universal product organization system that we’ve built at DataWeave. The output of our category classification system is used by other in-house machine-learning and heuristic-based systems to generate more detailed product categories, types, subcategories, attributes, and the like.

    Our universal product organization system is the backbone of the Competitive Pricing and Assortment Intelligence solutions we provide to online retailers, which enable them to evaluate their pricing and assortment against competitors along multiple dimensions, helping them compete effectively in the cutthroat eCommerce space.

    Click here to find out more about DataWeave’s solutions and how modern retailers harness the power of data to drive revenue and margins.

  • Advantage Flipkart: The Motives Behind Acquiring eBay India

    Advantage Flipkart: The Motives Behind Acquiring eBay India

    Flipkart recently acquired eBay’s India business in an announcement that made a huge splash across the country. With Flipkart already having acquired Myntra and Jabong, and talks of a Snapdeal acquisition picking up steam, this level of consolidation comes clearly as a direct response to internet behemoth Amazon’s aggressive expansion strategies in India.

    With this acquisition play, Flipkart stands to gain primarily on two fronts.

    eBay’s Seller Network

    Firstly, eBay has built a strong network of authorized and highly-rated global sellers, something that Flipkart can leverage to drive increased sales and market share.

    Per Flipkart’s announcement to the press — “Flipkart and eBay have signed an exclusive cross-border trade agreement, as a result of which customers of Flipkart will gain access to the wide array of global inventory on eBay, while eBay’s customers will have access to unique Indian inventory provided by Flipkart sellers. Thus, sellers on Flipkart will now have an opportunity to expand their sales globally.”

    At DataWeave, we ran our proprietary data aggregation and analysis algorithms over eBay’s websites and unearthed some interesting numbers about their seller network.

    eBay.com has a global network of 17,361 sellers, 41% of whom ship to India. Therefore, this acquisition opens the door for Flipkart to gain access to over 7000 global eBay.com sellers who ship to India — a huge boost to the range of products Flipkart can host on its platform.

    Additionally, a sizable chunk — 14% — of eBay.in sellers ship to international destinations. This provides Flipkart with opportunities to expand its reach globally.

    The other, rather lesser known advantage that Flipkart stands to gain from this acquisition is in the refurbished and pre-owned goods space.

    The Emergence of Refurbished and Pre-Owned Goods

    The market for refurbished and pre-owned products is estimated to be between $15 billion and $20 billion globally, with exponential growth forecast for the near future.

    Part of the reason for growth in this segment is it yields higher returns on investment for retailers. While a retailer typically earns 3–5% margin by selling a new smartphone, refurbished smartphones fetch 7–8% margin, and pre-owned smartphones 9–10%.

    The Hidden Advantage

    eBay has established itself over the years as a reliable source of refurbished and pre-owned products, with impressive levels of authentication and warranties. We did a quick analysis of eBay.in, Flipkart, and Amazon to identify their relative strengths in this space.

    Unsurprisingly, Flipkart has close to zero refurbished or pre-owned products hosted on their website. With Amazon, 12% of mobile phones and 9% of Books on their website are refurbished or pre-owned, the largest selling categories in this space.

    eBay.in, though, has a significant share of these products across categories — 95% of books & magazines, 36% of mobile phones, and 28% of televisions — a substantial portion of eBay’s business.

    With this acquisition, Flipkart can now take a gigantic step into the relatively more profitable and exponentially growing refurbished and pre-owned products space. It will also be a strong competitive differentiator for the company as they go head to head with Amazon in India.

    While the refurbished and pre-owned goods space poses a series of advantages for retailers, it sits well with consumer preferences as well, drawing more shoppers, and retaining existing ones.

    Influence of Shopping Behavior on Product Assortment

    Refurbished and pre-owned products provide consumers with attractive alternatives, both in terms of price and variety. Shoppers today explore and research new, pre-owned and refurbished products, all at the same time, and compare prices across e-commerce websites before deciding on a purchase.

    As a result, comprehensive product assortments across price ranges and attributes drive higher engagements, traffic and improve customer conversion and retention rates, as they cater to a more diverse set of consumers.

    For modern retailers, this reinforces the importance of investing in tools that enable to them to identify high-value gaps in their assortment and plug them. To achieve this, they need up-to-date, accurate data, at scale, on the assortments of their competitors.

    DataWeave’s Assortment Intelligence solution is designed to give retailers near-real-time insights on competing retailers’ product mix and suggests product additions to retailer catalogs.

    Click here to know more about how Assortment Intelligence can help your retail business manage assortment efficiently and profitably.

     

  • Baahubali 2: Dissecting 75,000 Tweets to Uncover Audience Sentiments

    Baahubali 2: Dissecting 75,000 Tweets to Uncover Audience Sentiments

    Why did Katappa kill Baahubali?

    Two years ago, not many would have foreseen this sentence capturing the imagination of the country like it has. Demolishing all regional barriers, the movie has grossed over INR 500 crores across the world in only its first three days.

    While the first movie received lavish praise for its ambition, technical values, and story, the sequel, bogged by bloated expectations, has polarized the critics fraternity. Some critics compare the movie’s computer graphics favorably to Hollywood productions like Lord of the Rings. Others find the movie lacking in pacing and plot.

    The masses, however, have reportedly lapped the movie up. Social media channels are brimming with opinions, and if one is to attempt finding out the aggregate views of audiences, Twitter is a good place to start.

    At DataWeave, we ran our proprietary, AI-powered ‘Sentiment Analysis’ algorithm over all tweets about Baahubali 2 the first three days of its release, and observed some interesting insights.

    Twitterati Reactions to Baahubali 2

    Overall, the Twitterati’s views on the movie were overwhelmingly positive. We analysed over 75,000 tweets and identified the sentiments expressed on several facets of the movie, such as, Visuals, Acting, Prabhas, etc. The following graphic indicates how the movie fared in some of these categories.

    The Baahubali team, Anushka (actor), Rajamouli (director), and Prabhas (actor), are all perceived as huge positive influences on the movie. Rajamouli, specifically, met with almost universal approval for his dedication and execution. Several viewers cheered the movie on as a triumph of Indian cinema, one which has redefined the cinema landscape of the country. There was considerable praise for the story, Rana (actor), and acting performances, as well.

    The not-so-positive sentiments were reserved for the reason behind Katappa killing Baahubali (no spoilers!), the visuals, and the second half of the movie. Many viewers found the second half to be slow, with unrealistic visuals and action sequences. For example, one of the tweets read:

    “First half was good, but the second half is beyond Rajnikanth movies: humans uprooting trees!”

    While these insights seem simple enough to understand, the technology to filter inevitably chaotic online content and extract meaningful information is incredibly complex.

    Unearthing Meaning from Chaos

    At DataWeave, we provide enterprises with Competitive Intelligence as a Service by aggregating and analyzing millions of unstructured data points on the web, across multiple sources. This enables businesses to better understand their competitive environment and make data-driven decisions to grow their business.

    One of our solutions — Sentiment Analysis — helps brands study customer preferences at a product attribute level by analyzing customer reviews. We used the same technology to analyze the reaction of audiences globally to Baahubali 2. After data acquisition, this process consists of three steps –

    Step 1: Features Extraction

    To identify the “features” that reviewers are talking about, we first understand the syntactical structure of the tweets and separate words into nouns, verbs, adjectives, etc. This needs to account for complexities like synonyms, spelling errors, paraphrases, noise, etc. Our AI-based technology platform then uses various advanced techniques to generate a list of “uni-features” and “compound features” (more than one word for a feature).

    Step 2: Identifying Feature-Opinion Pairs

    Next, we identify the relationship between the feature and the opinion. One of the reasons this is challenging with twitter is, most of the time, twitter users treat grammar with utter disdain. Case in point:

    “I saw the movie visuals awesome bad climax felt director unnecessarily dragged the second half”

    In this case, the feature-opinion pairs are visuals: awesome, climax: bad, second half: unnecessarily dragged. Clearly, something as simple as attributing the nearest opinion-word to the feature is not good enough. Here again, we use advanced AI-based techniques to accurately classify feature-opinion pairs.

    We classified close to 1000 opinion words and matched them to each feature. The infographic below shows groups of similar words that the AI algorithm clustered into a single feature, and the top positive and negative sentiments expressed by the Twitterati for each feature.

    While our technology can associate words with similar meaning, such as, ‘part after interval’ and ‘second half’, it can also identify spelling errors by identifying and grouping ‘Rajamouli’ and ‘Raajamouli’ as a single feature.

    Adjectives like ‘magnificent’ and ‘creative’ were used to describe the Baahubali team positively, while words like ‘boring’, ‘disappointed’, and ‘tiring’ were used to describe the second half of the movie negatively.

    Step 3: Sentiment Calculation

    Lastly, we calculate the sentiment score, which is determined by the strength of the opinion-word, number of retweets and the time of tweet. A weighted average is normalized and we generate a score on a scale of 0% to 100%.

    A Peephole into the Consumer’s Mind

    As more and more people express their views and opinions in the online world, there is more of an opportunity to use these data points to drive business strategies.

    Consumer-focused brands use DataWeave’s Sentiment Analysis solution as a key element of their product strategy, by reinforcing attributes with positive sentiments in reviews, and improving or eliminating attributes with negative sentiments in reviews.

    Click here to find out more about the benefits of using DataWeave’s Sentiment Analysis!

     

  • How to Survive the Loss of Brick & Mortar Retail Stores

    How to Survive the Loss of Brick & Mortar Retail Stores

    For years, the consumer electronics chain Radioshack has endeavored to stay alive in our ever-changing world. Despite their efforts, they have filed for bankruptcy for the second time, in as many years. As of now, the company is closing 200 of their 1,500 stores, slightly more than 13% of their locations

    This one-time retail “giant” isn’t alone on the path of reduction in force. Macy’s has announced that they will close 63 stores, and Sears will lock their doors for the final time on 150 of their stores this fiscal year.

    Brands too are feeling the heat. Ralph Lauren recently announced the closure of an unspecified number of stores (including its Polo store on Fifth Avenue, New York City), and a reduction in its workforce.

    The internet is impacting brick and mortar sales the way that Sears Roebuck and Montgomery Ward catalog mail order sales impacted the general store at the turn of the last century.

    Online Retail Plays the Spoiler

    The disruption of the retail industry following the onset of e-commerce is largely due to the change in shopping behavior. Shoppers today can sit at home and compare multiple retailers before making a purchase. This has a significant impact on consumer expectations and how retailers do business today.

    Smartphone apps make comparing prices, and downloading coupons simple. So, we now see e-retailers compete tooth-and-nail on price, and even willing to take the “loss leader” route to drive adoption. Consequently, consumers expect rock bottom prices. Many brick-and-mortar retailers like Walmart have responded by simply matching online prices.

    While there are tens of thousands of e-commerce companies in the world today, this disruption is led primarily by the behemoth of global retail — Amazon.

     

    The Torchbearer of Modern Retail

    Amazon’s retail business strategy rests on three pillars: price perception, broad assortments, and customer experience.

    Price has long been the primary driving factor in retail. Therefore, there is need to optimize price efficiently to drive revenue and margins. What Amazon has smartly done is to drive the perception among shoppers that the company is always the lowest priced, even though it’s untrue. They do this by ensuring they are the lowest priced in the top 20% selling SKUs by volume. The resulting perception among consumers is a key differentiator.

    Also, to deliver superior customer experience compared to competing retailers, Amazon ensures high quality of online catalogs, provides a wide selection of products, and offers fast shipping to a broad coverage area, at no additional cost.

    When you factor in the Amazon Prime service, consumers have become spoiled with receiving their purchases within 48 hours. Sunday deliveries, and scheduling within the hour means buyers are in the driving seat.

    Some of Amazon’s competitors are following suit. Mega box stores like Costco, in an endeavor to meet their customers’ desire for options, are partnering with Google Express to provide fast delivery of household items, apparel, electronics, pantry staples such as bread and cereal, and more.

    The message is clear — today’s brick-and-mortar retailers need to have an omni-channel approach to retail, and an online presence if they are to stay competitive and relevant. However, this move has its fair share of obstacles –

    The Challenge of Moving Online

    Brick-and-mortar retailers moving online are confronted with several questions that carry more weight today than they used to in the past:

    • How do I deliver a high-quality shopping experience?
    • How can I drive price perception among shoppers?
    • What products do I promote and when?
    • What product assortment do I build to drive sales and retention?
    • How do I manage my logistics to reduce shipping cost and time?

    Traditional retailers looked largely at only internal data — like POS data, product sell-through rates, inventory, etc. to answer these questions. Today, it is mission-critical for retailers to absorb and utilize external competitive data as well — and here lies the problem. When you are benchmarking yourself against the competition online, it is that much harder, as it’s more dynamic and significantly more complex than before.

    For example, Forbes estimated that through Christmas season in 2014, Amazon made a total of 80 million price changes per day to stay competitive. These are extraordinary numbers, and reflect how dynamic online retail is, and its contrast to traditional retail.

    Retailers today have no choice but to automate as much as possible, so they can make quick, timely merchandising decisions and keep pace with modern e-retail. Retail technology providers like DataWeave have stepped in to meet this demand.

    DataWeave’s Retail Intelligence

    At DataWeave, we enable retailers gain a competitive advantage in the online world by providing Competitive Intelligence as a Service. We do this by harnessing public information on the competition, structuring it, and presenting it in a form that is easily consumable and actionable, enabling easy, automated decision-making.

    Our AI-based technology platform facilitates smarter pricing decisions by providing retailers with price change (increase and decrease) opportunities as they occur. Retailers can also plug gaps in their product portfolio by identifying opportunities to expand their assortments. In addition, they can benchmark their shipping speed and cost against competition, to enhance customer experience. And there’s more where these come from!

