The DataWeave Blog

Category: Strategy

  • 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.

  • 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.

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

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

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

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

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

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

    Poor Data Refinement vs. Good Refinement

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

    Retailer A

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

    Now, imagine that your pricing data is outdated and unrefined when you go to price your new sneakers.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

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

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

    Retailer B

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

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

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

    The Hidden Cost of Unrefined Data

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

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

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

    The Two Pillars of Data Refinement

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

    Competitive Matches

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

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

    Internal Portfolio Matches

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

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

    Leveraging AI for Enhanced Match Rates

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

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

    How AI helps convert raw data to pricing and assortment intelligence

    From Refinement to Business Value

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

    Price Management

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

    Price Reporting

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

    Competitive Intelligence

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

    Implementation Framework

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

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

    What’s Next?

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

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

  • 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.

  • Cracking the Code: How Retailers Can Adapt to Plummeting Egg Prices in 2024

    Cracking the Code: How Retailers Can Adapt to Plummeting Egg Prices in 2024

    Virtually every cuisine in the world uses eggs. They’re in your breakfast, lunch, dinner, and dessert — which is perhaps why the global egg market is expected to generate $130.70 billion in revenue in 2024 and is projected to grow to approximately $193.56 billion by 2029.

    More specifically, the United States is the fourth-largest egg producer worldwide. The country’s egg market is projected to generate $15.75 billion in 2024 and increase to $22.51 billion by 2029.

    This growth is driven by several factors, most notably:

    • Health-consciousness among consumers: Consumers value eggs for their essential nutrients and rich protein content.
    • Demand for convenience foods: Consumers’ preferences are shifting toward quick and easy foods, which drives demand for shell eggs and pre-packaged boiled or scrambled eggs.
    • Population Growth: A growing worldwide population increases the demand for eggs.
    • Affordability and accessibility: Eggs are an affordable and accessible nutrient-dense food source for many.

    Despite these factors contributing to the U.S. egg market’s growth, recent times have seen egg prices fall dramatically.

    Based on a sample of 450 SKUs, DataWeave discovered that egg prices in the U.S. fell by 6.7% between April 2023 and April 2024, dipping to its lowest (-12.6%) in December 2023.

    Egg Price Chart: Egg Prices USA Going Down 98.95% between April 2023 and April 2024

    So, what’s causing the decrease in egg prices?

    The Rise and Fall of Egg Prices: A Recent History

    In 2022, avian influenza severely impacted the United States. The disease affected wild birds in nearly every state and devastated commercial flocks in approximately half of the country.

    The 2022 incident was the first major outbreak since 2015 and led to the culling of more than 52.6 million birds, mainly poultry, to prevent the disease from spreading uncontrollably.

    With almost 12 million fewer egg-laying hens, the United States produced around 109.5 billion eggs in 2022 — a drop of nearly two billion from the previous year.

    Consequently, the cost of eggs soared, peaking at $4.82 a dozen — more than double the price of eggs in the previous year.

    The avian flu continues to affect egg-laying hens and other poultry birds across the United States. As of April 2024, farms have killed a total of 85 million poultry birds in an attempt to contain the disease.

    Despite the disease’s effects, production facilities have made significant efforts to repopulate flocks, leading to a steady increase in supply – and a much anticipated decrease in egg prices.

    According to the U.S. Bureau of Labor Statistics, there was an increase in producer egg prices in 2022, reaching a peak in November 2022, at which point they began to fall.

    Retailer’s egg prices followed suit. The egg price chart below depicts retailers’ declining egg prices over one year, from April 2023 to April 2024, with Giant Eagle showing the most significant price reductions and Walmart the least.

    Egg Price Chart Featuring Leading Retailers 2023-2024

    What Does the Future Hold for Egg Prices?

    The USDA reported recent severe avian flu outbreaks in June 2024. These outbreaks are estimated to have affected 6.23 million birds.

    With a reduction in egg-laying hens, egg prices are likely to increase — time will tell.

    Nonetheless, the annual per capita consumption of eggs in the U.S. is projected to reach 284.4 per person in 2024 from 281.3 per person in 2023. So for now, producers and retailers can rest assured of the growing demand for eggs.

    How Can Retailers Adapt to the Unpredictability of Egg Prices?

    Egg prices were down to $2.69 for a dozen in May 2024. However, they are still significantly higher than consumers were used to just a few years ago—eggs were, on average, $1.46 a dozen in early 2020.

    Additionally, while the avian flu puts pressure on producers, inflation and supply chain disruptions exert pressure on retailers.

    With such challenging egg market conditions, what can retailers do to maintain customer loyalty amid reduced consumer spending while maintaining profitability?

    1. Give the Customer What They Want: Increase Offerings of Organic, Cage-Free, and Free-Range Eggs

    As mentioned, Data Bridge Market Research’s trends and forecast report highlighted a significant increase in consumer health consciousness. Additionally, animal welfare increasingly influences consumers’ purchasing decisions when buying meat and dairy products.

    DataWeave data shows that the prices of organic, cage-free, and free-range eggs—such as those by brands like Happy Eggs and Marketside—have fallen less than those of non-organic, caged egg brands.

    Egg Price Chart Featuring Leading Egg Brand Prices 2023-2024

    2. Increase Private-Label Offerings

    Private labels typically offer retailers higher margins than national brands. These margins can shield consumers from sudden wholesale egg price swings, helping to preserve brand trust and consumer loyalty without sacrificing profitability.

    Moreover, eggs are particularly suited to private labeling, given their uniform appearance and taste and the lack of product innovation opportunities.

    Undoubtedly, this is why sales of private-label eggs dwarf sales of national egg brands in the United States. Statista reports that across three months in 2024, private label egg sales amounted to $1.55 billion U.S. dollars, while the combined sales of the top nine national egg brands totaled just $617.88 million U.S. dollars.

    3. Price Intelligently

    With the current and predicted fluctuations in egg prices over the foreseeable future, price competitiveness is paramount to margin management and customer loyalty.

    This is especially true when lower prices are the primary factor influencing the average consumer’s choice of supermarket for daily essentials purchases.

    AI-driven pricing intelligence tools like DataWeave give retailers valuable highly granular and reliable insights on competitor pricing and market dynamics. In today’s data-motivated environment, these insights are necessary for competitiveness and profitability.

    Final Thoughts

    Egg prices have fluctuated significantly due to the impact of avian flu. Despite recent price drops, future egg price increases are possible due to ongoing outbreaks. Retailers should adapt to unstable egg prices by increasing organic, free-range, cage-free, and private-label egg offerings while leveraging AI-driven pricing tools to maintain margins and customer loyalty.

    Speak to us today to learn more!

  • How Healthy is Your Assortment?

    How Healthy is Your Assortment?

    In 2025, both consumers and retailers continue to prioritize better health – albeit with evolving definitions and expectations.

    The pandemic fundamentally transformed how consumers approach wellness, with this shift becoming entrenched in shopping behaviors years later. As shopping habits have permanently altered, retailers now face increased pressure to rapidly adapt their assortments with in-demand health and wellness products that enhance customer experience across various channels – online and offline.

    Let’s explore how leading retailers are keeping consumers – and their own bottom lines – healthy by responding effectively to market trends to drive online sales and market share.

    Health & Wellness Influence The Product Mix Across Categories

    Consumption habits have changed dramatically since the onset of the pandemic. A McKinsey study shows that 82% and 73% of US, and UK consumers respectively now consider health & wellness a top priority. Typically shoppers adjust grocery shopping and meal planning at the start of the year, with many focusing on fresh, organic, and nutrient-rich foods.

    The influential health and wellness mega-trend spans diverse retail channels, including grocery, pharmacy and mass. It extends across numerous categories like:

    • Food and beverage (natural, organic, vegan, plant-based food)
    • Health and personal care
    • Beauty
    • Cleaning products
    • Fitness equipment 
    • Athleisure (apparel)
    • Consumer electronics like health wearables.

    Today’s health movement is so powerful and compelling that retailers have revised their business strategies to better serve health-conscious consumers. For instance, drugstores are reinventing themselves as healthcare destinations, with CVS and Kroger expanding into personalized care delivery and value-based clinics to enhance their health offerings.

    Major retailers like Amazon, Walmart, and Target report robust sales in health and wellness categories. For example, Walmart saw a 4.6% increase in comparable sales in early 2024, driven significantly by grocery, consumables, and health-related products.

    New product categories are gaining traction:

    • Functional foods and beverages are seeing unprecedented growth, with Target launching over 2,000 wellness items in the category, including exclusive products priced under $10.
    • Personalized nutrition and mental health products are surging, including tailored dietary solutions and stress-reducing items.
    • Health wearables and wellness tech continue to rise in popularity, with over 150 new wellness tech items launched at Target this year, including innovative red-light therapy devices.
    • Transparency and sustainability certifications like organic, non-GMO, and vegan labels are increasingly driving purchasing decisions.
    • Clinically proven benefits offered by health & wellness products are gaining traction among Gen Z.

    Retail’s Survival Of The Fittest Moves Online

    As the omnichannel retail sector continues to grow, more shoppers now make purchase decisions within minutes using just a few clicks rather than physically visiting brick-and-mortar stores. In some cases, AI agents like Operator from Chat-GPT or Gemini (Google’s Chatbot) even make personalized, curated lists and reduce the time taken to make purchase decisions. Traditional retail paradigms are rapidly becoming obsolete as consumers grow savvier, more empowered, and better informed than ever before.

    To stay competitive, more retailers are embracing AI-driven data insights to adjust their assortments to reflect consumer demand for health and wellness products.

    According to industry experts, data insights have emerged as a critical retail strategy that continues to gain momentum. This is because retailers can no longer afford to guess how to approach their omnichannel strategy. They need the accuracy, clarity, and efficiency of data insights to guide their assortment and pricing decisions to outmaneuver competitors, maximize sales, and win market share as shopping evolves online.

    Among its retail best practices, Bain & Company recommends retailers “lead with superior assortments that use a customer-centric lens to reduce complexity and increase space for the products customers love.” Insights can help retailers discover the optimal mix of national brands, private labels, limited-time offers, and value-added bundles.

    “Lead with superior assortments …
    increase space for the products consumers love    “
    ~ Bain & Company

    Determining the optimal mix of products also includes bestsellers and unique items that help retailers distinguish their offerings. Assortment insights help retail executives track competitors’ assortment changes and spot gaps in their own product assortment to adapt to emerging consumer trends and in-demand products.

    Why Effective Assortment Planning Matters

    Assortment planning sits at the heart of retail success, directly influencing profitability, customer satisfaction, and competitive differentiation. In today’s health-conscious market, getting your assortment right means:

    • Meeting Customer Expectations: Today’s health-conscious consumers expect relevant, high-quality products that match their wellness goals. A well-planned assortment signals that a retailer understands its customers’ evolving needs.
    • Optimizing Inventory Investment: Strategic assortment planning ensures capital is allocated to products with the highest return potential while minimizing investments in slow-moving items.
    • Creating Competitive Advantage: A distinctive assortment that includes popular health and wellness products alongside unique offerings helps retailers stand out in a crowded marketplace.
    • Reducing Lost Sales: Effective assortment planning minimizes the risk of stockouts on high-demand health and wellness items, preventing customers from shopping elsewhere.
    • Supporting Omnichannel Strategies: Well-executed assortment planning ensures consistency across physical and digital touchpoints, creating a seamless customer experience.
    • Improving Operational Efficiency: A thoughtfully curated assortment reduces complexity throughout the supply chain, from procurement to warehouse management to in-store operations.

    As health and wellness continues to drive consumer spending, retailers who excel at assortment planning can capitalize on these trends more effectively than their competitors, turning market insights into tangible business results.

    AI-Powered Assortment Analytics Driving Retail Success

    The synergy of AI and data analytics into retail assortment planning is changing how businesses approach inventory management. Retailers using AI-driven predictive analytics have achieved a 36% SKU reduction while increasing sales by 1-2%, showcasing the efficiency of data-driven approaches according to a McKinsey report.

    Retailers face several challenges that can hinder strategic assortment planning:

    • Limited Understanding of Competition: Retailers struggle to gain comprehensive insights into their product assortments relative to competitors, often lacking visibility into their strengths and weaknesses across categories.
    • Data Overload: Assortment planning involves handling vast amounts of data, making it challenging for category managers to extract actionable insights without user-friendly tools and visualization.
    • Cross-Channel Consistency: With omnichannel retailing, ensuring consistency across physical stores, e-commerce, and other channels is complex. Misalignment can lead to customer dissatisfaction and loss of loyalty.
    • Adapting to Changing Market Trends: Identifying top-selling products and tracking consumer preferences is challenging. Balancing the right mix of products is crucial; without analytics, retailers risk lost sales or excess slow-moving inventory.
    • Scalability and Efficiency: As retailers expand into new markets or categories, scaling their assortment planning processes efficiently becomes a challenge. Legacy systems and manual methods often fail to support the agility needed for quick decision-making at scale.

    DataWeave’s Assortment Analytics helps retailers address these challenges by providing a robust, easy-to-use platform that delivers actionable insights into product assortments and competitive positioning. With AI-driven, contextual insights and alerts, retailers can effortlessly identify high-demand, unique products, capitalize on catalog strengths, optimize pricing and promotions, improve stock availability, and refine assortments to maintain a competitive edge.

    Beyond Data: Actionable Insights That Drive Results

    DataWeave’s platform provides a comprehensive, insight-led view into assortments through several key dimensions:

    • Stock Insights: Monitor stock changes across retailers to stay updated on availability.
    • Category and Sub-Category Insights: Analyze assortment changes, identify newly introduced or discontinued categories, and track leading retailers in specific segments.
    • Brand Insights: Identify newly introduced, missing, or discontinued brands, as well as leading brands within chosen categories.
    • Product Insights: Identify bestsellers and evaluate their impact on your portfolio, analyzing pricing and promotions.
    • Personalized Recommendations: Receive suggestions tailored to your behavior and user profile to refine decision-making.
    • User-Configured Alerts: Stay informed with alerts designed to highlight significant changes or opportunities.

    The platform addresses data overload by providing an intuitive, insight-driven view of your assortment. Category managers gain a comprehensive, bird’s-eye perspective of key changes within specified timeframes, allowing them to focus on what matters most.

    Preparing for the Future of Retail Health

    To avoid supply chain bottlenecks, inventory shortages, and out-of-stock scenarios, retailers are strategically using data insights to anticipate fluctuations in demand and proactively plan how to manage disruptions that could affect their assortments.

    For variety that satisfies consumers’ diverse product needs, retailers are using data insights to determine whether to collaborate with nimble suppliers to promptly fill any gaps.

    To further strengthen their assortments’ attractiveness, retailers are using AI-powered pricing analytics to offer the right product at the right price. These analytics help retailers know exactly how they compare to rivals’ pricing moves with relevant data so they can keep up with market fluctuations and stay competitive by earning consumer engagement, sales, and trust.

    To Conclude…

    Like nourishing habits that improve consumers’ health, data insights improve retailers’ e-commerce health. Advanced assortment and pricing analytics, powered by artificial intelligence, help retailers make better decisions faster to boost their agility, outmaneuver rivals, and fuel online growth.

    In a retail landscape where consumer preferences for health and wellness continue to evolve rapidly, the retailers who thrive will be those who leverage data and AI to understand, anticipate, and meet these changing demands with the right products at the right time. Reach out to us to know more.

  • How DataWeave Enhances Transparency in Competitive Pricing Intelligence for Retailers

    How DataWeave Enhances Transparency in Competitive Pricing Intelligence for Retailers

    Retailers heavily depend on pricing intelligence solutions to consistently achieve and uphold their desired competitive pricing positions in the market. The effectiveness of these solutions, however, hinges on the quality of the underlying data, along with the coverage of product matches across websites.

    As a retailer, gaining complete confidence in your pricing intelligence system requires a focus on the trinity of data quality:

    • Accuracy: Accurate product matching ensures that the right set of competitor product(s) are correctly grouped together along with yours. It ensures that decisions taken by pricing managers to drive competitive pricing and the desired price image are based on reliable apples-to-apples product comparisons.
    • Freshness: Timely data is paramount in navigating the dynamic market landscape. Up-to-date SKU data from competitors enables retailers to promptly adjust pricing strategies in response to market shifts, competitor promotions, or changes in customer demand.
    • Product matching coverage: Comprehensive product matching coverage ensures that products are thoroughly matched with similar or identical competitor products. This involves accurately matching variations in size, weight, color, and other attributes. A higher coverage ensures that retailers seize all available opportunities for price improvement at any given time, directly impacting revenues and margins.

    However, the reality is that untimely data and incomplete product matches have been persistent challenges for pricing teams, compromising their pricing actions. Inaccurate or incomplete data can lead to suboptimal decisions, missed opportunities, and reduced competitiveness in the market.

    What’s worse than poor-quality data? Poor-quality data masquerading as accurate data.

    In many instances, retailers face a significant challenge in obtaining comprehensive visibility into crucial data quality parameters. If they suspect the data quality of their provider is not up to the mark, they are often compelled to manually request reports from their provider to investigate further. This lack of transparency not only hampers their pricing operations but also impedes the troubleshooting process and decision-making, slowing down crucial aspects of their business.

    We’ve heard about this problem from dozens of our retail customers for a while. Now, we’ve solved it.

    DataWeave’s Data Statistics and SKU Management Capability Enhances Data Transparency

    DataWeave’s Data Statistics Dashboard, offered as part of our Pricing Intelligence solution, enables pricing teams to gain unparalleled visibility into their product matches, SKU data freshness, and accuracy.

    It enables retailers to autonomously assess and manage SKU data quality and product matches independently—a crucial aspect of ensuring the best outcomes in the dynamic landscape of eCommerce.

    Beyond providing transparency and visibility into data quality and product matches, the dashboard facilitates proactive data quality management. Users can flag incorrect matches and address various data quality issues, ensuring a proactive approach to maintaining the highest standards.

    Retailers can benefit in several ways with this dashboard, as listed below.

    View Product Match Rates Across Websites

    The dashboard helps retailers track match rates to gauge their health. High product match rates signify that pricing teams can move forward in their pricing actions with confidence. Low match rates would be a cause for further investigation, to better understand the underlying challenges, perhaps within a specific category or competitor website.

    Our dashboard presents both summary statistics on matches and data crawls as well as detailed snapshots and trend charts, providing users with a holistic and detailed perspective of their product matches.

    Additionally, the dashboard provides category-wise snapshots of reference products and their matching counterparts across various retailers, allowing users to focus on areas with lower match rates, investigate underlying reasons, and develop strategies for speedy resolution.

    Track Data Freshness Easily

    The dashboard enables pricing teams to monitor the timeliness of pricing data and assess its recency. In the dynamic realm of eCommerce, having up-to-date data is essential for making impactful pricing decisions. The dashboard’s presentation of freshness rates ensures that pricing teams are armed with the latest product details and pricing information across competitors.

    Within the dashboard, users can readily observe the count of products updated with the most recent pricing data. This feature provides insights into any temporary data capture failures that may have led to a decrease in data freshness. Armed with this information, users can adapt their pricing decisions accordingly, taking into consideration these temporary gaps in fresh data. This proactive approach ensures that pricing strategies remain agile and responsive to fluctuations in data quality.

    Proactively Manage Product Matches

    The dashboard provides users with proactive control over managing product matches within their current bundles via the ‘Data Management’ panel. This functionality empowers users to verify, add, flag, or delete product matches, offering a hands-on approach to refining the matching process. Despite the deployment of robust matching algorithms that achieve industry-leading match rates, occasional instances may arise where specific matches are overlooked or misclassified. In such cases, users play a pivotal role in fine-tuning the matching process to ensure accuracy.

    The interface’s flexibility extends to accommodating product variants and enables users to manage product matches based on store location. Additionally, the platform facilitates bulk match uploads, streamlining the process for users to efficiently handle large volumes of matching data. This versatility ensures that users have the tools they need to navigate and customize the matching process according to the nuances of their specific product landscape.

    Gain Unparalleled Visibility into your Data Quality

    With DataWeave’s Pricing Intelligence, users gain the capability to delve deep into their product data, scrutinize match rates, assess data freshness, and independently manage their product matches. This approach is instrumental in fostering informed and effective decisions, optimizing inventory management, and securing a competitive edge in the dynamic world of online retail.

    To learn more, reach out to us today!

  • AI-powered Product Matching: The Key to Competitive Pricing Intelligence in eCommerce

    AI-powered Product Matching: The Key to Competitive Pricing Intelligence in eCommerce

    With thousands of products and hundreds of online retailers to choose from, the average modern-day shopper usually compares prices across several e-commerce sites effortlessly before often settling for the lowest priced option. As a result, retailers today are forced to execute millions of price changes per day in a never-ending race to be the lowest priced – without losing out on any potential margin.

    Identifying, classifying, and matching products is the first step to comparing prices across websites. However, there is no standardization in the way products are represented across e-commerce websites, causing this process to be fairly complex.

    Here’s an example:

    What’s needed is a pricing intelligence solution that first matches products across several websites swiftly and accurately, and then enables automated tracking of competitor pricing data on an ongoing basis.

    Pricing intelligence solutions already exist. What’s wrong with using them?

    There are several challenges with the incumbent solutions in the market – the biggest one being that they don’t work in a timely manner. In essence, it’s like deferring the process of finding actionable information that helps retailers acquire a competitive advantage, and instead doing it in hindsight. Like an autopsy of sorts.

    Here are the various solution types we have in the market today:

    • Internally developed systems – Solutions developed by retailers themselves often rely on heavy manual data aggregation and have poor product matching capabilities. Since these solutions have been developed by professionals not attuned to building data crunching machines, they pose significant operational challenges in the form of maintenance, updates, etc.
    • Web scraping solutions – These solutions have no data normalization or product matching capabilities, and lack the power to deliver relevant actionable insights. What’s more, it’s a struggle to scale them up to accommodate massive volumes of data during peak times such as promotional campaigns.
    • DIY solutions – These solutions require manual research and entry of data. It goes without saying that due to the level of human intervention and effort required, they’re expensive, difficult to scale, slow, and of questionable accuracy.

    As common as it is nowadays, AI has the answer

    DataWeave’s competitive pricing intelligence solution is designed to help retailers achieve precisely the competitive advantage they need by providing them with accurate, timely, and actionable pricing insights enabled by matching products at scale. We provide retailers with access to detailed pricing information on millions of products across competitors, as frequently as they need it.

    Our technology stack broadly consists of the following.

    1. Data Aggregation

    At DataWeave, we can aggregate data from diverse web sources across complex web environments – consistently and at a very high accuracy. Having been in the industry for close to a decade, we’re sitting on a lot of data that we can use to train our product matching platform.

    Our datasets include data points from tens of millions of products and have been collected from numerous geographies and verticals in retail. The datasets contain hierarchically arranged information based on retail taxonomy. At the root level, there’s information such as category and subcategory, and at the top level, we have product details such as title, description, and other <attribute, value> relationships. Our machine learning architectures and semi-automated training data building systems, augmented by the skills of a strong QA team, help us annotate the necessary information and create labeled datasets using proprietary tools.

    2. AI for Product Matching

    Product matching at DataWeave is done via a unified platform that uses both text and image recognition capabilities to accurately identify similar SKUs across thousands of e-commerce stores and millions of products. We use an ensemble deep learning architectures tailored to NLP and Computer Vision problems specific to us and heuristics pertinent to the Retail domain. Products are also classified based on their features, and a normalization layer is designed based on various text/image-based attributes.

    Our semantics layer, while technically an integral part of the product matching process, deserves particular mention due to its powerful capabilities.

    The text data processing consists of internal, deep pre-trained word embeddings. We use state-of-the-art, customized word representation techniques such as ELMO, BERT, and Transformer to capture deeply contextualized text with improved accuracy. A self-attention/intra-attention mechanism learns the correlation between the word in question and a previous part of the description.

    Image data processing starts with object detection to identify the region of interest of a given product (for example, the upper body of a fashion model displaying a shirt). We then leverage deep learning architectures such as VggNet, Inception-V3, and ResNet, which we have trained using millions of labeled images. Next, we apply multiple pre-processing techniques such as variable background removal, face removal, skin removal, and image quality enhancing and extract image signatures via deep learning and machine learning-based algorithms to uniquely identify products across billions of indexed products.

    Finally, we efficiently distribute billions of images across multiple stores for fast access, and to facilitate searches at a massive scale (in a matter of milliseconds, without the slightest compromise on accuracy) using our image matching engine.

    3. Human Intelligence in the Loop

    In scenarios where the confidence scores of the machine-driven matches are low, we have a team of Quality Assurance (QA) specialists who verify the output.

    This team does three things:

    • Find out why the confidence score is low
    • Confirm the right product matches
    • Figure out a way to encode this knowledge into a rule and feed it back to the algorithm

    In this way, we’ve built a self-improving feedback loop which, by its very nature, performs better over time. This system has accumulated knowledge over the 8 years of our operations, which is going to be hard for anyone to replicate. Essentially, this process enables us to match products at massive scale quickly and at very high levels of accuracy (usually over 95%).

    4. Actionable Insights Via Data Visualization

    Once the matching process is completed, the prices are aggregated at any frequency, enabling retailers to optimize their prices on an ongoing basis. Pricing insights are typically consumed via our SaaS-based web-portal, which consists of dashboards, reports, and visualizations.

    Alternatively, we can integrate with internal analytics platforms through APIs or generate and deliver spreadsheet reports on a regular basis, depending on the preferences of our customers.

    To summarize

    The benefits of our solution are many. Detailed price improvement opportunity-related insights generated in a timely manner empower retailers to significantly enhance their competitive positioning across categories, product types, and brands, as well as ability to influence their price perception among consumers. These insights, when leveraged at a higher granularity over the long term, can help maximize revenue through price optimization at a large scale.

    Our solution also helps drive process-based as well as operational optimizations for retailers. Such modifications help them better align themselves to effectively adopt a data-driven approach to pricing, in turn helping them achieve much smarter retail operations across the board.

    All of this wouldn’t be possible if the product matching process, inherent to this system, was unreliable, expensive, or time-consuming.

    If you would like to learn more about DataWeave’s proprietary product matching platform and the benefits it offers to eCommerce businesses and brands, talk to us now!

  • 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.

  • U.S. Prime Day Deals 2022: Promotion Intelligence First Look

    U.S. Prime Day Deals 2022: Promotion Intelligence First Look

    As inflation hits another 40-year high at 9.1 percent, U.S. consumers geared up for their first sign of hope and relief in the form of anticipated discount buys – 2022 Amazon Prime Days, or so we thought. While Prime Days have grown to become a promotional period almost as important as Black Friday to digital shoppers, the combination of economic uncertainty, inflationary pressures, and supply chain challenges seemed to alter the discount strategy expected given activity seen during 2021 Prime Days.

    Our analyst team has been hard at work aiming to provide a ‘first look’ at 2022 Prime Day Promotional Insights, tracking discounts offered across 46,000+ SKUs within key categories like Electronics, Clothing, Health & Beauty and Home, on seven major retailer websites – Amazon, Target, Best Buy, Sephora, Ulta, Lowe’s and Home Depot. Our analysis compares prices seen during Amazon Prime Day 2022 on July 12th, to pre-Prime Day maximum value prices seen in the ten days leading up to Prime Days, to determine the average change in discounts offered during the promotional period. Below is a summary of our findings.

    Competitive Promotions Give Amazon a Run for their Money

    Amazon offered the greatest average discount enhancements for Electronics at 5.6 percent followed by Health & Beauty items at 5.1 percent, and Home products at 4.2 percent versus pre-Prime Day discounts seen across the categories considered within our analysis. The only category reviewed where average discounts were greater on a competitor’s website was on Target.com within the Clothing category. As seen below, Clothing on Target.com average discounts were 6.8 percent greater than pre-Prime Day offers, which was 2.6 percent higher than the average discounts offered for Clothing on Amazon.

    Target Capitalizes on Growth Opportunity in Clothing Category

    Diving deeper into the details of where Target won within the Clothing category, you can see a majority of their promotional activity took place within Women’s Accessories where discounts offered were 18.5 percent greater than those seen pre-Prime Day 2022, which was almost 15 percent greater than the discount enhancements seen on Amazon for Women’s Accessories. In fact, Women’s Shoes and Sneakers were the only two categories where the average discounts offered were greater on Amazon than on Target.com.

    Overall, the discounts offered on Target.com within the Clothing category were primarily concentrated within items priced $40 and lower, but what was most interesting is that within the $10 and under price bucket, Target offered average discounts of over 11 percent whereas Amazon increased prices for these items on average by over 9 percent.

    While most of the Clothing available on both Amazon and Target.com during Prime Days 2022 were offered without a price change, the greatest discount percentages tracked were within the range of 10-25 percent off on Amazon whereas Target chose to offer the bulk of their promotions at 25 percent off an up.

    Strategic Promotional Strategies Defined at the Electronics Subcategory Level

    When it comes to the Electronics category on Prime Day, the big question is always who will win the battle of the brands. Below shows the difference in average pricing and promotions discounts offered between products manufactured by Samsung versus Apple across each retailer platform, noting discounts were almost 3 percent greater on average for Apple versus Samsung products on Amazon, and Apple discounts were almost 5 percent greater on Amazon versus than those seen on Target.com.

    Amazon wasn’t going all in on Apple however, as we saw ‘Alexa’ devices (Amazon products) available on Best Buy and Target websites also, but the discounts were almost 4 percent greater on Amazon versus Target and over 7 percent greater than the discounts seen on BestBuy.com.

    While the average discounts offered within the Electronics category were greatest on Amazon (5.6 percent) versus Best Buy (3.9 percent) and Target (3.4 percent) as noted within the first chart of this blog and across brands and technologies considered above, the discounts offered on Amazon were strategically focused between 10-25 percent as seen below.

    Amazon’s Electronics promotions were also targeted at smaller price points, items priced between $20-500, whereas Best Buy and Target offered greater promotions for electronics priced $500 and up than Amazon.

    Below is a snapshot of price buckets tracked for Electronics available on BestBuy.com, highlighting where most of the promotional activity was targeted at products priced $50 and up during Prime Days 2022, with discounts ranging from 10 percent up to greater than 25 percent greater than pre-Prime day prices.

    The standout categories were TVs on Target.com with discounts averaging nearly 12 percent greater than those seen pre-Prime day, and smartphones on BestBuy.com with discounts averaging just over 11 percent greater than those seen pre-Prime Day. The category with the greatest average discount enhancements seen on Amazon during Prime Days 2022 was for Wireless Headphones with an average discount of 8.7 percent.

    Home is Where Amazon’s Heart Was on Prime Day

    Amazon dominated offers within the Home categories, especially for products within mid ($40-100) and higher price ranges (items priced $200-500), with the bulk of the discounts offered between 10-25 percent. There was little to no promotional activity seen across all price points on Lowe’s or Home Depot’s websites within the categories we tracked, and most other competitive offers on Home products were seen on BestBuy.com for products priced from $50-500. Even a subcategory like Tools offered deeper average discounts on Amazon (4.7 percent) than discounts seen on HomeDepot.com (1.1 percent) or Lowes.com (0 percent).

