The DataWeave Blog

Category: Image recognition

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

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

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

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

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

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

    The Critical Data Gap In Traditional MMM

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

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

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

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

    How Digital Shelf Analytics Completes The Picture

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

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

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

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

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

    The DSA + MMM Advantage in Retail Media

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

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

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

    How DSA Enhances Retail Media Optimization

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

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

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

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

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

    Digital Shelf Analytics for Marketing Mix Modeling - 3 Step Framework

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

    Step 1: Map DSA Variables to MMM Inputs

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

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

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

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

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

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

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

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

    Step 3: Validate and Iterate

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

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

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

    Executive Decision Support in Uncertain Times

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

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

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

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

    Future-Proofing with DSA-Driven MMM

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

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

    DataWeave’s Unique Advantage

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

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

    Conclusion – From Hindsight to Foresight

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

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

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

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

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

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

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

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

    Understanding the Distinction

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

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

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

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

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

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

    Characteristics of Competitive Intelligence

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

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

    Here are some of the essential elements of competitive intelligence:

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

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

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

    Different Audiences, Different Needs

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

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

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

    Price Reporting for Operational Teams

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

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

    Key functions of standard price reporting include:

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

    Competitive Intelligence for Leadership

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

    Core strategic functions of competitive intelligence include:

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

    From Data to Strategy – Transforming Basic Price Data

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

    Transforming from basic price data to competitive intelligence involves:

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

    Leveraging Technology for Competitive Intelligence

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

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

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

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

    Maximizing Competitive Match Rates: The Foundation of Effective Price Intelligence

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

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

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

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

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

    The Match Rate Challenge

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

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

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

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

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

    Why Match Rates Matter

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

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

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

    The Business Impact

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

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

    Current Industry Challenges

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

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

    The Matching Hierarchy

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

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

    The AI Advantage

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

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

    Implementation Strategy

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

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

    Future-Proofing Match Rates

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

    Key Takeaways

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

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

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

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

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

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

    The Data Quality Challenge for Retailers and Brands

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

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

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

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

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

    Improving the Accuracy of Product Matching

    Image Matching for Data Quality

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

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

    How ‘Embeddings’ Enhance Scoring

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

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

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

    Vector Databases for Enhanced Accuracy

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

    Evolution of Embeddings and Scoring: A Multimodal Perspective

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

    DataWeave’s AI framework can:

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

    Quantified Improvements: Model Accuracy and Stats

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

    Business Use Case: Multimodal Matching for Price Leadership

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

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

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

    DataWeave’s AI-Driven Data Quality Framework

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

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

    Scoring Data Quality

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

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

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

    DataWeave's Data Quality Check framework

    Statistical Process Control

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

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

    Transparent Quality Assurance

    The platform provides complete visibility into data quality through:

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

    Human-in-the-Loop Validation (Véracité)

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

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

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

    In Conclusion

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

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

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

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

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

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

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

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

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

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

    Why Product Attribute Tagging is Important in eCommerce

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

    Taxonomy Comparison and Assortment Gap Analysis

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

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

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

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

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

    Assortment Depth Analysis

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

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

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

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

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

    Enhancing Product Matching Capabilities

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

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

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

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

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

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

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

    Elevating the Search Experience

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

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

    Pitfalls of Conventional Product Tagging Methods

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

    Scalability

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

    Inconsistencies and Errors

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

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

    Speed

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

    How DataWeave’s Advanced AI Capabilities Revolutionize Product Tagging

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

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

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

    RLMs for Enhanced Semantic Understanding

    Semantic Understanding of Product Descriptions

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

    Attribute Extraction

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

    Identifying Implicit Relationships

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

    Synonym Recognition in Product Descriptions

    Synonym Matching with Context

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

    Overcoming Brand-Specific Terminology

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

    Dealing with Ambiguities

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

    Contextual Understanding for Improved Accuracy and Precision

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

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

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

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

    Case Study: Niche Jewelry Attributes

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

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

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

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

    Unparalleled Scalability

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

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

    Normalizing Size and Color in Fashion

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

    Normalizing Size and Color in Fashion for Product Matching

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

    Continuous Adaptation and Learning

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

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

    Stay Ahead of the Competition With Accurate Attribute Tagging

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

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

    To learn more, talk to us today!

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

    Mastering Grocery Pricing Intelligence: A Strategic Approach for Modern Retailers

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

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

    The Evolution of Grocery Pricing Intelligence

    Imagine these scenarios in the grocery industry:

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

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

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

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

    Three Pillars of Grocery Price Management

    1. Smart Data Collection: Building Your Foundation

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

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

    2. Intelligent Data Refinement: Making Sense of the Numbers

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

    Data refinement includes several key processes:

    Advanced Product Matching

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

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

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

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

    Custom Product Relationships for Consistent Pricing and Competitive Positioning

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

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

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

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

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

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

    The Role of AI and Data Sciences in Data Refinement

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

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

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

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

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

    3. Strategic Implementation: Turning Insights into Action

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

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

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

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

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

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

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

    Beyond Pricing: Comprehensive Data for Broader Insights

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

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

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

    Looking Ahead: The Future of Grocery Pricing Intelligence

    The grocery pricing landscape continues to evolve, driven by:

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

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

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

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

  • DataWeave’s AI Evolution: Delivering Greater Value Faster in the Age of AI and LLMs

    DataWeave’s AI Evolution: Delivering Greater Value Faster in the Age of AI and LLMs

    In retail, competition is fierce, and in its ever-evolving landscape, consumer expectations are higher than ever.

    For years, our AI-driven solutions have been the foundation that empowers businesses to sharpen their competitive pricing and optimize digital shelf performance. But in today’s world, evolution is constant—so is innovation. We now find ourselves at the frontier of a new era in AI. With the dawn of Generative AI and the rise of Large Language Models (LLMs), the possibilities for eCommerce companies are expanding at an unprecedented pace.

