The DataWeave Blog

Tag: similarity matching

  • 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!

  • 5 Must-Have Capabilities of Your Ideal Competitive Pricing Intelligence Solution

    5 Must-Have Capabilities of Your Ideal Competitive Pricing Intelligence Solution

    In the cutthroat world of retail, where razor-thin margins and fierce competition reign supreme, pricing becomes your secret weapon to driving sales. The magic bullet unlocks sales, attracts customers, and ultimately fuels your bottom line. But with ever-changing market trends and competitor tactics shifting constantly, effective pricing strategies become even more crucial.

    A recent Bain & Company study highlights this very point. 78% of respondents acknowledged that their pricing decisions could be improved, leaving significant revenue untapped. John Furner, President and CEO of Kroger, echoes this sentiment. In a press release announcing a new pricing strategy, he emphasized their commitment to “providing our customers with predictable, affordable prices on the products they need most.” This focus on transparent and consistent pricing reflects the growing importance of building trust with customers, where value goes beyond just the lowest price tag.

    The right pricing strategy can unlock a treasure trove of benefits for retailers, including attracting new customers, boosting sales, and ultimately increasing their bottom line.

    But here’s the challenge: keeping pace with market trends and competitor strategies requires constant vigilance. This is where an advanced, user-centric pricing intelligence tool comes into play. Retailers need a platform specifically designed to address their unique challenges. It should empower them to protect margins, create a seamless pricing process, and attract and retain price-sensitive customers. To help you navigate this landscape, we’ve identified the must-have capabilities of a pricing intelligence solution that will transform your pricing strategy and propel your business toward long-term success.

    1. Reliable and Accurate Data Collection

    Retailers need a competitive intelligence solution that goes beyond merely capturing information en masse from competitor sites. An ideal solution ensures that data is consistent, extensive, and highly accurate, with an added level of granularity. This is achieved through statistical process control methods for data quality assurance, enabling highly accurate data capture and processing.

    Such a platform should be capable of scraping data from various sources, including desktop sites, mobile sites, and mobile applications, as well as a variety of online platforms: aggregators, omnichannel retailers, delivery intermediaries, quick commerce platforms, D2C sites, and more. This versatility ensures that data is captured across any global region and in dozens of languages, making the system geography and language agnostic.

    DataWeave’s solution includes a fast and automated data source configuration system, enabling a swift setup of new web sources for data capture. This capability ensures that retailers can stay ahead of the curve as the market landscape and competitor strategies evolve.

    An effective competitive pricing intelligence solution allows retailers to move away from working with incomplete or inaccurate data and instead leverage a comprehensive information stream to create strategic pricing decisions and optimize their overall business strategy. At the end of the day, the insights you base your decisions on are only as good as the data you aggregate. Even with the world’s best analytics engine, it’s always a case of “garbage in, garbage out.”

    2. Hyperlocal Insights From Store-Level Data

    Monitoring pricing and availability across specific stores is crucial for retailers to gain critical insights into a vast network of locations, enabling them to make strategic decisions that enhance pricing strategies and supply chain effectiveness, thereby minimizing stockouts or pricing inefficiencies in key markets. A platform like DataWeave provides retailers with a comprehensive view of store-level data across ZIP codes, maintaining a hyperlocal competitive strategy. It offers detailed visibility into product availability, highlighting out-of-stock scenarios across different competitors. This capability is invaluable, allowing quick identification of price improvement opportunities and providing retailers with a bird’s eye view of where products can be priced higher than usual to gain margins.

    The system operates at configurable intervals—daily, weekly, or monthly—enabling retailers to keep a vigilant eye on pricing, product availability, and delivery timelines based on the selected fulfillment option. Unlike many other providers who offer limited insights from a sample of stores, this solution delivers exhaustive analytics from every storefront. This comprehensive approach grants retailers (and brands) a strategic edge, facilitating efficient inventory tracking, precise pricing adjustments, and rapid responses to fluctuating market dynamics.

    3. Sophisticated, AI-Powered Product Matching

    A solution that matches products accurately at scale is essential for a robust and reliable competitive pricing strategy. Advanced platforms use unified systems for both text and image recognition to accurately match similar SKUs across thousands of eCommerce stores and millions of products. Deep learning architecture is employed to develop unique AI that matches text and images, grouping similar products based on their features, ensuring accurate matches even for private label products.

    This AI identifies critical elements of products in images, such as focusing on the top half of a model wearing a shirt, the sleeve length, the color of the product, etc.. Deep learning models, trained on extensive datasets of images, enhance these images by removing irrelevant background details and improving the quality of the core product image. Innovative AI then extracts unique signatures from the photos, allowing for quick and efficient identification and grouping of products across billions of indexed items.

    No matter how powerful the AI, combining it with human expertise is key to achieving true data veracity—ensuring accuracy, freshness, and comprehensive coverage required for reliable product matching. A human-in-the-loop approach elevates the AI-powered product matching process by addressing key challenges. AI algorithms may initially identify product matches with 80-90% accuracy, but human validation corrects errors, pushing accuracy closer to 100%. Humans apply contextual judgment for subjective criteria like aesthetics and design, making nuanced decisions that quantitative rules might miss. Continuous learning through an iterative feedback loop allows AI models to quickly adapt to changing trends and preferences as human experts provide context and re-label incorrect predictions. By integrating AI’s automation and scale with human validation, judgment, and knowledge curation, pricing intelligence solutions can achieve the accuracy and coverage necessary for actionable competitive pricing insights.