    For Large Appliances, Amazon was the only retailer to off any significant discount across each major subcategory with the greatest average discount being on Ovens at 6 percent, followed by Refrigerators at 4 percent. One caveat with this category, when we reviewed Large Appliance prices two weeks prior to Prime Days, we saw average price increases around 16.7 percent occurring on Amazon.

    During Prime Days 2022 however, Amazon also offered top average discounts for small appliances, except for on Instant Pots which appeared to have greater average discounts on Target.com (5.9 percent versus 4.2 percent on Amazon), and Vacuum Cleaners which appeared to have the best promotion of appliances small and large at 13.8 percent average discount on BestBuy.com. Another subcategory deeply discounted on BestBuy.com was weighted blankets, which averaged discounts around 18.5 percent versus Amazon’s average discount at only 6.2 percent.

    Health & Beauty Retailer Pricing Strategies Revealed

    Given the importance Health & Beauty Brands placed on Prime Day sales last year, we had anticipated to see more offers, especially within pure-play beauty retail channels, than we did for this booming category.

    Amazon drove most of the Health & Beauty offers seen averaging 5.1% discounts versus other retailers only offering less than 1% on average, but discounts were aimed at a targeted group of SKUs on Amazon, bringing the average discount lower overall. Most of the promotions offered on Amazon fell within mid-range price points ($20-50) and were discounted between 10-25 percent versus pre-Prime Day prices.

    Target.com offered the most comparable discounts to Amazon for Health & Beauty products on average, but their strategy primarily focused on items within the $20 and lower price range with discounts ranging primarily between 10-25 percent.

    More 2022 Prime Day Insights Coming Soon

    We know the significance visibility to critical pricing and promotional insights play in enabling retailers and brands to offer the right discounts to stay competitive, especially during promotional periods like Prime Days. While this blog is intended to provide a ‘sneak peek’ into 2022 Prime Day insights for the U.S. market, we will be providing more extensive, global coverage and will proactively share new insights with the marketplace as they become available throughout the month of July.

    Be sure to also check out our Press page for access to the latest media coverage on Prime Day insights and more. Don’t hesitate to reach out to our team if there is any particular category you are interested in seeing in more detail, or for access to more information on our Commerce Intelligence and Digital Shelf solutions.

  • The challenges in scaling a ‘House of Brands’

    The challenges in scaling a ‘House of Brands’

    Let’s start with the basics – what is a ‘House of Brands.’

    House of Brands is a portfolio management strategy that defines how a family of brands owned by one parent company, each independent of one another and each with its own audience, marketing, look & feel operate in harmony with each other. 

    Advantages of a House of Brands Strategy

    • The Profit Playbook: The playbook generated by the success of one brand can be leveraged to scale other brands.
    • Economies of Scale: Cost across Marketing, Supply chain, Advertising, and Operations gets shared across multiple brands helping optimize costs.
    • Market Coverage: Multiple products enable brands to cover multiple market niches and audiences while maintaining unique messaging for each niche. 
    • Future-Proofing: By hedging bets across multiple brands, it cushions the parent company against changes in customer preferences and trends. 

    … for these reasons and more, it’s no surprise that every digital-first consumer brand today aspires to leverage a portfolio strategy to become a House of Brands.

    More and more companies are slowly adopting this strategy

    • In the US the brands like P&G, Newell, and Unilever which found early success in the online space are quickly acquiring more brands and betting on the “House of Brands” strategy to scale.
    • In India, Unicorn D2C start-ups like MamaEarth, Good Glamm Group, Sugar Cosmetics, Rebel, Boat, and Lenskart to name a few, are already knee-deep into this strategy as their brand portfolio keeps growing.
    • And then there are brand roll-ups like Thrasio, Perch, HeyDay in the USA, Branded, Hero in the UK and Mensa, and GlobalBees in India which started as a House of Brands from the get-go.

    More Brands. More Data. More need for Monitoring!

    You cannot improve what you cannot measure! In order to scale these brands, the first thing needed is DATA. Data across all digital platforms – data on social media performance, customer engagement, eCommerce sales, product stock availability, pricing, reviews, and customer sentiment to name a few. This data will unlock huge value for brands and it gives them a sense of what’s working and what needs to be improved in order to increase sales & scale. 

    All brands need to track this information – but here’s a challenge unique to a House of Brands – it is the sheer volume & scale of data needed across multiple brands across multiple digital platforms! For example, a House of Brands with let’s say 10+ brands, each brand with 50 SKUs, selling on 10 eCommerce platforms is the equivalent of managing 10 retail shops with 500 SKUs! 

    Let’s look at some of the questions the analytics, marketing, and brand management teams at House Of Brands would ask. And the data they would need almost on a daily basis for every single brand. 

    • What is the search ranking for all of our SKUs across each and every single eCommerce store it is available on? How does this benchmark to the closest competitor? And are competitors using aggressive advertising strategies to outperform & overshadow our SKUs?
    • Are competitors offering discounts? Are those discounts higher than what we’re offering leading customers to purchase their products instead of ours?
    • Are my products & SKUs available and not out of stock across every single marketplace and online store?
    • Are positive ratings & reviews driving my customers to purchase my product? Or do our competitors have a better customer perception than my brand does?
    • Are Amazon and other marketplaces displaying my product content correctly so customers have enough information to make an informed purchase decision?

    … if the sheer scale across multiple brands was not a big enough challenge when this data needs to be tracked hyper-locally for each brand, it becomes anyone’s worst data nightmare!

    Need Data? Lots of it? No problem!

    To get ample data, across key KPIs brands need to invest in a Digital Shelf Solution. However, traditional Digital Shelf Solutions were built for brands that got a majority of their revenue from in-store sales and only a part of their revenue was being generated online. 

    That’s where DataWeave is different. DataWeave’s AI-Powered Digital Shelf Solutions was built with Digital Native brands in mind. 

    What KPIs do we help House of Brands track?

    • Keyword Search Ranking: Track & improve your search rankings for priority keywords. Boost product visibility and sales
    Keyword Analysis
    Keyword Analysis
    • Content: Optimize your brand’s product content to drive up conversions
    Content Quality Analysis
    Content Quality Analysis
    Availability Analysis
    Availability Analysis

    The following metrics are available to view in one single dashboard, across multiple online stores and multiple geographies making it so easy to get a consolidated view of the health of the entire portfolio of products! What’s more, we’ve created a dashboard with multiple views – brand-wise, function-wise & even hierarchy-wise. This means a brand manager can see all KPIs specific for only the brand they manage, while the marketing team can look at keyword search rankings across all brands and the leadership team can see a brand-level daily scorecard for a quick health check. And that’s not all! Our dashboard highlights insights that can be “actioned asap” to make it easier to understand what critical tweaks and changes can help improve sales. Lastly, as a House of Brands adds more Brands & SKUs to its portfolio, our solution has the full flexibility to add and delete SKUs on the go!

    If you are a House of Brand and wish to explore how some of the problems you face daily can be solved – please email: contact@dataweave.com.

    Brand Roll-Ups and House of Brands are always scouting for new brands to acquire. DataWeave has a unique product to help you track a category daily, highlighting brands that show exceptional KPIs across – Ranking, Reviews, Ratings, Bestseller ranks, Sales Estimates, etc. Read more about how VC’s & Brand Rolls up are using Data for faster Acquisitions. 

  • Share of Keyword Search Cinco de Mayo 2022

    Share of Keyword Search Cinco de Mayo 2022

    As inflation continues to hike costs for consumers and supply chains challenge them to maintain loyalty, there is still an active audience willing to pay the ultimate price for the convenience of food and alcohol delivery. That being said, we analyzed 8 popular Retail and Delivery Intermediary websites and 11 popular ‘Cinco de Mayo’ keywords to see which Brands are predicted to win the battle of Digital Shelf Share of Search this holiday.

    2022 Cinco de Mayo Share of Search Insights - Top Brands for 'Cinco de Mayo'
    2022 Cinco de Mayo Share of Search Insights – Top Brands for ‘Cinco de Mayo’

    Opportunities for Food & Bev on Cinco de Mayo

    While most of our analysis focused on popular Cinco de Mayo food and beverage products, none of these brands populated on either Target (pictured on left below) or Walmart (pictured on right below) page 1 search results for the term ‘Cinco de Mayo’. Keyword search results for this term are dominated primarily by décor brands as indicated below.

    Brands Achieving Top Share of Search for Food and Beverage Categories on Cinco de Mayo 2022
    Brands Achieving Top Share of Search for Food and Beverage Categories on Cinco de Mayo 2022

    Share of Keyword Search Results – Alcohol Category

    Three of the most popular alcohol types sought out during Cinco de Mayo are ‘Mexican Beer’, ‘Mezcal’, and ‘Tequila’. Below are the brands dominating Share of Keyword Search results on each of the major retail websites we researched.

    AmazonFresh, Meijer, Kroger, and Sam's Club Share of Search - Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022
    AmazonFresh, Meijer, Kroger, and Sam’s Club Share of Search – Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022

    We also reviewed the same keyword performance across popular delivery intermediaries to see how Share of Keyword Search altered for ‘Mexican Beer’, ‘Mezcal’, and ‘Tequila’. The results are below for TotalWine, Instacart, Drizly and GoPuff:

    TotalWine, Instacart, Drizly, and GoPuff of Search - Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022
    TotalWine, Instacart, Drizly, and GoPuff of Search – Beer, Mezcal, and Tequila Keywords on Cinco de Mayo 2022

    The keyword ‘Agave’ is also a popular search term within the alcohol category during the time leading up to Cinco de Mayo. We reviewed keyword search performance at various zip codes to see how price points that populated on page 1 search results varied given the change in median income. Below are the results:

    Share of Search for Alcohol by Price Point and Zip Code on AmazonFresh
    Share of Search for Alcohol by Price Point and Zip Code on AmazonFresh

    Share of Keyword Search Results – Grocery Categories

    We also reviewed some of the most popular grocery items purchased during Cinco de Mayo by Keyword Share of Search results to see which brands are primed to win the Digital Shelf this year. Below are the results for Target.com and Walmart.com.

    Walmart and Target Share of Search - Food and Beverage Keywords on Cinco de Mayo 2022
    Walmart and Target Share of Search – Food and Beverage Keywords on Cinco de Mayo 2022

    Below are the results for the same popular grocery items and alcohol keywords related to Cinco de Mayo and the page 1 results seen for Brand Share of Search on Safeway.com.

    Safeway Share of Search - Food and Beverage Keywords on Cinco de Mayo 2022
    Safeway Share of Search – Food and Beverage Keywords on Cinco de Mayo 2022

    Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions and help drive profitable growth in an intensifying competitive environment. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis, and let us know what other holiday insights you’d be interested in seeing this year. Happy Cinco de Mayo!

  • 11 Reasons why your eCommerce Business is failing

    11 Reasons why your eCommerce Business is failing

    No matter where your eCommerce business sells, there are some fundamentals that brands have to get right to achieve sales targets. Brands need to find the right product/market fit, nail their lead acquisition strategy, and design a qualified sales funnel to turn prospects into leads and eventually returning customers. They will also have to analyze their customer’s buying journey and get insights into competitors’ strategies to understand what works for their business.

    If your eCommerce business is struggling, read this blog to learn about steps you can take to increase sales and keep your business afloat. 

    1. Lack of social proof

    Customers often check for reviews or testimonials before making a purchase. Our decisions are consciously or unconsciously influenced by the opinions, choices, and actions of people around us. Social proof helps brands build customer trust, adds credibility to their business, improves brand presence, and validates customers’ buying decisions. 92% of consumers are more likely to trust user-generated content (UGC) and non-paid recommendations than any other type of advertising. Additionally, brands should also find ways to combat negative reviews since bad reviews can sometimes be extremely damaging. 

    Understanding these reviews or the impact of your brand’s social proof is critical. At DataWeave, we help brands analyze online reviews to understand customer sentiment and adapt to feedback to enhance their experience with your brand. 

    2. Slow site speed

    Site speed of the home page and checkout page on your D2C website can be a roadblock. Slow sections on your site like My Accounts, checkout, and cart are often overlooked when it comes to tracking site speed. Brands should run their checkout process at least once a month to ensure it’s fast, smooth, and bug-free. You can optimize images, strip unused scripts, implement HTTP/2, etc., to improve site speed and performance. 

    3. Poor customer service

    69% of US consumers say customer service is very important when it comes to their loyalty to a brand. Guaranteeing a return customer is important to maintaining customer loyalty. While the focus is on the first purchase for new customers, your brand’s customer service will determine if first-time customers become repeat buyers. Loyal customers are known to spend 67% more on a brand product than new customers, even if they make up only 20% of your audience. 

    Types of customer service
    Types of customer service

    4. Failure to send traffic to popular products

    Be it your own D2C website, or when selling on a marketplace, you should be able to drive traffic to your best-selling products. One of the best ways for sending traffic to popular products on your website is to run paid ad campaigns and reach new audiences with influencer marketing on social media. Brands can also attract customers with organic media such as writing blogs and producing podcasts. 

    If you’re looking at driving traffic to key products on Amazon & other such marketplaces, sponsored ads are the way to go! Sponsored ads help your best-selling products more discoverable & helps shoppers find your brand with ease. 

    5. Inadequate pricing

    Finding the right pricing strategy for your eCommerce business is crucial for optimizing sales and increasing revenue. The first step is to perform a competitor and historical data analysis to get a general idea of the market and then develop a pricing strategy that is the right fit for your products. Brands also have to ensure that they have dynamic pricing that can adjust according to supply and demand. 

    Our Digital Shelf solution at DataWeave helps brands track pricing for products across different pack sizes & variants across multiple online retailers and marketplaces helping them stay competitive in the market. 

    Optimize the right pricing strategy
    Optimize the right pricing strategy

    6. Not targeting the right audience

    One of the biggest mistakes that eCommerce businesses can make is targeting the wrong audience. It’s crucial for brands to define that target audience and then tailor products and marketing toward them. To increase sales as an eCommerce business, brands have to understand their audience, their interests, and how to appeal to their interest. Start by creating ideal buyer personas that represent your ideal customers. Also, segmenting audiences and targeting various groups based on buyer personas for ad campaigns will lead to better sales and revenue. 

    Targeting the right audience
    Targeting the right audience

    7. Poor product descriptions

    One of the major and common mistakes by eCommerce brands is using irrelevant product descriptions that are not optimized for the product. Customers don’t add products to their cart if they have difficulty finding sufficient information relevant to the product. Brands should write attention-grabbing descriptions optimized for SEO that are informative for the users. Here are some tips to optimize content to drive more eCommerce sales.

    At DataWeave, our AI-Powered solution helps brands optimize content and visuals across product pages to improve discoverability. 

    8. Not having multiple revenue streams

    Due to COVID-19, many businesses have had to modify or temporarily shut down their daily operations. However, finding new revenue streams can be a great way for eCommerce businesses to make up for the lost income and keep the company afloat. The best solution is to diversify your product offerings by offering commonly purchased products in bundles. 

    9. Low-quality visuals

    Businesses fail to hit their sales targets because of low-quality visuals in product descriptions. High-quality and custom images can improve conversion rates from both marketplaces and image-based channels like social media. Social media users are attracted to exciting, high-quality content that conveys a desirable lifestyle. Brands should use high-resolution, attractive pictures of their products. Brands can also utilize UGC and influencers to help build up their content libraries.

    Low-quality visuals
    Low-quality visuals

    10. Wrong Assortment. Poor Availability

    When your target audience lands on your eCommerce store and cannot find what they’re looking for, it leads to a poor shopping experience, but more importantly a lost sale for your brand! While you cannot have endless inventory, it’s essential to optimize your assortment & product availability to decrease the chances of your customer walking away. Assortment & availability optimization begins with analyzing current and historical inventory trends. If done manually, assortment can be a time-consuming task. A healthy assortment can increase retail sales by creating a positive shopping experience for your customers and encouraging them to return to your store again.

    11. Bad eCommerce UX

    Offering a sub-standard user experience is a common reason why eCommerce businesses find it difficult to increase sales. According to a study, the conversions can fall by up to 7% for every one-second delay in page load time. Businesses can use a countdown clock on their landing page and exit pop-ups to improve conversations. Your landing page and product descriptions should provide information that helps your users make a better and more informed decision. 

    Conclusion

    If your eCommerce’s business sales are tanking, improving site speed, customer service, social proof, and product descriptions are some of the levers you can pull to remedy the situation. Brands should also work on improving online reviews & ratings, availability, assortment, visuals, and website UX to improve customer experience. These steps not only increase loyalty but also improve customer retention. 

    Need help tracking online pricing for your eCommerce business? Or decoding customer sentiment from reviews they’ve left for your products? Or do you need insights into your product assortment and availability? Sign up for a demo with our team to know how DataWeave can help!  

  • eCommerce Performance Analytics for CPG Private Label

    eCommerce Performance Analytics for CPG Private Label

    The combination of economic uncertainty, inflation, and perceived affordability has increased consumer’s willingness to buy and try more private label products, challenging National brands to differentiate their eCommerce strategies, especially those related to price positioning, in other ways.

    Our previously released report, Inflation Accelerates Private Label Share and Penetration, confirmed 8 out of 10 brands with the highest SKU count carried across all grocery retailer websites to be private label, signaling the strength of their digital Share of Voice. Given the growing shift in consumer preference toward private label brands, we are providing access to the latest trends seen from September 2021 through March 2022. Below you will find a summary of what the data revealed about the growing presence of private label brands on the Digital Shelf.

    Private Label Account and Category Penetration

    We analyzed private label penetration at an account level to understand which private label brands have the greatest presence on retailer digital shelves, and to see which retailers may be leaving product assortment opportunities on the table.

    Private Label Penetration Across Retail Grocer Websites

    As a retailer, it is important to understand how your private label penetration stacks up against the industry average at a category level, especially given the performance tracked for retailers included within our analysis and the vast number of SKUs they offer online (over 20,000).

    Private Label Penetration by Category Across Retail Grocer Websites

    The Private Label and National Brand Price Gap Widens

    Private label brands tried out of necessity mid-pandemic increased in popularity as grocery prices continued to rise, providing an opportunity for retailers to increase brand affinity and loyalty for their online shoppers. Retailers alike were able to keep affordability at the forefront of their strategies and maintain a price gap of 23% or more, despite inflationary pressures to increase prices.

    Private Label / National Brand Price Gap by Retailer

    Looking at the results at a category level, we can see that Meat is the only category found within our analysis where private label brands are priced higher than National brands at an average of 8% greater. The Alcohol & Beverages category tends to always see the greatest price gap between private label and National brands given the price variances by unit (ranging from under $10 to over $100), in this case averaging a 148% price gap.

    Private Label & National Brand Price Gap by Category

    Private Label Total Basket Value Comparison Across Retailers

    While SKU-level pricing is extremely important to product strategy, for a retailer, it is equally as important to be as mindful of the total basket value even more so now as consumers further their private label loyalty across various categories. A few SKU-level missteps in pricing decisions can exacerbate cart abandonment and negatively impact shopper loyalty in a world where prices can be compared instantly in the palm of your hand.

    Based on our analysis, Walmart and H-E-B private label products offered the lowest priced total basket of goods at $42.90 and $45.06 respectively, whereas AmazonFresh and Safeway offered the highest total at $73.19 and $69.52 respectively.

    Private Label Item Level Price Comparison by Retailer

    Inflation-driven Price Changes are on the Rise with Room to Grow

    Based on the 20,000+ SKUs analyzed, we saw a continual price increase every month since September 2021 when comparing future monthly prices to those we tracked in September. The greatest price increase happened in March 2022 at 12.5% on average, however, there are still 48% of SKUs that have yet to see a price increase even as inflationary pressures rise.

    When viewing the split between National and private label brand price increases in March 2022 versus September 2021, we saw National brands increased prices on average by 13% where private label brand prices only increased an average of 7%.

    Private Label & National Brand Price Change
    Private Label & National Brand Price Change (%)

    Price decreases are still occurring across all categories, despite inflation, but to varying degrees ranging from 5% for Deli items to 17% for Dairy & Eggs. Within the Dairy & Eggs and Pantry categories, private label brands reduced prices for an additional 10% of total SKUs compared to National brands.

    The greatest category of opportunity for price increases within private label were found within Beauty & Personal Care with 67% of private label products yet to see a price change since September 2021.

    Price Change (%) by Category and Brand Type

    Private Label Price Change Correlation to Product Availability

    The category with the greatest magnitude of price increase seen within private label brands occurred within Baby at 16.3% followed by Home at 14.3% on average. Private label products within Home and Baby categories were also showing the lowest availability rates, 75.9% and 79.5% respectively, indicating a high demand for these items even as prices increased.

    The private label categories with the smallest price increase on average were Dairy & Eggs at 2.4% and Other Foods and Pantry at 3.4% and 3.6%, respectively.

    Private Label Price Change Magnitude & Availability
    Private Label Price Change Magnitude & Availability

    While in many accounts both private label and National brands struggled with stock availability in March 2022, National brand availability is much lower (around 10% on average) than private label availability.

    H-E-B had the lowest overall product availability at 76% across both private label and National brands on average. Only Walmart had lower availability for Private Label at 75% compared to 93% for National brands, but they also had the greatest price gap between private label and National brands.

    Private Label & National Brand Product Stock Availability

    The Future of eCommerce Growth for Private Label

    Our greatest learning from this analysis is that it’s time for retailers to start thinking and planning more like the National brands they carry when it comes to positioning their private label brands for success. Successful retailers are taking this time to reset their private-label strategies and transfer short-term switching behavior into long-term customer loyalty.

    Retailers playing catch up have the opportunity to address some of the gaps highlighted throughout this analysis, for example, relative to pricing and assortment changes. Below are some of the highlighted opportunities:

    • Though inflation is driving price hikes, more than 50% of products analyzed have yet to see a price increase indicating an opportunity to protect margin
    • Narrowing the price gap between a store’s brand and National brands should not be the only focus as competitive private label brands are becoming a greater threat at a category and basket level
    • Modifying and expanding assortments as demand increases for private label can improve customer retention and loyalty, especially for cross-shopping consumers

    According to The Food Industry Association (FMI), only 20% of food retailers currently promote private brands on their homepages, and only 48% include detailed product descriptions indicating even more opportunities left on the table for retailers to optimize private label digital performance.

    Many leading retailers are leveraging real-time digital marketplace insights and eCommerce analytics solutions like ours to further their online brand presence and optimize sales performance. This report highlights only a small sample of the types of near real-time insights we provide our clients to effectively build competing strategies, make smarter pricing and merchandising decisions, and accomplish eCommerce growth goals. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.

    For access to a previously recorded webinar presented in partnership with the Private Label Manufacturers Association and conducted by DataWeave’s President and COO, Krish Thyagarajan, click here.

  • Critical Features of an Effective Price Intelligence Tool For Retailers

    Critical Features of an Effective Price Intelligence Tool For Retailers

    In the age of a mature eCommerce and omni-channel retail ecosystem, pricing is the premier competitive battleground. It’s both the biggest offensive weapon to capture market share – and the biggest vulnerability if you stumble. In fact, a recent Statista survey revealed that 70% of US online users prioritize competitive pricing in their digital shopping choices. Yet most retailers still struggle with consistent, profitable pricing often replying on instincts rather than data-led intelligence.

    That’s where Pricing Intelligence (PI) comes in. PI is a fast-evolving discipline powering data-driven, continually optimized pricing strategies to help merchants make rapid, surgical adjustments that attract customers and protect margins. Most retailers are aware of Pricing Intelligence tools, but they miss out on getting one that serves their needs and proves its ROI consistently.

    Because of course, not all pricing intelligence solutions are created equal. Here’s top features retailers looking to invest in a Pricing Intelligence tool should look out for.

    1. Accurate Product Matching

    Of course, accurate pricing data is table stakes for any PI solution – The core premise of any pricing intelligence tool is enabling robust product tracking and price monitoring of your own catalog against the competition. 

    So, a PI tool must take care of matching each of your product across all other sources, so that you can make a straightforward comparison and take actions.

    But since the internet is not a one standard entity and even the same or similar products can have different titles, descriptions, specs and images, most retailers end up capturing incomplete or inaccurate data completely undermining their intelligence. A good Pricing Intelligence tool like DataWeave’s should be able to leverage Similarity Matching and AI-based image tracking to bring more products under product matches and present a more complete picture.



    2. Width of pricing types and factoring in real net effective prices

    Product accuracy must extend far beyond just basic “landed” or “street” pricing and cover more types of specialized pricing situations. A robust pricing intelligence tool should automatically detect and handle nuanced mechanics like:

    – Bundled/kit/packaged pricing 

    – Pricing regulated by manufacturer policies (MSRP, MAP, etc.)

    – Complex promotional structures (% off, BOGO, BXGX, etc.)

    – Inventory-level or stocking threshold-based pricing

    – Zonal/regional taxes, fees and price variations

    – Segment-based pricing for members, loyalty tiers, etc.

    – Pricing tiers or breaks based on volume/purchase quantities

    Properly capturing and classifying these additional pricing nuances by retail vertical is key. Otherwise you’ll have major blind spots and inaccuracies that leave you open to being undercut or overpriced compared to real-world market dynamics.

    3. Real-Time, Continuous Monitoring and High Data Update Frequency

    Data points like product prices and offers get stale fairly quickly. Ideally, we want to see real time data. Real time is not achievable at scale, or might even be an overkill in many cases.

    However, an effective PI tool must present up-to-date data to the extent possible. Based on requirement this can vary from a day to a few hours thus helping the business stay ahead of the price curve.

    4. Scalable Coverage and Contextual Enrichment For Full Product Information

    For many retailers, one of the biggest pricing intelligence challenges is scaling comprehensive, accurate monitoring across their full product catalog and relevant competitor ecosystem. This is especially true for those operating regionally or with multiple banners/brands. 

    You need robust data collection capabilities to ingest and process pricing data on everything from big box retailers and national sellers all the way down to small mom-and-pop shops that may only sell locally – but could still impact your pricing perception.

    A best-in-class PI solution should have the ability to dynamically monitor millions of products and tens of thousands of competitor sources globally, processing all those inputs in a normalized, unified way. Additionally, your PI solution needs to be flexible to adapt seasonal or special requirements – whether that involves tracking key value items more frequently, or getting updates on pricing changes during festive seasons.

    But beyond just raw data collection scale, leading PI solutions also enrich and add context around that pricing data to make it far more actionable through technologies like:

    – Machine learning models to extract intelligent insights 

    – Semantic processing to identify nuanced pricing mechanics

    – Competitive product knowledge graphs to map relationships

    – Location data appending for geographic/zonal context  

    This enrichment bridges the gap between simple “list prices” and real-world factors like localized promotions, inventory levels, demand elasticity and other variables that should be driving more nuanced, profitable pricing decisions.

    5. Pricing Opportunities

    A good PI tool should present data at different levels of granularity: category, sub-category, brand, and individual product. This helps the category/merchandizing team or the pricing analysts to surgically strike problem areas. For instance, when you are tracking 1000s or even 100s of products, it’s next to impossible to go over every product and take pricing decisions.

    Furthermore, with large, diverse product catalogs, it’s impossible for category managers to manually monitor pricing on every SKU. Your pricing intelligence tool must automatically analyze and highlight prioritized pricing opportunities where action is needed – enabling efficient pricing decisions at a glance.

    6. Historical Pricing

    “Prediction is very difficult, especially if it’s about the future.” But they also say, history can be a useful predictor of the future. Nowhere is it truer than in competitive price intelligence.

    An analysis of historical data almost always shows a trend that can be capitalized on for competitive pricing. A good PI tool stores and presents historical pricing data in a useful manner.

    7. “It’s not [just] about the money”

    Retail is a highly competitive and commoditized sector. So, price is an important factor for a consumer when making a decision to buy a product. Having said that, as a retailer, you don’t always want to compete on pricing.

    You may want to compete through better packaging, or giving the user more choice (variants/colours/sizes), or better SLAs. This is where a Price Intelligence tool needs to go beyond just pricing. It needs to capture and present all other relevant data points associated with a product.

    8. Uncluttered User Experience

    Any tool built for a user needs to be usable, intuitive, and uncluttered. More so for busy managers who need to take several decisions quickly day on day. A Price Intelligence tool is in essence a Data Product. A data product is built on top of a lot of data; however, a good data product is one “where data recedes to the background”.

    A data product is not one that delivers a lot of data, but one that delivers actionable data and insights based on data. Data presentation is also another important aspect. A good PI tool delivers the most important data points in formats and templates that a customer can easily consume.

    DataWeave provides Competitive Intelligence for retailers, brands, and manufacturers. It is built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches.

    DataWeave is powered by distributed data crawling and processing engines that enables serving millions of data points around products data refreshed on a daily basis. This data is presented through dashboards, notifications, and reports. PriceWeave brings the ability to use BigData in compelling ways to retailers.

    PriceWeave lets you track any number of products across any categories against your competitors. Still not convinced? Try us out. Just send us a request for a demo.

  • 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. 

  • 2021 Cost-Push Inflationary Trends Ran Rampant, Impacting Holiday Discounts

    2021 Cost-Push Inflationary Trends Ran Rampant, Impacting Holiday Discounts

    Business has been anything but usual this holiday season, especially in the digital retail world. The holiday hustle and bustle historically seen in stores was once again occurring online, but not as anticipated given the current strength of consumer demand and the reemergence of COVID-19 limiting in-store traffic. While ‘Cyber Weekend’, Thanksgiving through Cyber Monday, continues to further its importance to retailers and brands, this year’s performance fell short of expectation due to product shortages and earlier promotions that pulled forward holiday demand.

    Holiday promotions were seen beginning as early as October in order to compete with 2020 Prime Day sales, but discounting, pricing and availability took an opposite direction from usual. This shift influenced our team to get a jump start on our 2021 digital holiday analysis to assess how drastic the changes were versus 2020 activity, and to understand how much of this change has been influenced by inflationary pressures and product scarcity.

    Scarcity Becomes a Reality

    Our initial analysis started by reviewing year-over-year product availability and pricing changes from January through September 2021, leading up to the holiday season, as detailed in our 2021 Cyber Weekend Preliminary Insights blog. We reviewed popular holiday categories like apparel, electronics, and toys, to have a broad sense of notable trends seen consistently throughout various, applicable marketplaces. What we found was a consistent decline in product availability over the last six months compared to last year, alongside an increase in prices.

    Although retailers significantly improved stock availability in November and early December 2021, even digital commerce giants like Amazon and Target were challenged to maintain consistent product availability on their website as seen below. While small in magnitude, there is also a declining trend occurring again closer toward the end of our analysis period, post Cyber Weekend, across all websites included in our analysis.

    Inventory Availability 2021 Holidays
    Source: Commerce Intelligence – Product Availability insights for Home & Garden, Jewelry & Watches, Clothing & Shoes, Bed N Bath, Lighting & Ceiling Fans categories

    Greater Discounts, Higher Prices?