    These technologies aren’t just a step forward; they’re a leap—propelling our capabilities to new heights. The insights are deeper, the recommendations more precise, and the competitive and market intelligence we provide is sharper than ever. This synergy between our legacy of AI expertise and the advancements of today positions DataWeave to deliver even greater value, thus helping businesses thrive in a fast-paced, data-driven world.

    This article marks the beginning of a series where we will take you through these transformative AI capabilities, each designed to give retailers and brands a competitive edge.

    In this first piece, we’ll offer a snapshot of how DataWeave aggregates and analyzes billions of publicly available data points to help businesses stay agile, informed, and ahead of the curve. These fall into four broad categories:

    • Product Matching
    • Attribute Tagging
    • Content Analysis
    • Promo Banner Analysis
    • Other Specialized Use Cases

    Product Matching

    Dynamic pricing is an indispensable tool for eCommerce stores to remain competitive. A blessing—and a curse—of online shopping is that users can compare prices of similar products in a few clicks, with most shoppers gravitating toward the lowest price. Consequently, retailers can lose sales over minor discrepancies of $1–2 or even less.

    All major eCommerce platforms compare product prices—especially their top selling products—across competing players and adjust prices to match or undercut competitors. A typical product undergoes 20.4 price changes annually, or roughly once every 18 days. Amazon takes it to the extreme, changing prices approximately every 10 minutes. It helps them maintain a healthy price perception among their consumers.

    However, accurate product matching at scale is a prerequisite for the above, and that poses significant challenges. There is no standardized approach to product cataloging, so even identical products bear different product titles, descriptions, and attributes. Information is often incomplete, noisy, or ambiguous. Image data contains even more variability—the same product can be styled using different backgrounds, lighting, orientations, and quality; images can have multiple overlapping objects of interest or extraneous objects, and at times the images and the text on a single page might belong to completely different products!

    DataWeave leverages advanced technologies, including computer vision, natural language processing (NLP), and deep learning, to achieve highly accurate product matching. Our pricing intelligence solution accurately matches products across hundreds of websites and automatically tracks competitor pricing data.

    Here’s how it works:

    Text Preprocessing

    It identifies relevant text features essential for accurate comparison.

    • Metadata Parsing: Extracts product titles, descriptions, attributes (e.g., color, size), and other structured data elements from Product Description Pages (PDP) that can help in accurately identifying and classifying products.
    • Attribute-Value Normalization: Normalize attributes names (e.g. RAM vs Memory) and their values (e.g., 16 giga bytes vs 16 gigs vs 16 GB); brand names (e.g., Benetton vs UCB vs United Colors of Benetton); mapping category hierarchies a standard taxonomy.
    • Noise Removal: Removes stop words and other elements with no descriptive value; this focuses keyword extraction on meaningful terms that contribute to product identification.

    Image Preprocessing

    Image processing algorithms use feature extraction to define visual attributes. For example, when comparing images of a red T-shirt, the algorithm might extract features such as “crew neck,” “red,” or “striped.”

    Image Preprocessing using advanced AI and other tech for product matching in retail analytics.

    Image hashing techniques create a unique representation (or “hash”) of an image, allowing for efficient comparison and matching of product images. This process transforms an image into a concise string or sequence of numbers that captures its essential features even if the image has been resized, rotated, or edited.

    Before we perform these activities there is a need to preprocess images to prepare them for downstream operations. These include object detection to identify objects of interest, background removal, face/skin detection and removal, pose estimation and correction, and so forth.

    Embeddings

    We have built a hybrid or a multimodal product-matching engine that uses image features, text features, and domain heuristics. For every product we process we create and store multiple text and image embeddings in a vector database. These include a combination of basic feature vectors (e.g. tf-idf based, colour histograms, share vectors) to more advanced deep learning algorithms-based embeddings (e.g., BERT, CLIP) to the latest LLM-based embeddings.

    Classification

    Classification algorithms enhance product attribute tagging by designating match types. For example, the product might be identified as an “exact match”, “variant”, “similar”, or “substitute.” The algorithm can also identify identical product combinations or “baskets” of items typically purchased together.

    What is the Business Impact of Product Matching?

    • Pricing Intelligence: Businesses can strategically adjust pricing to remain competitive while maintaining profitability. High-accuracy price comparisons help businesses analyze their competitive price position, identify opportunities to improve pricing, and reclaim market share from competitors.
    • Similarity-Based Matching: Products are matched based on a range of similarity features, such as product type, color, price range, specific features, etc., leading to more accurate matches.
    • Counterfeit Detection: Businesses can identify counterfeit or unauthorized versions of branded products by comparing them against authentic product listings. This helps safeguard brand identity and enables brands to take legal action against counterfeiters.

    Attribute Tagging

    Attribute tagging involves assigning standardized tags for product attributes, such as brand, model, size, color, or material. These naming conventions form the basis for accurate product matching. Tagging detailed attributes, such as specifications, features, and dimensions, helps match products that meet similar criteria. For example, tags like “collar” or “pockets” for apparel ensure high-fidelity product matches for hard-to-distinguish items with minor stylistic variations.

    Attributes that are tagged when images are matched for retail ecommerce analytcis.

    Including tags for synonyms, variants, and long-tail keywords (e.g., “denim” and “jeans”) improves the matching process by recognizing different terms used for similar products. Metadata tags categorize similar items according to SKU numbers, manufacturer details, and other identifiers.

    Altogether, these capabilities provide high-quality product matches and valuable metadata for retailers to classify their products and compare their product assortment to competitors.