    Click here to find out more about how we can help modern retailers stay competitive in the online world.

     

  • Dissonance in Online MRP Prices Across Retailers | DataWeave

    Dissonance in Online MRP Prices Across Retailers | DataWeave

    We all know, online shopping offers a lot of benefits to shoppers. Apart from the convenience it offers access to a wide-assortment base and, of course, discounts are an added benefit. Often we see, retailers claiming large discounts on products.

    Many-a-time, the percentage discount that is mentioned drives price perception. Customers when comparing prices across stores view larger percentage discounts as a better deal. However, this is not necessarily the case. To present this case, let us look into how discounts are calculated:

    Percentage discounts are a function of the MRP / MSRP and the Selling Price. The MRP / MSRP is set by the manufacturer and the selling price is more often than not determined by the retailer.

    Selling price of products being different across retailers is a well-known fact. When the MRP of the same products tend to vary across retailers, it gets confusing for a customer, which in turn leads to a brand equity dilution of the brand or manufacturer.

    To analyse how deep this discord is, we decided to dive deeper into its working dynamics. Amongst all the data that we aggregate at DataWeave, analysing discounts of the same product across retailers gives us the ability to discern pricing strategies of retailers. We used this dataset to monitor and analyse MRPs.

    What we found

    1. We analysed MRPs of around 400 brands across 10 categories. Around 44% of products in these brands have no variance in MRPs across retailers

    2. This also means there is a variance in 56% of products

    3. Products in the ‘Mobile Phones and Tablets’ category have the most price variance; 65% of the products have price variance

    4. Fashion and Fashion accessories have the least price variance; around 20%

    5. Brands having the most variance:

    6. Brands having the least variance:

    What are the implications of the above insights?

    1. Brands & manufacturers need to be aware of how their brand products are being represented and sold online
    2. Consumers shopping online need to look at end prices, and not focus on the discount percentage, before making a purchase-decision on a particular store

    This article was previously published on Yourstory

    DataWeaves Brand Intelligence provides consumer brands with the ability to track their products, pricing, discoverability vis-a-vis their competitors across e-commerce platforms.

  • DataWeave Wins 2016 BI Software Awards From FinancesOnline

    DataWeave Wins 2016 BI Software Awards From FinancesOnline

    After a thorough assessment of our product FinancesOnline, a well-known software review platform and SaaS leads generation source, awarded DataWeave Retail Intelligence with two of their prestigious industry awards. According to FinancesOnline, our specialized competitive intelligence product is a rare tool that handles different languages with ease, and it allows businesses to improve the margin of their products and be more competitive.

     

    Currently, DataWeave Retail Intelligence holds two of the platform’s prominent awards: the 2016 Great User Experience Award given to products which facilitate complex operations and allow users to navigate an easy and familiar interface; and the 2016 Expert’s Choice Award, confirming that DataWeave employs a variety of unique mechanisms to produce valuable competitors’ insights, compares and measures metrics that matter to every online store. Both awards were given for the platform’s business intelligence software reviews category.

     

    According to their DataWeave review here the experts believe DataWeave genuinely focused on making businesses more competitive instead of simply listing data that may not be actionable by the company. They were particularly fond of the advanced identification of weak and strong points, actionable insights, and assortment intelligence, but mentioned as well the positive aspects of combining internal analytics with market data the way DataWeave does it. They praised our efforts to surpass traditional functionality gaps arising from language and location restrictions, and seem to firmly believe that out well-planned integrations make DataWeave usable for all type of analysis. Continuing with this tempo, FinancesOnline’s B2B professional foresee DataWeave performing successfully in many areas other than retail.

     

  • Smart Practices for Pricing Products

    Smart Practices for Pricing Products

    Top pricing strategies for online retailers

    “When it comes to retail markets, law of one price is no law at all” — Hal Varian

    Hal Varian, in his seminal paper “A Model of Sales”, further remarks that most retail markets are instead characterized by a rather large degree of price dispersion.

    Do you know how much your products are worth? How low are you willing to price an item to compete with another ecommerce retailer?

    Today, online retail has become increasingly competitive. If you are priced higher than your competitors, you may end up losing customers who are sensitive to prices. With the advent of highly competitive pricing tools, winning the online pricing war is an uphill task. Having a differentiated competitive strategy is critical to your e-commerce success.

    We bring to you a list of smart practices that we have seen being played out across online retailers in 10 countries that we actively monitor and analyze.

    Analyzing Competitor Prices And Stock Availability

    Product pricing is one of the largest driver of profitability. So you know who your main competitors are, but do you know how they are priced? Compare prices and stock availability of products that are popular across all your competitors and do the same for products that are popular at your store. If you know that certain products are “not in stock”, you know you need not discount. Look at products that are popular across competition and know your price position. Try for an opportunity to increase prices without losing your price position. However, for products popular on your store, you may want to stay competitive.

    Knowing Price Variations

    You get the right price, and then it’s not right anymore. That’s the story of online retail. But when you are equipped with the knowledge of price variations on popular marketplaces, it gives you an idea of where the market is heading. This, in turn, will help you adjust your prices to get the consumers. For instance, Amazon changed prices of more than 50% of their products in Hair Care category more than once in a week including ~20% of the products at least 4 times in the same week.

    Product Bundling

    A marketer of a successful product may bundle a new or less successful product with its stronger product to edge its way into a new market. This allows you to charge a unique, competitive price that can’t be copied by others. If you realize that you may not be able to compete on direct discounts, bundle products together and offer them at a lower price. You can either bundle in multiples of the same product or pack different products together. One of the more famous examples of this is Microsoft’s bundling of various software applications. In the onsite retail space, for example, on a particular day we noticed ~400+ combo offers from SnapDeal in the camera & accessories category whereas PayTM has ~200+ combo offers and Amazon has ~3000+ combo offers in the same category. Similarly, in hair care category we observed significant variance in combo offers across marketplaces (~900+ by Amazon, ~250+ by PayTM and ~100 by SnapDeal on a specific day). We also noticed that marketplaces have varied number of products sold in packs across different brands (~2500 in Amazon, ~800+ in PayTM and ~500 in Snapdeal on a specific day).

    Shipping Fees & Delivery Time

    Free shipping attracts customers to e-commerce platforms like a moth to a flame. Monitor shipping fees across competition for products you are interested in. There will be cases where your competitor is pricing a product at a lower price than you, but does not offer free shipping. That is your signal to promote your platform.

    Price Match Guarantees

    Price match is an easy way for customers to save money on their day-to-day purchases. During Black Friday sales in the US, a lot of popular stores go for the price match guarantee feature to drive sales. It’s a smart trick to let your customers show you the lowest price and then match them accordingly.

    No Discounts On Unique Products

    No matter how much you dress it up, cutting prices hurts. It might be unavoidable, but you can get rid of discounts on unique products. When you analyze gaps and strengths of your catalog and realize that there are products that are available only on your store, why would you need to provide discounts? So, for instance, it seems that only Flipkart is carrying Icon LaserJet Pro Black Toner currently and it is being sold at 75% discount. Unless the objective is to get rid of the inventory, this product could be priced higher. Another example is, Nikon Coolpix S1100PJ Point & Shoot Camera is out of stock with most of the key marketplaces. Hence if anyone gets this replenished, this should not be discounted. Similarly, unique brands in hair care category, say LeModish, is sold primarily on PayTM. So, PayTM could look at reducing discount for this brand.

    Don’t Price Above Market Rate

    Some retailers price products above the market rate (MRP / MSRP) so that they can show substantial discounts. But your customers are smart and research well. If they realize that this is not really ‘low price’, you may end up losing them.

    Dynamic Pricing

    This is one trend you should definitely follow. Constantly monitor competitor prices and drop or increase prices whenever you see an opportunity. This process is highly tech-driven, so ensure that you work with a vendor who provides the same or you have the in-house capability to do this in a sustained and scalable manner.

    There are multiple product strategies that have to be considered, including cross-border commerce and highly spread out markets like SEA where there exists a lot more C2C marketplaces. However, as with many things in ecommerce, one size does not fit all. Combine the powers of your service and price to drive your bottom line and emerge as an undisputed leader in the retail space.

    Note: This article has been previously published on Inc42 and on Indian Retailer.

    DataWeave Retail Intelligence provides competitive intelligence solution to retailers. DataWeave’s solution is both language and geography agnostic and is built for significant scale

  • Introducing the new PriceWeave

    Introducing the new PriceWeave

    PriceWeave provides Competive Intelligence for eRetailers, brands, and manufacturers. Competitive Intelligence helps businesses understand their competition better, take timely decisions, and increase sales. Our retail pricing intelligence tool serves the following major purposes:

    Compare: PriceWeave lets you access products from across any number of sources and organize them for a straightforward apples-to-apples comparison.

    Monitor: Our intuitive dashboards help you monitor prices, assortments, products, brands, and deals across competition on a daily basis.

    Discover: Discover gaps in your product catalog. Discover products that are unique to you. Discover new brands and categories your competitors have introduced. Find new competitors.

    Analyze: Get customized alerts and reports on anything that you want to track. Access historical pricing data to understand pricing strategies. Visualize data across facets at different levels of granularity.

    If you are an eRetailer, PriceWeave powers your sales, marketing, and analytics team with actionable data–for both day to day operations, as well as long term strategy. With retail pricing intelligence, an eRetailer can:

    • understand pricing opportunitiesand implement an effective pricing strategy
    • get pricing variation for the products you are tracking across competition
    • get apples-to-apples product comparison and historical pricing data
    • optimize assortment planningthrough assortment intelligence
    • continuously monitor product assortment width and depth
    • understand gaps in your (and your competition’s) product catalog
    • manage featured products and promotions
    • develop overall sales and marketing strategy
    • big picture as well multi-dimensional faceted views: price bands, discount bands, categories, brands, and features

    If you are a brand or a manufacturer who sell your products through retailers, PriceWeave helps you as well. A Brand (or a Manufacturer) can:

    • ensure brand equity
    • monitor MOP violations and discover unauthorized resellers
    • increase market penetration
    • track retailer assortment across competing brand products.
    • discover new retailers — new distribution channels
    • increase engagement with retailers as well as customers
    • get regular reports on availability, pricing, offers, and discounts

    In short, PriceWeave is a product that gives you all the data and tools to help you gain and sustain an edge over your competition.

    For a demo of the product do reach out to us at (contact@dataweave.com). You can sign up for a free evaluation at dataweave.com.

  • Benefits of Competitive Marketing Intelligence | DataWeave

    Benefits of Competitive Marketing Intelligence | DataWeave

    In the aggressive business of online retail every detail you know about your competitor gives you an edge over them. To help you stay ahead of your competition we have designed a series of blog posts that familiarize you with competitive intelligence and equip you to get maximum mileage out of competitive intelligence tools. This is the first post of the series.

    Let’s begin at the beginning.

    What is Competitive Intelligence?

    Competitive intelligence (CI) is the gathering of publicly-available information about an enterprise’s competitors and the use of that information to gain a business advantage.

    Competitive marketing intelligence helps managers and executives to make data-driven decisions both in the short term, as well as formulate medium to long term strategy.

    Why is Competitive Intelligence important?

    Competitive marketing intelligence is critical because it helps businesses stay ahead of the competition by:

    1. Augmenting one’s experience and instincts with hard data and analyses on a regular basis
    2. Delivering reasonable assessments of one’s own business vis-a-vis competitors’ businesses
    3. Identifying and alerting new business opportunities as well as threats
    4. Helping shape short term and long term strategies to grow and consolidate one’s business

    How does Competitive Intelligence help achieve the core objectives of retail business?

    Retail is a particularly competitive sector. Given the volume of transactions that happen in the retail sector, even a slight improvement in metrics has a huge impact. Thus, competitive Intelligence has a direct effect on the bottom line. It helps in the following ways:

    > Improve margins

    This is a result of optimized pricing of products. Knowing the competitors pricing goes a long way in pricing your products right and improving margins. With Competitive Intelligence on your side, you can take pricing decisions backed by data.

    > Reduce customer acquisition costs

    By improving your assortment mix more users looking for products that your site offers become your users. This helps reduce customer acquisition costs. This also helps in retaining existing customers

    > Optimize marketing spend

    Competitive Intelligence brings more clarity and sharper objectives for the marketing team. You get good indicators which products/categories your competitors are promoting, and which new brands/categories they have introduced. This helps streamline and optimize your market spend.

    This is where DataWeave comes in. DataWeave provides Competitive Intelligence for retailers, brands, and manufacturers. DataWeave is built on top of huge amounts of product data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches.

    DataWeave is powered by distributed data crawling and processing engines that enables serving millions of data points around products data refreshed on a daily basis. This data is presented through dashboards, notifications, and reports. PriceWeave brings the ability to use BigData in compelling ways to retailers.

    DataWeave lets you track any number of products across any categories against your competitors. If you wish to try this out, just book a free discovery call with us.

    In the next few posts, we will dig deeper into DataWeave and introduce its major features. We will also talk about how each of these features help you in improving your business metrics.

  • Dataweave – Smartphones vs Tablets: Does size matter?

    Dataweave – Smartphones vs Tablets: Does size matter?

    Smartphones vs Tablets: Does size matter?

    We have seen a steady increase in the number of smartphones and tablets since the last five years. Looking at the number of smartphones, tablets and now wearables ( smart watches and fitbits ) that are being launched in the mobiles market, we can truly call this ‘The Mobile Age’.