    With inflation at a thirty-nine year high, retailers and manufacturers have realized they can command higher prices without impacting demand as consumers have shown their willingness to pay the price, especially when threatened by product scarcity. Our assessment is that while some products and categories have responded drastically, manufacturers’ suggested retail prices (MSRPs) have increased nearly seven percent on average from January to December 2021. MSRP adjustments are not taken lightly either, as this is an indication increased prices will be part of a longer-term shift in product strategy.

    2021 MoM Retail Inflation Tracker
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com & Target.com each month in 2021 comparing price increases from January 2021 base

    Our 2021 pre-Cyber Weekend analysis reviewed MSRP changes for select categories (Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion) on Amazon and Target.com, and found around forty-eight percent of products on Amazon and thirty-five percent of products on Target.com have increased their MSRPs year-over-year, but kept pre-holiday discount percentages the same.

    Looking more specifically as to what year-over-year changes occurred on Black Friday in 2021, we observed MSRPs increasing across the board for all categories at various magnitudes. This indicates why 2021 discounts appeared to be greater than or equivalent to 2020 for many categories, when in reality consumers paid a higher price than they would have in 2020 for the same items.

    2021 Black Friday MSRP Increases
    Source: Commerce Intelligence – MSRP Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Black Friday November 27th, 2021, versus average MSRP pricing for the same SKU count from November 20-26th 2021

    On Amazon.com, categories like health & beauty have already increase MSRPs by a much greater percentage and magnitude versus Target.com leading up to and during Black Friday 2021, while other categories like furniture have increased MSRPs evenly on average across both retail websites. The below chart cites a few specific examples of year-over-year SKU-level MSRP, promotional price, and discount changes within found within the electronics, furniture, fashion, and health & beauty categories.

    Black Friday 2021 vs. 2020 SKU-level Price Changes
    Source: Commerce Intelligence – MSRP Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Black Friday November 27th, 2021, versus average MSRP pricing for the same SKUs on Black Friday November 26th, 2020.

    Fewer, but Deeper Discounts

    From October through early November 2021, fewer products were discounted compared to this same period in 2020, and the few that were saw much deeper discounts apart from the home improvement category. The most extreme example we saw in discounts offered was within furniture where only three percent of SKUs were on discount in 2021 compared to twenty-six percent in 2020. Interestingly, the magnitude of discount was also higher pre-Cyber Weekend 2021 versus 2020, but this trend was not exclusive to furniture and was also seen within electronics, health & beauty, and home improvement.

    Pre-Black Friday 2021 and 2020 SKUs on Discount and Magnitude
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com & Target.com Pre-Black Friday average selling price during November 20-26th 2021 versus average selling price from November 13-19th 2021 compared to Pre-Black Friday average selling price during November 19-25th 2020 versus average selling price from November 12-18th, 2020.

    Within the furniture category, the subcategories offering the greatest number of SKUs with price decreases on Black Friday 2021 were rugs by a wide margin, followed by cabinets, bed and bath, and entertainment units, but the magnitude of discounts offered were all under twenty percent.

    2021 Black Friday Furniture Category Price Decreases
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    Accounting for this phenomenon could have been retailers’ attempts to clear inventory for SKUs which hadn’t sold even during the period of severe supply chain shortages. With more products selling at higher prices this year, retailers were also able to use fewer SKUs with greater discounts to attract buyer in hopes of filling their digital baskets with more full-priced goods, helping to protect margins heading in to Cyber Weekend. Scarcity threats also encouraged consumers to buy early, even when not on promotion, to ensure they would have gifts in time for the holidays.

    The same trends seen pre-Cyber Weekend 2021 were also seen on Black Friday with a year-over-year decrease in the percentage of SKUs offered on discount versus 2020, and steeper price reductions for the discounted products which can also be attributed to the increase in MSRPs.

    Black Friday 2021 and 2020 SKUs on Discount and Magnitude
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    2021 Black Friday Price Increases?

    We all know Black Friday is all about price reductions, discounts and deals and so it’s rare to see actual price increases, yet for Black Friday 2021, trends ran counter to this. We observed price increases across all categories for around thirteen to nineteen percent of SKUs, with an average price increase of around fifteen percent in 2021 versus an average of only two percent in 2020.

    SKUs with Price Increases Black Friday 2021 and 2020
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    At an account level, we noticed a few interesting differences happening on Black Friday 2021 versus 2020 regarding category price changes. On Target.com, almost ninety percent of the bed and bath SKUs analyzed had a price change on Black Friday in 2021 versus 2020 with eighty-two percent presenting a higher price year-over-year versus only around seven percent showing a decrease, where on Amazon nearly forty-four percent of bed and bath SKUs showed an increase in price and around thirty-eight percent showed a decrease. Except for the health and beauty category on Target.com, more than half of the SKUs in each category saw a price increase on Black Friday versus a price decrease.

    2021 YoY Price Changes on Black Friday
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs on Black Friday November 26th, 2020.

    The magnitude of year-over-year price changes seen on Black Friday 2021 was significant across all categories, but the magnitude of price increases found on Amazon.com within the health and beauty category outpaced the rest by far. We reviewed three hundred and sixty-five SKUs on Amazon.com within the health & beauty category and saw almost eighty-three percent of them had a price change with around thirty-one percent decreasing prices and around fifty-two percent increasing prices. This means that within the health & beauty category on Amazon.com, more than fifty percent of the SKUs tracked were sold at a one hundred and seventy-six percent higher price on average during Black Friday 2021 versus 2020.

    Magnitude of Black Friday 2021 Price Increases
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs on Black Friday November 26th, 2020.

    The subcategories offering the greatest number of SKUs with price increases on Black Friday 2021 were cameras, followed by men’s fragrances, laptops, and desktops & accessories, but the magnitude of discounts offered were all under ten percent.

    2021 Subcategories with Price Increases during Black Friday
    Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

    The Aftermath Post-2021 Cyber Weekend

    Extending this analysis beyond the holiday weekend, we analyzed price change activity from December third through the ninth across the top US retailers (chart below) and found that price decreases have been very minimal, comparatively speaking. Though there was a spike in number of price decreases from December 8th to the 9th, the percentage of SKUs with price decreases was still very low (less than three percent). We anticipate this trend will continue into 2022.

    SKUs with Price Decrease Post Cyber Weekend 2021
    Source: Commerce Intelligence – Pricing insights for Home & Garden, Jewelry & Watches, Clothing & Shoes, Bed N Bath, Lighting & Ceiling Fans categories

    A Sign of Things to Come

    A confluence of inflationary trends, product shortages and consumer liquidity have driven many marketplace changes to occur simultaneously. Government programs in the form of stimulus checks, have put extra money in consumers’ hands, and so they’ve been more willing to spend. That, coupled with the shock in the supply chain, has motivated people to buy far ahead of the 2021 holiday season. Hence, retailers have needed to rely much less on across-the-board discounts. Promotions have been more strategic – we’ve seen deeper discounts over fewer products, likely used to draw consumers in to buy certain items, and once they’re there, customers are buying everything else at a non-discount level. When these factors once again normalize, we could see a return to the “race to the bottom” that has occurred since the financial crisis of 2008-2009, but for once, retailers may be able to maintain some pricing power as the 2021 holiday shopping season played out.

    Even though performance was not as anticipated and holiday sales did not grow as rapidly as they did in 2020, Cyber Monday was still the greatest online shopping day in 2021. Through it all, retailers managed to keep their digital shelves stocked and orders filled in time for the holidays for the most part, running the risk of housing aged inventory if goods didn’t arrive in time. Despite predictions for steep promotions in January 2022, with supply chains still challenged and inflationary pressures still full steam ahead, we don’t anticipate much in the way of enhanced discounts to continue beyond the holidays.

    Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions like how and when to address inflationary pressures, while also supporting many other day-to-day operations and help drive profitable growth in an intensifying competitive environment. Continue to follow us in the coming weeks for a detailed 2021 year-end review across more retailers and categories. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.         

  • Are Your Digital Shelves Prepared for Green Monday?

    Are Your Digital Shelves Prepared for Green Monday?

    Traditionally, retailers have staged multiple promotions between Black Friday and before Christmas Day to keep consumers excited about holiday shopping, so it’s easy to see why one more promotional day might fall into relative obscurity. As if ‘Early Start’ offers to Black Friday and extended ‘Cyber Weekend’ promotions weren’t enough to plan for, eBay added another day into the mix called ‘Green Monday’, much to the benefit of consumers, as it furthers the window of opportunity to secure a bargain during the holiday season. 

    Green Monday falls on the second Monday of December and has historically been one of the greatest sales days of the year for eBay, often attracting last-minute shoppers or those searching for last-minute deals. However, because of the 2021 Global Shipping Crisis, there is speculation that Green Monday may be the last chance this year to have items delivered in time for Christmas. For this reason, we believe it could turn into quite a fruitful event for participating retailers if it encourages procrastinating shoppers that traditionally spend closer to December 25th to buy earlier in the season.

    This isn’t the first year retailers outside of eBay have offered Green Monday promotions, however. Our team has been actively monitoring activity on this day from 2017 through present, to not only assess which retailers participate in the event, but also to understand how the discounts may change surrounding the event. The categories monitored include Apparel (Clothing, Shoes & Jewelry), Bed and Bath, and Home and Garden, and we’ve identified products offered on discount by comparing each applicable product’s price on Green Monday versus the most commonly seen price for the product offered throughout the month of December.

    Better Promotions Than Boxing Day

    Taking a closer look at 2020 Green Monday discounts within the categories and retailers analyzed, apart from Wayfair.com, we see all offered more SKUs on discount on Green Monday versus the days leading up to and out of the event. Kohls.com led the pack with around 93% of SKUs offered on discount, followed by Macys.com with 95%, and Wayfair.com with 83%. Overall, the number of SKUs on discount on Green Monday were greater than the SKUs offered on discount on Boxing Day, which is traditionally known as a great day to bargain shop.

    Source: DataWeave Commerce Intelligence – Promotional Insights tracking Apparel, Bed & Bath, and Home & Garden category product’s online price on Green Monday 2020 in the US versus regular prices for the same products in the month of December each year.

    What’s in Store for Green Monday 2021?

    The insights we’ve tracked over the last four years have not indicated any signs to an end for Green Monday any time soon. As we see it, for consumers it is an extremely convenient time to order holiday gifts, and for retailers it is a good time to build brand trust and loyalty by fulfilling last minute orders at a great value, in time for the holidays.

    Our prediction for the categories analyzed is to expect to see more retailers participate in Green Monday 2021 to a greater degree (more SKUs on sale and enhanced promotions). For retailers in this analysis, we would anticipate HomeDepot.com to enhance the number of offers to match 2020 competitive activity, and for Wayfair.com to look at increasing the number of offers on Green Monday versus the period leading into the event.

    If you are interested in learning more about the details behind this analysis or our Promotional Insights solution, be sure to contact us. We can help you evaluate the effectiveness of your holiday promotional spend with access to near real-time marketplace insights on the brands, categories, and products your rivals promote, including discounts, campaign frequency and duration and more.

  • How Brands Boost Sales & Satisfaction on Walmart.com

    How Brands Boost Sales & Satisfaction on Walmart.com

    The explosive growth of online shopping has forced brands to re-examine their e-commerce processes to stay competitive and profitable. In particular, out-of-stocks are a common, costly retail challenge, as product shortages frustrate online shoppers – and even prompt them to leave brands.

    According to McKinsey & Company, forty-eight percent of consumers switched to a different brand in 2020 because those products were in stock. Among these consumers, seventy-three percent plan to keep using the new brands, linking product availability gaps to the erosion of sales and loyalty. Conversely, brands with effective inventory planning and replenishment can keep items in stock, drive sales and improve the customer experience.

    Retailers like Walmart, collaborating with these brands to meet customer demand, are still facing inventory challenges but, as noted in 2021 Q3 earnings, inventory was up almost twelve percent year-over-year as they worked to stay ahead of increased holiday demand. They have also adjusted in-store operations to accommodate ever-growing e-commerce demands, especially within grocery-centric categories, as digital grocery buyers now amount to more than half the U.S. population.

    Maximizing Conversions with Category Insights

    Walmart’s dot-com strategy is paying off in spades, considering they surpassed Amazon as the leading U.S. grocery e-commerce retailer in 2020 and grew another forty-one percent in Q3, 2021. Our team has been actively tracking digital shelf analytic KPIs on Walmart.com to identify inventory and promotional performance improvement opportunities at a category level to support brands in capitalizing on these digital growth opportunities.

    The latest analysis is summarized below, reviewing average category availability and discount trends occurring each week of the month, from May to August 2021, at a category level. A recent report found the 29th of each month to be the busiest day for online sales because consumers often get paid at the end of the month, which made DataWeave analysts wonder:

    • Which categories are maximizing their growth potential on Walmart.com and where are the greatest opportunities for improvement during periods of increased demand?
    • How do increased demand periods (like payday) impact category online availability?
    • Are category promotions offered at the right times throughout the month to best support demand?

    When Seasonal Demand for Groceries and Payday Merge

    Across all Walmart.com food categories tracked, Week 5 – where payday commonly falls for most consumers, had the lowest average product availability, while Week 4 had the highest average product availability for all categories except Deli and Fruits and Vegetables. These findings may inspire Walmart’s brand partners to rethink their inventory and assortment planning, replenishment and even pricing efforts to maintain a healthy stock closer toward the end of the month to match higher demand.

    The categories with the greatest difference in average availability during Week 5 versus the rest of the month were Snacks & Candy, Beverages and Alcohol, indicating consumers consistently made these types of purchases closest to payday, when income was highest throughout the month. Seasonality is a secondary factor that influenced demand for these items given events like Memorial Day, Fourth of July, Summer Break, and Back-to-School shopping all took place during our analysis. Additionally, most holidays overlapped payday, which also furthered Week 5 demand.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Coupling availability with discounts allows us to consider whether consumers buy more in Week 5 due to high discounts or increased purchasing power, or both. In reviewing the average category discounts offered within the same grocery-centric categories analyzed above, we found almost every grocery category showed a higher discount in Week 5 compared to the rest of the month, except for Bread & Bakery and Alcohol.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Regarding Alcohol, during Week 4, when average availability was the highest, the average discounts offered were the lowest. This can indicate inventory was primed for payday shoppers (and the holidays of course). Bread & Bakery offers the greatest average discounts when inventory levels are lowest on average, indicating Week 3 is a great time to stock up, while Week 4 might be a great time to buy the freshest inventory.

    The greatest average discounts in Week 5 were in Snacks & Candy, Pantry and Fruits & Vegetables. Deeper discounts for Snacks & Candy in Week 5 may have helped brands compete for consumers’ disposable income despite being a discretionary category. Pantry brands’ discounts may have reflected a need to compete for shoppers’ attention. During this period, consumers were out of the house more and less likely to use these grocery staples compared to earlier lockdown periods and cooler months.

    Making Specialty Categories and Health a Priority for Online Shoppers

    Interestingly, the only two categories where inventory was higher in Week 5 versus all other weeks each month were ‘Special Diets’ foods and ‘Summer Flavors’, although ‘Special Diets’ foods consistently maintained the lowest level of average availability each week across all food categories analyzed. This consistent lack of inventory could indicate a great opportunity for brands to increase inventory for dietary products sold on Walmart.com.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    The average availability for ‘Summer Flavors’ foods verifies brands are maintaining a solid replenishment strategy for these seasonal items, and a high likelihood consumers will happily find what they need to plan their Summer gatherings on Walmart.com. One alarming factor we found was the change in average discounts offered during Week 5 versus Weeks 1 through 4, indicating promotions surrounding payday may be driving sales volume versus organic demand.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Digital Growth Opportunity in Meal Kits and Kids’ Meals

    Two categories primed for growth, according to Statista, are meal kits and kids’ food and beverages. Their research indicates retail sales for kids’ food has grown steadily year-over-year since 2013, and a recent report also indicates meal kit sales are expected to more than double 2017 sales in 2022, reaching $11.6 billion in the U.S., spurred by pandemic-induced demand. A concerning find in our research indicates both categories, ‘Easy Meal Solutions’ and ‘Kid Friendly Foods’ on Walmart.com, showed great volatility when it comes to in-stock availability. For example, in Week 1, ‘Easy Meal Solutions’ had an average availability nearly half the average of the rest of the month (around nineteen percent versus nearly thirty-eight percent), and in Week 5, payday week, ‘Kid Friendly Foods’ saw the biggest drop in average availability compared to Weeks 1 through 4 (over sixty-seven percent versus seventy-five percent) indicating supply may not be keeping up with the heightened demand.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    The heightened average discounts offered during Week 5 for ‘Baby’ and ‘Pets’ items indicate two categories consumers will most likely stock up on during payday.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Back to School Stock-Outs

    U.S. retail sales unexpectedly increased in August, likely boosted by back-to-school shopping and child tax credit payments. Meanwhile, product shortages and other supply chain issues slowed 2021’s back-to-school sales, possibly affecting school supplies’ and clothing availability on Walmart.com. According to our analysis, the average product availability in Walmart.com’s school supplies category fell from over sixty-two percent during Weeks 1 through 4 to nearly forty-two percent in Week 5.

    Warmer weather, seasonal events, reduced lockdowns, and vaccination efforts led more Americans to resume in-person socializing, giving reason to update their spring and summer wardrobes. In July, Forbes shared that three-quarters of shoppers are purchasing apparel, accessories and shoes the most. On average, only around sixty-three percent of clothing items were available on Walmart.com during Weeks 1 through 4. However, in Week 5, that figure plummeted to just over thirty-eight percent, the most significant drop among all categories.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Demand for new fashion remained high throughout this period, seemingly fueled organically, as only moderate additional discounts took place in Week 5, and although the average discount on school supplies was only around twenty-seven percent during Weeks 1 through 4, it surged to just over forty-seven percent in Week 5. Generous additional discounts in Week 5 may have inspired online shoppers to shift spending from clothing to school supplies in late July and August ahead of students’ return to the classroom.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Prioritizing Product Availability with Digital Advertising Strategies

    Seventy-eight percent of B2C marketers increased their 2021 digital advertising spend to fuel online product discoverability (Share of Search), and sales and market share, but out-of-stock experiences simultaneously surged 172% this year from pre-pandemic levels. Paying for ads that drive traffic to your out-of-stock products can be as detrimental to your brand as a bad user experience. Our review of the ‘Featured Products’ sold on Walmart.com show consistent, low-levels of product availability each week throughout the months reviewed.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average availability percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    Additionally, the average discount offered on these products tended to be higher than most other categories reviewed, indicating brands participating in the featured product section of the website were not only investing in digital ads, but also doubling down with promotional activity as well.

    Source: DataWeave Digital Shelf Analytics for Brands – Category average discount percentages from May to August 2021 between Week 1 (the 1st to the 7th day of the month) and Week 5 (the 29th, 30th and 31st day of the month).

    How Brands can Replenish Their Digital Shelf

    It is well known just how important it is to have products available during the right time of day, week, month, or season to improve customer satisfaction rates, but with your e-commerce store open 24/7 and omnichannel fulfillment strategies in place, it drastically changes the way in which strategic execution is prioritized for a retailer to reduce basket abandonment and for brands to build loyalty.

    Our greatest takeaway from this analysis is realizing how crucial it is for brands to proactively track product availability and competitive pricing insights to stay ahead of the curve and achieve their digital growth goals. Early visibility to stock replenishment could help brands align with heightened cyclical and seasonal demand to avoid out-of-stocks and grow e-commerce sales.

    This is why more leading brands now rely on our Digital Shelf Analytics solutions, including Pricing and Availability insights, to keep eCommerce planning agile, to maximize online conversions, and ultimately maintain shopper satisfaction and loyalty.

  • 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.

  • How an American QSR (Quick Service Restaurants) improved its Business ROI Food Apps

    How an American QSR (Quick Service Restaurants) improved its Business ROI Food Apps

    Traditionally, Quick Service Restaurants (QSRs) such as McDonald’s or Burger King, have been strategically operating on a brick and mortar model. However, according to some studies, an average QSR generates as much as 75% of its sales from online orders.

    With the advent of delivery apps such as Uber Eats and Doordash, a significant portion of QSRs’ business has moved to these platforms. The war to top rank on one of these platforms is an even greater feat. With each brand competing for the top listing, it’s much less about the dollars you pay and much more about optimizing your investments.

    The relationship between QSR chains and food delivery apps has its advantages and disadvantages. One of the critical grouses QSRs have against food apps is the incremental marketing spend required to participate on the platform and the inability to measure the impact of their investment. What makes matters worse is the limitation in metrics even available to measure the impact – neither the food apps provide them, nor does anyone else.

    At DataWeave, we have made it our mission to enable QSRs to not only define measurable metrics to achieve a positive ROI for food app marketing investments, but we also equip QSRs with the tools to track their competitive performance at granular, zip code-based level so that localized strategies can be modified as needed. Below is an example of a 1000+ store chain QSR we partnered with to optimize a pre-existing investment made with a large food aggregator app. Within months of engagement with us, they were able to achieve a 3X increase in sales without adding any additional marketing dollars.

    Below are the pain points we identified and solved together:

    1. No Defined Metric

    Problem – No leading metric to track marketing performance

    One of the first issues we realized was that sales was not a good metric for tracking marketing performance as it’s a lagging metric and doesn’t capture the issues that help grow or suppress sales.

    Most of the sales are driven by rank in the cuisine category and searches for branded keywords. But, the QSR chain had no way to track these ranks.

    In fact, 70%+ sales go to the first five restaurants for the category and keyword

    Comparing ranking on food delivery platforms
    Comparing ranking on food delivery platforms across different categories and times

    Solution – Establish ranking as a clear marketing metric

    By aggregating data across different food app platforms comprehensively, i.e. across locations, at different times of the day, we established the ranking of the QSR chain in critical categories and for priority keywords, identifying where they under or over-performed relative to the competition. As we did this daily- this became a straightforward metric that helped establish the performance of their marketing campaign.

    2. Geographical & Categorical Challenges

    Problem: Identifying poor-performing stores and zip codes

    We realized  it was not a simple exercise to identify well performing stores on food apps since sales depend on many factors such as competition, population of the area, local cuisine preference, etc.

    Solution: Zip Code Ranking and Attributes

    We tracked the ranking of each store within each Zip Code for keywords and created a list of poor-performing stores. We also extracted attributes such as estimated time of arrival (ETAs), Delivery Fee, Ratings, Reviews, etc., for each of these poor performing stores, to identify the reasons for the poor ranking. 

    Analysing key metrics at a store level
    Analysing key metrics at a store level – identifying worst & best performing stores

    E.g., We realized 356 of the stores were not populating on first page results, primarily because of poor ratings and High ETAs. After the focused initiative, 278 of these stores started showing on the first page and increased sales by 23%. 

    3. Sensitivity Analysis Deficiency

    Problem: Not clear about the contribution of Rating, ETAs, Fees, etc. on the Ranking

    The exact ranking algorithms of these food apps are not publicly shared – so the QSR chain wasn’t clear which variable of rating, ETAs, fees, ad spend, or availability contributed more or less to the overall ranking. 

    Solution: Sensitivity analysis for measuring contribution 

    Comprehensive data for multiple zip codes in various timestamps was analyzed to determine which variable contributes most significantly to the rankings and when. We also conducted A/B testing – simultaneously testing two different variables, such as reducing ETAs at one store and improving ad spend at another, calculating which led to greater rank and sales impact.

    For example, we realized reducing publicized ETA’s (even by decreasing the delivery radius) contributed much more to improve the rankings than changes to ratings.

    4. An Unknown Competitive Landscape

    Problem: Tracking competitor performance

    For example, we found the QSR chain performed well in key urban centers, but the competition was doing even better, but there wasn’t a good way to track and compare the performance of the competitors.

    Solution:

    We started tracking the QSR chain and the competition for each of the metrics and started comparing performance.

    Analysing competitive performance
    Analysing competitive performance on key metrics such as ETA, Availability etc

    We quickly realized ranking started quickly improving as we gained a slight edge in each metric against the competitors. For example, 5 minutes less ETA adds to higher ranking.

    In six months of this exercise with the QSR chain, we improved the average ranking from 24 to 11 for the QSR chain, getting them featured on the first page.

    5. Blind Advertising Investment Opportunities

    Problem: 

    The QSR chain was not clear on which banners (Popular near you, National Favorites, etc.)  to choose to invest in, and had to depend on the recommendations of the food platforms entirely. 

    They weren’t even provided a clear view of which position made the banner visible and at what rank among those banners was their promo visible. They were at times the 7th promo in the 6th banner, which has almost zero probability of being discovered by the user – this happened despite paying heavily for the banners.

    Solution: 

    We aggregated data for all banners populated within each zip code and found out the ranking and in which position the QSR chain was visible.

    Analysing right banners
    Identifying and analysing right banners for advertising spends

    The QSR chain invested in 630 zip code-based banners with guaranteed visibility, but our assessment indicated the banners were only visible in 301 zip codes. After selecting suitable banners for promotions, we improved visibility to 533 zip codes within enhancing the budget.  

    We are now using the same strategy for refining discounts, offers, promotions, and coupons. 

    6. Lack of Campaign Performance Monitoring

    Problem: Unsure of the long-term impact of marketing spend

    In general, increasing marketing spend does give a temporary boost to sales, but the QSR chain’s question was, how can we measure the long-term impact i.e., ranking keywords and the targeted zip codes.

    Solution: 

    We created a simple widget for every marketing campaign which showed the rank for the keywords for selected zip codes before the campaign, during the campaign, and post the campaign, clearly establishing the midterm impact of the campaign. This constant monitoring allowed the QSR to also quickly pivot on their strategy on account of national holidays etc, and act accordingly.

    7. Non-Existent ROI Measurement

    Problem: Establishing the impact of ranking on sales

    Though the QSR chain could track sales that were coming via the food app channel, they had no way of knowing incremental organic volume driven by marketing efforts. 

    One missing variable here was how much of extra sales could be attributed to improvement of QSR ranking? 

    Solution: 

    By combining the sales data with aggregated insights over time, we established for the QSR chain how much increase in sales they could anticipate from an increase in ranking, also knowing which changed variables led to the percentage of change increase.

    So, in essence, we were able to tell the QSR chain that for each store how much sales would increase by improving ETAs, rating, ad visibility, availability, etc., enabling precise ROI calculations for each intervention they make for their stores.

    Increasing sales by 3x within six months was only the beginning, and the journey of driving marketing efficiency using competitive and channel data has only just begun. 

    DataWeave for QSRs

    DataWeave has been working with global QSR chains, helping them drive their growth on aggregator platforms by enabling them to monitor their key metrics, diagnose improvement areas, recommend action, and measure interventions’ impact. DataWeave’s strategy eliminates the dependence on food apps for accurate data. We aggregate food app data and websites to help you with analysis and the justification of marketing spend and drive 10-15% growth.

    DataWeave’s strategy eliminates the dependence on food apps for accurate data. We aggregate food app data and websites to help you with analysis and the justification of marketing spend and drive 10-15% growth.

    If you want to know learn how your brand can leverage Dataweave’s data insights and improve sales, then click here to sign up for a demo

  • Amazon Prime Day Secrets all Brands need to know.

    Amazon Prime Day Secrets all Brands need to know.

    Prime Day or not, brands need to make sure their Digital Shelf is well stocked, highly discoverable in crowded marketplaces, have the right offers and discounts to stay competitive, all while making sure their products have glowing reviews, ratings and optimized content. While this is a year-round effort, brands go the extra mile on Prime Day to make sure they’re putting their best foot forward.

    Methodology
    To understand how brands adapted their digital shelf for Prime Day, we examined data insights across Amazon in 6 markets and compared the following brand KPIs:

    • Share of voice (SOV): The percentage of a brands products that appear in the search results page for relevant keywords on Amazon.
    • Availability: The percentage of products in stock on Amazon for Prime Day.
    • Additional discounts: The reduction in the listing price of a product during Prime Day compared to before or after the event to see how brands adapted their pricing strategies to stand out from rivals

    Winning brands made sure their products were ‘highly’ discoverable

    With all the global lockdowns, home entertainment hit a new high. So we looked at the word “TV” to see which brand had the highest share against this keyword during the Prime Day event.

    • In the US – Samsung won hands down with close to 15% SOV. LG came in at a not so close 2nd with 9% SOV.
    • In the UK – Samsung won again with a whooping 16.7% SOV with Sharp at # 2 at 12%.

    Now let’s look at some key European markets

    • On Amazon Italy we saw a similar trend – Samsung & LG, neck to neck at 22% & 19% respectively.
    • Amazon Germany was no different – Samsung had the highest SOV at 15% and Philips far behind at 7%.

    Samsung has such a strong association with the keyword TV. This means, when customers are searching for TVs on Amazon in these regions – the brand that has the largest selection up on display for them to choose from is Samsung! That’s definitely going to have a positive impact on sales, don’t you think?

    Do you know which keywords you should be tracking for your brand? And do you know your Brand’s SOV against those keywords?

    … & finally, an outlier!

    • In Amazon France, LG took the lead for a change – with 15% SOV. But we have Samsung not far behind at 13%.

    Kudos to team Samsung!

    Winning Brands kept a close eye on product availability

    Poor product availability leads to lost sales. But not on Amazon Prime Day! Bigger brands that sell over 500 products created artificial scarcity by listing a chunk of products out of stock before the sale. And restocked aggressively during the sale.

    In contrast, the smaller brands that sold fewer than 100 products didn’t dare make such bold moves and stayed stocked up even pre-event to avoid even a single day of lost sales.

    Let’s look at some data from the US

    • The average availability for bigger brands selling 500+ products before the sale was 41% and then went up dramatically to 81.4% the day of the sale when they aggressively restocked.

    Similar trend in the UAE

    • Availability pre-sale was 26.2% and during the sale shot up to 87.6%!

    Various other markets displayed similar patterns. And this was only possible because these brands were able to track their availability with precision and plan their stock levels accordingly.

    How are you tracking your availability across marketplaces? Do you know when your products are out of stock and are in immediate need of replenishment?

    Winning Brands made strategic pricing and discounting decisions

    Discounts matter. Period. And brands that use discounts strategically, win.

    Let’s look at Airpods on Amazon in the US.

    • During the event, Apple had the highest SOV for the keyword Airpod at 7.5% followed by SkullCandy, an American audio accessory manufacturer at 5.6%. 
    • Here’s the interesting part – during the sale Apple offered just 3.4% additional discounts while SkullCandy offered 32.1% additional discount to try and win sales from Apple. And looks like it worked! Apples SOV dropped from 13.5% before the event to 7.5% during the event and SkullCandy’s SOV improved 

    Now let’s look at the same data cut in the UAE – a market where Apple products have a fair penetration, but not as high as in the US. They needed a more aggressive discounting strategy in this market.

    • In the US, during the sale, Apple offered additional discounts on just 30% of products. However, in the UAE that number rose to 90% – a clear strategy to make their product pricing more attractive to customers to win sales

    Discounts and markdowns aren’t always the answer to improving sales, but when used strategically can drive significant impact to your bottom line.