    User-Generated Content (UGC) Analysis

    Customer reviews and ratings are rich sources of information, enabling brands to gauge consumer sentiment and identify shortcomings regarding product quality or service delivery. However, while informative, reviews constitute unstructured “noisy” data that is actionable only if parsed correctly.

    Here’s where DataWeave’s UGC analysis capability steps in.

    • Feature Extractor: Automatically pulls specific product attributes mentioned in the review (e.g., “battery life,” “design” and “comfort”)
    • Feature Opinion Pair: Pairs each product attribute with a corresponding sentiment from the review (e.g., “battery life” is “excellent,” “design” is “modern,” and “comfort” is “poor”)
    • Calculate Sentiment: Calculates an overall sentiment score for each product attribute
    The user generated content analysis framework used by DataWeave to calculate sentiment.

    The final output combines the information extracted from each of these features, which looks something like this:

    • Battery life is excellent
    • Design is modern
    • Not satisfied with the comfort

    The algorithm also recognizes spammy reviews and distinguishes subjective reviews (i.e., those fueled by emotion) from objective ones.

    DataWeave's image processing tool also analyses promo banners.

    Promo Banner Analysis

    Our image processing tool can interpret promotional banners and extract information regarding product highlights, discounts, and special offers. This provides insights into pricing strategies and promotional tactics used by other online stores.

    For example, if a competitor offers a 20% discount on a popular product, you can match or exceed this discount to attract more customers.

    The banner reader identifies successful promotional trends and patterns from competitors, such as the timing of discounts, frequently promoted product categories or brands, and the duration of sales events. Ecommerce stores can use this information to optimize their promotion strategies, ensuring they launch compelling and timely offers.

    Other Specialized Use Cases

    While these generalized AI tools are highly useful in various industries, we’ve created other category—and attribute-specific capabilities for specialty goods (e.g., those requiring certifications or approval by federal agencies) and food items. These use cases help our customers adhere to compliance requirements.

    Certification Mark Detector

    This detector lets retailers match items based on official certification marks. These marks represent compliance with industry standards, safety regulations, and quality benchmarks.

    Example:

    • USDA Organic: Certification for organic food production and handling
    • ISO 9001: Quality Management System Certification

    By detecting these certification marks, the system can accurately match products with their certified counterparts. By identifying which competitor products are certified, retailers can identify products that may benefit from certification.

    Image analysis based product matching at DataWeave also detects certificate marks.

    Nutrition Fact Table Reader

    Product attributes alone are insufficient for comparing food items. Differences in nutrition content can influence product category (e.g., “health food” versus regular food items), price point, and consumer choice. DataWeave’s nutrition fact table reader scans nutrition information on packaging, capturing details such as calorie count, macronutrient distribution (proteins, fats, carbohydrates), vitamins, and minerals.

    The solution ensures items with similar nutritional profiles are correctly identified and grouped based on specific dietary requirements or preferences. This helps with price comparisons and enables eCommerce stores to maintain a reliable database of product information and build trust among health-conscious consumers.

    Image processing for product matching also extracts nutrition table data at DataWeave.

    Building Next-Generation Competitive and Market Intelligence

    Moving forward, breakthroughs in generative AI and LLMs have fueled substantial innovation, which has enabled us to introduce powerful new capabilities for our customers.

    How Gen AI and LLMs are used by DataWeave to glean insights for analytics

    These include:

    • Building Enhanced Products, Solutions, and Capabilities: Generative AI and LLMs can significantly elevate the performance of existing solutions by improving the accuracy, relevance, and depth of insights. By leveraging these advanced AI technologies, DataWeave can enhance its product offerings, such as pricing intelligence, product matching, and sentiment analysis. These tools will become more intuitive, allowing for real-time updates and deeper contextual understanding. Additionally, AI can help create entirely new solutions tailored to specific use cases, such as automating competitive analysis or identifying emerging market trends. This positions DataWeave to remain at the forefront of innovation, offering cutting-edge solutions that meet the evolving needs of retailers and brands.
    • Reducing Turnaround Time (TAT) to Go-to-Market Faster: Generative AI and LLMs streamline data processing and analysis workflows, enabling faster decision-making. By automating tasks like data aggregation, sentiment analysis, and report generation, AI dramatically reduces the time required to derive actionable insights. This efficiency means that businesses can respond to market changes more swiftly, adjusting pricing or promotional strategies in near real-time. Faster insights translate into reduced turnaround times for product development, testing, and launch cycles, allowing DataWeave to bring new solutions to market quickly and give clients a competitive advantage.
    • Improving Data Quality to Achieve Higher Performance Metrics: AI-driven technologies are exceptionally skilled at cleaning, organizing, and structuring large datasets. Generative AI and LLMs can refine the data input process, reducing errors and ensuring more accurate, high-quality data across all touchpoints. Improved data quality enhances the precision of insights drawn from it, leading to higher performance metrics like better product matching, more accurate price comparisons, and more effective consumer sentiment analysis. With higher-quality data, businesses can make smarter, more informed decisions, resulting in improved revenue, market share, and customer satisfaction.
    • Augmenting Human Bandwidth with AI to Enhance Productivity: Generative AI and LLMs serve as powerful tools that augment human capabilities by automating routine, time-consuming tasks such as data entry, classification, and preliminary analysis. This allows human teams to focus on more strategic, high-value activities like interpreting insights, building relationships with clients, and developing new business strategies. By offloading these repetitive tasks to AI, human productivity is significantly enhanced. Employees can achieve more in less time, increasing overall efficiency and enabling teams to scale their operations without needing a proportional increase in human resources.

    In our ongoing series, we will dive deep into each of these capabilities, exploring how DataWeave leverages cutting-edge AI technologies like Generative AI and LLMs to solve complex challenges for retailers and brands.