    We, at DataWeave, deal with millions of data points related to products which vary from electronics to apparel. One of the main challenges we encounter while dealing with this data is the amount of noise and variation present for the same products across different stores.

    One particular problem we have been facing recently is detecting whether a particular product is a mobile phone (smartphone) or a tablet. If it is mentioned explicitly somewhere in the product information or metadata, we can sit back and let our backend engines do the necessary work of classification and clustering. Unfortunately, with the data we extract and aggregate from the Web, chances of finding this ontological information is quite slim.

    To address the above problem, we decided to take two approaches.

    • Try to extract this information from the product metadata
    • Try to get a list of smartphones and tablets from well known sites and use this information to augment the training of our backend engine

    Here we will talk mainly about the second approach since it is more challenging and engaging than the former. To start with, we needed some data specific to phone models, brands, sizes, dimensions, resolutions and everything else related to the device specifications. For this, we relied on a popular mobiles/tablets product information aggregation site. We crawled, extracted and aggregated this information and stored it as a JSON dump. Each device is represented as a JSON document like the sample shown below.

    { "Body": { "Dimensions": "200 x 114 x 8.7 mm", "Weight": "290 g (Wi-Fi), 299 g (LTE)" }, "Sound": { "3.5mm jack ": "Yes", "Alert types": "N/A", "Loudspeaker ": "Yes, with stereo speakers" }, "Tests": { "Audio quality": "Noise -92.2dB / Crosstalk -92.3dB" }, "Features": { "Java": "No", "OS": "Android OS, v4.3 (Jelly Bean), upgradable to v4.4.2 (KitKat)", "Chipset": "Qualcomm Snapdragon S4Pro", "Colors": "Black", "Radio": "No", "GPU": "Adreno 320", "Messaging": "Email, Push Email, IM, RSS", "Sensors": "Accelerometer, gyro, proximity, compass", "Browser": "HTML5", "Features_extra detail": "- Wireless charging- Google Wallet- SNS integration- MP4/H.264 player- MP3/WAV/eAAC+/WMA player- Organizer- Image/video editor- Document viewer- Google Search, Maps, Gmail,YouTube, Calendar, Google Talk, Picasa- Voice memo- Predictive text input (Swype)", "CPU": "Quad-core 1.5 GHz Krait", "GPS": "Yes, with A-GPS support" }, "title": "Google Nexus 7 (2013)", "brand": "Asus", "General": { "Status": "Available. Released 2013, July", "2G Network": "GSM 850 / 900 / 1800 / 1900 - all versions", "3G Network": "HSDPA 850 / 900 / 1700 / 1900 / 2100 ", "4G Network": "LTE 800 / 850 / 1700 / 1800 / 1900 / 2100 / 2600 ", "Announced": "2013, July", "General_extra detail": "LTE 700 / 750 / 850 / 1700 / 1800 / 1900 / 2100", "SIM": "Micro-SIM" }, "Battery": { "Talk time": "Up to 9 h (multimedia)", "Battery_extra detail": "Non-removable Li-Ion 3950 mAh battery" }, "Camera": { "Video": "Yes, 1080p@30fps", "Primary": "5 MP, 2592 x 1944 pixels, autofocus", "Features": "Geo-tagging, touch focus, face detection", "Secondary": "Yes, 1.2 MP" }, "Memory": { "Internal": "16/32 GB, 2 GB RAM", "Card slot": "No" }, "Data": { "GPRS": "Yes", "NFC": "Yes", "USB": "Yes, microUSB (SlimPort) v2.0", "Bluetooth": "Yes, v4.0 with A2DP, LE", "EDGE": "Yes", "WLAN": "Wi-Fi 802.11 a/b/g/n, dual-band", "Speed": "HSPA+, LTE" }, "Display": { "Multitouch": "Yes, up to 10 fingers", "Protection": "Corning Gorilla Glass", "Type": "LED-backlit IPS LCD capacitive touchscreen, 16M colors", "Size": "1200 x 1920 pixels, 7.0 inches (~323 ppi pixel density)" } }

    From the above document, it is clear that there are a lot of attributes that can be assigned to a mobile device. However, we would not need all of them for building our simple algorithm for labeling smartphones and tablets. I had decided to use the device screen size for separating out smartphones vs tablets, but I decided to take some suggestions from our team. After sitting down and taking a long, hard look at our dataset, Mandar had an idea of using the device dimensions also for achieving the same goal!

    Finally, the attributes that we decided to use were,

    • Size
    • Title
    • Brand
    • Device dimensions

    Screen sizeI wrote some regular expressions for extracting out the features related to the device screen size and resolution. Getting the resolution was easy, which was achieved with the following Python code snippet. There were a couple of NA values but we didn’t go out of our way to get the data by searching on the web because resolution varies a lot and is not a key attribute for determining if a device is a phone or a tablet.

    size_str = repr(doc["Display"]["Size"]) resolution_pattern = re.compile(r'(?:\S+\s)x\s(?:\S+\s)\s?pixels') if resolution_pattern.findall(size_str): resolution = ''.join([token.replace("'","") for token in resolution_pattern.findall(size_str)[0].split()[0:3]]) else: resolution = 'NA'

    But the real problems started when I wrote regular expressions for extracting the screen size. I started off with analyzing the dataset and it seemed that screen size was mentioned in inches so I wrote the following regular expression for getting screen size.

    size_str = repr(doc[“Display”][“Size”]) screen_size_pattern = re.compile(r'(?:\S+\s)\s?inches’) if screen_size_pattern.findall(size_str): screen_size = screen_size_pattern.findall(size_str)[0].split()[0] else: screen_size = ‘NA’

    However, I noticed that I was getting a lot of ‘NA’ values for many devices. On looking up the same devices online, I noticed there were three distinct patterns with regards to screen size. They are,

    • Screen size in ‘inches’
    • Screen size in ‘lines’
    • Screen size in ‘chars’ or ‘characters’

    Now, some of you might be wondering what on earth do ‘lines’ and ‘chars’ mean and how do they measure screen size. On digging it up, I found that basically both of them mean the same thing but in different formats. If we have ‘n lines’ as the screen size, it means, the screen can display at most ‘n’ lines of text at any instance of time. Likewise, if we have ‘n x m chars’ as the screen size, it means the device can diaplay ‘n’ lines of text at any instance of time with each line having a maximum of ‘m’ characters. The picture below will make things more clear. It represents a screen of 4 lines or 4 x 20 chars.

    Thus, the earlier logic for extracting screen size had to be modified and we used the following code snippet. We had to take care of multiple cases in our regexes, because the data did not have a consistent format.

    Thus, the earlier logic for extracting screen size had to be modified and we used the following code snippet. We had to take care of multiple cases in our regexes, because the data did not have a consistent format.

    size_str = repr(doc["Display"]["Size"]) screen_size_pattern = re.compile(r'(?:\S+\s)\s?inc[h|hes]') if screen_size_pattern.findall(size_str): screen_size = screen_size_pattern.findall(size_str)[0] .replace("'","").split()[0]+' inches' else: screen_size_pattern = re.compile(r'(?:\S+\s)\s?lines') if screen_size_pattern.findall(size_str): screen_size = screen_size_pattern.findall(size_str)[0] .replace("'","").split()[0]+' lines' else: screen_size_pattern = re.compile(r'(?:\S+\s)x\s(?:\S+\s)\s?char[s|acters]') if screen_size_pattern.findall(size_str): screen_size = screen_size_pattern.findall(size_str)[0] .replace("'","").split()[0]+' lines' else: screen_size = 'NA'

    Mandar helped me out with extracting the ‘dimensions’ attribute from the dataset and performing some transformations on it to get the total volume of the phone. It was achieved using the following code snippet.

    dimensions = doc['Body']['Dimensions'] dimensions = re.sub (r'[^\s*\w*.-]', '', dimensions.split ('(') [0].split (',') [0].split ('mm') [0]).strip ('-').strip ('x') if not dimensions: dimensions = 'NA' total_area = 'NA' else: if 'cc' in dimensions: total_area = dimensions.split ('cc') [0] else: total_area = reduce (operator.mul, [float (float (elem.split ('-') [0])/10) for elem in dimensions.split ('x')], 1) total_area = round(float(total_area),3)

    We used PrettyTable to output the results in a clear and concise format.

    Next, we stored the above data in a csv file and used Pandas, Matplotlib, Seaborn and IPython to do some quick exploratory data analysis and visualizations. The following depicts the top ten brands with the most number of mobile devices as per the dataset.

    Then, we looked at the device area frequency for each brand using boxplots as depicted below. Based on the plot, it is quite evident that almost all the plots are right skewed, with a majority of the distribution of device dimensions (total area) falling in the range [0,150]. There are some notable exceptions like ‘Apple’ where the skew is considerably less than the general trend. On slicing the data for the brand ‘Apple’, we noticed that this was because devices from ‘Apple’ have an almost equal distribution based on the number of smartphones and tablets, leading to the distribution being almost normal.

    Based on similar experiments, we noticed that tablets had larger dimensions as compared to mobile phones, and screen sizes followed that same trend. We made some quick plots with respect to the device areas as shown below.

    Now, take a look at the above plots again. The second plot shows the distribution of device areas in a kernel density plot. This distribution resembles a Gaussian distribution but with a right skew. [Mandar reckons that it actually resembles a Logistic distribution, but who’s splitting hairs, eh? ;)] The histogram plot depicts the same, except here we see the frequency of devices vs the device areas. Looking at it closely, Mandar said that the bell shaped curve had the maximum number of devices and those must be all the smartphones, while the long thin tail on the right side must indicate tablets. So we set a cutoff of 160 cubic centimeters for distinguishing between phones and tablets.

    We also decided to calculate the correlation between ‘Total Area’ and ‘Screen Size’ because as one might guess, devices with larger area have large screen sizes. So we transformed the screen sizes from textual to numeric format based on some processing, and calculated the correlation between them which came to be around 0.73 or 73%

    We did get a high correlation between Screen Size and Device Area. However, I still wanted to investigate why we didn’t get a score close to 90%. On doing some data digging, I noticed an interesting pattern.

    After looking at the above results, what came to our minds immediately was: why do phones with such small screen sizes have such big dimensions? We soon realized that these devices were either “feature phones” of yore or smartphones with a physical keypad!

    Thus, we used screen sizes in conjunction with dimensions for labeling our devices. After a long discussion, we decided to use the following logic for labeling smartphones and tablets.

    device_class = None if total_area >= 160.0: device_class = 'Tablet' elif total_area < 160.0: device_class = 'Phone' if 'lines' in screen_size: device_class = 'Phone' elif 'inches' in screen_size: if float(screen_size.split()[0]) < 6.0: device_class = 'Phone'

    After all this fun and frolic with data analysis, we were able to label handheld devices correctly, just like we wanted it!

    Originally published at blog.priceweave.com.

  • Why is Product Matching Difficult? | DataWeave

    Why is Product Matching Difficult? | DataWeave

    Product Matching is a combination of algorithmic and manual techniques to recognize and match identical products from different sources. Product matching is at the core of competitive intelligence for retail. A competitive intelligence product is most useful when it can accurately match products of a wide range of categories in a timely manner, and at scale.

    Shown below is PriceWeave’s Products Tracking Interface, one of the features where product matching is in action. The Products Tracking Interface lets a brand or a retailer track their products and monitor prices, availability offers, discounts, variants, and SLAs on a daily (or a more frequent) basis.

     

    A snapshot of products tracked for a large online mass merchant

     

    Expanded view for a product shows the prices related data points from competing stores

    Product Matching helps a retailer or a brand in several ways:

    • Tracking competitor prices and stock availability
    • Organizing seller listings on a marketplace platform
    • Discovering gaps in product catalog
    • Filling the missing attributes in product catalog information
    • Comparing product life cycles across competitors

    Given its criticality, every competitive intelligence product strives hard to make its product matching accurate and comprehensive. It is a hard problem, and one that cannot be complete addressed in an automated fashion. In the rest of this post, we will talk about why product matching is hard.

    Product Matching Guidelines

    Amazon provides a guideline to sellers about how they should write product catalog information in order to achieve a good product matching with respect to their seller listings. These guidelines apply to any retail store or marketplace platform. The trouble is, more often than not these guidelines are not followed, or cannot by retailers because they don’t have access to all the product related information. Some of the challenges are:

    • Products either don’t have a UPC code or it is not available. There are also non-standard products, unbranded products, and private label products.
    • There are products with slights variations in technical specifications, but the complete specs are not available.
    • Retailers manage a huge catalog of accessories, for instance Electronics Accessories (screen guards, flip covers, fancy USB drives, etc.).
    • Apparels and Lifestyle products often have very little by way of unique identifiers. There is no standard nomenclature for colors, material and style.
    • Products are often bundled with accessories or other related products. There are no standard ways of doing product bundling.

    In the absence of standard ways of representing products, every retailer uses their own internal product IDs, product descriptions, and attribute names.

    Algorithmic Product Matching using “Document Clustering”

    Algorithmic product matching is done using some Machine Learning, typically techniques from Document Clustering. A document is a text document or a web page, or a set of terms that usually occur within a “context”. Document clustering is the process of bringing together (forming clusters of) similar documents, and separating our dissimilar ones. There are many ways of defining similarity of documents that we will not delve into in this post. Documents have “features” that act as “identifiers” that help an algorithm cluster them.

    A document in our case is a product description — essentially a set of data points or attributes we have extracted from a product page. These attributes include: title, brand, category, price, and other specs. Therefore, these are the attributes that help us cluster together similar products and match products. The quality of clustering — that is how accurate and how complete the clusters are — depends on how good the features are. In our case, most of the times the features are not good, and that is what makes clustering, and in turn product matching, a hard problem.