    Are you tracking your competitor pricing? Do you know if they’re keeping tabs on your pricing strategy to get ahead of you?

    Winning Brands made it to the Amazon Best Seller list

    Amazon Best Sellers are products that have the highest sales on Amazon. Products with a higher Amazon Best Seller Rank have higher sales.

    • In the US, Nintendo had the highest share in the Electronics Best Seller category during the sale at 18.6%. Before the sale their share stood at 22.5% – so they lost ground a little ground with a 27.2% drop in their Best Seller share. While they gave additional discounts of 17%, only 27% of their catalogue was discounted.
    • But here’s a brand that knocked it out of the park! The Razer had an SOV of just 1.18% before the sale and during Prime Day it shot up to 6%! A clear indication that sales for the Razer spiked exponentially during Prime Day. Could this be because Razer offered 100% of their catalogue on an additional discount of 31%? It’s a bold move that could have paid off and contributed to super high sales. 

    Now let’s look at France – the Fashion Capital of the World and which brand came out on top in the Fashion Best Seller Category

    • Footwear brand Havaianas had the highest SOV on Prime Day (13.48%) Not too surprising because before the sale they were at 14.09%
    • Now let’s look at the Best Seller Rank – Lacoste secured BSR #1 at the event. Pat on the back for them because before the sale they were at Rank 46! And post-sale they dropped to #6. Definitely a combination of techniques involved here that got them from #46 to #1 at super speed!

    What techniques have you tried to boost sales for your products on Amazon?

    Brands that do not optimize their Digital Shelf risk losing out on their share of basket. If you’ve been thinking about how to optimize your Brands Digital Shelf, then get in touch & learn how DataWeave can help!

  • This Mother’s Day, Dazzle Moms With These Beauty & Fashion Trends

    This Mother’s Day, Dazzle Moms With These Beauty & Fashion Trends

    Show moms extra love this year. With Mother’s Day coming up fast, savvy beauty and fashion brands will use this special occasion to inspire pampering and gift giving to fuel their e-commerce sales growth.


    This year, beauty and fashion are poised to boom, as 40% of consumers plan to buy beauty products and 37% will buy new outfits for going out. 1 According to eMarketer, apparel and accessories e-commerce sales will grow nearly 19% this year due to pent-up demand for clothing, while health and beauty sales will rise 16%. 2

    “People will be happy to go out again …
    there will be a fiesta in makeup and in fragrances.”

    ~L’Oréal CEO and Chairman Jean-Paul Agon

    After beauty and apparel sales declined last year, brands now seize every opportunity to capitalize on the categories’ resurgence in 2021. To differentiate their goods, brands can use e-commerce marketing best practices to position their fashion and beauty items as spectacular gifts that moms will love.


    Aligning with the latest trends can help brands boost online growth.

    Hot trends dominating beauty and fashion

    This Mother’s Day, shoppers can delight moms with beauty bestsellers like:

    • Mask-friendly makeup: As we continue to wear masks over the short-term, cosmetics like false lashes, smudge-proof mascara and ultra-hypoallergenic eyeshadow will remain popular. 3
    • Fragrances: Online fragrance sales rose 45% year-over-year in 2020. Clean and organic beauty categories grew 56% with fragrance brands growing the most. 4
    • Purpose-led brands: Consumers crave companies that care. More online searches contain keywords like “ethical beauty” and “sustainable makeup” for products that help consumers look good and feel good. 5

    Online fashion is in vogue

    Before the pandemic, consumers bought less than one-third of their apparel or footwear online; last year, the proportion surged to an astounding 51%. In 2021, consumers will invest even more in their wardrobes, including trends like:

    • Comfort: Athleisure will remain in demand as many consumers still prefer comfortable clothing when they work from home. 7
    • Beloved staples: Classic pieces like jeans, dresses and simple yet elegant tops are making a comeback as consumers start to go out more. 8
    • Retro ‘80s: Ladies are ready to party like it’s 1984. Bright and metallic colors and sequins for occasionwear (and even NFL linebacker-inspired shoulderpads) are recreating a fun, indulgent ’80s vibe. 9

    Brands’ secret weapon for a competitive advantage

    For successful Mother’s Day campaigns, more fashion and beauty brands will use digital shelf analytics for marketing decisions that maximize their ROI and e-commerce sales.

    To ensure online shoppers discover Mother’s Day products with ease, brands are using Share of Search insights to measure their share of digital shelf. These DataWeave analytics tell brands which keywords perform best. Brands can also benchmark their search and navigation visibility against rivals’ rankings across e-commerce categories, websites and geographic regions.


    Using Content Audit insights tells brands how their content is performing. They can discover and fill content gaps so their products show up more prominently. Optimizing content (like keywords, product page titles, descriptions, ads and sponsored space) and images to align with the retailers’ search algorithms ensures a consistent brand experience across all online channels. Improving content helps brands connect to consumers with marketing that resonates and inspires them to buy.
    Brands also use

    Pricing and Promotions insights to measure the effectiveness of their online promotions and secure sales. Brands can stay competitive by ensuring their pricing and promotions are in line with rivals’ offers, such as identifying first movers and rivals with the deepest discounts across retailers and SKUs. Brands can even determine how imitating rivals’ pricing and promotional moves could impact revenue and sales volume.

    Help shoppers make Mom’s day

    Since Mother’s Day is almost here, beauty and fashion brands can apply these data insights to connect consumers with a variety of products moms will love. Digital shelf analytics from DataWeave can help brands deliver timely campaigns, improve their return on digital marketing spend and make effective marketing decisions to drive e-commerce sales.

    1 Howland, Daphne. Wells Fargo sees permanent behavior shifts from the pandemic. Retail Dive. March 29, 2021.
    2 Droesch, Blake. US Ecommerce by Category 2021. eMarketer. April 27, 2021.
    3 Wood, Dana. Is Makeup Dead? The Robin Report. April 18, 2021.
    4 Larson, Kristin. Fragrance Sales Pick Up As Consumers Reengage With The Outside World. Forbes. April 27, 2021.
    5 What Can Brands Learn About Sustainability From Green Beauty Consumers? Beauty Business Journal. June 15, 2020.
    6 Howland, Daphne. Wells Fargo sees permanent behavior shifts from the pandemic. Retail Dive. March 29, 2021.
    7 Ibid.
    8 Bhattarai, Abha. Americans are starting to buy real clothes again. The Washington Post. March 18, 2021.
    9 Warren, Liz. Loose Denim and Bold Occasionwear on Full Display for Fall 2021. Sourcing Journal. April 2, 2021.

  • How Brands Make Their Marketing Magnetic

    How Brands Make Their Marketing Magnetic

    E-commerce is getting crowded.

    The proliferation of informed shoppers, e-commerce sites, and competitors of all sizes has increased the complexity of – and lucrative opportunities in – brand management.

    Now more brands rely on data insights to uncover specific ways to make their digital marketing more arresting, effective and profitable. Many brands struggle with e-commerce profitability due, in part, to advertising expenses that often yield lackluster results.1

    Analytics are growing in retail significance, as 88% of retail and consumer goods marketers say data improves their marketing by allowing them to personalize touchpoints. Relevant marketing and great marketers helps brands connect with consumers. Let’s see why leading brands are adding data insights to their 2021 marketing strategies to fuel online sales growth.

    Brands discover how to get discovered

    Consumer goods brands no longer leave it up to chance that consumers will find them online. The digital migration of companies and consumers over the past year means more noise for brands to breakthrough.

    Now search is growing in importance to improve brands’ online product discovery. Here’s why:

    • 87% of shoppers begin their hunt in digital channels3
    • 17% rise in paid search in late 20204
    • 24% rise in paid social advertising during the same period5

    To grab consumers’ attention by being easier to see, more brands are turning to data insights to track their online visibility.

    Brands need to look for ways to mitigate the high costs of acquiring customers online6

    Brands use marketing analytics related to keywords and navigation searches help brands know exactly how much space on the digital shelf they occupy across different online platforms.

    These DataWeave’s Share of Search solutions help brands understand what percentage of the digital shelf they command through either keywords or navigation. These insights can help brands decide whether to boost their brand visibility using sponsored ads to ensure their products show up more prominently in online search results to boost brand reach and awareness on each channel. For instance, brands can tell whether consumers search for products using branded, generic or category-specific keywords to align their marketing accordingly.

    In addition, brands can see how their organic and sponsored results rank compared to their competitors to spot ways to improve their visibility rank and decrease customer acquisition costs.

    Content differentiates a brand’s digital shelf

    For a striking digital presence and enhanced discoverability, leading brands measure how effectively their content inspires online shoppers to choose them.

    Brands can improve their digital marketing results by using Content Audit insights to spot patterns among their top-performing campaigns. They can also benchmark their content with category bestsellers to discover how to optimize their online performance to grow sales volume and market share.


    Strategic advertising requires high-quality photography and data-driven content7

    Using these data insights from DataWeave helps brands determine how well their content (including product description pages and images) align with e-commerce algorithms and lead to online traffic, engagement and sales. Brands also adapt faster by adjusting underperforming campaigns to reduce costs and optimize their digital marketing spends.

    Brands can fill content gaps across online channels with enhanced product information that aligns content and images with brands’ product information management (PIM). Using analytics to deliver a consistent brand experience across all online channels can help brands build relationships with consumers and earn their trust.


    Alluring promotions help brands secure the sale

    As e-commerce evolves, brands have matured beyond Google AdWords and Facebook campaigns to offer targeted promotions across digital touchpoints, which increases marketing reach and complexity.

    To boost clarity, be in demand and drive sales across online platforms, more leading brands use data insights to measure the effectiveness of their digital Promotions. Promotional insights from DataWeave keep brands informed of trending categories and products to keep their online offerings relevant and timely. Brands can pinpoint exactly which products to promote and which e-commerce sites help them drive the most profitable results with compelling digital offers.

    Brands that respond quickly to their customers’ needs have the upper hand8

    Analytics also keep brands competitive and relevant by benchmarking their promotional strategies with their rivals’ and continuously monitoring rivals’ online moves. For instance, brands can track the promotions their competitors offer for similar products across different e-commerce sites. These competitive insights help brands quickly spot opportunities to optimize their online conversions with appealing promotions that reflect market trends.

    Better marketing decisions can help brands grow sales and share

    Data insights make brands more enticing by connecting the dots among their online visibility, content and promotions. Brands uncover ways to make smarter marketing decisions faster to improve their top line and decrease customer acquisition costs. DataWeave analytics also help brands stand out and improve product discovery, engagement and sales. As a result, brands save time and boost their agility with relevant marketing that resonates and inspires shoppers to keep coming back.

    1 Jansen, Caroline, Cara Salpini and Maria Monteros. 8 DTC trends to watch in 2021. Retail Dive. February 3, 2021
    2 Casna, Kathryn. Ecommerce Trends That Are Shaping the Way Businesses Sell Online. Salesforce. 2021.
    3 Casna, Kathryn. Ecommerce Trends That Are Shaping the Way Businesses Sell Online. Salesforce. 2021.
    4 The Future of eCommerce in 2021. Shopify Plus. 2021.
    5 The Future of eCommerce in 2021. Shopify Plus. 2021.
    6 Jansen, Caroline, Cara Salpini and Maria Monteros. 8 DTC trends to watch in 2021. Retail Dive. February 3, 2021.
    7 Glasheen, Jasmine. 2021 Forecast: Next Gens in a Brand-New World. The Robin Report. January 3, 2021.
    8 Monteros, Maria. Forrester: Few brands can anticipate and act on consumer needs. Retail Dive. February 10, 2021.

  • [INFOGRAPHIC] 2020: The Year the World Navigated Uncertainty Together

    [INFOGRAPHIC] 2020: The Year the World Navigated Uncertainty Together

    The start of 2020 brought with it the promise of global economic growth. Markets in the US were on a steady rise we also witnessed demand from brands and retailers in Europe and the Middle East. All seemed to be on track to make it a year of plenty.

    Out of nowhere, the end of the first quarter saw the world coming to a grinding halt. The world was held hostage by a global pandemic and the force with which we were hit, was unprecedented.

    From February to mid-May we saw things come to a sharp halt. We at DataWeave seized this intermittent downtime to bolster our product offerings.

    On the flip side, when the world did start opening May onwards, we saw completely new categories take center stage digitally. With new habits and trends taking shape, the pandemic single-handedly caused exceptional growth in the Food and Grocery Delivery intermediaries. Predictably, the rest of the world followed. Our existing customers saw the competition rise steeply with everyone coming online. We invested substantially in our Digital Shelf Analytics solutions after noticing that e-commerce was seeing a boom. 2020 saw brands making their online presence the new norm. This meant that small, medium and large enterprises had to now divert their spending to analytics and e-commerce. 

    It is interesting to note that the rise in the food and grocery delivery segment gave brands another channel to focus on vis a vis their presence. Brands that were available on these sites focused on how they could optimize their sales on these channels, which proved to be the front runners during the height of the pandemic. While the challenges and opportunities for both these segments overlapped and seemed similar, our solutions helped measure and optimize brand performance across all online channels. Some of the in-demand solutions and analytics we saw our customers use were; share of search, content audit, assortment and availability, pricing and promotions, and ratings and reviews. 

    There were mixed emotions in the market, with regard to the best use of marketing spends. Human resource and client cutbacks happened across the board. At DataWeave however, we had the pleasure of onboarding 25 new clients including retailers and brands ranging from food and grocery delivery, home improvement from across multiple geographies.

    Infographics

    Throughout the year, the work never ceased at DataWeave. The team showed incredible resilience while working remotely, making sure our deliverables were being taken care of, at all times. Due to the e-commerce boom and immense pressure from existing and new entrants in the digital space, our clients saw a need to gather more insights. With the given uptick, we are happy to report that our stellar 95%+ accuracy record for in-depth insights at scale, was maintained through the course of all the work done.

    Looking forward to the year 2021:

    In the US, the adoption of e-commerce accelerated as traditional brick and mortar stores shut down and pivoted. To put things into perspective, e-commerce adoption grew only by 4.3% from 2014 to 2019. In just three months in 2020, e-commerce adoption grew at 4.3%! Add to that, with approved vaccines making their way slowly to the public, we do anticipate the travel sector to open up and we look forward to working with new clients.

    Nike’s Chief Executive, John Donahoe recently said, ” We know that digital is the new normal. The consumer today is digitally grounded and simply will not revert back…the shift to online sales could be a permanent trend.” We could not agree more! With online sales here to stay, brand and retailers’ requirements to keep their competitive edge will only continue to grow. We at DataWeave, look forward to delivering the results they want in this new year, and for the years to come.

  • Prime Day 2020: Home categories fuel retail rivalry & desirable discounts

    Prime Day 2020: Home categories fuel retail rivalry & desirable discounts

    According to our preliminary analysis of Prime Day 2020, Amazon’s rivals offered more generous discounts within Home categories to stay competitive as more consumers invest in their homes this year.

    This year the COVID-19 pandemic has transformed consumers into homebodies who increasingly work, learn and shop from home. This year also marks the first time Prime Day took place in the Fall, jumpstarting the holiday sales season.

    At DataWeave, we wanted to know whether Prime Day 2020 lived up to the hype and how Amazon’s deals compared to other retailers’ discounts. Our analysis examines products across four popular Home categories: Bed & Bath, Furniture, Kitchen and Pet Care.

    Our Methodology

    We tracked the pricing of several leading retailers (Home Depot, Target, Walmart and Amazon) selling the Home categories of Bed & Bath, Furniture, Kitchen and Pet Care to assess their pricing and assortment strategies during this annual sales event. Our analysis focused on additional discounts offered during the sale to estimate the true value that the sale represented to consumers. Our calculations compared product prices on Prime Day versus the prices prior to the sale. The sample consisted of up to the top 750 ranked products across 16 popular product types for the home.

    The Verdict 

    Overall, Amazon reported the lowest price reduction in all Home categories (12.4%), compared to Target (22.1%), Home Depot (16.5%), and Walmart (15.1%). Yet Amazon reported the second-highest percentage of additionally discounted products (9.6% vs. 11.0% for Target).

    After Prime Day ended, certain retailers’ Home assortments saw more significant price increases than others. For instance, 88% of Target’s 760 products in Bed & Bath, Furniture, Kitchen and Pet Care received a price increase during the post-sale period, compared to 47% of Walmart’s 1005 products. Walmart’s everyday low price strategy helps to explain the difference between the two big box retailers.

    These results suggest that Prime Day 2020 may boost Amazon’s marketing and PR engagement yet its rivals offered the most generous deals in Home categories. As home-related categories’ sales soared during the pandemic, Amazon’s competitors offered deep discounts to stand out online and grow their market share. As such, consumers may want to embrace the habit of comparing multiple retailers’ websites to discover the best Prime Day deals in Home categories.

    Top product types by additional discount

    In Bed & Bath, Target offered the biggest average additional discount (27.4%) and Amazon offered the lowest (13.3%). Bed sheets and pillowcases were a popular product category for additional discounts across all four retailers, with Target offering the best average additional discount at 31.3%. Other popular product types among rival retailers included blankets, comforters and bathroom furniture.


    In Furniture, Home Depot (20.5%) offered the biggest overall additional discount, closely followed by and Target (19.2%). Living room furniture was a popular subcategory for all four retailers, with Home Depot offering the highest additional discount (29.1%). Other popular product types included furniture for the bedroom, home office, kitchen and dining room.

    In the Kitchen category, Target offered the biggest average additional discount for small appliances (21.8%), a subcategory in which all four retailers offered discounts. Within the large appliance subcategory, Walmart’s additional discounts were nearly triple Amazon’s (15.7% vs. 5.6%).

    Within the Pet Care category, Target offered the biggest average additional discount (18.5%). Cat food was a popular product category, with Target offering the best average additional discount (50.0%). Other popular product types across all four retailers included dog collars, leashes and dry dog food.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into low, medium and high premiumness levels, with high indicating higher selling prices.

    In Bed & Bath, most retailers showed an inverse relationship between their additional discounts and the products’ level of premiumness. Target offered the biggest additional discounts across all levels of premiumness, more than double Amazon’s discounts (27.2% vs. 12.3%). Target’s bold discounting strategy shows a commitment to protecting its competitive position across the entire Bed & Bath category.

    By far, Amazon offered the greatest percentage of additional discounts in Bed & Bath compared to its rivals across all levels of premiumness. Comparatively pervasive discounts help the e-commerce giant offer a greater variety of appealing deals within this category.

    In Furniture, most retailers showed a direct relationship between their additional discounts and the level of premiumness. Notably, Home Depot offered massive additional discounts at the high premium level, nearly triple Amazon and Walmart (34.5% vs. 12.7%). This move suggests Home Depot is serious about winning the business of upscale consumers in the Furniture category.

    Target differentiated its assortment by discounting by far the greatest portion of its Furniture at all premiumness levels (22.4%) and Home Depot discounted the least (4.4%). Amazon and Walmart distributed the greatest portion of their additional discounts to the moderate level of premiumness. Target’s strategy tries to attract all Furniture shoppers while Amazon and Walmart try to make their mid-market offerings affordable to more consumers.

    Across all levels of premiumness for Kitchen products, Target offered the biggest additional discounts, including almost double Amazon’s discounts at the medium level (22.5% vs. 13.4%). Target’s aggressive discounting shows a desire to be more competitive by attracting consumers at all levels of the Kitchen category.

    In the Kitchen category, most retailers offered a direct relationship between the proportion of additional discounts and the level of premiumness, yet Home Depot showed an inverse relationship. Amazon’s proportion of additional discounts across all levels of premiumness nearly tripled Home Depot’s (10.1% vs. 3.7%). This discount strategy shows Amazon’s willingness to offer shoppers deals across a broader variety of Kitchen items.

    In Pet Care, Walmart offered the highest overall additional discounts (16.2%), which could fortify its low-cost leadership position for pet lovers at all price points.


    While Target offered the greatest overall percentage of additional discounts in Pet Care, Amazon applied more discounts to the higher end of the premium spectrum and Target focused on the lower end.

    Additional discounts across visibility levels

    In Bed & Bath, Target offered the highest overall additional discounts across all levels of visibility (27.3%) and Amazon offered the lowest (12.4%). Amazon focused its additional discounts on the most visible Bed & Bath products to help online shoppers discover those items with ease and make them appealing enough to add to their cart.


    Amazon offered the lowest additional discounts in the Furniture category across all levels of product visibility. Yet, among the Furniture category’s most visible items, Amazon offered its highest additional discounts. Home Depot’s additional discounts approach was the most aggressive except among the lowest product visibility levels. Home Depot’s discount strategy shows a desire to compete for Furniture’s most visible items.

    In the Kitchen category, Home Depot consistently offered the lowest additional discounts among products at the higher visibility levels. Conversely, Target was the most aggressive in this category, offering additional discounts of up to 43.2% at moderate levels of visibility and double Home Depot’s discounts (26.3% vs. 13.4%) among the most visible items. Amazon may feel confident that men already choose Amazon for their apparel needs.

    In Pet Care, the retailers generally offered the most additional discounts for items in the middle of the visibility spectrum. Walmart offered the most aggressive additional discounts among the most visible Pet Care items, more than double Target’s discounts (13.5% vs. 6.5%).

    Overall, Prime Day 2020 offered an ideal time for Amazon to attract homebound consumers to invest in domestic products, yet its rivals offer much higher additional discounts in Bed & Bath, Furniture, Kitchen and Pet Care. How about other categories? Watch this space for more insights!

  • How Prime Day 2020 Deals Influenced Retail Pricing Strategies

    How Prime Day 2020 Deals Influenced Retail Pricing Strategies

    Our preliminary analysis reveals that Prime Day 2020 motivated Amazon’s rivals to offer deeper discounts in key categories to try to make their merchandise more magnetic and lure consumers away from the e-commerce giant.

    This year’s Prime Day is momentous, as the COVID-19 pandemic has encouraged more consumers to make online shopping a more regular habit. It also marks the first time Prime Day took place in the strategically significant final quarter of the year, kicking off the holiday sales season.

    At DataWeave, we wanted to know whether Prime Day 2020 lived up to the hype and how Amazon’s deals compared to other retailers’ discounts. Our analysis examines products across three popular categories: electronics, beauty and fashion.

    Our Methodology

    We tracked the pricing of several leading retailers (Best Buy, Target, Walmart and Amazon) selling consumer electronics, beauty and fashion to assess their pricing and assortment strategies during this annual sales event.

    Our analysis focused on additional discounts offered during the sale to estimate the true value that the sale represented to consumers. Our calculations compared product prices on Prime Day versus the prices prior to the sale. The sample consisted of up to the top 750 ranked products across 21 popular product types in consumer electronics, beauty and fashion.

    The Verdict

    Overall, Amazon reported the lowest price reduction in the Electronics, Beauty and Fashion categories (13.4%), compared to Best Buy (22.5%), Target (21.7%) and Walmart (16.3%). Yet Amazon reported the second-highest percentage of additionally discounted products (12.0% vs. 15.7% for Target).

    After Prime Day ended, certain assortments reflected more significant price increases than others. For instance, 97% of Target’s 158 products in Electronics, Beauty and Fashion had a price increase during the post-sale period, compared to 49% of Walmart’s 986 products. This discrepancy makes sense given Walmart’s everyday low price strategy.


    These results suggest that although Prime Day generates tremendous media buzz for Amazon, the most generous deals come from its rivals. To stand out and lure shoppers away from Amazon, competitors offered comparatively deeper discounts, especially in categories in which they want to grow their market share. This means online shoppers would be wise to compare prices across retailers’ websites to find the best cross-category deals on Prime Day.

    Top product types by additional discount

    In Electronics, Best Buy offered the biggest average additional discount (22.4%) and Amazon offered the lowest (9.4%). Tablets were a popular product category among Amazon, Best Buy and Walmart, with Best Buy offering the best average additional discount at 19.1%. Other popular product types among rival retailers included TVs, desktops and laptops.


    In Beauty, Target (13.2%) and Walmart (13.1%) almost tied for the biggest overall additional discount. Makeup was a popular beauty subcategory, with Walmart offering the highest additional discount at 19.7%. Other popular product types included hair care, skin care and fragrance.

    In Men’s Fashion, Target offered the biggest average additional discount of 28.1%. Suits and blazers were a popular fashion subcategory, in which Target offered the highest average additional discount at 50.0%. Other popular product types included T-shirts and tank tops, shirts and jeans.


    Within the Women’s Fashion category, Walmart offered the biggest average additional discount of 20.5%. Tops and tees were a popular product category across all three fashion rivals, with Walmart offering the best average additional discount at 23.6%. Other popular product types included dresses, jumpsuits and jeans.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into low, medium and high premiumness levels, with high indicating higher selling prices.


    In Electronics, Amazon showed a direct relationship between its additional discounts and the level of premiumness; Best Buy and Walmart showed an inverse relationship. Best Buy offered the biggest additional discounts across all levels of premiumness, nearly triple Amazon’s discounts (20.7% vs. 7.0% ) at the low end of the premium spectrum, and more than double Amazon’s discounts (18.5% vs. 7.3%) at the moderate level. Best Buy’s discounting strategy show it’s serious about protecting its competitive position in electronics.

    Best Buy and Walmart offered the most additional discounts at the high end of the premiumness spectrum, making both retailers more competitive in the high-ticket electronics category. By contrast, Amazon offered nearly double the additional discounts of its rivals within the low segment, which helps to protect its margins while making products even more affordable and appealing.


    In Beauty, Amazon and Walmart offered their biggest additional discounts at the low premium level, possibly to position those products as loss leaders. Meanwhile Target nearly doubled and tripled its rivals’ additional discounts at the high premium level (30.0% vs. 16.0% for Walmart and 11.0% for Amazon) to stand out in this intensely competitive category.

    Amazon stood out by discounting the greatest portion of its Beauty offerings at all premiumness levels and Target discounted the least. Amazon and Walmart showed a direct relationship between their distribution of additional discounts and the beauty products’ premiumness level.


    Across all levels of premiumness for Men’s Fashion, Target offered the biggest additional discounts, including more than triple Amazon’s discounts at the high end (38.4% vs. 12.4%). Target’s aggressive discounting shows a desire to be more competitive within the most premium segment of Men’s Fashion.

    Amazon’s additional discounts accounted for the greatest percentage of its Men’s Fashions across all levels of premiumness, nearly triple Target’s overall average (15.4% vs. 5.3%). This approach shows Amazon’s willingness to give shoppers deals across a broader variety of Men’s Fashion items.

    In Women’s Fashion, Target’s and Walmart’s overall additional discounts were comparable, and Amazon’s discounts were consistently the lowest among all levels of premiumness. Walmart offered its most generous discounts at the low and medium level of premiumness, which could reinforce its low-cost leadership image.

    While Amazon and Target offered a comparable overall percentage of additional discounts in Women’s Fashions, Amazon applied more discounts to the higher end of the premium spectrum and Target focused on the lower end.

    Additional discounts across visibility levels

    In Electronics, Amazon offered the lowest average additional discounts across all levels of visibility. Among the most visible electronics, Amazon and Best Buy gave the most visible electronics higher additional discounts to make those items more alluring to help consumers find the items fast and add them to their online baskets.

    Among the Beauty category’s most visible items, Amazon and Target offered their highest additional discounts. Yet Target was most aggressive in beauty, offering a 30% additional discount at the most visible end of the spectrum as well as at the least visible. This discount strategy shows Target wants to compete in Beauty, spreading its generosity beyond an exclusive focus on highly visible items.

    In Men’s Fashion, Amazon consistently offered the lowest additional discounts at all visibility levels. Target was the most aggressive in this category, offering additional discounts of 50% at moderate levels of visibility and 34.5% among the most visible items. Amazon may feel confident that men already choose Amazon for their apparel needs.

    In Women’s Fashion, the retailers generally offered the most additional discounts for items at the higher end of the visibility spectrum. Walmart offered the most aggressive additional discounts among the most visible items in Women’s Fashion to try to boost its market share in this category.

    Overall, while Prime Day is an effective way for Amazon to boost brand engagement, its rivals overwhelmingly offer higher additional discounts in Electronics, Beauty and Fashion. How about other categories like the booming Home space? Watch this space for more insights!

  • Food Delivery Boom Fuels Competition Among Restaurants

    Food Delivery Boom Fuels Competition Among Restaurants

    This year, homebound consumers crave the convenience of food delivery.
    Growing 20% since 2015, restaurant delivery has sparked intense rivalry to reach consumers’ homes. Although the pandemic led to $165 billion in lost sales industry-wide between March and July, experts predict online food delivery sales will reach $220 billion by 2023, accounting for 40% of total restaurant sales.[1,2]

    This massive market opportunity makes food delivery an urgent priority for restaurants to stay competitive and solvent during the pandemic. This year nearly one in six U.S. restaurants have closed either permanently or long-term.[3]

    Also, 40% of U.S. operators say they will likely be out of business within six months if economic conditions persist and 60% of Canadian restaurants could close permanently by November.[4,5]

    COVID-19 compounds market complexity

    Powerful market trends are rattling restaurants. During the pandemic, nearly 70% of operators have added third-party delivery to lift sales.[6]

    This year, third-party delivery from food delivery apps like Uber Eats, Grubhub and DoorDash will grow 21% over 2018.[7] The global market for cloud kitchens (also called ghost kitchens or virtual kitchens), commercial kitchens intended for delivery-only orders, will grow from $650 million in 2018 to $2.6 billion by 2026.[8]


    To avoid the need to rely on delivery partners, many chains invest in their own last-mile delivery capability to serve their fleet of restaurants.
    E-grocery sales are poised to surge 40% in 2020 and meal kits have boomeranged back into popularity, nearly doubling 2019 sales.[9, 10]

    Consumers demand speed to keep their food fast, fresh and hot. Prompt service matters, as one survey found when consumers face a food delivery issue, 93% want it resolved within 10 minutes.[11]

    The recession and job losses mean more consumers now need affordable food options. Meanwhile, restaurants are investing more in technology to modernize operations for efficient omnichannel service.

    How restaurants are adapting to 2020’s disruption

    Restaurant prices have risen during the pandemic to cover operating costs. Third-party delivery fees have led 41% of consumers to prefer to order food by contacting the restaurant directly (vs. 16% for third-party delivery).[12] To optimize pricing competitiveness, more restaurants now compare their delivery fees and offerings with rivals’ to spot and correct gaps, and keep their prices affordable.

    To streamline operational processes and costs during the pandemic, 28% of restaurants shrank their menus.[13]

    For clarity on which items to keep, operators now use data insights on restaurant listings and menu items down to the ZIP code level. This information also helps them decide whether to adapt to consumers’ diverse tastes, including vegan, gluten-free and organic, for competitive local assortments.

    Outperform rivals: Restaurant operators seek proof of their brand visibility on food delivery apps’ homepages.

    Restaurants have discovered consumers welcome reasons to celebrate at home this year. One chain’s weekly virtual happy hours on Facebook Live drew 80,000 participants and a $40,000 sales increase from delivery and takeout orders.[14]

    More restaurants now compare their promotional strategies with rivals’ to evaluate marketing performance, including homepage discoverability and visibility ranking, to ensure consumers find their brand online with ease.