    In the meantime, talk to us to learn more!

  • Using Siamese Networks to Power Accurate Product Matching in eCommerce

    Using Siamese Networks to Power Accurate Product Matching in eCommerce

    Retailers often compete on price to gain market share in high performance product categories. Brands too must ensure that their in-demand assortment is competitively priced across retailers. Commerce and digital shelf analytics solutions offer competitive pricing insights at both granular and SKU levels. Central to this intelligence gathering is a vital process: product matching.

    Product matching or product mapping involves associating identical or similar products across diverse online platforms or marketplaces. The matching process leverages the capabilities of Artificial Intelligence (AI) to automatically create connections between various representations of identical or similar products. AI models create groups or clusters of products that are exactly the same or “similar” (based on some objectively defined similarity criteria) to solve different use cases for retailers and consumer brands.

    Accurate product matching offers several key benefits for brands and retailers:

    • Competitive Pricing: By identifying identical products across platforms, businesses can compare prices and adjust their strategies to remain competitive.
    • Market Intelligence: Product matching enables brands to track their products’ performance across various retailers, providing valuable insights into market trends and consumer preferences.
    • Assortment Planning: Retailers can analyze their product range against competitors, identifying gaps or opportunities in their offerings.

    Why Product Matching is Incredibly Hard

    But product matching stands out as one of the most demanding technical processes for commerce intelligence tools. Here’s why:

    Data Complexity

    Product information comes in various (multimodal) formats – text, images, and sometimes video. Each format presents its own set of challenges, from inconsistent naming conventions to varying image quality.

    Data Variance

    The considerable fluctuations in both data quality and quantity across diverse product categories, geographical regions, and websites introduce an additional layer of complexity to the product matching process.

    Industry Specific Nuances

    Industry specific nuances introduce unique challenges to product matching. Exact matching may make sense in certain verticals, such as matching part numbers in industrial equipment or identifying substitute products in pharmaceuticals. But for other industries, exactly matched products may not offer accurate comparisons.

    • In the Fashion and Apparel industry, style-to-style matching, accommodating variants and distinguishing between core sizes and non-core sizes and age groups become essential for accurate results.
    • In Home Improvement, the presence of unbranded products, private labels, and the preference for matching sets rather than individual items complicates the process.
    • On the other hand, for grocery, product matching becomes intricate due to the distinction between item pricing and unit pricing. Managing the diverse landscape of different pack sizes, quantities, and packaging adds further layers of complexity.

    Diverse Downstream Use Cases

    The diverse downstream business applications give rise to various flavors of product matching tailored to meet specific needs and objectives.

    In essence, while product matching is a critical component in eCommerce, its intricacies demand sophisticated solutions that address the above challenges.

    To solve these challenges, at DataWeave, we’ve developed an advanced product matching system using Siamese Networks, a type of machine learning model particularly suited for comparison tasks.

    Siamese Networks for Product Matching

    Our methodology involves the use of ensemble deep learning architectures. In such cases, multiple AI models are trained and used simultaneously to ensure highly accurate matches. These models tackle NLP (natural language processing) and Computer Vision challenges specific to eCommerce. This technology helps us efficiently narrow down millions of product candidates to just 5-15 highly relevant matches.

    The Tech Powering Siamese Networks

    The key to our approach is creating what we call “embeddings” – think of these as unique digital fingerprints for each product. These embeddings are designed to capture the essence of a product in a way that makes similar products easy to identify, even when they look slightly different or have different names.

    Our system learns to create these embeddings by looking at millions of product pairs. It learns to make the embeddings for similar products very close to each other while keeping the embeddings for different products far apart. This process, known as metric learning, allows our system to recognize product similarities without needing to put every product into a rigid category.

    This approach is particularly powerful for eCommerce, where we often need to match products across different websites that might use different names or images for the same item. By focusing on the key features that make each product unique, our system can accurately match products even in challenging situations.

    How Siamese Networks Work?

    Imagine having a pair of identical twins who are experts at spotting similarities and differences. That’s essentially what a Siamese network is – a pair of identical AI systems working together to compare things.

    How it works:

    • Twin AI systems: Two identical AI systems look at two different products.
    • Creating ‘fingerprints’ or ‘embedding’: Each system creates a unique ‘fingerprint’ of the product it’s looking at.
    • Comparison: These ‘fingerprints’ are then compared to see how similar the products are.

    Architecture

    The architecture of a Siamese network typically consists of three main components: the shared network, the similarity metric, and the contrastive loss function.

    • Shared Network: This is the ‘brain’ that creates the product ‘fingerprints’ or ‘embeddings.’ It is responsible for extracting meaningful feature representations from the input samples. This network is composed of layers of neural units that work together. Weight sharing between the twin networks ensures that the model learns to extract comparable features for similar inputs, providing a basis for comparison.
    • Similarity Metric: After the shared network processes the inputs, a similarity metric is employed. This decides how alike two ‘fingerprints’ or ‘embeddings’ are. The selection of a similarity metric depends on the specific task and characteristics of the input data. Frequently used similarity metrics include the Euclidean distance, cosine similarity, or correlation coefficient, each chosen based on its suitability for the given context and desired outcomes.
    • Loss Function: For training the Siamese network, a specialized loss function is used. This helps the system improve its comparison skills over time. It guides and trains the network to generate akin embeddings for similar inputs and disparate embeddings for dissimilar inputs.

      This is achieved by imposing penalties on the model when the distance or dissimilarity between similar pairs surpasses a designated threshold, or when the distance between dissimilar pairs falls below another predefined threshold. This training strategy ensures that the network becomes adept at discerning and encoding the desired level of similarity or dissimilarity in its learned embeddings.