    Noisy Small Factually Weak (NSFW) Documents

    The documents that we deal with, the product descriptions, are not well formed and so not readily usable for product matching. We at PriceWeave characterize them endearignly as Noisy Weak and Factually Weak (NSFW) documents. Let us see some examples to understand these terms.

    Noisy

    • Spelling errors, non-standard and/or incomplete representations of product features.
    • Brands written as “UCB” and “WD” instead of “United Colors of Benetton” and “Western Digital”.
    • Model no.s might or might not be present. A camera’s model number written as one of the following variants: DSC-WX650 vs DSCWX650 vs DSC WX 650 vs WX 650.
    • Noisy/meaningless terms might be present (“brand new”, “manufacturer’s warranty”, “with purchase receipt”)

    Small

    • Not much description. A product simply written as “Apple iPhone” without any mention of its generation, or other features.
    • Not many distinguishable features. Example, “Samsung Galaxy Note vs Samsung Galaxy Note 2”, “Apple ipad 3 16 GB wifi+cellular vs Apple ipad mini 16 GB wifi-cellular”

    Factually Weak

    • Products represented with generic and subjective descriptions.
    • Colours and their combinations might be represented differently. Examples, “Puma Red Striped Bag”, “Adidas Black/Red/Blue Polo Tshirt”.

    In the absence of clean, sufficient, and specific product information, the quality of algorithmic matching suffers. Product matching include many knobs and switches to adjust the weights given to different product attributes. For example, we might include a rule that says, “if two products are identical, then they fall in the same price range.” While such rules work well generally, they vary widely from category to category and across geographies. Further, adding more and more specific rules will start throwing off the algorithms in unexpected ways rendering them less effective.

    In this post, we discussed the challenges posed by product matching that make it a hard problem to crack. In the next post, we will discuss how we address these challenges to make PriceWeave’s product matching robust.

    PriceWeave is an all-around Competitive Intelligence product for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide real-time actionable insights. PriceWeave’s offerings include: pricing intelligence, assortment intelligence, gaps in catalogs, and promotion analysis. Please visit PriceWeave to view all our offerings. If you’d like to try us out request for a demo.

    Originally published at blog.priceweave.com.

  • How Colors Influence Consumer Buying Patterns | DataWeave

    How Colors Influence Consumer Buying Patterns | DataWeave

    Research shows that the colour of the clothes we wear significantly affect our day to day lives. For instance wearing black might help us appear powerful and authoritative at the workplace, while a red dress can make us look more attractive to a date. A yellow top might brighten up one’s day and a blue one land us a nifty bonus.

    Oftentimes buyers navigating the myriad nuances of current fashion look for help from friends, popular media and retailers themselves. Retailers, for their part, try to stay ahead of fashion trends by meticulously studying trends from magazines, keeping a close eye on competitors and wading through the chatter on social media and fashion blogs.

    Now that most of retail is metrics driven and becoming smarter by the day, we asked ourselves whether there is a more optimal way to analyse the influence of colors on customer buying decisions. Here’s how we went about doing it:

    Method:

    Thanks to the internet, a huge mine of valuable fashion data is available to us through e-commerce sites, brand Pinterest pages and fashion blogs, which regularly update their content streams with the newest fashion offerings. Data ranging from featured fashion of the current season including the complete product catalogue of brands as well as combinations of dresses that go together (even between brands) are all available for us to collect and analyse.

    By crawling these sites, pages and blogs periodically we can extract the colors on each of the images shared. This data is very helpful for any online/offline merchant to visualize the current trend in the market and plan out their own product offering. It is also possible to plot monthly data to capture the timeline of trends across different fashion websites.

    How is it Useful?

    Let us assess the applications made possible from this data. How would color analysis assist product managers, category heads and merchandising heads?

    1.Spotting current trends:

    Color analysis can spot current trends across brands and various filters. This gives decision makers the ability to gauge and respond to current trends and offerings. Some filters that can be used to analyse this are price, colors, categories, subcategories etc

    2.Predictive trends:

    Using historical color data future trends can be spotted with greater accuracy. With this data decision makers can stay ahead of the demands and the predictions of the market and gain a foothold on the ever changing nature of fashion.

    3.Assortment Analysis:

    Assortment Analysis can become more in depth and insightful with color analysis. Assortment comparisons of one’s offerings v/s competitor’s offerings can give a clear cut decision pointers on both one’s color offerings present and categories one can focus on to get ahead of the competition.

    4.Recommendations

    A strong recommendation feature is vital in driving up sales by offering the right products to buyers at the right time. Analysis of colors helps recommendations become smarter and more relevant. For instance, the algorithm can help understand what tops go with which jeans or which shirts go with what ties.

    Colours add a new dimension to current business analytics. Decision makers will be able to access enhanced analytics on existing products and compare across sources based on parameters such as price, categories, subcategories etc.

    Color Analysis in retail is largely unexplored and rife with possibilities. Doing it at scale presents a number of unique challenges that we are addressing. We’re excited to bring novel techniques and the power of large scale data analytics to retail.

    Color analysis will add to a retailer’s understanding of consumer buying patterns. This will help retailers sell better and improve profit margins. We are currently working on integrating this feature into PriceWeave so that our customers can do a comparative assortment analysis with color as an additional dimension.

    About Priceweave:

    PriceWeave provides Competitive Intelligence for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches. PriceWeave lets you track any number of products across any number of categories against your competitors. If you’d like to try us out request for a demo.

    Originally published at blog.priceweave.com.

     

  • Tips on How to Price Your Products | DataWeave

    Tips on How to Price Your Products | DataWeave

    Picture this. You’re approaching the biggest sale of the year for your business, the number of offerings are ever growing and your competitors are inching in on your turf. How then are you to tackle the complex & challenging task of pricing your offerings? In short how do you know if the price is right?

    Here’s how we think it’s possible:

    1. Prioritize your objectives

    Pricing can be modified based on your priorities. A good pricing intelligence tool lets you understand pricing opportunities across different dimensions (categories/brands, etc.). Which categories do you want to score on? Which price battles do you choose to fight? Once you have decided your focus areas, you can make pricing decisions accordingly.

    2. Trading off margins for market share (or vice versa)

    Trading off profits for larger market shares often decreases overhead and increases profits due to network effects. This means that the value of your offerings increases as more people use them (e.g., the iOS or the Windows platform). If margins are crucial do not hesitate to make smart and aggressive pricing decisions using inputs from pricing intelligence tools.

    3. Avoiding underpricing and overpricing

    Underpricing brings down the bottom line and overpricing alienates customers. Walking the thin line between these is both an art and a science. An effective path to a balanced pricing is employing a pricing intelligence tool. A pricing intelligence tool helps you in getting the price right with ease for any number of your products.

    4. Understanding consumers and balancing costs

    Who IS your buyer? How much is she willing to shell out for the products you are selling? How much should you mark up your products to recuperate your costs? What can you do retain your consumers and attract new ones? What steps are my competitors taking to achieve this (discounts/combos/coupons/loyalty points)? Answer these questions and you are closer to the ideal price.

    5. Monitor competition

    The simplest and the most effective way to price your product right is to monitor your competitors. Every pricing win contributes to your profits and boosts your bottom line. Competitive Intelligence products let you monitor your products across any of your competitors.

    Conclusion

    There are many tips on how to price your products. An effective pricing tool goes a long way in helping you determine the right price for your products. It augments your experience, intuition, and your internal analytics with solid competitive pricing data.

    Why not give pricing intelligence a test ride then? Email us today at contact@dataweave.in to get started.

    About PriceWeave

    PriceWeave provides Competitive Intelligence for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches. PriceWeave lets you track any number of products across any number of categories against your competitors. If you’d like to try us out request for a demo.

    Originally published at blog.priceweave.com.

  • Benefits of Assortment Intelligence

    Benefits of Assortment Intelligence

    In retail, product assortment plays a critical role in selling effectively. It impacts the everyday decision making of category managers, brand managers, the merchandising, planning, and logistics teams. A good assortment mix helps achieve the following objectives:

    1. Reduce acquisition costs for new customers (as well as retain existing customers)
    2. Increase penetration by catering to a variety of customer segments
    3. Optimize planning and inventory management costs.

    Increasingly, retailers are moving away from a generic one-size-fits all assortment planning model, to a more dynamic and data driven approach. As a result, assortment benchmarking followed by assortment planning are activities that take place round the year. The breadth and depth of one’s assortment achieved through assortment benchmarking can define how and when products get bought.

    A number of factors are crucial for assortment planning: analytics over internal data, intuition, experience, and understanding gained through trends. In addition to these, tracking assortment changes on competitors’ websites helps retailers track and adjust their product mix by adjusting features such as brands, colors, variants, and pricing. The goal is to help users find exactly what they are looking for, the moment they are looking for it.

    Let’s see how we can achieve this through Assortment Intelligence tools in a moment. But first, some basics.

    What is Assortment Intelligence?

    Assortment intelligence refers to online retailers tracking, analysing a competitor’s assortment, and benchmarking it against one’s one assortment. Assortment intelligence tools make this process efficient. A good assortment intelligence tool such as PriceWeave gives you information the breadth and depth of your competitors’ assortment across categories and brands. It helps you analyze assortment through different lenses: colors, variants, sizes, shapes, and other technical specifications. With the help of an assortment intelligence tool, a retailer can get a good understanding about what products competitors have, how they perform and whether they should add these products to their existing catalog.

    Who uses Assortment Intelligence?

    Assortment tracking is used by retailers operating across categories as varied as footwear, electronics, jewelry, household goods,appliances, accessories, tools, handbags, furniture, clothing, baby products, and books among others.

    Some Uses of Assortment Intelligence

    Gaps in Catalog: Discover products/brands your competitors are offering that are not on your catalog, and add them.

    Unique Offerings: Find products/brands that only you are offering and decide whether you are pricing them right. May be you want to bump up their prices.

    Compare and analyze product assortment across dimensions: Benchmark your assortments across different dimensions and combinations thereof. Understand your as well as competitors’ focus areas. You can do this in aggregate as well as at the category/brand/feature level. Below we show a few examples.

    Effectively measure discount distributions across brands and/or sources. Understand your competitors’ “sweet spots” in terms of discounts.

    Understand assortment spread across price ranges. Are you focusing on all price ranges or only a few? Is that a decision you made consciously?

    Deep dive using smart filters — monitor specific competitors, brands and sets of products with filters such as colors, variants, sizes and other product features.

    Why do it?

    Assortment Intelligence not only increases sales and improves margins, but also helps reduce planning and inventory costs. It allows retailers to strike the right balance between assortment and inventory while maximizing sales. Retailers can take informed decisions by analyzing one’s own as well as competitors’ assortments. Businesses gain an edge by identifying opportunities around changes in product mix and make quick decisions. By identifying areas that need focus, and taking timely actions, an assortment intelligence tool will help improve the bottom line.

    What does PriceWeave bring in?

    With a feature-rich product such as PriceWeave, you can do all of the above and more everyday (or more frequently if you like). In addition, you can get all assortment related data as reports in case you want to do your own analysis. You can also set alerts on any changes that you want to track.

    PriceWeave lets you drill down as deep as you like. Assortments do not have to be based on high level dimensions or standard features like colors and sizes. You can analyze assortments based on technical specs of products (RAM size, cloth material, style, shape, etc.) or their combinations.

    Assortment Intelligence is an important part of the PriceWeave offering. If you’d like us to help you make smarter assortment intelligence decisions talk to us

    About Priceweave

    PriceWeave provides Competitive Intelligence for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches. PriceWeave lets you track any number of products across any number of categories against your competitors. If you’d like to try us out request for a demo.

    Originally published at blog.priceweave.com.

  • Analyzing Social Trends Data from Google & YouTube

    Analyzing Social Trends Data from Google & YouTube

    In today’s world dominated by technology and gadgets, we often wonder how well a particular product or technology is being perceived by society and if it is truly going to leave a long lasting impression. We regularly see fan wars breaking out between Apple and Android fanatics (no offence to Windows mobile lovers!) on social media where each claim that they are better than the rest. Another thing we notice quite often is that whenever a new product is launched or in the process of being launched, it starts trending on different social media websites.

    This made me wonder if there was a way to see some of these trends and the impact it is causing on social media. There are different social media channels where people post a wide variety of content ranging from personal opinions to videos and pictures. A few of the popular ones are listed below.

    • Facebook
    • Twitter
    • YouTube
    • Instagram
    • Pinterest

    Today, I will discuss two such ways we can do this, namely Google Trendsand YouTube. If you want to know how we can perform data mining using Twitter, refer to my earlier post “Building a Twitter Sentiment Analysis App using R” which deals with getting data from Twitter and analyzing it.

    Now, we will be looking at how easy it is to visualize trending topics on Google Trends without writing a single line of code. For this, you need to go to the Google Trends website. On opening it, you will be greeted by an interactive dashboard, showing the current trending topics summarized briefly just like the snapshot shown below. You can also click on any particular panel to explore it in detail.

    This is not all that Google Trends has to offer. We can also customize visualizations to see how specific topics are trending across the internet by specifying then in the interface and the results are shown in the form of a beautiful visualization. Google gets the data based on the number of times people have searched for it online. A typical comparison of people’s interest in different mobile operating systems over time is shown below.

    Interestingly, from the above visualization, we see that ‘Windows Mobile’ was quite popular from 2007 till mid 2009 when the popularity of ‘Android’ just skyrocketed. Apple’s ‘iOS’ gained popularity sometime around 2010. One must remember however that this data is purely based on data tracked by Google searches.