    Delivery speed and precision also matter. A survey found 70% of consumers had food delivery order complaints, including late delivery (50%), incorrect order (37%) and cold or stale food (36%).[15] Using accurate geographic data can help restaurants improve speed and the customer experience.

    To gain a competitive advantage in today’s booming food delivery market, a growing number of leading chains and food delivery providers are collaborating with DataWeave to access actionable insights to make better strategic and operational decisions faster. Using trusted insights to make data-driven pricing, menu and promotional decisions help restaurants save time, reduce risk and gain clarity in today’s evolving market.

    Applying DataWeave’s accurate, up-to-date information also helps restaurants deliver affordability, convenience and variety to remain responsive to consumers and agile among competitors. To see how DataWeave helps restaurants stay relevant and competitive, contact us today.

    [1] Rogers, Kate. Winter is coming, bringing a new challenge to already-struggling restaurants. CNBC. September 14, 2020.
    [2] Zahava Dalin-Kaptzan. Food Delivery: Industry Trends for 2020 and beyond. Bringg. April 30, 2020.
    [3] Klein, Danny. 100,000 Restaurant Closures Expected in 2020. QSR. September 14, 2020.
    [4] Rogers, Kate. Winter is coming, bringing a new challenge to already-struggling restaurants. CNBC. September 14, 2020.
    [5] Charlebois, Sylvain. Don’t Want to Save the Restaurant Industry? Fine, but Use it to Save the Canadian Economy. Retail Insider. September 11, 2020.

    [6] Rogers, Kate. Winter is coming, bringing a new challenge to already-struggling restaurants. CNBC. September 14, 2020.
    [7] US Food Delivery App Usage Will Approach 40 Million Users in 2019. eMarketer. July 2, 2020.
    [8] Levy, Ari. Virtual Kitchen, founded by ex-Uber execs to help restaurants with delivery, raises $20 million. CNBC. Sept. 8 2020
    [9] Redman, Russell. Online grocery sales to grow 40% in 2020. Supermarket News. May 11, 2020.
    [10] De Leon, Riley. How the coronavirus pandemic delivery surge created a lifeline for Blue Apron meal kits. CNBC. May 22, 2020.
    [11] Guszkowski, Joe. Delivery services have room to improve, consumers say. Restaurant Business Online. Sept. 1, 2020.
    [12] Guszkowski, Joe. Consumers’ desire to order directly from restaurants is a big opportunity. Restaurant Business Online. Aug. 27, 2020.
    [13] Romeo, Peter. Best practices for weathering a second COVID wave. Restaurant Business Online. Aug. 28, 2020.
    [14] Ibid. 
    [15] Guszkowski, Joe. Delivery services have room to improve, consumers say. Restaurant Business Online. Sept. 1, 2020.

  • JioMart Launches Online Grocery Store

    JioMart Launches Online Grocery Store

    JioMart, the online channel for Reliance Retail Limited, launched in December 2019 as a contender in the e-grocery segment. Currently in India, this segment is being dominated by bigbasket, Amazon, Flipkart Supermart, Grofers, etc. After less than a year and from their initial launch in Mumbai, they now have their presence in 205 cities across India.

    According to their recent press release, they claim to be clocking over 250,000 daily orders, compared to bigbasket’s 220,000 and Amazon’s 150,000. To get an understanding of this rapid penetration, we had a look at the PIN codes that JioMart serves, spanning the country.

    The map below represents the percentage of PIN codes that are being served by JioMart’s online grocery in each state:

    **Disclaimer -Map for representation purposes only

    While states like Chandigarh, Delhi and Punjab in the North are covered extensively, JioMart has a stronger distribution in the Southern states.

    The image below shows the top ten states in India where JioMart’s online grocery has the highest presence:

    They’re yet to launch in 14 more states but it’s interesting to note that in this limited time, they’ve managed to cover 14% of the PIN codes in the country and all this, in the midst of lockdowns.

    Assortment

    To get an idea of the assortment in their range, we analyzed select PIN codes across three tiers of cities in India. The parameters we looked at were categories, brands and discounts to get an understanding of how JioMart is stacking up against its competitors. The cities we examined were:

    • Tier 1 – Bangalore, Delhi, Kolkata, Mumbai
    • Tier 2 – Ahmedabad, Jaipur, Kochi, Visakhapatnam
    • Tier 3 – Mohali, Mysore, Nagpur, Siliguri

    In its range, they offer eight broad categories, of which, we focussed on the four that offer the highest selection of products: home care, personal care, snacks & branded food and staples.

    The table below represents the average selection of products offered across each tier.

    Overview of discounts offered and the private label split

    Out of the assortment we looked at in the three tiers, we noticed that an average of 18% of the products are JioMart’s private labels. What stood out further is that private labels accounted for 48% in the Staples category and 24% in Personal Care. We noticed this trend (increase in the private label) when we did an analysis of Amazon.

    When it comes to discounts, we noticed that a near-total 91% of the products listed are being sold at a discount. Out of this, the highest discounts were witnessed in the Home Care and Staples categories.

    The brands with the highest number of products listed were Good Life, Reliance, Amul, Gillette and items sold loosely. All these accounted for 14% of the assortment. Out of these, Good Life, Reliance and the loose items are JioMart’s private labels.

    Competitor analysis

    To get an idea of where JioMart stands with relation to its competitors, we focussed on food and essentials in the Tier 1 cities. The table below highlights the number of product offerings in each category:

    It’s clear that in these categories (food and essentials), JioMart has the least number of products on discount. There’s no doubt that bigbasket is miles ahead in its product range/ assortment.

    To get a better idea of the discounting patterns, we analyzed the same categories to get a count of the number of products being discounted, as well as the average discount being offered. 

    We noticed that JioMart bookended our analysis – the least average discount, across the most number of products. Grofers offered the highest average discount of 23% with Flipkart Supermart and bigbasket closely behind. Lastly, bigbasket had the least number of products on discount with a little over 53%.

    Conclusion

    JioMart launched during a tumultuous and unprecedented time; the COVID-19 pandemic and the subsequent nation-wide lockdowns. Given this trial by fire, they managed to make an impact in this highly competitive space. Their expansion plans of tying up with mom and pop stores to fortify their penetration, had to take a back seat due to the ongoing situation but is sure to resume once conditions improve. This set-back did not however deter JioMart from attracting strategic investments from Facebook, Google and 12 other investors  in a span of 3 months. 

    In a study by Goldman Sachs, it found that India’s e-commerce business is expected to grow at a compound annual growth rate of 27% by 2024, resulting in a $99 billion market share. What’s even more shocking is that 50% of this market will be captured by Reliance Industries. It, therefore, stands to reason that all we’ve seen and heard of so far, is merely the tip of the iceberg and there’s surely more to come in the near future.

  • Market Intelligence Platform with Kenshoo

    Market Intelligence Platform with Kenshoo

    We’re thrilled to announce that we have teamed up with Kenshoo to offer an integrated marketing solution that combines DataWeave’s digital shelf analytics and commerce intelligence platform with Kenshoo’s ad automation platform. This in turn, provides better recommendations on promotions to retailers and consumer brands.

    As e-commerce surges, consumer brands can now promote their products through retail-intelligent advertising. Product discoverability, content audit, and availability across large marketplaces can be critical to a brand’s success. Using DataWeave’s digital shelf solutions, Kenshoo now can offer marketers greater visibility into a brand’s performance.

    Even large retailers and agencies can use our commerce intelligence platform to improve their price positioning, address category assortment gaps, and more.  

    Through this partnership, Kenshoo – a global leader in marketing technology, can help its significant base of consumer brands and retailers invest their marketing dollars intelligently and in a timely manner.

    At DataWeave, we have constantly strived to bring in a holistic approach to help our customers optimize their online sales channels. This partnership furthers our resolve in this direction. As we collectively strive to adjust to a post-COVID-19 world, we are observing an acceleration towards digital commerce. This acceleration and change in consumer behavior is going to be a lasting change, creating significant growth opportunities for both DataWeave and Kenshoo.

    With this partnership, we look forward to helping our customers make timely, intelligent, and data-driven decisions to grow their business.

  • Amazon Triples Down on its Private-label Product Portfolio

    Amazon Triples Down on its Private-label Product Portfolio

    Among Amazon’s most prominent and decisive steps in achieving retail dominance over the last few years has been its focus on expanding its private label portfolio.

    The most recent collaborative report between DataWeave and Coresight Research determines that Amazon’s private label assortment in early 2020 has grown three-fold over the previous two years, most of which is in categories outside of apparel and accessories.

    In addition, the report covers various facets of Amazon’s private label penetration and strategy. These include the size of Amazon’s private label portfolio, the distribution of private label products by category, the product ratings and number of reviews, the average price points across products and brands, and more.

    Our detailed and proprietary Amazon private label dataset includes information on over 20,000 products and 111 brands.

    Some of our key findings are:

    • Amazon’s private-label offering spans 22,617 products across 111 identified private labels.
    • Around half of the private-label products are in clothing, footwear and accessories, which is lower than the three-quarters found in our similar research from June 2018, indicating Amazon’s push into a broader range of categories.
    • The average Amazon private-label product generates a customer rating of 4.3 out of 5, representing positive customer feedback overall.

    Amazon’s Private-Label Offering Spans 22,617 Products across 111 Identified Private Labels

    The number of private-label products—22,617—is more than triple the total of 6,825 from June 2018 (see our previous report). The number of private-label brands also increased substantially (up 50% versus June 2018), indicating that the e-commerce giant has stepped up its private-label strategy.

    Around Half of Private-Label Products Are in Clothing, Footwear and Accessories

    Just over half of Amazon’s private-label products are in “clothing, footwear and accessories,” versus almost three-quarters when we undertook similar research in June 2018, indicating Amazon’s push into a broader range of categories. Other categories that feature more than 1,000 private-label products include “home and kitchen,” “grocery and gourmet food” and “tools and home improvement.”

    Source: DataWeave/Coresight

    The Average Amazon Private-Label Product Generates a Customer Rating of 4.3 out of 5

    We examined feedback provided by Amazon’s private-label customers: Customer satisfaction can be measured by the average star rating that customers have left in reviews. We chart both average star rating and average number of customer reviews per product in the graph below.

    The average Amazon private-label product generates a customer rating of 4.3 stars out of 5, suggesting overall solid customer satisfaction levels.

    Source: DataWeave/Coresight

    The full report is available for Coresight’s premium subscribers. It includes further details of categories and subcategories that suggest longer-term implications—including how Amazon targets a niche customer base through specific category labels but appeals to broader consumer needs by offering multicategory labels.

    To access DataWeave’s proprietary database on Amazon’s private label brands and products, reach out to us today!

  • Coronavirus Outbreak: Impact on E-Commerce Retailers and Consumer Brands

    Coronavirus Outbreak: Impact on E-Commerce Retailers and Consumer Brands

    The Coronavirus, otherwise known as COVID-19, has made landfall on U.S. shores. At the time of writing this article, there are over 230 confirmed cases in the country and 12 deaths. The growing unease about the virus, which has quickly accumulated 95,000+ confirmed cases globally, has, among other things, adversely affected businesses and stock markets the world over.

    In the wake of this outbreak, U.S. based retailers and brands would be prudent to brace themselves and plan ahead to minimize disruptions as much as possible.

    Businesses and consumers in China, the global epicenter of the epidemic, have been dealing with these challenges over the last couple of months. It’s likely that some of the trends observed in China would be mimicked in the U.S. as well, something that domestic retailers and brands would do well to study and prepare for.

    The Inadvertent E-commerce Wave

    When the outbreak happened in China, it caused an uptick in e-commerce adoption as shoppers were reluctant to step out of their homes and instead, opted to shop for their goods online.

    Reports indicate that Chinese online retailer JD.com’s online grocery sales grew 215% YoY over a 10-day period between late January and early February. Similarly, Carrefour’s vegetable deliveries grew by 600% YoY during the Lunar New Year period. Online sales of Dettol, a disinfectant produced by Reckitt Benckiser, rose 643% YoY between 10 February and 13 February on China’s Suning.com.

    In Singapore, another region affected by the virus more recently than in China, Lazada’s grocery arm, RedMart, and Supermarket chain, NTUC FairPrice, both reported an unprecedented surge in demand, which tested their delivery capabilities to the limit.

    This bump in online sales isn’t just restricted to grocery, but other categories as well. Jean-Paul Agon, CEO of L’Oréal, recently said that online sales of the brand’s beauty products increased in China in February.

    Given such a consistent shift in shopping behavior across coronavirus-affected regions, it’s logical to expect that a similar trend would be followed in the U.S. – in fact, it might already be underway.

    A recent survey by Coresight Research indicated that 27.5% of U.S. respondents are avoiding public areas at least to some extent, and 58% plan to if the outbreak worsens. Of those who have altered their routines, more than 40% say they are “avoiding or limiting visits to shopping centers/ malls” and more than 30% are avoiding stores in general. The survey also found consumers will likely begin to avoid restaurants, movie theaters, sporting events and other entertainment venues.

    Therefore, it’s essential for U.S. retailers and brands to swiftly energize their e-commerce readiness and be fully prepared to cater to the circumstances-induced shift in shopping behavior, inclined toward online.

    A Logistical Nightmare

    The most obvious area of impact for retailers and brands is in their supply chain and order fulfilment operations.

    A large portion of consumer product manufacturers rely to some extent on China, and the potential impact of the virus on supply chain processes is inescapable. Chinese factories have been operating at partial capacity, impacting supply chains globally. This has largely affected highly popular e-commerce categories like consumer electronics, fashion and furniture.

    Shares in the U.S. of furniture e-commerce retailer, Wayfair, fell as much as 26% toward the end of February, according to a Bloomberg report. The is particularly revealing, as the online retailer reportedly relies on China for half of its merchandise.

    Retailers struggling to cope with this stress in their supply chain systems would do well to warn their customers beforehand about delays in deliveries, like AliExpress has just done.

    For categories like CPG, as consumers increasingly shop online, retailers that offer Buy Online Pick Up In Store (BOPIS), should expect a surge in its adoption, and reinforce their online infrastructure and in-store operations to cater to the rising demand.

    In addition to disruptions in the supply chain, several other mission-critical areas are likely to get affected too.

    Keeping Up With The Online Surge

    As with any event of this magnitude, the business implications reach far and wide. The following are a few areas that we’ve identified as critical, based on our experience working with retailers and brands. Being aware of and focusing on these issues are likely to alleviate some of the issues faced by consumers today.

    Fair pricing: There have been several reports of price gouging on e-commerce platforms. Examples include 2-ounce Purell bottles being sold for $400 and face masks for up to $20. While these prices have mostly been set by third party merchants, brands are likely to face the flak from consumers. A recent Bloomberg article reported that online retailers still rely partly on employees to manually monitor these items. This approach has obvious limitations, such as products quickly reappearing on the website after being de-listed. Brands and e-commerce platforms will need to explore automated ways of controlling their online pricing practices at large scale.

    3P merchant and counterfeit management: Often, unauthorized third-party merchants selling an original manufacturer’s goods are the ones who unreasonably inflate prices. These merchants tend to test the markets on online marketplaces with their pricing, which adversely affects the brand image of the manufacturer. Further still, they sometimes list counterfeit or fake goods that make incorrect or extravagant claims. Brands will need to swiftly identify and de-list these merchants from online marketplaces.

    Ensuring stock availability: During times like these, it’s a common sight to see empty aisles at supermarkets selling items like canned food, water, paper products and personal care products. Consumers will benefit from brands monitoring their stock availability at stores, which will help them better align their supply chain operations to the rapidly changing demand patterns across the U.S. map. This way, efforts can be more targeted at regions with severe shortages.

    Content compliance: Helium 10, a technology provider for Amazon sellers, reported that since 26 February, 90% of searches on Amazon are coronavirus related, and searches for hand sanitizers spiked to 1.5 million searches in February compared to 90,000 in November. As a result, to arrest exploitative practices, some online marketplaces have announced policy guidelines on product content claiming health benefits. Words like ‘Coronavirus‘, ‘COVID-19‘, ‘Virus‘ and ‘epidemic’ are, in fact, prohibited.  Amazon has already de-listed several merchants claiming fraudulent cures. Ebay has gone as far as to ban all new listings for face masks, hand sanitizers, and disinfecting wipes, due to regulatory restrictions. In this context, retailers and brands will benefit from deploying tracking mechanisms that quickly identify offenders.

    The areas of business presented above are by no means a comprehensive list for retailers and brands to rely on during this time. Still, these are critical impact areas for them to address, even as huge efforts are made toward managing highly stressed supply chains.

    DataWeave Offers Support

    The coronavirus outbreak is likely to get worse before it gets better. As we enter unchartered territories, DataWeave is offering to contribute in small ways, pro bono, by leveraging our expert talent and competitive intelligence technology platform, to address some of the challenges faced by retailers and brands.

    We’re announcing a limited-time, no-cost offer to detect and report on price gouging, the presence of unauthorized third-party merchants, as well as stock availability across U.S. ZIP-codes. This offer will be valid for 4-6 weeks (timeline will be flexible based on how the outbreak develops) and limited to monitoring the top 10 U.S. online marketplaces, as well as critical product categories such as medicinal and hygiene-related products, emergency food items, survival-related products, fuel, etc.

    Reach out to us for further details.

  • [INFOGRAPHIC] 2019 at DataWeave: Blazing New Trails

    [INFOGRAPHIC] 2019 at DataWeave: Blazing New Trails

    As another year comes to a close, we look back at 2019 with fond memories and look forward to the exciting new prospects of 2020. Take a trip with us as we highlight some of DataWeave’s milestones of the last twelve months.

    Over the course of the year, DataWeave’s success has gone hand in hand with the evolution of retail and e-commerce, reinforcing the relevance of our technology platform.

    Our rapid growth in the North American market is a reflection of how intense competition in the region is triggering the need for accurate, timely, and actionable competitive and market insights, as well as other avenues for retailers and brands to gain a competitive edge.

    Last year, we saw a resurgence of big-box (omnichannel) retailers as they adopted innovative approaches to play to their strengths (their offline stores). Offering buy online, pick up in store (BOPIS) or click-and-collect options, rolling out price match guarantee programs, and expanding their partnerships with delivery services like Instacart, enabled these retailers to leverage the best of both the online and offline worlds to compete with e-commerce firms.

    Amazon continues to dominate e-commerce with a daunting 38% share in the US. Still, the partnerships between brands and Amazon are increasingly being tested. Nike and Ikea recently joined the likes of Swatch and Birkenstock to sever ties with the retail behemoth. This seemingly growing trend is largely due to counterfeits continuing to leak through the system.

    Brands that used to de-prioritize their focus on their eCommerce channel (as it often was only a small portion of their revenues) have come to realize that consumers use large marketplaces like Amazon not just to shop for products but also to perform product research. As a result, how these brands are represented and sold online impact their offline sales. And with the onset of BOPIS and click-and-collect initiatives, brands can now analyze this correlation even at a hyperlocal (ZIP-code) level.

    Large marketplaces, for their part, have started taking advantage of the increasingly brand-agnostic shopping behavior of consumers by launching ad-platforms for brands and manufacturers, enabling them to boost their visibility online.

    Due to such sweeping transformations to the market landscape, brands and retailers are increasingly looking more toward intelligent tech-based solutions to help them gain a competitive edge.

    In order to effectively serve the growing need for competitive and market insights, we’ve pushed our platform to its limits and beyond. It’s our constant endeavor to innovate and improve. This is evident with the launch of a host of new features on our product suite, especially Brand Analytics – designed to enable consumer brands to protect their brand equity and optimize e-commerce performance.

    One of the key factors that enabled us to achieve all the milestones we did is the aggressive hiring of some of the most skilled talent in the tech industry. Our team grew by 44% in 2019, giving us additional confidence to raise the bar on our capabilities and offer 95% accuracy in our data and insights to our customers consistently.

    We’re encouraged by the fact that we’ve more than doubled as a business, year-over-year, for the past several years, without depending solely on growing the team, but also by consolidating our technology stack, optimizing our processes, and scaling our products.

    Here’s a sneak peek into our performance in 2019:

    2020 Vision

    The upcoming year promises to be an exciting one for the retail industry and the consumer brand space at large. We plan to be at the helm and increase our footprint all around. There’s a strong focus to expand our US team and consequently, the business. While we continue to strengthen our roots in India, we will look toward other mature markets like the UK, Germany and the Middle East as well.

    On other fronts, we’re gathering steam on new partnership engagements – consulting firms, ad tech firms, marketing agencies and complementary technologies. We will also expand our foray into the travel and delivery services verticals.

    With our diversifying portfolio, we haven’t lost sight of one of the most important aspects of any successful company – its employees. We will continue to keep our employees engaged, motivated, and satisfied by providing vertical and horizontal career growth opportunities, conducting personalized training programs, organizing hackathons, fostering cross-team collaboration and learning, and encouraging everyone to periodically blow off some steam at company retreats and the ferociously fought in-house sports tournaments.

    Here’s to a stellar 2020 of empowered retailers and brands. We wish them well as they navigate the dense competitive landscape, knowing that they have an ally in their corner with DataWeave.

  • 3 Common Problems Brands Face in eCommerce | DataWeave

    3 Common Problems Brands Face in eCommerce | DataWeave

    Over the last three years, I have helped deploy eCommerce analytics solutions for several brands and manufacturers globally. During this time, I have conversed with day-to-day users up through C-Suite executives of some of the world’s most successful brands, while also working with the founders of startup brands who were simply trying to find their place in the world of commerce.

    As I look back on my time to date, I have noticed a few themes emerge from my diverse client conversations with brands, which are indicative of an ecosystem that’s only now coming to terms with retailers and consumers moving online. Here are three fundamental problems I’ve seen brands often run into as they adapt to the world of eCommerce:

    1. “We have no idea what we are doing”

    My favorite part about being an analytics solution provider is the introduction session with a new client. I always entered these conversations with a few key questions:

    – What are your top three eCommerce initiatives for the next 12 months?

    – How does your team and other internal resources align with these initiatives

    – How do you envision using this type of tool to help you succeed with your goals? What made you choose ours?

    Early in my career, what always amazed me was that these enormous brands – wildly successful brands – entered into a partnership without a clear plan to execute. Many would fumble through what I thought were very basic questions. After a few of these conversations, I came to the realization that most brands have a limited understanding of what they are doing in eCommerce.

    How could this be possible?

    I remember a conversation with a large CPG brand executive. He said, “Keep in mind, most of the people doing these jobs are from a bricks-and-mortar world. They don’t have eCommerce experience because no one does. It is too new. We don’t have the resources to hire more people because eCommerce makes up less than 1% of our total revenue.”

    As an industry, brands are collectively making it up as they go. Few admit it, but the industry is growing and evolving so fast, the best that some do is hold on for the ride (while taking a few calculated chances along the way).

    2. “We measure success poorly”

    I have noticed that, with time, many brands are starting to get a better grasp on how to operate online, though there is still a long way to go for many. The best evidence for this improvement is the growth in the number of job posts for eCommerce-focused roles, new vendors popping up in this space, and industry centers of excellence being developed. As more people choose eCommerce roles, the biggest challenge that I see is the lack of effective measurement and training processes.

    Often, the issue is that many brands take a long-standing, loyal executive and assign them as the eCommerce leader. When this person is not forward thinking, analytical or open to trying things a new way, brands fail. The reason startup brands are winning online is because they are entering the eCommerce game with an open and fresh perspective. Forcing old ways into eCommerce will surely lead to failure.

    I have worked with many brands that have developed eCommerce centers of excellence and have shared best practices on how to measure teams and success. The most painful to deal with were the organizations that brought their bricks-and-mortar measurements into the eCommerce world. The data used to measure success was the wrong data. The KPIs were set in a way that people would surely fail.

    In my opinion, the best measurements for success are sales growth (not share growth), digital shelf KPIs (search and content first), and a subjective measure on maturity in the industry. The best first step is to have someone lead the team who understands how to measure success and execute in a cutting-edge and evolving environment.

    3. “We sign up with either too many or the wrong service providers”

    The final observation is one that is costing many teams a lot of money. Many brands start to move into eCommerce based on their old team structures. Each team has a separate eCommerce objective, budget, and set of tools to execute with.

    Then, when the centralized eCommerce team (Center of Excellence) gets established, they will likely find many teams working with many tools. Sometimes, they see many teams signed up, via separate contracts, with the same tool. Worse still – it’s often the wrong type of tool.

    As brands evaluate tools, they need to ask questions such as:

    • Does this vendor provide global coverage, so that we can establish a global way of thinking and executing (with the ability to customize for local consumption when required)?
    • Does this vendor have the backbone (people and technology) to scale with my business?
    • (The best question, in my opinion) Does this vendor have people who are willing to listen and understand my business, or are they simply people who want to sell me a cookie-cutter solution?

    In my experience, I have seen brands spending way too much time, effort, and money on vendors who do not check the boxes listed above.

    Summing up…

    As I look back over my time serving brands in the eCommerce analytics space, I have seen an industry morph and transform time and time again. I have seen companies shift, re-shift, panic and pivot.

    If you’re a brand, my encouragement to your team is to hit the pause button. Ask the right questions. Evaluate your goals, your team structure, and your vendor partners. If the strategy, execution, people, and measurement, are not aligned, come up with a plan to get them back on track. Be willing to learn a new way to do business.

    Pause. Reset. Measure.

  • Prime Day 2019 Fashion: Were the Deals as Attractive as the Merchandise?

    Prime Day 2019 Fashion: Were the Deals as Attractive as the Merchandise?

    Target and Walmart offered more appealing discounts than Amazon during Prime Day 2019.

    Statista estimates that e-commerce fashion accounted for approximately 20.4% of overall fashion retail sales in the United States in 2018, which amounted to about $103 billion in absolute terms. According to Internet Retailer, apparel is the largest and among the most competitive retail categories in e-commerce. Moreover, as a share of total apparel and accessories sales, online apparel sales is growing at a faster rate than US e-commerce as a whole.

    Given the high-growth and competitive nature of the category, we at DataWeave were interested to find out how high the stakes got during the fifth annual Prime Day earlier this month.

    Our Methodology

    Since Prime Day is no longer necessarily an Amazon event (since competing websites often offer attractive discounts as well), we tracked the pricing of several leading retailers selling fashion apparel, footwear, and accessories to assess their pricing and product strategies during the sale event. Our analysis was focused on additional discounts offered during the sale to estimate the true value that the sale represented to its customers. We calculated this by comparing product prices on Prime Day versus the same prices prior to the sale.

    Our sample consisted of 20 product types across women’s as well as men’s fashion categories. While we did monitor exclusive fashion retailers Macy’s, Bloomingdales, Nordstrom, and Neiman Marcus, we did not find them offering any additional discounts – an interesting insight all on its own since they’ve clearly chosen not to compete with Amazon during the two days of the Prime Day sale. We therefore restricted the rest of our study to Amazon, Target, and Walmart – the latter two of which interestingly offered immensely aggressive discounts in their apparel categories.

    The Verdict

    Despite owning the day at least in name, Amazon was found to offer the lowest additional discounts among the retailers studied. Target and Walmart, on the other hand, ensured that they didn’t lose out on market share this Prime Day by offering substantially high discounts of their own. While Target was the most aggressive with a steep average markdown of 26.5%, Amazon closed out the bottom at 8.4%.

    Walmart and Target didn’t seem particularly focused on compensating their sharp discounts with price increases in other products – their focus seems to have been solely only on offering timely discounts during the sale. Amazon, on the other hand, marked up just about as many products as it marked down, with the markup margin being close to double that of the markdown in an effort to protect margins during the sale.

    Top product types by additional discount

    Target and Walmart both offered aggressive discounts across their top product categories. Walmart ended up with a marginally higher overall average additional discounts on product types like Shirts, T-shirts, and Tops.

    Interestingly, though Amazon offered moderate discounts across its top categories (Lingerie, Swimwear, and Underwear), the volume of marked down products was very limited.

    Additional discounts across popularity levels

    We determined popularity using a combination of average review rating and number of reviews, and the resulting scores were categorized as low, moderate, and high.

    When it came to discounting popular products, there were clear differences in strategy among all the three retailers. Amazon, which interestingly had close to 60% of its products in the low popularity bucket, chose to offer the highest discounts in the same category – indicating an effort to clear its stock of unpopular products. Target and Walmart, on the other hand, focused their discounts on moderate rated products.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into four percentile blocks, with higher percentile blocks indicating higher selling prices.

    As found in the electronics and furniture categories that were analyzed previously, most of the discounting activity was focused on the lower end of the premium spectrum with a view to protect margin – despite a largely equitable distribution of discounted products across percentile ranges (with the exception of Target, which had a discounted assortment heavily dominated by its least premium products).

    This indicates a clear strategy to protect margins, while still maintaining the perception of promoting attractive offers to draw traffic. Target and Walmart both offered substantial additional discounts of close to 30% on their least premium products, while at 12%, Amazon offered less than half that discount.

    Additional discounts across visibility levels

    Given the fairly large number of SKUs across the fashion category in general, the discounts across visibility levels understandably didn’t vary much when compared to the more pronounced fluctuations observed in the electronics and furniture categories. This is also largely because consumers tend to explore lower ranked products more so in the fashion category than in other categories.

    Across product categories, we’re seeing lower-than-expected additional discounts on Amazon this Prime Day, coupled with more aggressive pricing activity by Amazon’s competitors. While this puts more pressure on Amazon, this also is a strong validation of Prime Day as a key annual sale event on the US shopper’s calendar.

    Curious to know how Amazon and its competitors performed in other product categories this Prime Day? Watch this space for more!

  • Online Furniture Pricing Strategies on 2019 Prime Day

    Online Furniture Pricing Strategies on 2019 Prime Day

    Just as with electronics, other retailers actually offered far better discounts than Amazon during Prime Day 2019.

    Online furniture sales have risen significantly since the 2000s, driven largely by a growing array of products, and even more so by the convenience of avoiding travel and crowded stores. According to Statista, online furniture and homeware sales were estimated to reach approximately $190 billion in 2018, with China and the United States accounting for over $60 billion in revenue each.

    Thus, furniture has quickly become a key product category during sale events globally – and Prime Day was no different. At DataWeave, we got down to figuring out exactly how plum those deals were this year.

    Our Methodology

    We tracked the pricing of several leading retailers selling home and furniture products to assess their pricing and product strategies during the sale events. Our analysis was focused on additional discounts offered during the sale to estimate the true value that the sale represented to its customers. We calculated this by comparing product prices on Prime Day versus the same prices prior to the sale. Our sample consisted of the top 1,000 ranked products across 10 popular product types, including beds, dining table sets, sofas, entertainment units, and coffee tables – analyzed for five retailers (Amazon, Home Depot, Target, Walmart, and Wayfair).

    The Verdict

    As we found in the electronics category, there were surprising price spikes in this category too – with Target reporting an average increase as high as 14.7%, and Amazon clocking a still moderately high 9.4%. Target also reported the highest distribution of products with price markups. Home Depot indicated the lowest price increase at 4.6%.