    How DataWeave Uses Siamese Networks for Product Matching

    At DataWeave, we use Siamese Networks to match products across different retailer websites. Here’s how it works:

    Pre-processing (Image Preparation)

    • We collect product images from various websites.
    • We clean these images up to make them easier for our AI to understand.
    • We use techniques like cropping, flipping, and adjusting colors to help our AI recognize products even if the images are slightly different.

    Training The AI

    • We show our AI system millions of product images, teaching it to recognize similarities and differences.
    • We use a special learning method called “Triplet Loss” to help our AI understand which products are the same and which are different.
    • We’ve tested different AI structures to find the one that works best for product matching, including ResNet, EfficientNet, NFNet, and ViT. 

    Image Retrieval 

    • Once trained, our AI creates a unique “fingerprint” for each product image.
    • We store these fingerprints in a smart database.
    • When we need to find a match for a product, we:
      • Create a fingerprint for the new product.
      • Quickly search our database for the most similar fingerprints.
      • Return the top matching products.

    Matches are then assigned a high or a low similarity score and segregated into “Exact Matches” or “Similar Matches.” For example, check out the image of this white shoe on the left. It has a low similarity score with the pink shoe (below) and so these SKUs are categorized as a “Similar Match.” Meanwhile, the shoe on the right is categorized as an “Exact Match.”

    Similarly, in the following image of the dress for a young girl, the matched SKU has a high similarity score and so this pair is categorized as an “Exact Match.”

    Siamese Networks play a pivotal role in DataWeave’s Product Matching Engine. Amid the millions of images and product descriptions online, our Siamese Networks act as an equalizing force, efficiently narrowing down millions of candidates to a curated selection of 10-15 potential matches. 

    In addition, these networks also find application in several other contexts at DataWeave. They are used to train our system to understand text-only data from product titles and joint multimodal content from product descriptions.

    Leverage Our AI-Driven Product Matching To Get Insightful Data

    In summary, accurate and efficient product matching is no longer a luxury – it’s a necessity. DataWeave’s advanced product matching solution provides brands and retailers with the tools they need to navigate this complex landscape, turning the challenge of product matching into a competitive advantage.

    By leveraging cutting-edge technology and simplifying it for practical use, we empower businesses to make informed decisions, optimize their operations, and stay ahead in the ever-evolving eCommerce market. To learn more, reach out to us today!

  • How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    As eCommerce increasingly becomes a prolific channel of sales for consumer brands, they find that maintaining a consistent and trustworthy brand image is a constant struggle. In an ecosystem filled with dozens of marketplaces and hundreds of third-party merchants, ensuring that customers see what aligns with a brand’s intended image is quite tricky. With many fakes and counterfeit products doing the rounds, brands may further struggle to get the right representation.

    One way brands can track and identify inconsistencies in their brand representation across marketplaces is to use Digital Shelf Analytics solutions like DataWeave’s – specifically the Content Audit module.

    This solution uses advanced AI models to identify image similarities and dissimilarities compared with the original brand image. Brands could then use their PIM platform or work with the retailer to replace inaccurate images.

    But here’s the catch – AI can’t always accurately predict all the differences. Relying solely on scores given by these models poses a challenge in tracking the subtle differences between images. Often, image pairs with seemingly high match scores fail to catch important distinctions. Fake or counterfeit products and variations that slip past the AI’s scrutiny can lead to significant inaccuracies. Ultimately, it puts the reliability of the insights that brands depend on for crucial decisions at risk, impacting both top and bottom lines.

    Dealing with this challenge means finding a balance between the number-based assessments of AI models and the human touch needed for accurate decision-making. However, giving auditors the ability to pinpoint variations precisely goes beyond simply sharing numerical values of the match scores with them. Visualizing model-generated scores is important as it provides human auditors with a tangible and intuitive understanding of the differences between two images. While numerical scores are comparable in the relative sense, they lack specificity. Visual interpretation empowers auditors to identify precisely where variations occur, aiding in efficient decision-making.

    How AI-Powered Image Scoring Works

    At DataWeave, our approach involves employing sophisticated computer vision models to conduct extensive image comparisons. Convolutional Neural Network (CNN) models such as Resnet-50 or YOLO, in conjunction with feature extraction models, analyze images quantitatively. This AI-powered image scoring process yields scores that indicate the level of similarity between images.

    However, interpreting these scores and understanding the specific areas of difference can be challenging for human auditors. While computer vision models excel at processing vast amounts of data quickly, translating their output into actionable insights can be a stumbling block. A numerical score may not immediately convey the nature or extent of the differences between images

    In the assessment of these images, all fall within the 70 to 80 range of scores (out of a maximum of 100). However, discerning the nature of differences—whether they are apparent or subtle—poses a challenge for the AI models and human auditors. For example, there are differences in the placement or type of images in the packaging, as well as packing text that are often in an extremely small font size. It is, of course, possible for human auditors to identify the differences in these images, but it’s a slow, error-prone, and tiring process, especially when auditors often have to check hundreds of image pairs each day.

    So how do we ensure that we identify differences in images accurately? The answer lies in the process of visual highlighting.

    How Visual Highlighting Works

    Visual highlighting is a method that enhances our ability to comprehend differences in images by combining sophisticated algorithms with human understanding. Instead of relying solely on numerical scores, this approach introduces a visual layer, resembling a heatmap, guiding human auditors to specific areas where discrepancies are present.

    Consider the scenario depicted in the images above: a computer vision model assigns a score of 70-85 for these images. While this score suggests relatively high similarity, it fails to uncover major differences between the images. Visual highlighting comes into play to overcome this limitation, precisely indicating regions where even subtle differences are seen.

    Visual highlighting entails overlaying compared images and emphasizing areas of difference, achieved through techniques like color coding, outlining, or shading specific regions. The significance of the difference in a particular area determines the intensity of the visual highlight.