    Coming to YouTube, it is perhaps the most popular video sharing website and I am sure all of you have at least watched a video on YouTube. Interestingly, we can also get a lot of interesting statistics from these videos besides just watching them, thanks to some great APIs provided by Google.

    In the next part, I will discuss how to get interesting statistics from YouTube based on a search keyword and do some basic analysis. I won’t be delving into the depths of data analytics here but I will provide you enough information to get started with data mining from YouTube. We will be using Google’s YouTube Data API, some Python wrappers for the same and the pandasframework to analyze the data.

    First, we would need to go to the Google Developers Console and create a new project just like the snapshot shown below.

    Once the project is created, you will be automatically re-directed to the dashboard for the project: There you can choose to enable the APIs you want for your application. Go to the APIs section on the left and enable the YouTube Data API v3 just like it is depicted in the snapshot below (click it if you are unable to make out the text in the image).

    Now, we will create a new API key for public API access. For this, go to the Credentials section and click on Create new key and choose the Server key option and create a new API key which is shown in the snapshot below (click to zoom the image).

    We will be using some Python libraries so open up your terminal or command prompt and install the following necessary libraries if you don’t have them.

    [root@dip]# pip install google-api-python-client [root@dip]# pip install pandas

    Now that the initial setup is complete, we can start writing some code! Head over to your favorite Python IDE or console and use the following code segment to build a YouTube resource object.

    from apiclient.discovery import build from apiclient.errors import HttpError import pandas as pd DEVELOPER_KEY = "REPLACE WITH YOUR KEY" YOUTUBE_API_SERVICE_NAME = "youtube" YOUTUBE_API_VERSION = "v3" youtube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION, developerKey=DEVELOPER_KEY)

    Once this is complete, we will be using this YouTube resource object to search for videos with the android keyword. For this we will be using the search method to query YouTube and after getting back a list of results, we will be storing each result to its appropriate list, and then display the lists of matching videos, channels, and playlists using the following code segment.

    search_response = youtube.search().list( q="android", part="id,snippet", maxResults=50 ).execute() videos = [] channels = [] playlists = [] for search_result in search_response.get("items", []): if search_result["id"]["kind"] == "youtube#video": videos.append("%s (%s)" % (search_result["snippet"]["title"], search_result["id"]["videoId"])) elif search_result["id"]["kind"] == "youtube#channel": channels.append("%s (%s)" % (search_result["snippet"]["title"], search_result["id"]["channelId"])) elif search_result["id"]["kind"] == "youtube#playlist": playlists.append("%s (%s)" % (search_result["snippet"]["title"], search_result["id"]["playlistId"]))

    Based on the query I ran, most of the results seemed to be videos. The output I obtained is shown in the snapshot below.

    Since the playlists and channels we obtained are very less in number, I decided to go ahead and analyze the videos obtained. For that, we create a dict of video identifiers and video names. Then we pass a query to the YouTube API’s videos method, to get the relevant statistics for each video.

    videos = {} for search_result in search_response.get("items", []): if search_result["id"]["kind"] == "youtube#video": videos[search_result["id"]["videoId"]] = search_result["snippet"]["title"] video_ids_list = ','.join(videos.keys()) video_list_stats = youtube.videos().list( id=video_ids_list, part='id,statistics' ).execute()

    I know you must be interested by now to see what kind of data is present in video_list_stats. So for that, I will show you the relevant statistics obtained for a video from the API in the following snapshot.

    Now we will be using pandas to analyze this data. For that, the following code segment is used, to get this data into a pandas data frame.

    df = [] for item in videos_list_stats['items']: video_dict = dict(video_id = item['id'], video_title = videos[item['id']]) video_dict.update(item['statistics']) df.append(video_dict) df = pd.DataFrame.from_dict(df)

    Now, we can view the contents of this data frame. I will be showing the output of the first few rows with the relevant columns in the snapshot below. I have considered only the important data points which include viewCount, likeCount, dislikeCount, commentCount indicating the number of views, likes, dislikes and comments on the videos respectively.

    Once we have this table of clean and formatted data, we can do all sorts of analytics on it, like getting the mean and median for number of views, seeing which are really popular videos and so on. Some examples with required code segments are depicted below. I have used the IPython shell for analyzing the data.

    Mean and Median of different counts

    Top ten most viewed videos

    Top ten most liked videos

    Line chart showing counts of likes, views and comments

    Thus you can see by now that a lot of interesting analysis and visualizations can be built on top of this data. For more details on how to customize and use the Youtube API with Python, you can refer to this page for sample code segments.

    Originally published at blog.dataweave.in.

  • How to Build a Twitter Sentiment Analysis App Using R

    How to Build a Twitter Sentiment Analysis App Using R

    Twitter, as we know, is a highly popular social networking and micro-blogging service used by millions worldwide. Each status or tweet as we call it is a 140 character text message. Registered users can read and post tweets, but unregistered users can only view them. Text mining and sentiment analysis are some of the hottest topics in the analytics domain these days. Analysts are always looking to crunch thousands of tweets to gain insights on different topics, be it popular sporting events such as the FIFA World Cup or to know when the next product is going to be launched by Apple.

    Today, we are going to see how we can build a web app for doing sentiment analysis of tweets using R, the most popular statistical language. For building the front end, we are going to be using the ‘Shiny’ package to make our life easier and we will be running R code in the backend for getting tweets from twitter and analyzing their sentiment.

    The first step would be to establish an authorized connection with Twitter for getting tweets based on different search parameters. For doing that, you can follow the steps mentioned in this document which includes the R code necessary to achieve that.

    After obtaining a connection, the next step would be to use the ‘shiny’ package to develop our app. This is a web framework for R, developed by RStudio. Each app contains a server file ( server.R ) for the backend computation and a user interface file ( ui.R ) for the frontend user interface. You can get the code for the app from my github repository here which is fairly well documented but I will explain the main features anyway.

    The first step would be to develop the UI of the application, you can take a look at the ui.R file, we have a left sidebar, where we take input from the user in two text fields for either twitter hashtags or handles for comparing the sentiment. We also create a slider for selecting the number of tweets we want to retrieve from twitter. The right panel consists of four tabs, here we display the sentiment plots, word clouds and raw tweets for both the entities in respective tabs as shown below.

    Coming to the backend, remember to also copy the two dictionary files, ‘negative_words.txt’ and ‘positive_words.txt’ from the repository because we will be using them for analyzing and scoring terms from tweets. On taking a close look at the server.R file, you can notice the following operations taking place.

    – The ‘TweetFrame’ function sends the request query to Twitter, retrieves the tweets and aggregates it into a data frame. — The ‘CleanTweets’ function runs a series of regexes to clean tweets and extract proper words from them. — The ‘numoftweets’ function calculates the number of tweets. — The ‘wordcloudentity’ function creates the word clouds from the tweets. — The ‘sentimentalanalysis’ and ‘score.sentiment’ functions performs the sentiment analysis for the tweets.

    These functions are called in reactive code segments to enable the app to react instantly to change in user input. The functions are documented extensively but I’ll explain the underlying concept for sentiment analysis and word clouds which are generated.

    For word clouds, we get the text from all the tweets, remove punctuation and stop words and then form a term document frequency matrix and sort it in decreasing order to get the terms which occur the most frequently in all the tweets and then form a word cloud figure based on those tweets. An example obtained from the app is shown below for hashtags ‘#thrilled’ and ‘#frustrated’.

    For sentiment analysis, we use Jeffrey Breen’s sentiment analysis algorithm cited here, where we clean the tweets, split tweets into terms and compare them with our positive and negative dictionaries and determine the overall score of the tweet from the different terms. A positive score denoted positive sentiment, a score of 0 denotes neutral sentiment and a negative score denotes negative sentiment. A more extensive and advanced n-gram analysis can also be done but that story is for another day. An example obtained from the app is shown below for hashtags ‘#thrilled’ and ‘#frustrated’.

    After getting the server and UI code, the next step is to deploy it in the server, we will be using shinyapps.io server which allows you to host your R web apps free of charge. If you already have the code loaded up in RStudio, you can deploy it from there using the ‘deployApp()’ command.

    You can check out a live demo of the app.

    It’s still under development so suggestions are always welcome.

  • A Peek into GNU Parallel

    A Peek into GNU Parallel

    GNU Parallel is a tool that can be deployed from a shell to parallelize job execution. A job can be anything from simple shell scripts to complex interdependent Python/Ruby/Perl scripts. The simplicity of ‘Parallel’ tool lies in it usage. A modern day computer with multicore processors should be enough to run your jobs in parallel. A single core computer can also run the tool, but the user won’t be able to see any difference as the jobs will be context switched by the underlying OS.

    At DataWeave, we use Parallel for automating and parallelizing a number of resource extensive processes ranging from crawling to data extraction. All our servers have 8 cores with capability of executing 4 threads in each. So, we experienced huge performance gain after deploying Parallel. Our in-house image processing algorithms used to take more than a day to process 200,000 high resolution images. After using Parallel, we have brought the time down to a little over 40 minutes!

    GNU Parallel can be installed on any Linux box and does not require sudo access. The following command will install the tool:

    (wget -O - pi.dk/3 || curl pi.dk/3/) | bash

    GNU Parallel can read inputs from a number of sources — a file or command line or stdin. The following simple example takes the input from the command line and executes in parallel:

    parallel echo ::: A B C

    The following takes the input from a file:

    parallel -a somefile.txt echo

Or STDIN:


cat somefile.txt | parallel echo

    The inputs can be from multiple files too:

    parallel -a somefile.txt -a anotherfile.txt echo

    The number of simultaneous jobs can be controlled using the — jobs or -j switch. The following command will run 5 jobs at once:

    parallel --jobs 5 echo ::: A B C D E F G H I J

    By default, the number of jobs will be equal to the number of CPU cores. However, this can be overridden using percentages. The following will run 2 jobs per CPU core:

    parallel --jobs 200% echo ::: A B C D

    If you do not want to set any limit, then the following will use all the available CPU cores in the machine. However, this is NOT recommended in production environment as other jobs running on the machine will be vastly slowed down.

    parallel --jobs 0 echo ::: A B C

    Enough with the toy examples. The following will show you how to bulk insert JSON documents in parallel in a MongoDB cluster. Almost always we need to insert millions of document quickly in our MongoDB cluster and inserting documents serially doesn’t cut it. Moreover, MongoDB can handle parallel inserts.

    The following is a snippet of a file with JSON document. Let’s assume that there are a million similar records in the file with one JSON document per line.

    {“name”: “John”, “city”: “Boston”, “age”: 23} {“name”: “Alice”, “city”: “Seattle”, “age”: 31} {“name”: “Patrick”, “city”: “LA”, “age”: 27} ... ...

    The following Python script will get each JSON document and insert into “people” collection under “dw” database.

    import json

import pymongo

import sys

document = json.loads(sys.argv[1])

client = pymongo.MongoClient()

db = client[“dw”]

collection = db[“people”]

try:

    collection.insert(document)

except Exception as e:

    print “Could not insert document in db”, repr(e)

    Now to run this parallely, the following command should do the magic:

    cat people.json | parallel ‘python insertDB.py {}’

    That’s it! There are many switches and options available for advanced processing. They can be accessed by doing a man parallel on the shell. Also the following page has a set of tutorials: GNU Parallel Tutorials.

  • How to Conquer Data Mountains API by API | DataWeave

    How to Conquer Data Mountains API by API | DataWeave

    Let’s revisit our raison d’être: DataWeave is a platform on which we do large-scale data aggregation and serve this data in forms that are easily consumable. The nature of the data that we deal with is that: (1) it is publicly available on the web, (2) it is factual (to the extent possible), and (3) it has high utility (decided by a number of factors that we discuss below).

    The primary access channel for our data are the Data API. Other access channels such as visualizations, reports, dashboards, and alerting systems are built on top of our data APIs. Data Products such as PriceWeave, are built by combining multiple APIs and packaging them with reporting and analytics modules.

    Even as the platform is capable of aggregating any kind of data on the web, we need to prioritize the data that we aggregate, and the data products that we build. There are a lot of factors that help us in deciding what kinds of data we must aggregate and the APIs we must provide on DataWeave. Some of these factors are:

    1. Business Case: A strong business use-case for the API. There has to be an inherent pain point the data set must solve. Be it the Telecom Tariffs AP or Price Intelligence API — there are a bunch of pain points they solve for distinct customer segments.
    2. Scale of Impact: There has to exist a large enough volume of potential consumers that are going through the pain points, that this data API would solve. Consider the volume of the target consumers for the Commodity Prices API, for instance.
    3. Sustained Data Need: Data that a consumer needs frequently and/or on a long term basis has greater utility than data that is needed infrequently. We look at weather and prices all the time. Census figures, not so much.
    4. Assured Data Quality: Our consumers need to be able to trust the data we serve: data has to be complete as well as correct. Therefore, we need to ensure that there exist reliable public sources on the Web that contain the data points required to create the API.

    Once these factors are accounted for, the process of creating the APIs begins. One question that we are often asked is the following: how easy/difficult is it to create data APIs? That again depends on many factors. There are many dimensions to the data we are dealing with that helps us in deciding the level of difficulty. Below we briefly touch upon some of those:

    1. Structure: Textual data on the Web can be structured/semi-structured/unstructured. Extracting relevant data points from semi-structured and unstructured data without the existence of a data model can be extremely tricky. The process of recognizing the underlying pattern, automating the data extraction process, and monitoring accuracy of extracted data becomes difficult when dealing with unstructured data at scale.