    When it came to additional discounts, Amazon fell short of expectations – at 4.7%, it offered the lowest average among its competitors. Target, on the other hand, was extremely aggressive both in terms of additional discounts and volume of discounted products.

    To conclude, all the retailers observed seemed to be keeping a close watch on their margins by countering price reductions with nearly equivalent surges elsewhere in their assortment.

    While there was no single product type that was found to be popular across all five retailers, it was clear that Target was again the most aggressive at offering discounts. It also had among the largest product ranges on discount.

    Amazon chose to follow a very moderate route both in terms of average discount and discounted product volume.

    Additional discounts across popularity levels

    We determined popularity using a combination of average review rating and number of reviews, and the resulting scores were categorized as low, moderate, and high.

    There doesn’t seem to have been much of a focus on low-popularity products in terms of additional discounts. Most of the attention was focused on products with moderate popularity, since there isn’t much of a need to be aggressive on price for highly popular products, and products with lower popularity aren’t really worth promoting.

    The only retailer that offered a higher discount on its most popular products was Home Depot. Walmart, too, seemed reluctant to let go of the opportunity to capitalize on popularity – it chose to offer the same discount on moderately as well as highly popular products.

    Interestingly, Walmart seems to have a disproportionately large share of products in its low popularity category – something it should possibly evaluate in the future in terms of brand quality, products, and service.

    The percentage distribution of products mostly indicated a linear relationship, with the highest distribution usually being offered for highly popular products. The exception was Wayfair, which offered a much larger array in its moderately popular category.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into four percentile blocks, with higher percentile blocks indicating higher selling prices.

    Most of the discounting activity seems to have occurred in the lower end of the premium spectrum, with a view to protect margin – despite a largely healthy distribution of products across percentile ranges. This indicates a clear strategy to protect margins, while also promoting attractive offers to draw traffic.

    However, there are a couple of exceptions – Target was consistent throughout the “premiumness” spectrum, resulting in the highest overall discounting activity. Home Depot too was aggressive, but selectively so – it chose attractive pricing for the lower and higher ends of its assortment.

    As expected, many retailers showed higher discounting activity in the higher ranks of their listing pages. As usual, though, there are a few exceptions here too. Home Depot and Wayfair indicated unusual patterns – perhaps relying on search results as opposed to organic listing page results. On the other hand, Target again indicated a consistent pattern, with mostly similar discounts across visibility levels.

    Overall, across all parameters analyzed, both the Electronics and Furniture categories have been treated quite similarly in terms of pricing activity by most retailers. Is Prime Day really all about its marketing hype, or will it live up to its promise in at least one segment? Stay with us to find out as we follow through with our series of articles analyzing various product categories on this year’s Prime Day.

  • A Study of Deals on Amazon Prime Day 2019 | DataWeave

    A Study of Deals on Amazon Prime Day 2019 | DataWeave

    Our preliminary analysis reveals that Prime Day 2019 had other retailers offering better deals than Amazon in many cases.

    As Prime Day extended into an additional day this year, Amazon seems to be hitting the right note with its customers, going by the revenue it’s raking in. This year, the longest Prime Day event ever witnessed a sales increase of 72% – overtaking Black Friday and Cyber Monday combined.

    At DataWeave, we were curious to find out how prime these deals were, and if in fact other retailers were offering better discounts. We started with the electronics category, which remains among the most popular categories year on year.

    Our Methodology

    We tracked the pricing of several leading retailers selling consumer electronics to assess their pricing and product strategies during the sale event. Our analysis was focused on additional discounts offered during the sale to estimate the true value that the sale represented to its customers. We calculated this by comparing product prices on Prime Day versus the prices prior to the sale. Our sample consisted of up to the top 1,000 ranked products across 10 popular product types in consumer electronics on Amazon, Best Buy, Target, and Walmart.

    The Verdict

     

    What we found most surprising was that across retailers, some portions of the assortment underwent price increases as well. While Amazon indicated the lowest increase at 9.1%, Best Buy indicated an increase as high as 27.1%. However, Amazon reported the highest percentage of products (6.9%) that showed a price increase.

    Equally surprising was that Amazon reported the lowest price reduction at 6.3% – Walmart, Target, and Best Buy in fact reduced their prices by much larger margins than Amazon did. A point to note here, however, is that Amazon did report the highest percentage of additionally discounted products – with Best Buy coming in at a close second.

    This goes to show that Prime Day, for all its hype, does not in truth offer the best deals to Amazon shoppers. This, of course, is expected based on the competitors’ perspective of wanting to avoid losing market share. As a result, shoppers would be well advised to compare prices across websites to find the best deal.

    Top product types by additional discount

     

    USB flash drives were a popular product category across all four retailers analyzed, with Best Buy offering the best average additional discount at 40.7%. Other popular product types ranged from the usual personal devices such as mobile phones, tablets, and smartwatches to home appliances such as refrigerators and TVs.

    Additional discounts across popularity levels

    We determined popularity using a combination of average review rating and number of reviews, and the resulting scores were categorized as low, moderate, and high.

    Interestingly, discounts were not found to be directly proportional to popularity. Except Walmart, all the retailers tended to offer the best discounts on products that enjoyed moderate popularity. This makes sense, since there isn’t a strong need to be aggressive on price for highly popular products in any case. On the other hand, products with lower popularity aren’t really worth promoting. Walmart, which was the exception, reported a higher discount on low- and high-popularity products than it did on moderately popular products.

    The percentage distribution of products did mostly show a directly proportional relationship, with the highest distribution usually being offered for highly popular products. The exception in this case was Best Buy, which evidenced a much higher distribution in its moderately popular goods.

    Additional discounts across product “premiumness” levels

    Premiumness was calculated as the average selling price before the sale event. This was divided into four percentile blocks, with higher percentile blocks indicating higher selling prices.

    In general, all retailers were found to have slightly higher additional discounts in the lower end of the “premiumness” spectrum. This is still a smart move, as it enables sellers to save on margin while still promoting attractive discount percentages. Interestingly, Amazon offered the lowest additional discount – a flat 5% – across all categories, despite offering more or less competitive product distributions compared to other retailers.

    Additional discounts across visibility levels

    Here, too, the lower end of the spectrum mostly witnessed higher additional discounts. This tactic actually offers double benefits – one, the most attractive discounts are offered in the higher realms of visibility, thus effectively enticing consumers to buy these products, and two, it helps build a low price perception (despite this not holding good as one delves deeper into the higher ranks). Again, it’s interesting to note that Amazon didn’t offer the highest discounts here either – in fact, it mostly offered the lowest additional discounts.

    All in all, it seems that Prime Day isn’t all it’s hyped up to be, at least not in the Electronics segment. How about other categories? Watch this space for more insights!

  • The Importance of Pricing Parity for Brands

    The Importance of Pricing Parity for Brands

    With bricks-and-mortar stores steadily increasing their online presence, the balancing act of pricing online and in-store is now more important and complex than ever. Companies spend years building brands and brand equity. Yet, a misplaced or poorly executed pricing strategy to handle both online and offline pricing can erode that equity with consumers very quickly.

    This problem is not new. It first started when Clubs like Costco and Sam’s started popping up in the 80’s. Suddenly, brands had to figure out a way to balance Club and Grocery pricing while taking advantage of a new, fast-growing channel. The biggest difference between now and then is that consumers now can check prices within seconds on their phone.

    So, how do you avoid losing your brand equity while ensuring price parity across online and offline channels?

    The key areas to consider are:

    1. Product Mix

    Do you have a broad enough mix of product sizes and case configurations for each channel? To maximize your sales and minimize your price disruption, reviewing your supply chain and product mix to ensure you are able to deliver value to both online and offline retailers is critical. Each channel is looking for ways to improve and maximize your brand sales. If you do not give them the right size and case configuration to enable them to increase margins, you will end up relying disproportionately on trade spend (dollars a brand spends with a retailer to promote products) to do so, or find your product on page 212 of every search.

    Examples of this strategy can be seen with companies offering only “bundled” items such as 12 cans or a large case on online marketplaces, while other retailers offer individual cans for purchase. This allows your online partners to make up margin by shipping a full case and not going through the process of breaking down a case and shipping single units. Also, this allows bricks-and-mortar retailers to have a sharper price point to lure consumers into the store. This strategy has played out well for many brands as they dealt with the rise of Club stores and can be played successfully in e-commerce as well, benefiting all parties.

    2. Price Lists

    Do you have harmonized price lists that do not favor one channel over another? If you do not, you are likely subsidizing the higher list cost in a channel with trade spend, which is highly inefficient. A single price list that provides an adequate price slope between the various sizes across your product range will maximize your ability to manage both channel pricing and brand equity.

    The single largest mistake brands tend to make is thinking that offering “net price” price lists to online marketplaces will benefit them while they use trade dollars in bricks-and-mortar stores to cater to EDLP (Everyday low price) customers. This approach is quite inefficient in many ways, and consumes valuable time and resources that can otherwise be better utilized. Having a single price list with the same price offered to all retailers allows for a more manageable and equitable pricing environment. It also enables a more profitable distribution of trade spend across the most effective areas to invest in for each retailer.

    I have worked with two brands in the past – one that managed two separate price lists and one that we implemented as a single-standard. While the one with the single price list saw sales grow and trade spend remain constant, the other saw trade spend double in just two years as it got caught in a scenario of always having to placate one side of the equation or the other.

    3. Trade Spend

    Today’s brands need to focus on a balanced trade spend strategy to address each channel’s unique needs. Using trade spend with online retailers can be tricky, as the channel is usually assumed to be the lowest priced anyway. Still, it can be used to drive traffic and offset supply chain costs, in order to ensure sufficient margins for the retailer, which will keep you off the CRAP (Can’t Realize A Profit) lists. Meanwhile, as JC Penny quickly learned when it made the disastrous shift to EDLP, consumers still want in-store discounts and sales.

    The best approach I have worked with is to set a single dead net price inclusive of all trade. For example, if your product’s standard list cost is $6.80 and you have a dead net price for promotions (or EDLP) of $5.40, then all retailers – online and bricks-and-mortar – are on equal footing. The only variance in the price for consumers will be the margin each operator chooses to take. This approach is not without issues, as you have to apply all elements of trade spend (such as ad fees, etc.) to the promotional unit costs to ensure you are truly capturing the dead net cost of the retailer.

    Still, the advantage of utilizing this approach is that when a retailer complains about the price another is offering to consumers, the conversation turns to margins being taken and not the cost of the product. At the very least, this approach provides a common ground on which to have a constructive conversation with all retailers.

    So why does this all matter so much to a brand?

    The road to selling online is littered with disaster and missed expectations for sales. Most manufacturers that jumped to online sales without considering pricing quickly learned that abandoning one channel for another does not lead to increased sales. Conversely, we have seen a few brands go from online only to in-store as well. These brands seem to have learned from the others’ mistakes and rarely will you find price variances between the online and offline channels. Instead, you tend to see these brands growing, as online consumers start experiencing the brand in-store.

    A Business To Community study by Larisa Bedgood in 2019 showed that “lower price” was second to only “convenience” for why consumers shop online, while 51% of consumers said that the biggest drawback to shopping online was not being able to touch and feel the product. Brands that are able to bridge the gap and provide consumers with the convenience of online while also showing up well in-store at the right price point will be able to break out of the stagnate 1-2% (if they are lucky) growth most CPG companies are experiencing. If online selling is growing 40-50% a year, why are these companies only managing brand declines and flat growth? I believe it is mainly due to the lack of a proper pricing parity strategy for the two channels along with a lack of actionable e-commerce data.

    Brands that do not focus on all three areas listed above often find themselves in a constant churn of conversations with retailers on all sides, which will typically lead to either online marketplaces or bricks-and-mortar stores deprioritizing the brand in promotions or search. Finding and setting a level playing field will allow for a balanced trade spend and growth for brands on both platforms, while also enabling a brand to break out of the net 1% growth that is plaguing a lot of CPG brands today.

    Outside of deploying basic pricing principles for your brand, I would also suggest early and strong investments in data, systems and people to monitor your brand’s health and pricing. Many brands jumped online without any way to monitor the consumer conversation around the brand or the pricing of the brand online. Not having the tools and resources in place to do this can lead to a quick and long-lasting erosion of brand equity and sales. Most, if not all, large manufacturers have subscribed to POS data for years and fully understand how to analyze this data. But the world has shifted. If your organization has not invested in digital shelf analytics, you may be driving blind and unaware that your brand is losing equity, which equals losing consumers and sales.

    Using a combination of pricing principles and e-commerce data mining tools will help you maintain price parity and brand relevance, while keeping you from becoming the last brand of choice for consumers, regardless of where they shop.

  • Compete Profitably in Retail: Leveraging AI-Powered Competitive Intelligence at Massive Scale

    Compete Profitably in Retail: Leveraging AI-Powered Competitive Intelligence at Massive Scale

    AI is everywhere. Any retailer worth his salt knows that in today’s hyper-competitive environment, you can’t win just by fighting hard – you have to do it by fighting smart. The solution? Retailers are turning to AI in droves.

    The problem is that many organizations regard AI as a black box of sorts – where you can throw all your data (the digital era’s blessing that feels like a curse) in at one end and have miraculously meaningful output appearing out the other. The reality of how AI works, however, is a lot more complex. It takes a lot of work to make AI work for you – and then to derive value out of it.

    Image Source: https://xkcd.com/1838

    Following the advent of the digital era, businesses across industries, particularly retail, were left grappling with massive amounts of internal data. To make things worse, this data was unstructured and siloed, making it difficult to process effectively. Yet, businesses learned to leverage simple analytics to extract relevant data and insights to affect smarter decisions.

    But just as that happened, the e-commerce revolution stirred things up again. As businesses of all shapes, sizes, and types moved online, they suddenly became a whole lot more vulnerable to other players’ movements than they were just about a decade ago, when buyers rarely visited more than one store before they made a purchase. In other words, retailers are now operating in entire ecosystems – with consumers evaluating a number of retailers before making a purchase, and a disproportionate number of players vying for the same consumer mindshare and share of wallet.

    Thus, external data from the web – the largest source of data known to man at present – is becoming critical to business’ ability to compete profitably in the market.

    Competing profitably in the digital era: Can AI help?

    As organizations across industries and geographies increasingly realized that their business decisions were affected by what’s happening around them (such as competitors’ pricing and merchandize decisions), they started shifting away from their excessive obsession with internal data, and began to look for ways to gather external data, integrate it with their internal data, and process it all in entirety to derive wholesome, meaningful insights.

    Simply put, harnessing external data consistently and on a large scale is the only way for businesses to gain a sustainable competitive advantage in the retail market. And the only way to practically accomplish that is with the help of AI. Many global giants are already doing this – they’re analyzing loads of external data every minute to take smarter decisions.

    That said, though, what you need to know is that all this data, while publicly available and therefore accessible, is massive, unstructured, noisy, scattered, dynamic, and incomplete. There’s no algorithm in the world that can start working on it overnight to churn out valuable insights. AI can only be effective if enormous amounts of training data is constantly fed back into it, coaxing it to get better and more astute each time. However, given the scarcity of readily available training datasets, limited and unreliable access to domain-specific data, and the inconsistent nature of the data itself, a majority of AI initiatives have ended up in a “garbage in, garbage out” loop that they can’t break out of.

    What you need is the perfect storm

    At DataWeave, we understand the challenge of blindly dealing with data at such a daunting scale. We get that what you need is a practical way to apply AI to the abundant web data out there and generate specific, relevant, and actionable insights that enable you to make the right decisions at the right time. That’s why we’ve developed a system that runs on a human-aided-machine-intelligence driven virtuous loop, ensuring better, sharper outcomes each time.

    Our technology platform includes four modules:

    1. Data aggregation: Here, we capture public web data at scale – whatever format, size, or shape it’s in – by deploying a variety of techniques.

    2. AI-driven analytics: Since the gathered data is extremely raw, it’s cleaned, curated, and normalized to remove the noise and prepare it for the AI layer, which then analyzes the data and generates insights.

    3. Human-supervised feedback: Though AI is getting smarter with time, we see that it’s still far from human cognitive capabilities – so we’ve introduced a human in the loop to validate the AI-generated insights, and use this as training data that gets fed back to the AI layer. Essentially, we use human intelligence to make AI smarter.

    4. Data-driven decision-making: Once the data has been analyzed and the insights generated, they can either be used as it to drive decision-making, or then integrated with internal data for decision-making at a higher level.

    With intelligent, data-backed decision-making capabilities, you can outperform your competitors

    Understandably, pricing is one of the most popular applications of data analytics in retail. For instance, a leading, US-based online furniture retailer approached us with the mission-critical challenge of pricing products just right to maximize sell-through rates as well as gross margin in a cost-effective and sustainable manner. We matched about 2.5 million SKUs across 75 competitor websites using AI and captured pricing, discounts, and stock status data every day. As a result, we were able to affect an up to 30% average increase in the sales of the products tracked, and up to a 3x increase in their gross margin.

    DataWeave’s powerful AI-driven platform is essentially an engine that can help you aggregate and process external data at scale and in near-real time to manage unavoidably high competition and margin pressures by enabling much sharper business decisions than before. The potential applications for the resulting insights are diverse – ranging from pricing, merchandize optimization, determination of customer perception, brand governance, and business performance analysis.

    If you’d like to learn more about our unique approach to AI-driven competitive intelligence in retail, reach out to us for a demo today!

  • 6 Smart Pricing Strategies for eCommerce Success

    6 Smart Pricing Strategies for eCommerce Success

    Over the last decade, the proliferation of e-commerce and the consequent surge in competitiveness among retailers has brought focus to one of the most critical drivers of success in online retail: pricing. According to McKinsey, an average 1% increase in price can translate into an 8.7% increase in operating profits (with the assumption that there’s no loss of volume). Yet, the company estimates that up to 30% of pricing decisions fail to provide the best price – every year. That’s a potential impact of millions in lost revenue for most modern-day retailers, a fact only made worse by the irony that in today’s times of automation and big data, there’s no shortage of intelligence to facilitate the best decision-making.

    What you need is the ability to gather and rationalize all the data out there – of competitor prices, price perceptions, market dynamics, buyer behavior, etc. – in good time to price your products just right for maximum margin and revenue. The best part? Effective product pricing contributes significantly toward fostering a great customer experience, too.

    Once you have your intel in place, there are plenty of eCommerce pricing strategies to choose from – it’s only a matter of identifying the metrics that matter the most to your business goals. That said, there are several models that have gained widespread popularity and acceptance over the years, like the following six:

    1) Introductory pricing

    This is a common marketing strategy used in the e-commerce space, where you draw consumer focus to a newly launched product or service, or the fact that you’re a new entrant in a market. There are two ways to do this – one is to start with steep discounts (particularly during sale events, and often in partnership with the consumer brand) with the aim of winning over more market share. At the other end is the strategy of setting relatively high initial prices. This works best for “exclusive offer” or “limited edition” opportunities; for instance, the opportunity to be the first to own the latest iPhone model.

    2) Cost-linked pricing

    In this method, you calculate how much it costs to sell a product and add a pre-determined margin to the final cost. In the world of online retail, product cost amounts to a lot more than the mere sum of manufacturing costs. For instance, it includes the procurement, labor, software, sales and marketing, shipping, and overhead costs that contribute to the total cost of housing it as long as it’s unsold. Therefore, all these costs need to be factored when determining the final product price. While the advantages of this model are its simplicity and the promise of guaranteed returns for each product sold, the flip side is that it doesn’t factor in the competitive landscape. The trick, therefore, lies in finding the balance between higher margin and sell-through rates, particularly given the aggressively competitive nature of online retail.

    3) Competitive pricing

    Today’s digitally savvy customers are forever comparing prices across several websites in the quest for the lowest prices. In fact, price is among the most critical factors that influences purchase decisions across products as well as categories. The competitive eCommerce pricing strategy, therefore, determines product price based on how the same products are priced by various competitors. While this model allows you to modify prices as frequently as necessary to drive efficient pricing and maximize revenue and margin, the complexity lies in ensuring consistent access to competitor prices, particularly in today’s highly dynamic e-commerce environment. DataWeave’s Pricing Intelligence platform helps eCommerce businesses overcome this challenge by helping them identify price improvement opportunities based on timely competitive intelligence at a massive scale.

    4) Dynamic pricing

    This model takes into account competitor prices, demand, and inventory levels, which are set up as triggers for automated pricing rules. While this results in sustained competitiveness, it requires a price optimization model that determines the optimal price in real-time response to fluctuations in demand and competitive prices – all the time ensuring alignment with your business goals. In other words, this model allows you to ensure consistently competitive yet optimized prices, thus acquiring and retaining a competitive edge in the market.

    5) Price perception management

    The company most famous for following this strategy is Amazon. The retail giant frequently identifies its most popular products and offers its largest discounts on them, often undercutting competitors. In other words, in this model, you “invest” in customer acquisition through excessively aggressive discounts on a select group of products – following which, you can cross-sell or up-sell other higher-priced products. Thus, you boost your perceived value to customers. Another way to drive a positive perception is to display discounted products at higher ranks on featured listings. For instance, in a recent study that we conducted, we found that 9 out of 10 leading US retailers’ top 50 ranked products (in each category) were significantly cheaper than the rest of their products.

    6) Bundle pricing

    The principle for this model is simple. You sell a number of the same products (or a range of complementary ones) for a combined, economical price. This is different from customers adding products individually to their cart as it works on the consumer psyche, which is more likely to favor a purchase that offers considerable perceived value. Thus, not only are you offering enhanced value to your customers (and in turn improving overall customer experience), you’re also actually increasing sales. Bundle pricing works best for products that are likely to involve repeat purchases (such as batteries, cereal boxes, or socks), and also for those that may need accessories (for instance, a food processor with various attachments). However, for bundle pricing to be effective, it’s also important to understand how your competitors are bundling their products.

    Granted, it isn’t easy to identify the perfect pricing strategy for you. As customers increasingly engage with you at every stage of their decision-making process and market dynamics become exceedingly complex, pricing as a function has to keep pace. As a retailer, your objective is to unearth the actionable insights hidden in your big data and leverage the resulting opportunities to drive the maximum possible revenue and margin – without getting lost in the flood.

  • Retailers Adopt Aggressive Private Label Pricing Strategies in CPG

    Retailers Adopt Aggressive Private Label Pricing Strategies in CPG

    Nine out of 10 leading retailers price their private label products lower than the average prices of their respective categories, reveals the latest DataWeave study, drafted in collaboration with SunTrust Robinson Humphrey The study reveals that an increasing number of retailers are viewing private label brands as a way to ensure sustained profitability.

    “As the CPG space reels under intense competition, a number of retailers are doubling down on private labels to capture valuable additional margin. For instance, Kroger, Walmart, and Amazon Fresh have a higher degree of private label penetration than the other retailers we analyzed,” said Karthik Bettadapura, Co-founder & CEO at DataWeave. “Our study unveils several such key insights covering product assortment & distribution patterns, price perception, and private label dynamics, revealing a clear snapshot of the disruptive transformations sweeping across the US CPG landscape.”

    Other key findings from the report, which tracked and analyzed 450,000 products across 10 leading retailers and 10 ZIP codes each, include the following:

    • Product assortment is emerging as a driver that’s as critical as pricing when it comes to customer retention. Target, H-E-B, and Kroger have a head start here, offering the largest product assortments among the retailers analyzed.
    • A sharp assortment strategy customized to local tastes and preferences is key to sustaining and enhancing customer satisfaction. Albertsons, Walmart, and Amazon Fresh lead here, revealing a higher focus on localized assortments.
    • “Home” and “Beauty & Personal Care” categories lead the distribution of private label products across retailers. The focus on these categories echoes a similar focus among national brands as well. These categories have the highest overall brand concentration, with around 4,000 brands each.

    To download the entire report, click here.

  • Decoding Alibaba’s Singles Day Sales

    Decoding Alibaba’s Singles Day Sales

    An average of $11.7 million per second was the rate at which Alibaba clocked $1 billion in sales during the first 85 seconds of Singles’ Day. As Alibaba’s annual sale event continues to grow in scale, referring to it as a global retail phenomenon is an understatement. Alibaba closed the day having shipped 1.04 billion express packages based on sales of merchandize worth 213.5 billion yuan ($30.67 billion).

    This performance shredded any lingering concerns analysts may have harbored about the prospects of this year’s sale, given the international backdrop of the ongoing trade skirmish between the US and China.

    Along with attractive discounts across a range of product categories, Singles’ Day also promised an integrated experience fusing entertainment, digital and shopping, in stark contrast to other large global sale events like Black Friday, which focus predominantly on discounts.

    At DataWeave, we set out to investigate if all the hype resulted in actual price benefits to the shoppers and how the various categories and brands performed in terms of sales during the event. To do this, we leveraged our proprietary data aggregation and analysis platform to capture a range of diverse data points on Tmall Global, covering unit sales (reported by the website) and pricing associated with Tmall Global’s major categories over the Singles’ Day period.

    Our Methodology

    We captured 5 separate snapshots of data from Tmall.com during the period between October 25 and November 14, encompassing over 15,000 unique products each time, across 15 product categories.

    To calculate the average discount rate, we considered the percentage difference between the maximum retail price and the available price of each product. We also looked at the additional discount rate, for which we compared the available price during Singles’ Day to the available price from before the sale. This metric reflects the truest value to the shopper during Singles’ Day in terms of price.

    Our AI-powered technology platform is also capable of capturing prices embedded in an image. For example, the offer price of ¥4198 was extracted accurately from the accompanying image by our algorithms and attributed as the available price while ¥100 from the same image was ignored.

    This technology was employed across hundreds of products using DataWeave’s proprietary Computer Vision technology.

    Domestic Appliances and Digital/Computer Categories Powered Turnover

    The Domestic Appliances and Digital/Computer categories dominated the Singles Day Sale in terms of absolute sales turnover. This isn’t surprising, since the average order value for these categories are typically much higher compared to the other categories analyzed.

    What clearly stands out in the above infographic is that the two largest categories in terms of sales turnover had average additional discounts of only 2 per cent and 0 per cent — a rather surprising insight. In general, with the exceptions of Women’s skincare, Men’s skincare, and Women’s bags (11 per cent, 10 per cent, and 9 per cent respectively), all other categories saw low additional discounts during Singles’ Day.

    However, the absolute discounts across the board were consistently high, with only Luggage (6 per cent), Digital/Computer (9 per cent) and Women’s wear (12 per cent) staying significantly below the 20 per cent mark. In fact, eight categories enjoyed absolute discounts greater than 30 per cent.

    Among common categories between Men and Women, the Men clocked more sales in Men’s wear, shoes, and bags. Only skincare proved to be an exception, where Women’s skincare generated twice the turnover of their Men’s equivalent.

    The Infants category was another intriguing sector to emerge during the sale. Both Diapers (38 per cent) and Infant’s Formula (25 per cent) were substantially discounted, despite only receiving low additional discounts of 2 per cent and 0 per cent respectively – indicating aggressive pricing strategies in this category even during non-sale time periods.

    The biggest takeaway from our analysis is the lack of any correlation between sales turnover and additional discounts, or even the absolute discounts.

    International Brands Make Gains

    International brands continue to penetrate the Chinese market showing up amongst the Top 5 brands of 13 of the 16 categories on sale.

    In the Diaper category, Pampers delivered nearly twice the sales turnover of its next biggest competitor. As expected, Apple and Huawei battled it out for honors in the Digital/Computer category although Xiaomi enjoyed pleasing results, nearly matching Huawei’s sales to go with its sales leadership of the Domestic Appliances category. Local brands, though, swept the Domestic Appliances, Furniture and Women’s Wear categories.

    The challenge posed by Chinese brands was illustrated by Nike’s spot in the second place in the highly competitive Men’s Shoes category after Anta.

    International brands topped only five of the 16 categories and Top 3 positions in ten categories. Still, there’s a growing presence of international brands in China’s eCommerce.

    Gillette won handsomely over its competition in the Personal Care category while Skechers enjoyed a similar result in Women’s Shoes, racking up nearly twice the retail sales of its nearest competitor. Another category dominated by international brands was the Women’s Cosmetics category where international brands accounted for 4 of the Top 5 brands.

    Similarly, Samsonite’s acquisition of American Tourister gave it two top 5 brands in the Luggage category. Other global brands to make the cut during the Singles’ Day sale included L’Oréal, Canada’s Hershel, Playboy, South Korea’s Innisfree and Japan’s Uniqlo.

    It’s Not All About Price On Singles’ Day

    The dramatic rise in shopping during Singles’ Day is not driven solely by price reductions. Alibaba’s commitment to its “New Retail” strategic model has led the Chinese giant to channel its impressive resources to focus on bringing together the online elements of its business with the more traditional offline aspects of its retail distribution. This is combined with entertainment to create a larger story based around the shopper’s overall “experience” rather than just driving “attractive prices” as a short-term retail hook.

    Alibaba is betting big on erasing the line between online and offline and its futuristic vision of structuring retail around the way people actually want to shop. Based on the consistently impressive results of Singles’ Day year after year, “New Retail” has a promising future.

    If you wish to know more about how DataWeave aggregates data from online sources to provide actionable insights to retailers and consumer brands, check out our website!

  • CEO Speak: Serving the US Market, Hiring the Right Talent, And More

    CEO Speak: Serving the US Market, Hiring the Right Talent, And More

    Recently, Karthik Bettadapura, Co-founder & CEO at DataWeave, was interviewed by Vishal Krishna, Business Editor at YourStory, in the Bay Area, California. They discussed DataWeave’s focus on the US market, challenges that retailers face today, DataWeave’s technology platform and hiring practices, and more.

    The following is a transcript of the interview.

    (The transcript has been edited for clarity and brevity)

    Vishal Krishna (VK): You left India to come and conquer America, why is that?

    Karthik Bettadapura (KB) : Just a bit of history — we started in 2011 and product development and research was based in Bangalore, and still is. At the end of the first 5 years, we realized that we built great technology, but we were not able to scale beyond a certain point [in India]. If we had to build a growing business, we had to look at other markets as well.

    VK: Quickly, can you tell me what DataWeave does?

    KB: We provide Competitive Intelligence to retailers and customer brands. We work with some of the largest brands and retailers out there and we provide them with analyses to compete profitably.

    VK: You said you had marque clients in India, yet you didn’t want to stay there because you wouldn’t have scaled beyond a particular point. Why is that?

    KB :The ticket size in India is still on the lower side. If you must build a sustainable business, you need access to a much larger customer base and we found that in the US.

    VK: Let’s start from the basics. What are a few things that a startup should decide to do when coming to America?

    KB: A few things:

    • A good understanding of the market
    • Learn fast about the market
    • Build a team here, or a have a team here already doing some work initially
    • Consider how your team back in India will go about doing things in your absence
    • The last one is about your own personal journey. I was so used to walking into an office and interacting with people. You come here, and you are all alone!

    VK: It’s a lonely journey. Doors don’t open all that easily and you’ve got to hustle. Why?