    For instance, if there’s a change in the product’s color or a discrepancy in the packaging, these variations will be visually emphasized. This not only streamlines the auditing process but also enables human evaluators to make well-informed decisions quickly.

    Benefits of Visual Highlighting

    • Intuitive Understanding: Visual highlighting offers an intuitive method for interpreting and acting upon the outcomes of computer vision models. Instead of delving into numerical scores, auditors can concentrate on the highlighted areas, enhancing the efficiency and accuracy of the decision-making process.
    • Accelerated Auditing: By bringing attention to specific regions of concern, visual highlighting speeds up the auditing process. Human evaluators can swiftly identify and address discrepancies without the need for exhaustive image analysis.
    • Seamless Communication: Visual highlighting promotes clearer communication between automated systems and human auditors. Serving as a visual guide, it enhances collaboration, ensuring that the subtleties captured by computer vision models are effectively conveyed.

    The Way Forward

    As technology continues to evolve, the integration of visual highlighting methodologies is likely to become more sophisticated. Artificial intelligence and machine learning algorithms may play an even more prominent role in not only detecting differences but also in refining the visual highlighting process.

    The collaboration between human auditors and AI ensures a comprehensive approach to maintaining brand integrity in the ever-expanding digital marketplace. By visually highlighting differences in images, brands can safeguard their visual identity, foster consumer trust, and deliver a consistent and reliable online shopping experience. In the intricate dance between technology and human intuition, visual highlighting emerges as a powerful tool, paving the way for brands to uphold their image with precision and efficiency.

    To learn more, reach out to us today!


    (This article was co-authored by Apurva Naik)

  • The Future of eCommerce is Social: Demystifying the Social Commerce Revolution

    The Future of eCommerce is Social: Demystifying the Social Commerce Revolution

    Social commerce is the selling of goods and services within a social media platform. Brands use social platforms such as Instagram, Facebook, Snapchat, and Twitter to promote and sell products. These platforms have become an integral part of consumers’ everyday life because they continue to engage users with relatable content, making them scroll their feeds for hours. 

    The Social Commerce model capitalizes on this high user engagement & moves social media beyond its traditional role in the top-of-the-funnel marketing process by encouraging users to shop without leaving their preferred apps. According to the Social Media Investment Report, 91% of executives agree that social commerce is driving an increasing portion of their marketing revenue, and 85% report that social data will be a primary source of business intelligence.

    Let’s talk a little bit about why brands should consider selling via social media platforms:

    Social Commerce vs. eCommerce vs. QCommerce

    While they may fall under the same umbrella of online selling, social commerce, quick commerce, and eCommerce are three very different concepts

    • eCommerce refers to online shopping via a (retailer or brand) website or app. Customers can access these platforms via desktop or mobile devices. However, the sales funnel generally looks the same. These brands and retailers use top-of-the-funnel tactics like social media content, digital ads, and other marketing strategies to encourage customers to visit the online store. There are three main types of eCommerce businesses: Business-to-Business (Alibaba, Amazon Business, eWorldTrade), Business-to-Consumer (websites such as Amazon, Rakuten, and Zalando), and Consumer-to-Consumer (platforms such as eBay & Etsy).
    • Quick Commerce (or QCommerce) refers to eCommerce businesses that deliver goods within a couple of hours or even minutes. Although it’s sometimes used interchangeably with on-demand delivery or instant commerce, the idea of quick commerce has been around in the food industry for ages now. It has been recently ushered into the mainstream by evolving consumer preferences for quicker delivery of groceries and FMCG goods.
    • Social commerce brings the store to the customer rather than redirecting customers to an online store. It removes unnecessary steps and simplifies the buying process by letting the customer checkout directly through social media platforms, creating a frictionless buying journey for the customer. Additionally, social media platforms are mobile-friendly, a huge benefit for brands because increasingly more and more customers are accessing the internet through mobile devices.
    Social Commerce
    Social Commerce

    Rise of Social Commerce

    First used in 2005 by Yahoo!, ‘social commerce’ refers to collaborative shopping tools such as user ratings, shared pick lists, and user-generated content. Social media networks snowballed throughout the 2000s and 2010s, alongside a general increase in eCommerce, leading customers and merchants to quickly recognize the benefits of buying and selling through social media networks. Social media platforms have since evolved from merely a showcase tool for brands. They now serve as virtual storefronts and extensions of a company’s website or brick and mortar stores, capable of handling the buying experience.

    Top Social Commerce Platforms

    Social media platforms aim to keep visitors engaged on their platforms for as long as possible. Increased time in-app or on-site maximizes their opportunity to serve ads, a primary source of revenue generation. Social media platforms have millions of active users and they have a great power to help companies and individuals build their brands, interact with consumers, and support after-sales. Here are the top social commerce platforms:

    • Facebook

    Facebook introduced Facebook Shops to capitalize on the commercial opportunity by allowing vendors to advertise and sell directly through the platform. Facebook integrates social commerce with shopping, allowing users to purchase products smoothly. Facebook shops offer a smooth user experience where users can review products and get recommendations from trusted acquaintances. Customers can directly interact with the merchant’s customer service department post-purchase. 

    • Instagram

    60% of people discover new products on Instagram. Owned by Facebook, Instagram facilitates in-app shopping and handles the entire transactions within the app itself. Users scrolling on Instagram often wants to follow trends and replicate the looks of their role models or favorite influencers. By offering purchasing options in the app, Instagram benefits from the platform’s rich visual imagery and videos, allowing businesses to sell an idea rather than the traditional process of selling a product. 

    • TikTok

    Shopify partnered with TikTok to introduce shopping and drive sales through the younger and seemingly ever-expanding TikTok audience. With TikTok for Business Ads Manager, brands and merchants can create in-feed video-based content depending on their product offering. This partnership allows Shopify merchants to expand to the TikTok audience.