    2. Temporality: Data can be static or temporal in nature. Aggregating static datas sets is an one time effort. Scenarios where data changes frequently or new data points are being generated pose challenges related to scalability and data consistency. For e.g., The India Local Mandi Prices AP gets updated on a day-to-day basis with new data being added. When aggregating data that is temporal, monitoring changes to data sources and data accuracy becomes a challenge. One needs to have systems in place that ensure data is aggregated frequently and also monitored for accuracy.

    3. Completeness: At one end of the spectrum we have existing data sets that are publicly downloadable. On the other end, we have data points spread across sources. In order to create data sets over these data points, these data points need to be aggregated and curated in order for them to be used. These data sources publish data in their own format, update them at different intervals. As always, “the whole is larger than the sum of its parts”; these individual data points when aggregated and presented together have many more use cases than those for the individual data points themselves.

    4. Representations: Data on the Web exists in various formats including (if we are particularly unlucky!) non-standard/proprietary ones. Data exists in HTML, XML, XLS, PDFs, docs, and many more. Extracting data from these different formats and presenting them through standard representations comes with its own challenges.

    5. Complexity: The data sets wherein data points are independent of each other are fairly simple to reason about. On the other hand, consider network data sets such as social data, maps, and transportation networks. The complexity arises due to the relationships that can exist between data points within and across data sets. The extent of pre-processing required to analyse these relationships makes these data sets is huge even on a small scale.

    6 .(Pre/Post) Processing: There is a lot of pre-processing involved to make raw crawled data presentable and accessible through a data API. This involves, cleaning, normalization, and representing data in standard forms. Once we have the data API, there can be a number of way that this data can be processed to create new and interesting APIs.

    So, that at a high level, is the way we work at DataWeave. Our vision is that of curating and providing access to all of the world’s public data. We are progressing towards this vision one API at a time.

    Originally published at blog.dataweave.in.

  • API of Telecom Recharge Plans in India

    API of Telecom Recharge Plans in India

    Several months ago we released our Telecom recharge plans API. It soon turned out to be one of our more popular APIs, with some of the leading online recharge portals using it extensively. (So, the next time you recharge your phone, remember us :))

    In this post, we’ll talk in detail about the genesis of this API and the problem it is solving.

    Before that — -and since we are into the business of building data products — some data points.

    As you can see, most mobile phones in India are prepaid. That is to say, there is a huge prepaid mobile recharge market. Just how big is this market?

    The above infographic is based on a recent report by Avendus [pdf]. Let’s focus on the online prepaid recharge market. Some facts:

    1. There are around 11 companies that provide an online prepaid recharge service. Here’s the list: mobikwik, rechargeitnow, paytm, freecharge, justrechargeit, easymobilerecharge, indiamobilerecharge, rechargeguru, onestoprecharge, ezrecharge, anytimerecharge
    2. RechargeItNow seems to be the biggest player. As of August 2013, they claimed an annual transactions worth INR 6 billion, with over 100000 recharges per day pan India.
    3. PayTM, Freecharge, and Mobikwik seem to be the other big players. Freecharge claimed recharge volumes of 40000/day in June 2012 (~ INR 2 billion worth of transactions), and they have been growing steadily.
    4. Telcos offer a commission of approximately 3% to third party recharge portals. So, it means there is an opportunity worth about 4 bn as of today.
    5. Despite the Internet penetration in India being around 11%, only about 1% of mobile prepaid recharges happen online. This goes to show the huge opportunity that lies untapped!
    6. It also goes to show why there are so many players entering this space. It’s only going to get crowded more.

    What does all this have to do with DataWeave? Let’s talk about the scale of the “data problem” that we are dealing with here. Some numbers that give an estimate on this.

    There are 13 cellular service providers in India. Here’s the list: Aircel Cellular Ltd, Aircel Limited, Bharti Airtel, BSNL, Dishnet Wireless, IDEA (operates as Idea ABTL & Spice in different states), Loop Mobile, MTNL, Reliable Internet, Reliance Telecom, Uninor, Videocon, and Vodafone. There are 22 circles in India. (Not every service provider has operations in every circle.)

    Find below the number of telecom recharge plans we have in our database for various operators.

    In fact, you can see that between the last week and today, we have added about 300 new plans (including plans for a new operator).

    The number of plans varies across operators. Vodafone, for instance, gives its users a huge number of options.

    The plans vary based on factors such as: denomination, recharge value, recharge talktime, recharge validity, plan type (voice/data), and of course, circle as well as the operator.

    For a third party recharge service provider, the below are a daily pain point:

    • plans become invalid on a regular basis
    • new plans are added on a regular basis
    • the features associated with a plan change (e.g, a ‘xx mins free talk time’ plan becomes ‘unlimited validity’ or something else)

    We see that 10s of plans become invalid (and new ones introduced) every day. All third party recharge portals lose significant amount of money on a daily basis because: they might not have information about all the plans and they might be displaying invalid plans.

    DataWeave’s Telecom Recharge Plans API solves this problem. This is how you use the API.

    Sample API Request

    “http://api.dataweave.in/v1/telecom_data/listByCircle/?api_key=b20a79e582ee4953ceccf41ac28aa08d&operator=Airtel&circle=Karnataka&page=1&per_page=10”

    Sample API Output

    We aggregate plans from the various cellular service providers across all circles in India on a daily basis. One of our customers once mentioned that earlier they used to aggregate this data manually, and it used to take them about a month to do this. With our API, we have reduced the refresh cycle to one day.

    In addition, now that this is process is automated, they can be confident that the data they present to their customers is almost always complete as well as accurate.

    Want to try it out for your business? Talk to us! If you are a developer who wants to use this or any other APIs, we let you use them for free. Just sign upand get your API key.

    DataWeave helps businesses make data-driven decisions by providing relevant actionable data. The company aggregates and organizes data from the web, such that businesses can access millions of data points through APIs, dashboards, and visualizations.

  • Of broken pumpkins and wasted lemonade

    Of broken pumpkins and wasted lemonade

    [The idea for this post was given by Mahesh B. L., who is a friend of DataWeave. We are going to have some fun with data below.]

    Ayudha Puje (ಆಯುಧ ಪೂಜೆ) or Ayudha Puja marks the end of Navaratri. It is the day before Dasara. Ayudha Puje is essentially the worship of implements (or tools/weapons) that we use. While this is practiced in all southern states, since Dasara is Karnataka’s nADa habba (ನಾಡ ಹಬ್ಬ) or “the festival of the land” it is done so with added fervour in Karnataka. At least it does seem so if you see the roads for the next few days!

    As part of Ayudha Puje, everybody washes and decorates their vehicles (well, they are our major weapons in more than one sense) and worships them by squashing a suitable amount of nice and juicy lemons with great vengeance. Ash gourds (Winter Melons) are also shattered with furious anger.

    Just how many lemons got squashed on the last Ayudha Puje in and around Bangalore? We dug up some data from a few sources (Praja, Bangalore City Traffic Police, RTO, a Hindu article), and came with a quick and dirty estimate on the number of vehicles in Bangalore. We estimate that there are about 55 lac vehicles in and around Bangalore. You can download the data we used and our approximations here.

     

    So, assuming one lemon per wheel, upwards of 13 million lemons got squashed on October 12, 2013 that could otherwise have been put to some good use such as preventing cauliflowers from turning brown, or serving Gin Fizz to an entire country. (Of course, we know that not everyone performs Ayudha Puje. But let’s not be insensitive to the plight of our victims by digressing.)

    We don’t have an estimate on how many Ash Gourds were broken, but we are sure the quantity would have been at least enough to serve tasty Halwa to every person in Karnataka.

    Do you want to do some serious analysis on Lemons and Pumpkins yourself? Take a look at our Commodity Prices API. Register to get an API key and start using it. Like this:

    http://api.dataweave.in/v1/commodities/findByCommodity/?api_key=b20a79e582ee4953ceccf41ac28aa08d&commodity=Lemon&start_date=20131009&end_date=20131015&page=1&per_page=10

    DataWeave helps businesses make data-driven decisions by providing relevant actionable data. The company aggregates and organizes data from the web, such that businesses can access millions of data points through APIs, dashboards, and visualizations.

     

    Originally published at blog.dataweave.in.

  • Implementing API for Social Data Analysis

    Implementing API for Social Data Analysis

    In today’s world, the analysis of any social media stream can reap invaluable information about, well, pretty much everything. If you are a business catering to a large number of consumers, it is a very important tool for understanding and analyzing the market’s perception about you, and how your audience reacts to whatever you present before them.

    At DataWeave, we sat down to create a setup that would do this for some e-commerce stores and retail brands. And the first social network we decided to track was the micro-blogging giant, Twitter. Twitter is a great medium for engaging with your audience. It’s also a very efficient marketing channel to reach out to a large number of people.

    Data Collection

    The very first issue that needs to be tackled is collecting the data itself. Now quite understandably, Twitter protects its data vigorously. However, it does have a pretty solid REST API for data distribution purposes too. The API is simple, nothing too complex, and returns data in the easy to use JSON format. Take a look at the timeline API, for example. That’s quite straightforward and has a lot of detailed information.

    The issue with the Twitter API however, is that it is seriously rate limited. Every function can be called in a range of 15–180 times in a 15-minute window. While this is good enough for small projects not needing much data, for any real-world application however, these rate limits can be really frustrating. To avoid this, we used the Streaming API, which creates a long-lived HTTP GET request that continuously streams tweets from the public timeline.

    Also, Twitter seems to suddenly return null values in the middle of the stream, which can make the streamer crash if we don’t take care. As for us, we simply threw away all null data before it reached the analysis phase, and as an added precaution, designed a simple e-mail alert for when the streamer crashed.

    Data Storage

    Next is data storage. Data is traditionally stored in tables, using RDBMS. But for this, we decided to use MongoDB, as a document store seemed quite suitable for our needs. While I didn’t have much clue about MongoDB or what purpose it’s going to serve at first, I realized that is a seriously good alternative to MySQL, PostgreSQL and other relational schema-based data stores for a lot of applications.

    Some of its advantages that I very soon found out were: documents-based data model that are very easy to handle analogous to Python dictionaries, and support for expressive queries. I recommend using this for some of your DB projects. You can play about with it here.

    Data Processing

    Next comes data processing. While data processing in MongoDB is simple, it can also be a hard thing to learn, especially for someone like me, who had no experience anywhere outside SQL. But MongoDB queries are simple to learn once the basics are clear.

    For example, in a DB DWSocial with a collection tweets, the syntax for getting all tweets would be something like this in a Python environment:

    rt = list(db.tweets.find())

    The list type-cast here is necessary, because without it, the output is simply a MongoDB reference, with no value. Now, to find all tweets where user_id is 1234, we have

    rt = list(db.retweets.find({ 'user_id': 1234 })

    Apart from this, we used regexes to detect specific types of tweets, if they were, for example, “offers”, “discounts”, and “deals”. For this, we used the Python re library, that deals with regexes. Suffice is to say, my reaction to regexes for the first two days was much like

    Once again, its just initial stumbles. After some (okay, quite some) help from Thothadri, Murthy and Jyotiska, I finally managed a basic parser that could detect which tweets were offers, discounts and deals. A small code snippet is here for this purpose.

    def deal(id):

re_offers = re.compile(r'''

\b

(?:

deals?

|

offers?

|

discount

|

promotion

|

sale

|

rs?

|

rs\?

|

inr\s*([\d\.,])+

|

([\d\.,])+\s*inr

)

\b

|

\b\d+%

|

\$\d+\b

''',

re.I|re.X)

x = list(tweets.find({'user_id' : id,'created_at': { '$gte': fourteen_days_ago }}))

mylist = []

newlist = []

for a in x:

b = re_offers.findall(a.get('text'))

if b:

print a.get('id')

mylist.append(a.get('id'))

w = list(db.retweets.find( { 'id' : a.get('id') } ))

if w:

mydict = {'id' : a.get('id'), 'rt_count' : w[0].get('rt_count'), 'text' : a.get('text'), 'terms' : b}

else:

mydict = {'id' : a.get('id'), 'rt_count' : 0, 'text' : a.get('text'), 'terms' : b}

track.insert(mydict)

    This is much less complicated than it seems. And it also brings us to our final step–integrating all our queries into a REST-ful API.

    Data Serving

    For this, mulitple web-frameworks are available. The ones we did consider were Flask, Django and Bottle.

    Weighing the pros and cons of every framework can be tedious. I did find this awesome presentation on slideshare though, that succinctly summarizes each framework. You can go through it here.

    We finally settled on Bottle as our choice of framework. The reasons are simple. Bottle is monolithic, i.e., it uses the one-file approach. For small applications, this makes for code that is easier to read and maintainable.

    Some sample web address routes are shown here:

    #show all tracked accounts

    id_legend = {57947109 : 'Flipkart', 183093247: 'HomeShop18', 89443197: 'Myntra', 431336956: 'Jabong'}

@route('/ids')

  def get_ids():

    result = json.dumps(id_legend)

    return result

    #show all user mentions for a particular account @route(‘/user_mentions’)

    def user_mention():

  m = request.query.id

  ac_id = int(m)

  t = list(tweets.find({'created_at': { '$gte': fourteen_days_ago }, 'retweeted': 'no', 'user_id': { '$ne': ac_id} }))

  a = len(t)

  mylist = []

  for i in t:

    mylist.append({i.get('user_id'): i.get('id')})

  x = { 'num_of_mentions': a, 'mentions_details': mylist }

  result = json.dumps(x)

  return result

    This is how the DataWeave Social API came into being. I had a great time doing this, with special credits to Sanket, Mandar and Murthy for all the help that they gave me for this. That’s all for now, folks!