    KB: For people here, you are an unknown entity. Why should they be trusting someone who does not have enough customers here or has not raised money here? We had two US-based customers when we came in. It’s an uphill task to ensure that customers trust you.

    VK: Who was the first customer you personally met here and why was that meeting so important?

    KB: The first customer I met here was a large, big box retailer, and the meeting was primarily focused around why they should trust us — how can they know that we would survive and serve them, as well as how we are better than some of the other guys out there.

    VK: Can you tell us what DataWeave does for US retailers?

    KB: For retailers, we provide competitive intelligence, primarily around pricing optimization and assortment analytics. In the US, a lot of retailers are shutting shop and filing for bankruptcy.

    VK: Yeah, we saw Sears go through something like that.

    KB: The reasons fall broadly into 3 categories:

    • They failed to compete profitably with a lot of these new age businesses.
    • The new age retailers offer superior customer experience. They have figured out a better assortment/product strategy.
    • The third one is ‘Price’ — price is such an important feature.
      What we do is help these retailers optimize their strategies around pricing, assortment and promotions, eventually enabling them to compete profitably.

    VK: Typically, customers pay you on the outcome, pricing, license or subscription?

    KB: It’s a subscription-based model. There is a one-time setup fee and an ongoing subscription fee.

    VK: So you plug into their data management system?

    KB: Yes, but we can also have our product sit independently. Sitting out of their internal systems is a benefit for us as we don’t have to get into the entire loop of integrations into their internal systems right from Day 1. We prove our product works and then we integrate with their systems.

    VK: How do you integrate? Is the CIO your target?

    KB: No, we don’t sell to the CIO world. We sell to analytics, pricing, and merchandising teams.

    VK: Can pricing alone give retailers a competitive edge?

    KB: Yes, pricing is a big lever that retailers use. For example, last holiday season’s sale, Amazon and Walmart made 120 million price changes in just 2–3 days.

    VK: So they change the prices so dynamically to compete with each other. Is this price war coming to India?

    KB: It is happening in India already.

    VK: How much data can DataWeave’s infrastructure ingest?

    KB: We are a global platform — we have customers across the globe, not just the US or India. So, on a daily basis, we process data on around 120 million products.

    VK: Talk a little bit on R&D quickly. Do you have your marketing team in the US?

    KB: We have marketing teams in the US and India.

    VK: And the engineering team?

    KB: The engineering team is in Bangalore.

    VK: For people who want to work in your company, what kind of talent are you looking for?

    KB: We look at 4 broad talent areas:

    • One is in the world of data acquisition, which addresses issues like how data can be aggregated from thousands of websites and millions of pages on an ongoing basis, and how this data can be stored.
    • The second area is on what kind of insights can be generated using this data. This could be done using text analytics, image analytics, and other technologies. This includes process optimization, in terms of building efficient and scalable systems.
    • The third area is on how well the data can be represented if we have a customer who wants 60–70 million data points to be consumed on a weekly basis.
    • And the last area is on data modeling — what kind of insights can we eventually give to the customer? And, when I say insights, I mean specific actions.

    VK: You want people who can handle massive scale and for that they should be good at linear regression.

    KB: We value people who write good code. We primarily work in Python, and we use a lot of optimization techniques in the middle of the stack to help us scale.

    VK: Would you do something for supermarkets?

    KB: Absolutely. The largest offline supermarket in India is our customer.

    VK: So what can you do for supermarkets?

    KB: Offline retailers across the world are facing something that’s called showrooming. This is when a shopper walks up to a store, looks at and feels a product, then searches online to see it’s available at a better price. So we have retailers who are wary of this phenomenon. We also have retailers who are wary of diminishing customer loyalty. So they have to constantly ensure that they are priced better in the market and are not losing customers because of [online] pricing.

    VK: How powerful are your algorithms?

    KB: There is a dedicated team that works on our algorithms. These fall into several buckets. One is pure data scale algorithms — how do you build systems which ensure that you are able to efficiently query them in real time and get the desired output. The second one is — how do you keep improving your machine learning algorithms. For example, computer vision algorithms, text analytics algorithm, etc. The third — how do you keep experimenting effectively.

    VK: What role can an MBA degree holder play in DataWeave?

    KB: We have people who hold MBA degrees and are working in customer success, delivery management, marketing, and sales.

    VK: Do you spend time in training?

    KB: You do have some lead time if you are a fresher, but if you are a lateral hire, its expected that you keep the ball rolling. They should be able to learn and learn fast — learning is more important than knowing. So, we give a lot of importance to people who can learn and pick up things quickly – about our product, handling customer objections, etc.

    *

    Watch the whole video here or check out DataWeave’s website to know more about how we use data engineering and artificial intelligence to enable retailers and brands to compete profitably in the age of eCommerce.

  • Evolution of Amazon’s US Product Assortment

    Evolution of Amazon’s US Product Assortment

    As with many other product categories, Amazon has made a significant incursion in Apparel — a key battleground category in retail today. Recently, DataWeave once more collaborated with Coresight Research, a retail-focused research firm to publish an in-depth report revealing insights on Amazon’s approach to its US fashion offerings.

    Since our initial collaborative report in February this year, we have witnessed some seismic shifts in the category at both the brand and the product-type level.

    Research Methodology

    We aggregated our analytical data on more than 1 million women’s and men’s clothing products listed on Amazon.com in two stages:

    Firstly, we identified all brands included in the Top 500 featured product listings for each product subcategory in both the Women’s Clothing and Men’s Clothing sections featured on Amazon Fashion (e.g., the top 500 product listings for women’s tops and tees, the top 500 product listings for men’s activewear, etc.). We believe these Top 500 products reflect around 95 percent of all Amazon.com’s clothing sales. This represents 2,782 unique brands.

    We then aggregated the data on all product listings within the Women’s Clothing and Men’s Clothing sections for each of those 2,782 brands. This generated a total of 1.12 million individually listed products. This expansive list forms the basis for our highlights of the report.

    Third-Party Seller Listings Are Rising Sharply

    We identified a total of 1.12 million products across men’s and women’s clothing — a significant increase of 27.3 percent in the seven months between February and September 2018. The drivers of this sharp spike are third-party seller listings. In contrast, the report indicates only a 2.2 percent rise in first-party listings over the same period, compared to a 30.5 percent jump in third-party listings.

    In addition, Amazon has listed just 11.1 percent of all clothing products for sale, with third-party sellers offering the remaining 88.9 percent — an indication of the strength of Amazon’s open marketplace platform.

    A Major Brand Shift On Amazon Fashion Is Underway

    In just over six short months, major brand shifts on Amazon Fashion have taken place. The number of Nike listings has plummeted by 46 percent, driven by a slump in third-party listings following Amazon’s new partnership with Nike — a story recently covered by Quartz. Limited growth in Nike clothing first-party listings failed to compensate for this decline.

    Gildan’s spike in total product listings appears to be fueled by increased first-party listings off a low base. Calvin Klein’s 2017 agreement to supply Amazon with products appears to be driving the Calvin Klein brand’s double-digit uptick in first-party listings on Amazon Fashion.

    Aéropostale’s decline appears to be entirely driven by a drop in its third-party listings. The brand itself is not listed as a seller on Amazon.com.

    Amazon Is Rebalancing Its Apparel Portfolio and Switching Its Focus from Sportswear To Suits

    As its Fashion footprint rapidly matures, Amazon now appears to be rebalancing its portfolio with strong growth being shown in listings for formal categories such as suits and away from sportswear. We recorded a 98.6 percent increase in listings of women’s suits and blazers complemented by a 52.2 percent rise in men’s suit and sports coat listings between February and September 2018.

    Generic “Non-Brands” Are Surging On Top 25 Brands List

    Over the past six months, low-price generic brands have made major inroads into Amazon’s listings. Four unknown “brands” captured the top positions on the list of brands offered on Amazon Fashion. The WSPLYSPJY, Cruiize and Comfy brands appear to be shipped directly to customers from China.

    Source: Coresight/DataWeave (Amazon Fashion: Top 25 Brands’ Number of Listings, February 2018 vs. September 2018)

     

    Source: Coresight/DataWeave (Amazon Fashion: Top 25 Brands’ Number of Listings, February 2018 vs. September 2018)

    WSPLYSPJY alone accounts for fully 8.6 percent of Amazon men’s and women’s clothing listings. Cruiize accounts for a further 3.2 percent of listings while Comfy chips in another 3.1 percent.

    Amazon Appears To be Executing A Strategic Pivot

    Amazon’s fashion offering is fast maturing. We saw substantial growth in the number of listings for more formal categories. The realignment in third-party listings by Nike together with increased first-party listings for Calvin Klein and Gildan appear to be driven by alliances with Amazon.

    Simultaneously, ultralow-price generic clothing items delivered on order from China have inundated the “Most-Listed Products” rankings. Third parties now represent nearly 90 percent of Amazon Fashion’s offering.

    While Amazon Fashion shoppers enjoy a wider choice than they did even six months ago, we believe a stronger emphasis on first-party listings would grow the products eligible for Prime delivery. This tactic could strengthen Amazon Fashion’s long-term appeal as a shopping destination.

    If you’re interested in DataWeave’s technology, and how we aggregate data from online sources to provide unique and comprehensive insights on eCommerce products and pricing, check us out on our website!

  • Inside India’s eCommerce Battle: Attractive Offers Usher In The Festive Season

    Inside India’s eCommerce Battle: Attractive Offers Usher In The Festive Season

    It’s festival season in India again and shoppers took advantage of aggressive cutthroat competition between Indian online retailers to drive sales to unprecedented highs.

    All the major Indian eCommerce websites including, Amazon, Flipkart, Myntra, and Shopclues opted to go head to head by holding their first sale event this season over 4 to 5 days starting on the 10th of October. Still, as industry reports indicate, one retailer came out on top during this event — an insight supported by our analysis as well.

    A New Battleground

    The highlight this year was seeing how the announcement of global retail colossus Walmart’s acquisition of Flipkart would impact the sale events. The acquisition was the most influential development in India’s eCommerce sector, and it has transported a decades-long U.S. rivalry between Amazon and Walmart to Indian soil. As a result, this year’s sale event held out the promise of more attractive pricing and vast product selection for India’s consumers than ever before.

    Industry analysts estimate that the sale generated a cumulative Rs 15,000 crore in sales over the spread of the five sale days, a whopping outcome. In 2018, this translated into around a 64 per cent year-on-year growth outcome compared to the USD 1.4 billion (around Rs 10,325 crore) generated by the 2017 sales.

    The DataWeave Analysis

    At DataWeave, we analyzed the performance of each of the major eCommerce platforms including Amazon, Flipkart, Myntra, Paytm, and Shopclues. For each eCommerce website, we aggregated data on the Top 500 ranked products for over 40 product types spread across 6 product categories (Electronics, Men’s & Women’s Fashion, Furniture, Haircare, Skincare).

    We focused our analysis on only the additional discounts offered during the sale and compared them to prices prior to the sale, to reflect the true value of the sale to India’s shoppers.

     

    The battle of the discounts was led primarily by Flipkart and Amazon. Flipkart’s average additional discounts by category actually exceeded Amazon’s in three out of six categories, and it discounted more products that Amazon across all categories.

    Clearly, the focus for all e-tailers was skewed towards the main battlegrounds of Electronics and Fashion, compared to mainstream FMCG categories such as Hair and Skin Care. However, this is not surprising given FMCG functions on rather skinny margins.

    Across retailers, the Men’s and Women’s Fashion categories were the most aggressively discounted, attracting both the highest additional discounts and the highest percentage of products with additional discounts.

    The Furniture category too was an interesting battleground between Amazon and Flipkart, attracting attractive discounts on a wide range of products, particularly in Flipkart’s case.

    Prospective shoppers in search of relatively more expensive clothing products on discount during the sale would have established Myntra as their ideal destination, as it carried more premium products on discount during the sale, relative to all its competitors. For shoppers in search of an electronics bargain though, they would have done well to opt for Flipkart.

    Shoppers may have found some interesting deals on Paytm Mall too, especially in Men’s Fashion, while Shopclues largely held itself back from any dramatic price reductions.

    While Myntra capitalized on its niche though aggressive discounting in the Fashion category, most of the discounting action revolved unsurprisingly around Amazon and Flipkart. To drill down for a more complete understanding of just how the Amazon and Flipkart discounted their products, we conducted a more detailed follow-on analysis.

    We normalized additional discounts and popularity using a scale of 1 to 10 and plotted each product on a chart to analyze its distribution characteristics. Popularity was calculated as a combination of the average review rating and the number of reviews posted. Products with a popularity score of zero, as well as zero additional discounts were excluded from this analysis.

     

    The most obvious insight yield through this analysis is how Flipkart elected to distribute its additional discounts across a larger range of discount percentages. By contrast, Amazon went all in on the more limited range of products it decided to provide additional discounts on. This is a strategy we have seen Amazon adopt previously.

    One other intriguing insight is Flipkart’s decision to go for a much higher distribution of products falling below a popularity score of 0.5 compared to Amazon. Amazon’s strategy resulted in more of its discounted products having a higher popularity score, relative to Flipkart, albeit only by a comparatively minor amount. However, a shopper’s chances of buying a popular, positively reviewed product at a lower price were higher on Amazon than Flipkart during this sale.

    Achieving a Consistent Competitive Edge

    Flipkart claims to have recorded a 70 per cent plus share of entire Indian e-commerce market in the 4 day-BBD’18 sales. Flipkart further claimed to have cornered an 85 per cent share in the online Fashion category together with a 75 per cent share in the Electrical category’s large appliances during the sale. This includes a contribution by Flipkart’s subsidiary Myntra.

    As these numbers reflect, Amazon still has some way to go to entrench itself in the Fashion category of the Indian market. However, Amazon appears content to continue its surgical discounting philosophy.

    Overall, this year witnessed an impressive participation by Tier II and Tier III Indian city consumers — a sign of things to come in Indian online retail.

    With increasing competitive pressure, retailers simply cannot adopt discounting and product selection strategies in isolation and be successful. Having access to up to date insights on competitors’ products dynamically during the day is emerging as key to ensuring they’re able to sustain their lowest priced strategy for appropriate products. These insights are also proving critical in identifying gaps in their product assortment, which can hamper customer conversion and retention.

    During sale events, modern retailers need to rely on highly granular competitive insights on an hourly basis (or even more frequently) to inform their pricing and product strategies to ensure they consistently maintain a competitive edge for the consumer’s wallet. And while access to reliable competitive intelligence is critical, true value can only be derived when it gets integrated with a retailer’s core business and decision-making processes, such as assortment management, promotions planning, pricing strategies, etc.

    DataWeave’s Competitive Intelligence as a Service helps global retailers do just this by providing timely, accurate, and actionable competitive pricing and product insights, at massive scale. Check out our website to find out more!

  • Evaluating the Influence of Learning Models

    Evaluating the Influence of Learning Models

    Natt Fry, a renowned thought leader in the world of retail and analytics, published recently an article expounding the value and potential of learning models influencing business decision-making across industries over the next few years.

    He quotes a Wall Street Journal article (paywall) published by Steven A. Cohen and Matthew W. Granade who claim that, “while software ate the world the past 7 years, learning models will ‘eat the world’ in the next 7 years.”

    The article defines a learning model as a “decision framework in which the logic is derived by algorithm from data. Once created, a model can learn from its successes and failures with speed and sophistication that humans usually cannot match.”

    Narrowing this down to the world of retail, Natt states, “if we believe that learning models are the future, then retailers will need to rapidly transform from human-learning models to automated-learning models.”

    This, of course, comes with several challenges, one of which is the scarcity of easily consumable data for supervised learning algorithms to get trained on. This scarcity often results in a garbage-in-garbage-out situation and limits the ability of AI systems to improve in accuracy over time, or to generate meaningful output on a consistent basis.

    Enabling Retailers Become More Model-Driven
    As a provider of Competitive Intelligence as a Service to retailers and consumer brands, DataWeave uses highly trained AI models to harness and analyze massive volumes of Web data consistently.

    Far too often, we’ve seen traditional retailers rely disproportionately on internal data (such as POS data, inventory data, traffic data, etc.) to inform their decision-making process. This isn’t a surprise, as internal data is readily accessible and likely to be well structured.

    However, if retailers can harness external data at scale (from the Web — the largest and richest source of information, ever), and use it to generate model-driven insights, they can achieve a uniquely holistic perspective to business decision-making. Also, due simply to the sheer vastness of Web data, it serves as a never-ending source of training data for existing models.

    DataWeave’s AI-based model to leverage Web data

     

    Web data is typically massive, noisy, unstructured, and constantly changing. Therefore, at DataWeave, we’ve designed a proprietary data aggregation platform that is capable of capturing millions of data points from complex Web and mobile app environments each day.

    We then apply AI/ML techniques to process the data into a form that can be easily interpreted and acted on. The human-in-the-loop is an additional layer to this stack which ensures a minimum threshold of output accuracy. Simultaneously, this approach feeds information on human-driven decisions back to the algorithm, thereby rendering it more and more accurate with time.

    Businesses derive the greatest value when external model-based competitive and market insights are blended with internal data and systems to generate optimized recommendations. For example, our retail customers combine competitor pricing insights provided by our platform with their internal sales and inventory data to develop algorithmic price optimization systems that maximize revenue and margin for millions of products.

    This way, DataWeave enables retailers and consumer brands to utilize a unique model-based decision framework, something that will soon be fundamental (if not already) to business decision-making across industry verticals and global regions.

    As AI-based technologies become more pervasive in retail, it’s only a matter of time before they’re considered merely table stakes. As summarized by Natt, “going forward, retailers will be valued on the completeness of the data they create and have access to.”

    If you would like to learn more about how we use AI to empower retailers and consumer brands to compete profitably, check out our website!

    Read Natt’s article in full below:

    Steven A. Cohen and Matthew W. Granade published a very interesting article in the Wall Street Journal on August 19, 2018 — https://www.wsj.com/articles/models-will-run-the-world-1534716720

    Their premise is that while software ate the world (Mark Andreessen essay in 2011, “Why Software is Eating the World”) the past 7 years, learning models will “eat the world” in the next 7 years.

    A learning model is a decision framework in which the logic is derived by algorithm from data. Once created, a model can learn from its successes and failures with speed and sophistication that humans usually cannot match.

    The authors believe a new, more powerful, business opportunity has evolved from software. It is where companies structure their business processes to put continuously learning models at their center.

    Amazon, Alibaba, and Tencent are great examples of companies that widely use learning models to outperform their competitors.

    The implications of a model-driven world are significant for retailers.

    Incumbents can have an advantage in a model-driven world as they already have troves of data.

    Going forward retailers will be valued on the completeness of the data they create and have access to.

    Retailers currently rely on the experience and expertise of their people to make good decisions (what to buy, how much to buy, where to put it, etc.).

    If we believe that learning models are the future then retailers will need to rapidly transform from human-learning models to automated-learning models, creating two significant challenges.

    First, retailers have difficulty in finding and retaining top learning-model talent (data scientists).

    Second, migrating from human-based learning models to machine-based learning models will create significant cultural and change management issues.

    Overcoming these issues is possible, just as many retailers have overcome the issues presented by the digital age. The difference is, that while the digital age has developed over a 20 year period, the learning-model age will develop over the next 7 years. The effort and pace of change will need to be much greater.

  • Amazon’s Fashion & Apparel Product Assortment | DataWeave

    Amazon’s Fashion & Apparel Product Assortment | DataWeave

    Apparel remains one of the key battleground categories in retail today, and like in most other product categories, Amazon has made significant in-roads here. Beyond expanding the range of product offerings and brands in its marketplace, Amazon has also launched several private label brands in this vertical and looked to drive more sales as a first-party seller.

    Recently, DataWeave collaborated with Coresight Research, formerly known as Fung Global Retail & Technology, a retail-focused research arm of Li & Fung Group, to publish an in-depth report revealing Amazon’s strategic approach to product assortment in its fashion and apparel category.

    In this blog post, we’ll summarize some interesting insights into Amazon’s strategy from the report. For an in-depth and detailed view, check out the original article at — “Amazon Apparel: Who Is Selling What? An Exclusive Analysis of Nearly 1 Million Clothing Listings on Amazon Fashion”

    Research Methodology

    Our analysis focused on several critical areas, including the presence of Amazon’s private label, the demarcation between Amazon as a seller and its third-party sellers and the top brands and categories in women and men’s apparel.

    We aggregated data from Amazon.com in two stages:

    Firstly, we identified brands with a meaningful presence in Amazon’s clothing offering by identifying all brands included in the top 500 ranks of featured product listings for each product type in the Women’s Clothing and Men’s Clothing sections on Amazon (e.g., the Top 500 product listings for women’s tops and tees, the Top 500 product listings for men’s activewear, and so on.). This generated a total of 2,798 unique brands.

    Secondly, we aggregated our data on all product listings within the Women’s Clothing and Men’s Clothing sections for each of the 2,798 brands identified previously. This returned a total of 881,269 individually listed products. This extensive list forms the basis for the highlights in Coresight’s report.

    Coresight’s Analysis — Some Interesting Insights

    Strategically, Amazon remains heavily reliant on its third-party sellers in the clothing category. In total, just 13.7 percent of women’s and men’s clothing products featured on Amazon Fashion are listed for sale by Amazon itself (first-party sales), while third-party sellers account for 86.3 percent of listings.

    In womenswear, third-party sellers account for 85.7 percent of listings, while in menswear, they account for 87.1 percent of listings. Moreover, Amazon appears to be focusing its first-party clothing inventory on the higher-value categories. Clearly, the retailer’s reliance on third-party sellers underscores its opportunity to grow its sales of apparel volumes by bringing more of its current inventory in-house.

    The analysis found 834 Amazon private-label products on Amazon website, equivalent to 0.1 percent of all clothing available through Amazon Fashion. The company’s private labels appear to be clustered tightly in specific clothing categories.

    Womenswear brand Lark & Ro is by far the biggest of Amazon’s apparel private labels, as measured by the number of items.

    Nike is the most-listed brand on Amazon Fashion, with 16,764 listed products spanning womenswear and menswear. Lower-price brands such as Gildan and Hanes also rank very highly in terms of the number of products listed.

    Value-positioned brands that have traditionally focused on wholesaling to retailers, such as Gildan and Hanes, also rank very highly in terms of the number of products listed.

    What is clear is that currently, Amazon’s clothing listings are highly diluted, with no one major brand dominating the listings.

    Interestingly, casualwear and activewear clearly lead Amazon’s category rankings. Women’s tops and tees are the most heavily listed clothing category on Amazon Fashion, with 138,001 products listed.

    Men’s shirts, which includes a large number of casual shirts together with polo shirts and some T-shirts, comes in second, with 109,043 products listed. Echoing the prominence of the global Nike and Adidas brands on the Amazon website, activewear has achieved a centre of gravity status as a category, accounting for 76,930 men’s activewear products and 51,992 women’s activewear products listed on the site.

    Several Opportunities for Growth

    Amazon Fashion remains heavily dependent on third-party sellers. It’s a fair assumption that more first-party listings would attract greater numbers of shoppers, especially Amazon Prime members. Amazon’s private-label ranges represent another potential lever for growth.

    Also, the 30 most-listed brands on Amazon Fashion comprise 30 percent of all clothing products listed on the website, while just 189 brands have more than 1,000 products each listed on the website.

    This data indicates the presence of major growth opportunities across the board, be it Amazon private label brands, Amazon as a seller, and for several mid-range clothing brands.

    If you’re interested in DataWeave’s technology, and how we aggregate data from the Web to provide unique and comprehensive insights on eCommerce products and pricing, check us out on our website!

  • What Retailers Can Learn from the Lowe’s Board Announcement

    What Retailers Can Learn from the Lowe’s Board Announcement

    Last Friday, Reuters published, “Home Improvement chain Lowe’s said it has nominated two independent board members and plans to add a third following “constructive” talks with hedge fund D.E. Shaw Group, which has taken an activist stake.”

    It was reported that D.E. Shaw Group had utilized available external data to identify quantifiable opportunities to grow sales by several billion dollars and to reduce costs significantly.

    A question that comes immediately to mind is, “Why didn’t Lowe’s utilize this same available external data themselves?”

    Is it because Lowe’s and many other retailers spend their time focusing on internally generated data, rather than looking at available external data, or better yet, combining available external data with their internal data?

    There are huge opportunities to drive incremental sales, margins and profits through leveraging external data, like competitive intelligence data produced by firms like DataWeave.

    There are huge opportunities to drive incremental store sales, margins, and profits through leveraging digital data to drive better store specific assortments, prices and promotions by providing relevant local digital data to store executives using solutions by firms like Radius8.

    I expect to see more Lowe’s-like announcements in the near future as investment firms realize there are very substantial, untapped financial opportunities within retail.

  • Walmart’s Online Pricing Analysis | DataWeave

    Walmart’s Online Pricing Analysis | DataWeave

    In an increasingly competitive retail landscape and facing intense margin pressures, improving the profitability of online commerce is a growing area of focus for all retailers.

    When Amazon acquired Whole Foods in August, several media outlets and analysts speculated whether there would be a slashing of prices across the board. Instead, Amazon lowered prices only on those items that it knew would drive increased traffic to the stores, resulting in a 25% increase in footfall the first 30 days after the acquisition closed.

    (Read also: Amazon’s Whole Foods Pricing Strategy Revealed)

    Disrupting the Status Quo

    Walmart has now announced a shift in its online pricing to draw more shoppers to purchase from its brick-and-mortar stores and save on shipping costs.

    Sarah Nassauer wrote an interesting article for the Wall Street Journal recently, outlining Walmart’s online pricing strategy and its approach to pricing its products differently between its online and offline stores.

    Sarah reports, “Walmart wants to charge customers more to buy some products online than in stores, part of the company’s efforts to boost profits and drive store traffic as it competes with Amazon.”

    What’s interesting is Walmart’s move to display the lower offline store prices on its website for some grocery products, nudging shoppers to drive down to the nearest Walmart store.

    Again, Walmart did not raise prices for all items but only a few, select food and household items, “including boxes of Kraft Macaroni & Cheese, Colgate toothbrushes and bags of Purina dog food, according to people familiar with the matter and comparisons between online and in-store prices.”

    The article goes on to state that, “[T]he move is unusual for Walmart, which has long honed an ‘everyday low price’ message and has worked to keep online prices at least as low as shoppers find in its 4,700 U.S. stores. Walmart e-commerce workers responsible for product sales have been instructed to boost profits along with sales, according to the people familiar with the situation, and are ‘no longer obligated to follow store pricing.’”

    This move indicates a greater focus on online-to-offline (O2O) strategies by the world’s largest retailer in an effort to cut down on the crippling costs of transport operations and logistics. According to a cost analysis by consultants Spend Management Experts, “A $1.28 box of Kraft Macaroni & Cheese could cost a big retailer around $10 to ship from Chicago to Atlanta, depending on how remote the buyer’s address is . . . A smaller retailer would likely pay about double.”

    With this news, the days of providing the same price online and in stores are over, setting a precedent and reflecting important differences in costs and competitor capabilities.

    But how did Walmart know which items to focus on for lowering (or raising) prices?

    Cutting-Edge Competitive Intelligence Solutions

    Did Walmart pick items at random or guess? Not likely. With recent enhancements in competitive intelligence and data analysis solutions, the era of guesswork, gut-fuelled decisions, and manual number crunching is over.

    In today’s digital economy, actionable competitive intelligence has become a critical component in the transformation of retail. Retailers like Amazon and Walmart use competitive insights to identify categories and items that show the greatest potential for increased shopper interest, sales, and profits, to adjust their prices.

    Competitive intelligence providers like DataWeave provide unique, AI-driven, competitive insights and business recommendations by harnessing and analyzing competitive data from the Web.

    When retailers link these competitive insights and data to their internal pricing and inventory systems, they create a powerful engine that marries internal and external forces to produce highly accurate assortment, pricing, and promotion recommendations, all in near real-time.

    As retailers like Walmart experiment with their pricing and merchandizing across channels, they have come to rely on modern retail technology solutions that continue to evolve to help them reduce operational complexities and yield higher ROI.

  • Top 5 Drivers of Successful eCommerce | DataWeave

    Top 5 Drivers of Successful eCommerce | DataWeave

    Retail has undergone a dramatic transformation over the last decade. Once dominant retailers are today being given a run for their money amid a gradual decline in mall traffic and sharply growing consumer preference for shopping online.

    Surfing this online retail wave is Internet behemoth Amazon, which is raking in 43% of all new eCommerce dollars, leaving other retailers floundering in its wake.

    As it unfolded, this transformation has unleashed changes across many areas of retail, a phenomenon that’s been well documented by industry commentators in the media. Some of these shifts include:

    Customer preferences: Customers today are spoilt for choice, both in terms of being able to quickly and easily compare product prices across websites, as well as consistently driving the demand for new and unique products from retailers.

    Hyper-personalization: With shoppers increasingly relying on mobile apps, highly personalized shopping experiences are becoming the new normal.

    Delivery: e-Retailers are competing on faster home deliveries, stretching themselves to guarantee same day delivery, or even (as in the case of hyper-local grocery retailers) within a few hours. Drones, anyone?

    Payment Modes: Even the more tactical aspects of retail, like payment modes, have been forced to evolve. Starting with cash-on-delivery, this trend quickly spread to embrace card payments and digital wallets. These initiatives have posed significant technological and security challenges for retailers.

    As with a forced move in chess, traditional retailers have had to evolve and embrace changes like the ones listed above, in order to survive the incredibly cutthroat world of modern retail. Similar challenges exist for up-and-coming eCommerce companies as well.

    However, many pundits and retailers alike often forget that doing even simple, time-tested things correctly can go a long way in forging an effective competitive position, helping win both market share and customer affections. While digital transformation has altered how these strategies were routinely executed, the fundamentals remain as relevant today as they ever were.

    1. Smarter Pricing

    With 80 percent of first-time shoppers comparing products prior to buying, the need for an eCommerce website to offer competitive pricing has become a mandatory cost-of-entry capability. While dynamic pricing poses a challenge for e-retailers to stay competitive, it also presents them with an opportunity to track their competitors’ pricing and exploit that information to optimize their own pricing.

    However, e-retailers today are frequently forced to perform millions of price-changes every day in the eternal quest to either offer the lowest price or entrench a calculated premium price perception among shoppers.

    For instance, as far back as Christmas season 2014, Amazon is estimated to have made a total of 80 million price changes per day. Similarly, today’s hyper-local grocery retailers offer differentiated and targeted prices for shoppers living in specific zip codes.

    To achieve price controls on this level of scale demands sophisticated automated tracking of competitor pricing to facilitate timely, data-driven dynamic pricing decisions. This has, today, become a table stakes requirement.

    2. Variety and Depth of Product Range

    If customers cannot find what they are looking for on a website, all other aspects of how an eCommerce operator optimizes their retail strategy falls by the wayside.

    A website’s success remains dependent largely on it being able to cater effectively to the needs, wants and desires of its target audience. Simply put, a website offering a mammoth product range may still end up failing compared to a small niche website with a limited but highly targeted assortment that understands closely its customer’s sweet spot.