    • Snapchat

    Snapchat has recently launched Brand profiles, a feature that allows users to scroll through a merchant’s products and buy them in-app. This new experience is powered by Shopify too. Merchants can create Brand Profiles or Native Stores that allow users to purchase products from the app. 

    Pinterest users are there for Shopping Inspiration
    Pinterest users are there for Shopping Inspiration
    • Pinterest

    Pinterest is also an image-based platform where users create boards of their favorite wedding accessories, home decor, fashion trends, etc. Pinterest doesn’t specifically offer social commerce for the global audience. Rather, it allows business accounts to create ‘Product Pins’ that are displayed in the brand’s Pinterest shop. Only U.S. customers can purchase within the app. Users from other countries are redirected to the eCommerce site to complete the sale. We have added Pinterest to this list because 89% of Pinterest users are there for shopping inspiration.

    Pinterest is an image-based platform where users create boards
    Pinterest is an image-based platform where users create boards

    Why Should Brands Care About Social Commerce

    • To enhance social media presence and brand awareness

    If your target demographic is in the 18-to-34 age range, they’re already on social media and waiting to shop while they scroll. According to Sprout Social, over 68% of consumers have already purchased directly from social media and nearly all (98%) consumers plan to make at least one purchase through social or influencer commerce this year. You can enhance brand awareness by selling on social media platforms. Influencer marketing is an amazing way to build brand awareness since customers are now seeking authenticity from micro-influencers rather than big-name celebrities. 

    • To generate social proof

    90% of online shoppers say that they read online reviews before making an online purchase. Whether it’s an automated follow-up email or a message through the social media platform, ask for a review after your product has been delivered to the customer. You can also offer incentives like a contest to encourage previous customers to weigh in and share their experiences. These steps will allow you to collect social proof since it’s vital to build a positive reputation online. You can also ask customers to create small product review videos that you can share on your social feeds in creative ways. You can also post user-generated content, create a carousel of positive comments, or host a live video with happy customers.

    Social Proof
    Social Proof
    • To simplify the buying process for consumers

    Traditional eCommerce involves several steps. It starts with displaying ads on social media platforms and customers being redirected to the business website for completing the transaction. To complete the transaction, customers also have to create an account or manually fill in the credit card details and delivery address. On the other hand, social is only a three-step process — find, click and buy. 

    Counterfeit Products
    Counterfeit Products

    Conclusion

    While social commerce is proliferating, it also has a few setbacks like the rise of counterfeit products. Counterfeiting has expanded into social media and has become an under-reported but vital hub for counterfeiters. A counterfeit detection solution can help brands and merchants identify & remove fake and unauthorized products. Technologies like image recognition can help in counterfeit detection by capturing fake logos and discrepancies. Removing counterfeit products will help brands safeguard customer loyalty and prevent fake products from harming your bottom line. 

    Here’s how DataWeave helped Classic Accessories, a leading manufacturer of high-quality furnishings & accessories identify counterfeit products across multiple retail marketplace websites eliminating 22 hours of time spent per week conducting manual audits – read the case study here. 

    Are you a brand or a retailer worried about counterfeits? Sign up for a demo with our team to know how we can help you track, identify and eliminate fakes! 

  • Importance of Image Recognition in the Retail Industry

    Importance of Image Recognition in the Retail Industry

    When it comes to classifying and analyzing images, humans can easily recognize distinct features of objects and associate them with individual definitions. However, visual recognition is a highly complex task for machines because it involves identifying multiple objects and finding object relationships. Image recognition has been a long-standing research problem in the computer vision field. But, the recent development in AI has improved the process of object detection, image identification, and image classification. The image recognition market is assumed to rise globally to a market size of $42.2 billion by 2022. Various industries are adopting image recognition technology to improve augmented reality applications, optimize medical imagery, boost driverless car technology, predict consumer behavior, and much more. 

    Although image recognition is a relatively new aspect of analysis, it is also making its way into eCommerce. Image recognition is helping retailers to expand consumer reach, offer insights into trends, and improve customers’ online shopping experience for the eCommerce industry. The Global Image Recognition in Retail Market is estimated to be USD 1.8 Bn in 2021 and is expected to reach USD 4.5 Bn by 2026, growing at a CAGR of 20%.

    Image Recognition
    Global Image Recognition in Retail Market

    In this blog, you’ll learn about image recognition technology and its importance in the retail industry. 

    What is Image Recognition?

    Image recognition, a subcategory of computer vision, is a technology that can identify objects, entities, or attributes in digital images or videos. However, computer vision is a broader term, including methods for gathering, processing, and analyzing data from the real world. Image recognition can be performed at varying degrees of accuracy, depending on the type of information required.

    Image recognition can perform the following tasks:

    Object Detection, Semantic Segmentation & Instance Segmentation
    Object Detection, Semantic Segmentation & Instance Segmentation
    • Classification: It identifies the “class,” i.e., the category to which an image belongs. A picture can have only one class.
    • Tagging: It’s a classification task but involves a higher degree of accuracy. Tagging can recognize several concepts or objects within an image, and there can be more than one tag assigned to a particular image.
    • Detection and localization: This step helps locate object(s) in an image. Once the system locates the object in question, localization helps to place a bounding box around it. 
    • Segmentation: This is also a detection task but involves a higher degree of precision. Segmentation locates element(s) to the nearest pixel in an image. 
    • Instance segmentation: It helps differentiate multiple objects belonging to the same class. 

    Image Recognition in eCommerce and how it works

    Nowadays, increasing competition and customer expectations are forcing online retailers to constantly monitor market dynamics wrt their pricing, promotion & product assortment in order to stay competitive. To get these insights, retailers need to match and compare their products against their competitors to see where the gaps are. That’s where product matching comes in. 

