The DataWeave Blog

Category: Brand Perception

  • 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!

  • 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.

  • 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 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.

  • 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.

  • 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.

  • 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.

  • 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!

  • 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!

  • 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!

  • 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: 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.

  • 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.

  • 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!

  • 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. 

  • 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 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! 

  • 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.

  • 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. 

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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!

     

  • 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.

  • 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.

  • 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.