    However, with millions of products on offer online all day every day, gathering and harvesting deep insights into a competitor’s assortment mix can appear daunting. Include dynamically changing product assortments and different product taxonomies into the standard research mix, and many who lack access to automated competitive intelligence systems find themselves struggling to find the expertise required to gather and summarize this information in an actionable form.

    3. Customer Centricity

    Today, customers demand to be heard. As competitive pricing becomes an expected cost of doing business, retailers will need to place greater support resources and more effective processes to resolve customer problems and complaints in a timely fashion at the heart of their customer service model.

    Following the online social revolution, 9 out of 10 retail customers now expect a consistent response across all social media channels.

    Successful companies like Zappos, Best Buy and Amazon have been quick to understand this significant shift in customer preferences. These retailers have demonstrated their willingness to go the extra mile by establishing a robust, scalable omni-channel support structure.

    The level of this commitment can be seen in Amazon’s recent vision statement announcement, “Amazon today boasts of one of the most responsive omni-channel customer support and Zappos takes pride in sending a personalized response to customer queries. We seek to become Earth’s most customer centric company.” This aggressive customer centric sentiment drives a stake in the ground for all competing eCommerce companies’ to match via their customer service strategy.

    4. Superior Customer Experience

    While bricks and mortar retail stores continue to attract customers by enabling shoppers to touch, feel and test items before they purchase, online and omni-channel retailers have channelized their efforts into increasingly refining their web user experience.

    Several studies reveal it takes only a couple of seconds for a website visitor to decide whether to stay on or leave a website. Aspects such as visual design, ease of use, content attractiveness, website loading time and pervasive calls to action (CTA) are a few of the key user experience parameters that influence visitors to stay on a website.

    eCommerce sites such as Zara, Graze, Asos, and Amazon offer attractively organized and clutter-free designs, which are visually engaging and easy to navigate. While these design elements help them keep their customers engaged, it’s their disciplined focus on content that stimulates visitor conversions.

    Detailed product descriptions and high-quality images are helping these eCommerce sites educate their customers about their products while simultaneously boosting their website’s SEO ranking, helping it attract and engage still more online visitors.

    Complementing the online retailing revolution are substantial efforts by omni-channel retailers to optimize O2O (online to offline) strategies designed to bring together the best of both worlds — the discoverability of online, with the touch-and-feel of an offline environment.

    5. Optimized Promotional Strategies

    With so many options for a shopper to choose from in an increasingly cluttered and competitive online retailing environment, attracting new customers and entrenching customer loyalty is an ongoing challenge. Strategic online promotions are emerging as an effective technique in solving the customer recruitment and retention dilemma. Online promotions if executed effectively are doing wonders for generating inbound website traffic.

    However, for online promotions to be effective, it is critical for e-retailers to understand their competitor’s strategy if they are going to be able to sustain their competitiveness. Key questions to answer in this context are, what brands are they promoting more than others? For how long? At what frequency?

    Keeping a keen eye on and reacting to competitors’ promotions is a key aspect to designing effective online promotions. Being able to exploit this competitive intelligence not only boosts their own sales volumes but erodes that of their competitors as well.

    Competitive Intelligence As A Service

    Having understood the far-reaching impact of these evergreen drivers of eCommerce success, we at DataWeave work with omni-channel and online retailers to provide Competitive Intelligence as a Service and help them evaluate and optimize their strategic approach across the eCommerce landscape.

    If you’re interested in DataWeave’s solutions and would like to learn more about how we help retailers and brands optimize their retail strategies, visit our website!

  • Analysis of Target’s Discount Strategy

    Analysis of Target’s Discount Strategy

    Earlier this year, we witnessed Amazon and Walmart going head to head in a CPG goods price war of fluctuating intensity that soon rippled out to embrace the entire grocery industry.

    This further intensified with Amazon’s takeover of Whole Foods and the Whole Foods’ subsequent announcement hinting at significant discounts toward the end of August.

    (Read Also: Amazon’s Whole Foods Pricing Strategy Revealed)

    Soon, Target announced it was lowering prices on literally “thousands of items.” As Mark Tritton, Target executive vice president and the chief merchandising officer put it, “We want our guests to feel a sense of satisfaction every time they shop at Target.”

    To drive home the seriousness of their intent, Target nominated grocery staples such as cereal, paper towels, milk, eggs, baby formula, razors and bath tissue and vowed to, “eliminate more than two-thirds of their price.”

    At DataWeave, we focused our proprietary data aggregation and analysis platform on Target’s reported price reduction. Our team acquired data on the prices of over 160,000 products listed by Target across 12 zip-codes selected at random. The platform then took two snapshots. Firstly, between 23rd August and 30th August which included the Whole Foods’ price reduction (to study any possible reactions on price) and, secondly, between the 6th September and 13th September, which included Target’s discount strategy announcement.

    Of the categories Target identified as priorities for its discount strategy, only baby products, cereals, and Milk & Eggs displayed significant price drops. This price discounting effect varies, however, across brands in each category. In cereals, while KIND (30.4%) and Purely Elizabeth (24%) displayed high discounts, Apple Jacks, Corn Pops, and Krave more surprisingly increased their prices by up to 25% each.

    Similarly, in the Milk & Eggs category, Price’s (13.6%) and Coffee-Mate (10%) exemplified hefty discounts, while Moon Cheese and Challenge Butter increased their prices by 33% and 48% respectively in the same time period. By comparison, Razors and Paper Towels showed no price changes whatsoever across the review period.

    Interestingly, we observed greater price-change activity coinciding with the time of the Whole Foods’ announcement (between 23rd and 30th of August) than the later time period. Once again, however, no definite price discounting pattern emerged from the study, indeed the team found discount rates fluctuated significantly across categories.

    Looking across the spectrum of CPG categories pricing, we saw significant, sustained variation across both categories and zip-codes.

    Beauty products showed a 2 percent discount on average although this varied by zip-code, fluctuating between a 7 percent discount and an actual 10 percent price increase. F&B showed a 2 percent price increase, which jumped to 10 percent in some zip-codes. Personal care displayed a 2.5 percent increase on average, varying anywhere between an 8 percent discount and a 10 percent price increase. Baby products surprisingly recorded a 4 percent price increase on average during the study.

    So, What Does This All Mean?

    Based on our analysis, Target’s pricing strategy appears to be a combination of very closely concentrated discounting, complemented by selective price increases. Is discounting more a perception than a reality at this stage of the CPG cycle?

    Aggressive price discounting has never been a decisive factor in successfully building Target’s consumer franchise. However, given the current trading environment and the continued pressure applied by competitive omni-channel strategies, which has seen a host of new entrants elbowing their way into the market, we anticipate price will continue to play a prominent role in retailing.

    We suspect, based on evidence we gathered, that price discounts are more a highly targeted weapon in the fight for market share than a broadsword slashing of prices across the board. As Target’s CEO Brian Cornell noted during an earnings call, the company experienced “a meaningful increase in the percent of our business done at regular price and a meaningful decline in the percent on promotion.”

    If you’re interested in DataWeave’s data aggregation and analysis technology, and would like to learn more about how we help retailers and brands build and maintain a competitive edge, visit our website.

  • Amazon’s Whole Foods Pricing Strategy Analysis | DataWeave

    Amazon’s Whole Foods Pricing Strategy Analysis | DataWeave

    Amazon.com, America’s retail behemoth, dominated headlines in August when it completed its acquisition of Whole Foods in early August 2017. Having officially taken control of the up-market grocer, which focuses on premium quality produce, market observers and consumers alike are eagerly awaiting Amazon’s pricing strategy analysis.

    At the heart of Amazon.com’s seemingly unstoppable growth trajectory is the company’s ability to understand consumers, complemented by deep insights into buying cycles and purchase decisions and preferences. It also helps that Amazon.com boasts one of the planet’s mightiest marketing and publicity machines.

    Is Amazon.com About To Launch A Grocery Price War?

    Reports of Amazon.com dropping Whole Foods prices by up to 43 percent quickly made splashes across the news media. Given Jeff Bezos has been quoted in the past as saying, “your margin is our opportunity”, an aggressive promotional campaign to achieve dominance for its new Whole Foods acquisition was anticipated by some commentators.

    These sentiments ignited fears of a profit-sapping price war, immediately hit stock prices in the cutthroat grocery industry, which survives on famously thin margins. Memories of Amazon.com’s impact on US department store profitability quickly surfaced with analysts pointing to Walmart’s revenue/market share plunge from 26 percent in 2005 to just 11 percent in 2016 when the sector came under sustained pressure from Amazon.com.

    How Deep Are Amazon.com’s Price Cuts Really?

    At DataWeave, a Competitive Intelligence as a Service provider for retailers and brands, we put Amazon.com’s actual Whole Foods discounts under the microscope. The resulting careful analysis of price discounts revealed quite a different story to the one initially featured in the media. Scrutiny by our proprietary data aggregation and analysis platform showed the drop in retail grocery prices was minimal to almost negligible, depending on the category.

    In delivering near-real-time competitive insights to retailers and brands, we acquire and compile large volumes of data from the Web on an ongoing basis. A key differentiator is our ability to aggregate data down to a zip-code level.

    Our analysis of Amazon.com’s reported drop in prices was based on data acquired for 13 zip-codes distributed across the country and selected at random. Our platform compared market prices by zip code valid between 23rd August and 30th August.

    Each zip code indicated the overall average discount offered varied between 0.20 percent and -0.20 percent. When the discounts at a category-level were separated out, the discounts available to customers per category varied between -6.8 percent (an actual price increase) and 6.1 percent.

    Moving on to the “Fill the Grill” category, discounts again were modest, varying between -5.6 percent (another price increase) and 6.1 percent across the zip codes analyzed.

    This aligns with Amazon.com’s recognized preference for basing its strategy on competing on breadth and depth of product assortment rather than pure pricing discounts at the checkout.

    Some Sunshine For Foodies

    There was some good news for shoppers looking for higher discounts. Amongst those products attracting a higher discount were:

    • Belton Farm Oak Smoked Cheddar Cheese: 50 percent
    • Beemster Premium Dutch Cheese: 50 percent
    • Heritage Store Black Castor Oil: 50 percent
    • Organic French Lentils: 45 percent
    • Vibrant Health Pro Matcha Protein: 40 percent
    • Hass Avocado: 50 percent (confined to one zip-code).

    Final Word

    Amazon.com’s marketing engine is renowned for skillfully nurturing consumer price perceptions of the giant retail website as being the lowest priced retailer. We kept a keen eye on Amazon’s pricing these past weeks, and unearthed a carefully conceived and executed Whole Foods pricing campaign, which is yet another example of their market shaping expertise at work.

    If you’re intrigued by DataWeave’s technology and would like to learn more about how we help retailers and brands build and maintain a competitive edge, please visit our website!

  • The Role of Competitive Intelligence in Modern Retail

    The Role of Competitive Intelligence in Modern Retail

    When retailers today look to compete in the cutthroat world of online commerce, they face several challenges unique to the nature of modern retail. It is now significantly harder for retailers to benchmark their pricing, assortment, and promotions against their competition, as the online world is highly dynamic and significantly more complex than before.

    Trends like the growing adoption of mobile shopping apps, the rising influence of customer reviews in buying behavior, hyperlocal e-commerce websites differentiating themselves by fulfilling deliveries in a matter of hours — the list goes on — have only added to this complexity.

    However, this complexity also presents an opportunity for retailers to incorporate layers of external competitive information into their merchandising strategies to deliver more value to customers and personalize their experience.

    Vipul Mathur, Chief Branding and Merchandising Officer at Aditya Birla Online Fashion, recently published an article highlighting some of the areas in which Competitive Intelligence providers like DataWeave can strategically influence modern merchandising.

    “The consumer is often driven by the aesthetics of a product, more so in the fashion and lifestyle industries than others. Hence, the choices of buyers are hard to interpret. However, innovative modern technologies are helping us understand these decisions,” says Vipul.

    He provides an example of how using AI-based tools (like DataWeave’s) to unearth the sentiments behind thousands of online reviews can help retailers better channel and message their online promotions.

    “Deciphering the consumers’ comments and converting them into tangible insights is incredible proof of the refinement possible with data analysis tools. It’s like knowing that consumers are delighted by the quality of the soles of a pair of Adidas running shoes. Using this, marketing communication can be modified to highlight this specific product feature,” explains Vipul.

    And it’s not just merchandising. This data can percolate across multiple functions in retail, enabling greater efficiency in operations. “If we have data on the best-selling styles across websites, including other attributes like pricing, region/locality (through pin-code mapping), and possibly even rate of sales, it’s up to our supply-chain systems to ensure that the supply is in accordance with demand.”

    DataWeave’s Retail Intelligence offers global retailers and e-commerce websites with these benefits and more. Our AI-powered technology platform aggregates and analyzes vast volumes of online competitive data and presents them in an easily consumable and actionable form, aiding quick, data-driven merchandising decisions.

    “DataWeave, our partner, has helped us refine our merchandising decisions, saving cost and creating value,” sums up Vipul.

    Read the entire article here, and if you’re intrigued by what DataWeave can do for retail businesses and wish to learn more, visit our website!

     

  • Advantage Flipkart: The Motives Behind Acquiring eBay India

    Advantage Flipkart: The Motives Behind Acquiring eBay India

    Flipkart recently acquired eBay’s India business in an announcement that made a huge splash across the country. With Flipkart already having acquired Myntra and Jabong, and talks of a Snapdeal acquisition picking up steam, this level of consolidation comes clearly as a direct response to internet behemoth Amazon’s aggressive expansion strategies in India.

    With this acquisition play, Flipkart stands to gain primarily on two fronts.

    eBay’s Seller Network

    Firstly, eBay has built a strong network of authorized and highly-rated global sellers, something that Flipkart can leverage to drive increased sales and market share.

    Per Flipkart’s announcement to the press — “Flipkart and eBay have signed an exclusive cross-border trade agreement, as a result of which customers of Flipkart will gain access to the wide array of global inventory on eBay, while eBay’s customers will have access to unique Indian inventory provided by Flipkart sellers. Thus, sellers on Flipkart will now have an opportunity to expand their sales globally.”

    At DataWeave, we ran our proprietary data aggregation and analysis algorithms over eBay’s websites and unearthed some interesting numbers about their seller network.

    eBay.com has a global network of 17,361 sellers, 41% of whom ship to India. Therefore, this acquisition opens the door for Flipkart to gain access to over 7000 global eBay.com sellers who ship to India — a huge boost to the range of products Flipkart can host on its platform.

    Additionally, a sizable chunk — 14% — of eBay.in sellers ship to international destinations. This provides Flipkart with opportunities to expand its reach globally.

    The other, rather lesser known advantage that Flipkart stands to gain from this acquisition is in the refurbished and pre-owned goods space.

    The Emergence of Refurbished and Pre-Owned Goods

    The market for refurbished and pre-owned products is estimated to be between $15 billion and $20 billion globally, with exponential growth forecast for the near future.

    Part of the reason for growth in this segment is it yields higher returns on investment for retailers. While a retailer typically earns 3–5% margin by selling a new smartphone, refurbished smartphones fetch 7–8% margin, and pre-owned smartphones 9–10%.

    The Hidden Advantage

    eBay has established itself over the years as a reliable source of refurbished and pre-owned products, with impressive levels of authentication and warranties. We did a quick analysis of eBay.in, Flipkart, and Amazon to identify their relative strengths in this space.

    Unsurprisingly, Flipkart has close to zero refurbished or pre-owned products hosted on their website. With Amazon, 12% of mobile phones and 9% of Books on their website are refurbished or pre-owned, the largest selling categories in this space.

    eBay.in, though, has a significant share of these products across categories — 95% of books & magazines, 36% of mobile phones, and 28% of televisions — a substantial portion of eBay’s business.

    With this acquisition, Flipkart can now take a gigantic step into the relatively more profitable and exponentially growing refurbished and pre-owned products space. It will also be a strong competitive differentiator for the company as they go head to head with Amazon in India.

    While the refurbished and pre-owned goods space poses a series of advantages for retailers, it sits well with consumer preferences as well, drawing more shoppers, and retaining existing ones.

    Influence of Shopping Behavior on Product Assortment

    Refurbished and pre-owned products provide consumers with attractive alternatives, both in terms of price and variety. Shoppers today explore and research new, pre-owned and refurbished products, all at the same time, and compare prices across e-commerce websites before deciding on a purchase.

    As a result, comprehensive product assortments across price ranges and attributes drive higher engagements, traffic and improve customer conversion and retention rates, as they cater to a more diverse set of consumers.

    For modern retailers, this reinforces the importance of investing in tools that enable to them to identify high-value gaps in their assortment and plug them. To achieve this, they need up-to-date, accurate data, at scale, on the assortments of their competitors.

    DataWeave’s Assortment Intelligence solution is designed to give retailers near-real-time insights on competing retailers’ product mix and suggests product additions to retailer catalogs.

    Click here to know more about how Assortment Intelligence can help your retail business manage assortment efficiently and profitably.

     

  • How to Survive the Loss of Brick & Mortar Retail Stores

    How to Survive the Loss of Brick & Mortar Retail Stores

    For years, the consumer electronics chain Radioshack has endeavored to stay alive in our ever-changing world. Despite their efforts, they have filed for bankruptcy for the second time, in as many years. As of now, the company is closing 200 of their 1,500 stores, slightly more than 13% of their locations

    This one-time retail “giant” isn’t alone on the path of reduction in force. Macy’s has announced that they will close 63 stores, and Sears will lock their doors for the final time on 150 of their stores this fiscal year.

    Brands too are feeling the heat. Ralph Lauren recently announced the closure of an unspecified number of stores (including its Polo store on Fifth Avenue, New York City), and a reduction in its workforce.

    The internet is impacting brick and mortar sales the way that Sears Roebuck and Montgomery Ward catalog mail order sales impacted the general store at the turn of the last century.

    Online Retail Plays the Spoiler

    The disruption of the retail industry following the onset of e-commerce is largely due to the change in shopping behavior. Shoppers today can sit at home and compare multiple retailers before making a purchase. This has a significant impact on consumer expectations and how retailers do business today.

    Smartphone apps make comparing prices, and downloading coupons simple. So, we now see e-retailers compete tooth-and-nail on price, and even willing to take the “loss leader” route to drive adoption. Consequently, consumers expect rock bottom prices. Many brick-and-mortar retailers like Walmart have responded by simply matching online prices.

    While there are tens of thousands of e-commerce companies in the world today, this disruption is led primarily by the behemoth of global retail — Amazon.

     

    The Torchbearer of Modern Retail

    Amazon’s retail business strategy rests on three pillars: price perception, broad assortments, and customer experience.

    Price has long been the primary driving factor in retail. Therefore, there is need to optimize price efficiently to drive revenue and margins. What Amazon has smartly done is to drive the perception among shoppers that the company is always the lowest priced, even though it’s untrue. They do this by ensuring they are the lowest priced in the top 20% selling SKUs by volume. The resulting perception among consumers is a key differentiator.

    Also, to deliver superior customer experience compared to competing retailers, Amazon ensures high quality of online catalogs, provides a wide selection of products, and offers fast shipping to a broad coverage area, at no additional cost.

    When you factor in the Amazon Prime service, consumers have become spoiled with receiving their purchases within 48 hours. Sunday deliveries, and scheduling within the hour means buyers are in the driving seat.

    Some of Amazon’s competitors are following suit. Mega box stores like Costco, in an endeavor to meet their customers’ desire for options, are partnering with Google Express to provide fast delivery of household items, apparel, electronics, pantry staples such as bread and cereal, and more.

    The message is clear — today’s brick-and-mortar retailers need to have an omni-channel approach to retail, and an online presence if they are to stay competitive and relevant. However, this move has its fair share of obstacles –

    The Challenge of Moving Online

    Brick-and-mortar retailers moving online are confronted with several questions that carry more weight today than they used to in the past:

    • How do I deliver a high-quality shopping experience?
    • How can I drive price perception among shoppers?
    • What products do I promote and when?
    • What product assortment do I build to drive sales and retention?
    • How do I manage my logistics to reduce shipping cost and time?

    Traditional retailers looked largely at only internal data — like POS data, product sell-through rates, inventory, etc. to answer these questions. Today, it is mission-critical for retailers to absorb and utilize external competitive data as well — and here lies the problem. When you are benchmarking yourself against the competition online, it is that much harder, as it’s more dynamic and significantly more complex than before.

    For example, Forbes estimated that through Christmas season in 2014, Amazon made a total of 80 million price changes per day to stay competitive. These are extraordinary numbers, and reflect how dynamic online retail is, and its contrast to traditional retail.

    Retailers today have no choice but to automate as much as possible, so they can make quick, timely merchandising decisions and keep pace with modern e-retail. Retail technology providers like DataWeave have stepped in to meet this demand.

    DataWeave’s Retail Intelligence

    At DataWeave, we enable retailers gain a competitive advantage in the online world by providing Competitive Intelligence as a Service. We do this by harnessing public information on the competition, structuring it, and presenting it in a form that is easily consumable and actionable, enabling easy, automated decision-making.

    Our AI-based technology platform facilitates smarter pricing decisions by providing retailers with price change (increase and decrease) opportunities as they occur. Retailers can also plug gaps in their product portfolio by identifying opportunities to expand their assortments. In addition, they can benchmark their shipping speed and cost against competition, to enhance customer experience. And there’s more where these come from!

    Click here to find out more about how we can help modern retailers stay competitive in the online world.

     

  • Smart Practices for Pricing Products

    Smart Practices for Pricing Products

    Top pricing strategies for online retailers

    “When it comes to retail markets, law of one price is no law at all” — Hal Varian

    Hal Varian, in his seminal paper “A Model of Sales”, further remarks that most retail markets are instead characterized by a rather large degree of price dispersion.

    Do you know how much your products are worth? How low are you willing to price an item to compete with another ecommerce retailer?

    Today, online retail has become increasingly competitive. If you are priced higher than your competitors, you may end up losing customers who are sensitive to prices. With the advent of highly competitive pricing tools, winning the online pricing war is an uphill task. Having a differentiated competitive strategy is critical to your e-commerce success.

    We bring to you a list of smart practices that we have seen being played out across online retailers in 10 countries that we actively monitor and analyze.

    Analyzing Competitor Prices And Stock Availability

    Product pricing is one of the largest driver of profitability. So you know who your main competitors are, but do you know how they are priced? Compare prices and stock availability of products that are popular across all your competitors and do the same for products that are popular at your store. If you know that certain products are “not in stock”, you know you need not discount. Look at products that are popular across competition and know your price position. Try for an opportunity to increase prices without losing your price position. However, for products popular on your store, you may want to stay competitive.

    Knowing Price Variations

    You get the right price, and then it’s not right anymore. That’s the story of online retail. But when you are equipped with the knowledge of price variations on popular marketplaces, it gives you an idea of where the market is heading. This, in turn, will help you adjust your prices to get the consumers. For instance, Amazon changed prices of more than 50% of their products in Hair Care category more than once in a week including ~20% of the products at least 4 times in the same week.

    Product Bundling

    A marketer of a successful product may bundle a new or less successful product with its stronger product to edge its way into a new market. This allows you to charge a unique, competitive price that can’t be copied by others. If you realize that you may not be able to compete on direct discounts, bundle products together and offer them at a lower price. You can either bundle in multiples of the same product or pack different products together. One of the more famous examples of this is Microsoft’s bundling of various software applications. In the onsite retail space, for example, on a particular day we noticed ~400+ combo offers from SnapDeal in the camera & accessories category whereas PayTM has ~200+ combo offers and Amazon has ~3000+ combo offers in the same category. Similarly, in hair care category we observed significant variance in combo offers across marketplaces (~900+ by Amazon, ~250+ by PayTM and ~100 by SnapDeal on a specific day). We also noticed that marketplaces have varied number of products sold in packs across different brands (~2500 in Amazon, ~800+ in PayTM and ~500 in Snapdeal on a specific day).

    Shipping Fees & Delivery Time

    Free shipping attracts customers to e-commerce platforms like a moth to a flame. Monitor shipping fees across competition for products you are interested in. There will be cases where your competitor is pricing a product at a lower price than you, but does not offer free shipping. That is your signal to promote your platform.

    Price Match Guarantees

    Price match is an easy way for customers to save money on their day-to-day purchases. During Black Friday sales in the US, a lot of popular stores go for the price match guarantee feature to drive sales. It’s a smart trick to let your customers show you the lowest price and then match them accordingly.

    No Discounts On Unique Products

    No matter how much you dress it up, cutting prices hurts. It might be unavoidable, but you can get rid of discounts on unique products. When you analyze gaps and strengths of your catalog and realize that there are products that are available only on your store, why would you need to provide discounts? So, for instance, it seems that only Flipkart is carrying Icon LaserJet Pro Black Toner currently and it is being sold at 75% discount. Unless the objective is to get rid of the inventory, this product could be priced higher. Another example is, Nikon Coolpix S1100PJ Point & Shoot Camera is out of stock with most of the key marketplaces. Hence if anyone gets this replenished, this should not be discounted. Similarly, unique brands in hair care category, say LeModish, is sold primarily on PayTM. So, PayTM could look at reducing discount for this brand.

    Don’t Price Above Market Rate

    Some retailers price products above the market rate (MRP / MSRP) so that they can show substantial discounts. But your customers are smart and research well. If they realize that this is not really ‘low price’, you may end up losing them.

    Dynamic Pricing

    This is one trend you should definitely follow. Constantly monitor competitor prices and drop or increase prices whenever you see an opportunity. This process is highly tech-driven, so ensure that you work with a vendor who provides the same or you have the in-house capability to do this in a sustained and scalable manner.

    There are multiple product strategies that have to be considered, including cross-border commerce and highly spread out markets like SEA where there exists a lot more C2C marketplaces. However, as with many things in ecommerce, one size does not fit all. Combine the powers of your service and price to drive your bottom line and emerge as an undisputed leader in the retail space.

    Note: This article has been previously published on Inc42 and on Indian Retailer.

    DataWeave Retail Intelligence provides competitive intelligence solution to retailers. DataWeave’s solution is both language and geography agnostic and is built for significant scale

  • Benefits of Competitive Marketing Intelligence | DataWeave

    Benefits of Competitive Marketing Intelligence | DataWeave

    In the aggressive business of online retail every detail you know about your competitor gives you an edge over them. To help you stay ahead of your competition we have designed a series of blog posts that familiarize you with competitive intelligence and equip you to get maximum mileage out of competitive intelligence tools. This is the first post of the series.

    Let’s begin at the beginning.

    What is Competitive Intelligence?

    Competitive intelligence (CI) is the gathering of publicly-available information about an enterprise’s competitors and the use of that information to gain a business advantage.

    Competitive marketing intelligence helps managers and executives to make data-driven decisions both in the short term, as well as formulate medium to long term strategy.

    Why is Competitive Intelligence important?

    Competitive marketing intelligence is critical because it helps businesses stay ahead of the competition by:

    1. Augmenting one’s experience and instincts with hard data and analyses on a regular basis
    2. Delivering reasonable assessments of one’s own business vis-a-vis competitors’ businesses
    3. Identifying and alerting new business opportunities as well as threats
    4. Helping shape short term and long term strategies to grow and consolidate one’s business

    How does Competitive Intelligence help achieve the core objectives of retail business?

    Retail is a particularly competitive sector. Given the volume of transactions that happen in the retail sector, even a slight improvement in metrics has a huge impact. Thus, competitive Intelligence has a direct effect on the bottom line. It helps in the following ways:

    > Improve margins

    This is a result of optimized pricing of products. Knowing the competitors pricing goes a long way in pricing your products right and improving margins. With Competitive Intelligence on your side, you can take pricing decisions backed by data.

    > Reduce customer acquisition costs

    By improving your assortment mix more users looking for products that your site offers become your users. This helps reduce customer acquisition costs. This also helps in retaining existing customers

    > Optimize marketing spend

    Competitive Intelligence brings more clarity and sharper objectives for the marketing team. You get good indicators which products/categories your competitors are promoting, and which new brands/categories they have introduced. This helps streamline and optimize your market spend.

    This is where DataWeave comes in. DataWeave provides Competitive Intelligence for retailers, brands, and manufacturers. DataWeave is built on top of huge amounts of product data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches.

    DataWeave is powered by distributed data crawling and processing engines that enables serving millions of data points around products data refreshed on a daily basis. This data is presented through dashboards, notifications, and reports. PriceWeave brings the ability to use BigData in compelling ways to retailers.

    DataWeave lets you track any number of products across any categories against your competitors. If you wish to try this out, just book a free discovery call with us.

    In the next few posts, we will dig deeper into DataWeave and introduce its major features. We will also talk about how each of these features help you in improving your business metrics.

  • Tips on How to Price Your Products | DataWeave

    Tips on How to Price Your Products | DataWeave

    Picture this. You’re approaching the biggest sale of the year for your business, the number of offerings are ever growing and your competitors are inching in on your turf. How then are you to tackle the complex & challenging task of pricing your offerings? In short how do you know if the price is right?

    Here’s how we think it’s possible:

    1. Prioritize your objectives

    Pricing can be modified based on your priorities. A good pricing intelligence tool lets you understand pricing opportunities across different dimensions (categories/brands, etc.). Which categories do you want to score on? Which price battles do you choose to fight? Once you have decided your focus areas, you can make pricing decisions accordingly.

    2. Trading off margins for market share (or vice versa)

    Trading off profits for larger market shares often decreases overhead and increases profits due to network effects. This means that the value of your offerings increases as more people use them (e.g., the iOS or the Windows platform). If margins are crucial do not hesitate to make smart and aggressive pricing decisions using inputs from pricing intelligence tools.

    3. Avoiding underpricing and overpricing

    Underpricing brings down the bottom line and overpricing alienates customers. Walking the thin line between these is both an art and a science. An effective path to a balanced pricing is employing a pricing intelligence tool. A pricing intelligence tool helps you in getting the price right with ease for any number of your products.

    4. Understanding consumers and balancing costs

    Who IS your buyer? How much is she willing to shell out for the products you are selling? How much should you mark up your products to recuperate your costs? What can you do retain your consumers and attract new ones? What steps are my competitors taking to achieve this (discounts/combos/coupons/loyalty points)? Answer these questions and you are closer to the ideal price.

    5. Monitor competition

    The simplest and the most effective way to price your product right is to monitor your competitors. Every pricing win contributes to your profits and boosts your bottom line. Competitive Intelligence products let you monitor your products across any of your competitors.

    Conclusion

    There are many tips on how to price your products. An effective pricing tool goes a long way in helping you determine the right price for your products. It augments your experience, intuition, and your internal analytics with solid competitive pricing data.

    Why not give pricing intelligence a test ride then? Email us today at contact@dataweave.in to get started.

    About PriceWeave

    PriceWeave provides Competitive Intelligence for retailers, brands, and manufacturers. We’re built on top of huge amounts of products data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches. PriceWeave lets you track any number of products across any number of categories against your competitors. If you’d like to try us out request for a demo.

    Originally published at blog.priceweave.com.