    Product matching refers to finding the same or similar products against a target universe of products from across the web, across multiple competing retailers. Product matching uses AI-based image recognition to determine product attributes, find patterns, and detect text, product price, shipping information, and so on. 

    Here’s how DataWeave’s AI-powered analytics platform uses image recognition & aggregates insights & data for retailers from across the web to provide a comprehensive view of the online competitive environment.

    Image recognition use-cases in the retail industry

    a. Attribute tagging

    Attribute Tagging
    Attribute Tagging

    Getting shoppers to your eCommerce platform is one thing and getting them to complete a purchase is a steeper hill to climb. If your platform can’t provide search results that match with customers’ requirements, they’ll get lost, grow frustrated, and drop off. Attribute tagging with image recognition allows eCommerce stores to automatically generate attributes for all products so customers can quickly find products they are looking for. 

    Tags allow users to filter products based on the categories they want to explore. Product tags include everything the customer might specifically search for — color, type, size, brand, use, design, fabric, discount, etc. For example, a dress could have tags like red, evening, midi, summer, long-sleeve, silk, summer sale, etc. When a user looks for midi dresses or long-sleeve dresses, products with these tags will show up. 

    b. Search by image

    Visual Search
    Visual Search

    Visual Search allows users to look for similar products using a reference image from their camera roll or downloaded from the internet. The visual search feature also enables eCommerce businesses to implement image-based search into their software applications. It maximizes the searchable potential of their visual data. 

    Meanwhile, Gartner predicts a 30% increase in digital commerce revenue by 2021 for companies who start supporting visual and voice search on their websites and apps. The benefits of visual search include more personalized, easy product recommendations and enhanced product discovery.

    c. Fashion trend analysis

    similarity matching
    Similarity Matching

    Tapping into trending product categories is a goldmine for any eCommerce business. Having insights into trending categories and products means less competition on search engines, fewer ads, and intelligent pricing. All of which can boost any retailer’s margins. Image recognition technology provides information about colors, styling techniques, fabric textures, prints, and more to spark consumer demand. It works by scanning social media images to pinpoint trending attributes and predict fashion trends. For instance, while scanning images, technology understands that it’s seeing a photo of a color-blocked sweatshirt because it recognizes the product has a hooded neck, full sleeves, blocks of different colors, and even the type of fabric. This technology can analyze millions of images, helping retailers analyze the volume of color-blocked sweatshirts. 

    We do this seamlessly at DataWeave. Our similarity matching solution helps retailers gather insights into attributes for products similar to the ones they’re carrying on their site. Similarity matching helps retailers gain visibility into their entire competitive landscape to keep their e-commerce strategy responsive to price & product assortment shifts among consumers and rivals

    d. Augmented reality

    According to Statista, the AR market is valued at $9.5 billion, with around 810 million active mobile users. Since shoppers want the full sensory product experience before shopping online, augmented reality (AR) can help them understand what they’re buying and how the product will work for them. There are AR applications for trying makeup, clothing, accessories, and even eyeglasses. IKEA was one of the pioneers in using AR for eCommerce retail. In 2017, IKEA launched the Place app, allowing shoppers to see how thousands of items will look in their homes, with 98% accuracy. 

    Image recognition helps AR applications anchor virtual content with the real world. For instance, Sephora has a Virtual Artist that allows users to try different makeup looks and even take pictures of an outfit they’re planning to wear to match the shade. Users can even check out full-face looks and learn how to do their makeup with virtual tutorials. 

    e. Counterfeit Detection

    Counterfeit Detection
    Counterfeit Detection

    Another application of image recognition that has proven to be very successful is counterfeit product detection. It has become increasingly difficult for brands and retailers to find and eliminate fake items on eCommerce sites. U.S. Customs seized over 13,500 counterfeit goods worth $30 Million in November 2021, indicating how brands and online marketplaces have struggled in the past to find an effective solution. 

    Essentially, image recognition technology allows eCommerce sites to detect products with fake logos and designs attempting to sell as legitimate brands by capturing discrepancies in images and content. The system flags and delists the products and sellers when a fake is detected.

    Here’s how DataWeave helped Classic Accessories, a leading manufacturer of high-quality covers, furnishings, and accessories automate their counterfeit detection process using our super Image Recognition capabilities. 

    f. User-generated content analysis

    Visual content plays a vital role in eCommerce sites, especially when it comes to product photos and videos. Today, branded visual content isn’t as effective as it’s one-dimensional. As a matter of fact, 93% of marketers agree that customers trust user-generated content more than content produced by brands. However, user-generated content that features product images or videos is way more exciting, realistic, and creative. It gives customers an appealing view of products being used in real life. 

    The most common form of UGC, i.e., reviews and ratings, have been the key for eCommerce brands as they are quantitative and qualitative metrics about a product/service quality, worth, value, reliability, etc. With image recognition, retailers can access insights into strengths and gaps in all product offerings by understanding what consumers are saying about them. 

    Here’s how DataWeave can help retailers and brands analyze consumer reviews & help them adapt to customer needs.

    Conclusion

    Because of its massive influence, image recognition technology is becoming widely adopted by eCommerce companies. It benefits both retailers and customers. Image recognition based on deep learning can provide retailers with helpful capacities like customer analytics, counterfeit detection, personalized searches, and more. Retailers can also use the data gathered from image recognition eCommerce technology to design effective marketing campaigns and improve their ROI.

    With super sharp image recognition capabilities, DataWeave offers 90% accuracy in matching eCommerce products, allowing us to provide comprehensive and precise insights into pricing and assortments. Sign up for a demo with our team to know more.