  • How to Extract Colors From an Image

    How to Extract Colors From an Image

    We have taken a special interest in colors in recent times. Some of us can even identify and name a couple of dozen different colors! The genesis for this project was PriceWeave’s Color Analytics offering. With Color Analytics, we provide detailed analysis in colors and other attributes related to retailers and brands in Apparel and Lifestyle products space.

    The Idea

    The initial idea was to simply extract the dominating colors from an image and generate a color palette. Fashion blogs and Pinterest pages are updated regularly by popular fashion brands and often feature their latest offerings for the current season and their newly released products. So, we thought if we can crawl these blogs periodically after every few days/weeks, we can plot the trends in graphs using the extracted colors. This timeline is very helpful for any online/offline merchant to visualize the current trend in the market and plan out their own product offerings.

    We expanded this to include Apparel and Lifestyle products from eCommerce websites like Jabong, Myntra, Flipkart, and Yebhi, and stores of popular brands like Nike, Puma, and Reebok. We also used their Pinterest pages.

    Color Extraction

    The core of this work was to build a robust color extraction algorithm. We developed a couple of algorithms by extending some well known techniques. One approach we followed was to use standard unsupervised machine learning techniques. We ran k-means clustering against our images data. Here k refers to the number of colors we are trying to extract from the image.

    In another algorithm, we extracted all the possible color points from the image and used heuristics to come up with a final set of colors as a palette.

    Another of our algorithms was built on top of the Python Image Library (PIL) and the Colorific package to extract and produce the color palette from the image.

    Regardless of the approach we used, we soon found out that both speed and accuracy were a problem. Our k-means implementation produced decent results but it took 3–4 seconds to process an entire image! This might not seem much for a small set of images, but the script took 2 days to process 40,000 products from Myntra.

    Post this, we did a lot of tweaking in our algorithms and came up with a faster and more accurate model which we are using currently.

    ColorWeave API

    We have open sourced an early version of our implementation. It is available of github here. You can also download the Python package from the Python Package Index here. Find below examples to understand its usage.

    Retrieve dominant colors from an image URL

    from colorweave import palette print palette(url="image_url")

Retrive n dominant colors from a local image and print as json:




print palette(url="image_url", n=6, output="json")

Print a dictionary with each dominant color mapped to its CSS3 color name




print palette(url="image_url", n=6, format="css3")

Print the list of dominant colors using k-means clustering algorithm




print palette(url="image_url", n=6, mode="kmeans")

    Data Storage

    The next challenge was to come up with an ideal data model to store the data which will also let us query on it. Initially, all the processed data was indexed by Solr and we used its REST API for all our querying. Soon we realized that we have to come up with better data model to store, index and query the data.

    We looked at a few NoSQL databases, especially column oriented stores like Cassandra and HBase and document stores like MongoDB. Since the details of a single product can be represented as a JSON object, and key-value storage can prove to be quite useful in querying, we settled on MongoDB. We imported our entire data (~ 160,000 product details) to MongoDB, where each product represents a single document.

    Color Mapping

    We still had one major problem we needed to resolve. Our color extraction algorithm produces the color palette in hexadecimal format. But in order to build a useful query interface, we had to translate the hexcodes to human readable color names. We had two options. Either we could use a CSS 2.0 web color names consisting on 16 basic colors (White, Silver, Gray, Black, Red, Maroon, Yellow, Olive, Lime, Green, Aqua, Teal, Blue, Navy, Fuchsia, Purple) or we could use CSS 3.0 web color names consisting of 140 colors. We used both to map colors and stored those colors along with each image.

    Color Hierarchy

    We mapped the hexcodes to CSS 3.1 which has every possible shades for the basic colors. Then we assigned a parent basic color for every shades and stored them separately. Also, we created two fields — one for the primary colors and the other one for the extended colors which will help us in indexing and querying. At the end, each product had 24 properties associated with it! MongoDB made it easier to query on the data using the aggregation framework.

    What next?

    A few things. An advanced version of color extraction (with a number of other exciting features) is being integrated into PriceWeave. We are also working on building a small consumer facing product where users will be able to query and find products based on color and other attributes. There are many other possibilities some of which we will discuss when the time is ripe. Signing off for now!

     

     

    Originally published at blog.dataweave.in.

  • Difference Between Json, Ultrajson, & Simplejson | DataWeave

    Difference Between Json, Ultrajson, & Simplejson | DataWeave

    Without argument, one of the most common used data model is JSON. There are two popular packages used for handling json — first is the stockjsonpackage that comes with default installation of Python, the other one issimplejson which is an optimized and maintained package for Python. The goal of this blog post is to introduce ultrajson or Ultra JSON, a JSON library written mostly in C and built to be extremely fast.

    We have done the benchmark on three popular operations — load, loadsanddumps. We have a dictionary with 3 keys — id, name and address. We will dump this dictionary using json.dumps() and store it in a file. Then we will use json.loads() and json.load() separately to load the dictionaries from the file. We have performed this experiment on 10000, 50000, 100000,200000, 1000000 dictionaries and observed how much time it takes to perform the operation by each library.

    DUMPS OPERATION LINE BY LINE

    Here is the result we received using the json.dumps() operations. We have dumped the content dictionary by dictionary.

     

    We notice that json performs better than simplejson but ultrajson wins the game with almost 4 times speedup than stock json.

    DUMPS OPERATION (ALL DICTIONARIES AT ONCE)

    In this experiment, we have stored all the dictionaries in a list and dumped the list using json.dumps().

    simplejson is almost as good as stock json, but again ultrajson outperforms them by more than 60% speedup. Now lets see how they perform for load and loads operation.

    LOAD OPERATION ON A LIST OF DICTIONARIES

    Now we do the load operation on a list of dictionaries and compare the results.

    Surprisingly, simplejson beats other two, with ultrajson being almost close to simplejson. Here, we observe that simplejson is almost 4 times faster than stock json, same with ultrajson.

    LOADS OPERATION ON DICTIONARIES

    In this experiment, we load dictionaries from the file one by one and pass them to the json.loads() function.

    Again ultrajson steals the show, being almost 6 times faster than stock json and 4 times faster than simplejson.

    That is all the benchmarks we have here. The verdict is pretty clear. Use simplejson instead of stock json in any case, since simplejson is well maintained repository. If you really want something extremely fast, then go for ultrajson. In that case, keep in mind that ultrajson only works with well defined collections and will not work for un-serializable collections. But if you are dealing with texts, this should not be a problem.

     

    This post originally appeared here.

  • Mining Twitter for Reactions to Products & Brands | DataWeave

    Mining Twitter for Reactions to Products & Brands | DataWeave

    [This post was written by Dipanjan. Dipanjan works in the Engineering Team with Mandar, addressing some of the problems related to Data Semantics. He loves watching English Sitcoms in his spare time. This was originally posted on the PriceWeave blog.]

    This is the second post in our series of blog posts which we shall be presenting regarding social media analysis. We have already talked about Twitter Mining in depth earlier and also how to analyze social trends in general and gather insights from YouTube. If you are more interested in developing a quick sentiment analysis app, you can check our short tutorial on that as well.

    Our flagship product, PriceWeave, is all about delivering real time actionable insights at scale. PriceWeave helps Retailers and Brands take decisions on product pricing, promotions, and assortments on a day to day basis. One of the areas we focus on is “Social Intelligence”, where we measure our customers’ social presence in terms of their reach and engagement on different social channels. Social Intelligence also helps in discovering brands and products trending on social media.

    Today, I will be talking about how we can get data from Twitter in real-time and perform some interesting analytics on top of that to understand social reactions to trending brands and products.

    In our last post, we had used Twitter’s Search API for getting a selective set of tweets and performed some analytics on that. But today, we will be using Twitter’s Streaming API, to access data feeds in real time. A couple of differences with regards to the two APIs are as follows. The Search API is primarily a REST API which can be used to query for “historical data”. However, the Streaming API gives us access to Twitter’s global stream of tweets data. Moreover, it lets you acquire much larger volumes of data with keyword filters in real-time compared to normal search.

    Installing Dependencies

    I will be using Python for my analysis as usual, so you can install it if you don’t have it already. You can use another language of your choice, but remember to use the relevant libraries of that language. To get started, install the following packages, if you don’t have them already. We use simplejson for JSON data processing at DataWeave, but you are most welcome to use the stock json library.

    Acquiring Data

    We will use the Twitter Streaming API and the equivalent python wrapper to get the required tweets. Since we will be looking to get a large number of tweets in real time, there is the question of where should we store the data and what data model should be used. In general, when building a robust API or application over Twitter data, MongoDB being a schemaless document-oriented database, is a good choice. It also supports expressive queries with indexing, filtering and aggregations. However, since we are going to analyze a relatively small sample of data using pandas, we shall be storing them in flat files.

    Note: Should you prefer to sink the data to MongoDB, the mongoexportcommand line tool can be used to export it to a newline delimited format that is exactly the same as what we will be writing to a file.

    The following code snippet shows you how to create a connection to Twitter’s Streaming API and filter for tweets containing a specific keyword. For simplicity, each tweet is saved in a newline-delimited file as a JSON document. Since we will be dealing with products and brands, I have queried on two trending products and brands respectively. They are, ‘Sony’ and ‘Microsoft’ with regards to brands and ‘iPhone 6’ and ‘Galaxy S5’ with regards to products. You can write the code snippet as a function for ease of use and call it for specific queries to do a comparative study.

    Let the data stream for a significant period of time so that you can capture a sizeable sample of tweets.

    Analyses and Visualizations

    Now that you have amassed a collection of tweets from the API in a newline delimited format, let’s start with the analyses. One of the easiest ways to load the data into pandas is to build a valid JSON array of the tweets. This can be accomplished using the following code segment.

    Note: With pandas, you will need to have an amount of working memory proportional to the amount of data that you’re analyzing.

    Once you run this, you should get a dictionary containing 4 data frames. The output I obtained is shown in the snapshot below.

    Note: Per the Streaming API guidelines, Twitter will only provide up to 1% of the total volume of real time tweets, and anything beyond that is filtered out with each “limit notice”.

    The next snippet shows how to remove the “limit notice” column if you encounter it.

    Time-based Analysis

    Each tweet we captured had a specific time when it was created. To analyze the time period when we captured these tweets, let’s create a time-based index on the created_at field of each tweet so that we can perform a time-based analysis to see at what times do people post most frequently about our query terms.

    The output I obtained is shown in the snapshot below.

    I had started capturing the Twitter stream at around 7 pm on the 6th of December and stopped it at around 11:45 am on the 7th of December. So the results seem consistent based on that. With a time-based index now in place, we can trivially do some useful things like calculate the boundaries, compute histograms and so on. Operations such as grouping by a time unit are also easy to accomplish and seem a logical next step. The following code snippet illustrates how to group by the “hour” of our data frame, which is exposed as a datetime.datetime timestamp since we now have a time-based index in place. We print an hourly distribution of tweets also just to see which brand \ product was most talked about on Twitter during that time period.

    The outputs I obtained are depicted in the snapshot below.

    The “Hour” field here follows a 24 hour format. What is interesting here is that, people have been talking more about Sony than Microsoft in Brands. In Products, iPhone 6 seems to be trending more than Samsung’s Galaxy S5. Also the trend shows some interesting insights that people tend to talk more on Twitter in the morning and late evenings.

    Time-based Visualizations

    It could be helpful to further subdivide the time ranges into smaller intervals so as to increase the resolution of the extremes. Therefore, let’s group into a custom interval by dividing the hour into 15-minute segments. The code is pretty much the same as before except that you call a custom function to perform the grouping. This time, we will be visualizing the distributions using matplotlib.

    The two visualizations are depicted below. Of course don’t forget to ignore the section of the plots from after 11:30 am to around 7 pm because during this time no tweets were collected by me. This is indicated by a steep rise in the curve and is insignificant. The real regions of significance are from hour 7 to 11:30 and hour 19 to 22.

    Considering brands, the visualization for Microsoft vs. Sony is depicted below. Sony is the clear winner here.

    Considering products, the visualization for iPhone 6 vs. Galaxy S5 is depicted below. The clear winner here is definitely iPhone 6.

    Tweeting Frequency Analysis

    In addition to time-based analysis, we can do other types of analysis as well. The most popular analysis in this case would be frequency based analysis of the users authoring the tweets. The following code snippet will compute the Twitter accounts that authored the most tweets and compare it to the total number of unique accounts that appeared for each of our query terms.

    The results which I obtained are depicted below.

    What we do notice is that a lot of these tweets are also made by bots, advertisers and SEO technicians. Some examples are Galaxy_Sleeves and iphone6_sleeves which are obviously selling covers and cases for the devices.

    Tweeting Frequency Visualizations

    After frequency analysis, we can plot these frequency values to get better intuition about the underlying distribution, so let’s take a quick look at it using histograms. The following code snippet created these visualizations for both brands and products using subplots.

    The visualizations I obtained are depicted below.

    The distributions follow the “Pareto Principle” as expected where we see that a selective number of users make a large number of tweets and the majority of users create a small number of tweets. Besides that, we see that based on the tweet distributions, Sony and iPhone 6 are more trending than their counterparts.

    Locale Analysis

    Another important insight would be to see where your target audience is located and their frequency. The following code snippet achieves the same.

    The outputs which I obtained are depicted in the following snapshot. Remember that Twitter follows the ISO 639–1 language code convention.

    The trend we see is that most of the tweets are from English speaking countries as expected. Surprisingly, most of the Tweets regarding iPhone 6 are from Japan!

    Analysis of Trending Topics

    In this section, we will see some of the topics which are associated with the terms we used for querying Twitter. For this, we will be running our analysis on the tweets where the author speaks in English. We will be using the nltklibrary here to take care of a couple of things like removing stopwords which have little significance. Now I will be doing the analysis here for brands only, but you are most welcome to try it out with products too because, the following code snippet can be used to accomplish both the computations.

    What the above code does is that, it takes each tweet, tokenizes it and then computes a term frequency and outputs the 20 most common terms for each brand. Of course an n-gram analysis can give a deeper insight into trending topics but the same can also be accomplished with ntlk’s collocations function which takes in the tokens and outputs the context in which they were mentioned. The outputs I obtained are depicted in the snapshot below.

    Some interesting insights we see from the above outputs are as follows.

    • Sony was hacked recently and it was rumored that North Korea was responsible for that, however they have denied that. We can see that is trending on Twitter in context of Sony. You can read about it here.
    • Sony has recently introduced Project Sony Skylight which lets you customize your PS4.
    • There are rumors of Lumia 1030, Microsoft’s first flagship phone.
    • People are also talking a lot about Windows 10, the next OS which is going to be released by Microsoft pretty soon.
    • Interestingly, “ebay price” comes up for both the brands, this might be an indication that eBay is offering discounts for products from both these brands.

    To get a detailed view on the tweets matching some of these trending terms, we can use nltk’s concordance function as follows.

    The outputs I obtained are as follows. We can clearly see the tweets which contain the token we searched for. In case you are unable to view the text clearly, click on the image to zoom.

    Thus, you can see that the Twitter Streaming API is a really good source to track social reaction to any particular entity whether it is a brand or a product. On top of that, if you are armed with an arsenal of Python’s powerful analysis tools and libraries, you can get the best insights from the unending stream of tweets.

    That’s all for now folks! Before I sign off, I would like to thank Matthew A. Russell and his excellent book Mining the Social Web once again, without which this post would not have been possible. Cover image credit goes to TechCrunch.

  • Mining Twitter to Analyze Product Trends | DataWeave

    Mining Twitter to Analyze Product Trends | DataWeave

    Due to the massive growth of social media in the last decade, it has become a rage among data enthusiasts to tap into the vast pool of social data and gather interesting insights like trending items, reception of newly released products by society, popularity measures to name a few.

    We are continually evolving PriceWeave, which has the most extensive set of offerings when it comes to providing actionable insights to retail stores and brands. As part of the product development, we look at social data from a variety of channels to mine things like: trending products/brands; social engagement of stores/brands; what content “works” and what does not on social media, and so forth.

    We do a number of experiments with mining Twitter data, and this series of blog posts is one of the outputs from those efforts.

    In some of our recent blog posts, we have seen how to look at current trends and gather insights from YouTube the popular video sharing website. We have also talked about how to create a quick bare-bones web application to perform sentiment analysis of tweets from Twitter. Today I will be talking about mining data from Twitter and doing much more with it than just sentiment analysis. We will be analyzing Twitter data in depth and then we will try to get some interesting insights from it.

    To get data from twitter, first we need to create a new Twitter application to get OAuth credentials and access to their APIs. For doing this, head over to the Twitter Application Management page and sign in with your Twitter credentials. Once you are logged in, click on the Create New App button as you can see in the snapshot below. Once you create the application, you will be able to view it in your dashboard just like the application I created, named DataScienceApp1_DS shows up in my dashboard depicted below.

    On clicking the application, it will take you to your application management dashboard. Here, you will find the necessary keys you need in the Keys and Access Tokens section. The main tokens you need are highlighted in the snapshot below.

    I will be doing most of my analysis using the Python programming language. To be more specific, I will be using the IPython shell, but you are most welcome to use the language of your choice, provided you get the relevant API wrappers and necessary libraries.

    Installing necessary packages

    After obtaining the necessary tokens, we will be installing some necessary libraries and packages, namely twitter, prettytable and matplotlib. Fire up your terminal or command prompt and use the following commands to install the libraries if you don’t have them already.

    Creating a Twitter API Connection

    Once the packages are installed, you can start writing some code. For this, open up the IDE or text editor of your choice and use the following code segment to create an authenticated connection to Twitter’s API. The way the following code snippet works, is by using your OAuth credentials to create an object called auth that represents your OAuth authorization. This is then passed to a class called Twitter belonging to the twitter library and we create a resource object named twitter_api that is capable of issuing queries to Twitter’s API.

    If you do a print twitter_api and all your tokens are corrent, you should be getting something similar to the snapshot below. This indicates that we’ve successfully used OAuth credentials to gain authorization to query Twitter’s API.

    Exploring Trending Topics

    Now that we have a working Twitter resource object, we can start issuing requests to Twitter. Here, we will be looking at the topics which are currently trending worldwide using some specific API calls. The API can also be parameterized to constrain the topics to more specific locales and regions. Each query uses a unique identifier which follows the Yahoo! GeoPlanet’s Where On Earth (WOE) ID system, which is an API itself that aims to provide a way to map a unique identifier to any named place on Earth. The following code segment retrieves trending topics in the world, the US and in India.

    Once you print the responses, you will see a bunch of outputs which look like JSON data. To view the output in a pretty format, use the following commands and you will get the output as a pretty printed JSON shown in the snapshot below.

    To view all the trending topics in a convenient way, we will be using list comprehensions to slice the data we need and print it using prettytable as shown below.

    On printing the result, you will get a neatly tabulated list of current trends which keep changing with time.

    Now, we will try to analyze and see if some of these trends are common. For that we use Python’s set data structure and compute intersections to get common trends as shown in the snapshot below.

    Interestingly, some of the trending topics at this moment in the US are common with some of the trending topics in the world. The same holds good for US and India.

    Mining for Tweets

    In this section, we will be looking at ways to mine Twitter for retrieving tweets based on specific queries and extracting useful information from the query results. For this we will be using Twitter API’s GET search/tweets resource. Since the Google Nexus 6 phone was launched recently, I will be using that as my query string. You can use the following code segment to make a robust API request to Twitter to get a size-able number of tweets.

    The code snippet above, makes repeated requests to the Twitter Search API. Search results contain a special search_metadata node that embeds a next_results field with a query string that provides the basis of making a subsequent query. If we weren’t using a library like twitter to make the HTTP requests for us, this preconstructed query string would just be appended to the Search API URL, and we’d update it with additional parameters for handling OAuth. However, since we are not making our HTTP requests directly, we must parse the query string into its constituent key/value pairs and provide them as keyword arguments to the search/tweets API endpoint. I have provided a snapshot below, showing how this dictionary of key/value pairs are constructed which are passed as kwargs to the Twitter.search.tweets(..) method.

    Analyzing the structure of a Tweet

    In this section we will see what are the main features of a tweet and what insights can be obtained from them. For this we will be taking a sample tweet from our list of tweets and examining it closely. To get a detailed overview of tweets, you can refer to this excellent resource created by Twitter. I have extracted a sample tweet into the variable sample_tweet for ease of use. sample_tweet.keys() returns the top-level fields for the tweet.

    Typically, a tweet has some of the following data points which are of great interest.

    The identifier of the tweet can be accessed through sample_tweet[‘id’]

    • The human-readable text of a tweet is available through sample_tweet[‘text’]
    • The entities in the text of a tweet are conveniently processed and available through sample_tweet[‘entities’]
    • The “interestingness” of a tweet is available through sample_tweet[‘favorite_count’] and sample_tweet[‘retweet_count’], which return the number of times it’s been bookmarked or retweeted, respectively
    • An important thing to note, is that, the retweet_count reflects the total number of times the original tweet has been retweeted and should reflect the same value in both the original tweet and all subsequent retweets. In other words, retweets aren’t retweeted
    • The user details can be accessed through sample_tweet[‘user’] which contains details like screen_name, friends_count, followers_count, name, location and so on

    Some of the above datapoints are depicted in the snapshot below for the sample_tweet. Note, that the names have been changed to protect the identity of the entity that created the status.

    Before we move on to the next section, my advice is that you should play around with the sample tweet and consult the documentation to clarify all your doubts. A good working knowledge of a tweet’s anatomy is critical to effectively mining Twitter data.

    Extracting Tweet Entities

    In this section, we will be filtering out the text statuses of tweets and different entities of tweets like hashtags. For this, we will be using list comprehensions which are faster than normal looping constructs and yield substantial perfomance gains. Use the following code snippet to extract the texts, screen names and hashtags from the tweets. I have also displayed the first five samples from each list just for clarity.

    Once you print the table, you should be getting a table of the sample data which should look something like the table below but with different content ofcourse!

    Frequency Analysis of Tweet and Tweet Entities

    Once we have all the required data in relevant data structures, we will do some analysis on it. The most common analysis would be a frequency analysis where we find out the most common terms occurring in different entities of the tweets. For this we will be making use of the collection module. The following code snippet ranks the top ten most occurring tweet entities and prints them as a table.

    The output I obtained is shown in the snapshot below. As you can see, there is a lot of noise in the tweets because of which several meaningless terms and symbols have crept into the top ten list. For this, we can use some pre-processing and data cleaning techniques.

    Analyzing the Lexical Diversity of Tweets

    A slightly more advanced measurement that involves calculating simple frequencies and can be applied to unstructured text is a metric called lexical diversity. Mathematically, lexical diversity can be defined as an expression of the number of unique tokens in the text divided by the total number of tokens in the text. Let us take an example to understand this better. Suppose you are listening to someone who repeatedly says “and stuff” to broadly generalize information as opposed to providing specific examples to reinforce points with more detail or clarity. Now, contrast that speaker to someone else who seldom uses the word “stuff” to generalize and instead reinforces points with concrete examples. The speaker who repeatedly says “and stuff” would have a lower lexical diversity than the speaker who uses a more diverse vocabulary.

    The following code snippet, computes the lexical diversity for status texts, screen names, and hashtags for our data set. We also measure the average number of words per tweet.

    The output which I obtained is depicted in the snapshot below.

    Now, I am sure you must be thinking, what on earth do the above numbers indicate? We can analyze the above results as follows.

    • The lexical diversity of the words in the text of the tweets is around 0.097. This can be interpreted as, each status update carries around 9.7% unique information. The reason for this is because, most of the tweets would contain terms like Android, Nexus 6, Google
    • The lexical diversity of the screen names, however, is even higher, with a value of 0.59 or 59%, which means that about 29 out of 49 screen names mentioned are unique. This is obviously higher because in the data set, different people will be posting about Nexus 6
    • The lexical diversity of the hashtags is extremely low at a value of around 0.029 or 2.9%, implying that very few values other than the #Nexus6hashtag appear multiple times in the results. This is relevant because tweets about Nexus 6 should contain this hashtag
    • The average number of words per tweet is around 18 words

    This gives us some interesting insights like people mostly talk about Nexus 6 when queried for that search keyword. Also, if we look at the top hashtags, we see that Nexus 5 co-occurs a lot with Nexus 6. This might be an indication that people are comparing these phones when they are tweeting.

    Examining Patterns in Retweets

    In this section, we will analyze our data to determine if there were any particular tweets that were highly retweeted. The approach we’ll take to find the most popular retweets, is to simply iterate over each status update and store out the retweet count, the originator of the retweet, and status text of the retweet, if the status update is a retweet. We will be using a list comprehension and sort by the retweet count to display the top few results in the following code snippet.

    The output I obtained is depicted in the following snapshot.

    From the results, we see that the top most retweet is from the official googlenexus channel on Twitter and the tweet speaks about the phone being used non-stop for 6 hours on only a 15 minute charge. Thus, you can see that this has definitely been received positively by the users based on its retweet count. You can detect similar interesting patterns in retweets based on the topics of your choice.

    Visualizing Frequency Data

    In this section, we will be creating some interesting visualizations from our data set. For plotting we will be using matplotlib, a popular Python plotting library which comes inbuilt with IPython. If you don’t have matplotlib loaded by default use the command import matplotlib.pyplot as plt in your code.

    Visualizing word frequencies

    In our first plot, we will be displayings the results from the words variable which contains different words from the tweet status texts. Using Counter from the collections package, we generate a sorted list of tuples, where each tuple is a (word, frequency) pair. The x-axis value will correspond to the index of the tuple, and the y-axis will correspond to the frequency for the word in that tuple. We transform both axes into a logarithmic scale because of the vast number of data points.

    Visualizing words, screen names, and hashtags

    A line chart of frequency values is decent enough. But what if we want to find out the number of words having a frequency between 1–5, 5–10, 10–15… and so on. For this purpose we will be using a histogram to depict the frequencies. The following code snippet achieves the same.

    What this essentially does is, it takes all the frequencies and groups them together and creates bins or ranges and plots the number of entities which fall in that bin or range. The plots I obtained are shown below.

    From the above plots, we can observe that, all the three plots follow the “Pareto Principle” i.e, almost 80% of the words, screen names and hashtags have a frequency of only 20% in the whole data set and only 20% of the words, screen names and hashtags have a frequency of more than 80% in the data set. In short, if we consider hashtags, a lot of hashtags occur maybe only once or twice in the whole data set and very few hashtags like #Nexus6 occur in almost all the tweets in the data set leading to its high frequency value.

    Visualizing retweets

    In this visualization, we will be using a histogram to visualize retweet counts using the following code snippet.

    The plot which I obtained is shown below.

    Looking at the frequency counts, it is clear that very few retweets have a large count.

    I hope you have seen by now, how powerful Twitter APIs are and using simple Python libraries and modules, it is really easy to generate very powerful and interesting insights. That’s all for now folks! I will be talking more about Twitter Mining in another post sometime in the future. A ton of thanks goes out to Matthew A. Russell and his excellent book Mining the Social Web, without which this post would never have been possible. Cover image credit goes to Social Media.