    This approach results in retailers being able to match products and compare prices between identical products, similar products, and private label brands.

    4. Unit of Measure Normalization

    Effective product matching and grouping are crucial for maintaining competitive pricing, but this requires a tech stack that can normalize units of measure across various sites. For example, a 10.75oz can of chicken noodle soup priced at $3 may seem cheaper than a 12.90oz can priced at $3.20, but this isn’t always the case. Initially, the larger package might appear more expensive, but when prices are compared based on the same unit amount, it often offers better value. Therefore, it is essential for retailers to standardize units to accurately compare prices. Advanced technology goes beyond simply matching products; it ensures accurate comparisons by normalizing unit measurements, including weight, quantity, and volume—crucial factors for establishing a clear pricing picture across competitors.

    Imagine comparing soup prices regardless of whether they are advertised in ounces, milliliters, or liters. By normalizing unit measurements, retailers can develop tailored pricing strategies on a level playing field, eliminating the risk of being misled by seemingly lower prices that conceal smaller quantities. Unit normalization allows retailers to uncover hidden value propositions by accurately determining the cost per unit, enabling them to set competitive prices, highlight the true value of their products, and make data-driven decisions.

    5. Ease of Actionability

    The most valuable insights are ineffective if they cannot be easily accessed and acted upon. Imagine a solution that not only provides industry insights but also customizes alerts and dashboards to show exactly how your prices compare to competitors in your specific categories and product groupings. An ideal solution would offer all this in one centralized platform, giving retailers easy access to data through intuitive dashboards, seamless data export options, and flexible API integrations. This enables a smooth, effortless process for adopting and utilizing the platform.

    Ease of use and actionable insights should be at the core of such a solution. A SaaS-based web portal can provide businesses with access to insights through user-friendly dashboards, detailed reports, and impactful visualizations. Customized insights tailored for each persona within the organization facilitate swift actions on relevant competitive intelligence. Whether it’s day-to-day tactical recommendations or inputs for long-term strategies, the platform should ensure that all insights are easily consumable and actionable.

    Moreover, the data should be accessible using plug-and-play APIs, enabling businesses to integrate external data with their internal pricing or ERP systems and BI tools. This integration generates predictive intelligence, enhances decision-making, and drives more robust business outcomes.

    Choosing the Right Pricing Intelligence Solution Will Determine Your Success

    Retailers need to leave behind generic pricing intelligence tools. For true success, retailers need a solution built to tackle their specific challenges. With capabilities like comprehensive data collection capturing granular details across regions and languages, local insights into store-level data for informed decision-making, accurate price comparisons with unit normalization, and access to actionable insights, retailers gain a complete and holistic picture of the pricing landscape, setting them up for success. Additionally, AI-powered and human-aided product matching ensures accurate competitor analysis

    These are just some of the essential capabilities DataWeave offers to retailers. By prioritizing these, retailers can transform their pricing strategy into a profit-generating machine, keeping them ahead of the curve and exceeding customer expectations in a competitive market to help them stay at the forefront of their categories.

    To learn more, talk to us today!

  • 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)

  • Similarity matching keeps retailers competitive: Know your rivals

    Similarity matching keeps retailers competitive: Know your rivals

    Soaring e-commerce growth has made retail more crowded, complex and competitive. Now retailers face an urgent need to keep an eye on more rivals with potential substitute products to maximize their own e-commerce growth.

    Consider these recent figures, which illustrate online shoppers’ abundance of product choices:
     

    • 24% year-over-year increase in direct-to-consumer (DTC) brands in the U.S. alone was estimated for 2020 as more brands bypass retailers1
    • 55% of shoppers have purchased private label in the past year and many retailers are investing more in their own brands2
    • 110% average increase in small retailers’ 2020 online holiday sales, as more players launched new e-commerce shops during the pandemic3
    • 39% of U.S. consumers have changed brands, with the level of brand switching doubling in 2020 compared to 2019, especially among Gen Z and Millennial consumers, as loyalty declines4

    These statistics prove that in 2021 retailers need to navigate more online players and products. Now retailers need a new approach to stay on top of market trends to keep their e-commerce strategies competitive, profitable and attractive to discerning online shoppers. 


    Retailers reduce the risk of substitutes with similarity matching

    In response to online crowding, more leading retailers are turning to similarity matching. Similarity matching is a type of retail analytics that scour global e-commerce sites to find products that exactly match a specific item as well as products that closely match it. Similarity matching insights have grown in strategic significance because they increase retailers’ visibility into potential substitute products, so they can respond to all rivals’ moves with greater agility and efficiency to stay competitive.


    In terms of e-commerce applications, similarity matching helps retailers gather insights on potential substitute products so they can adjust their pricing and assortment strategies accordingly. Retailers can align their pricing with rivals’ pricing moves for similar items to protect their margins and maximize profitability. They can also make informed assortment decisions, including which product mix of bestsellers, unique items and private labels could optimize their online sales performance.

    Online shoppers search for products differently across different categories

    Consumer behavior plays a role, as online search habits differ across product categories, which influences the type of similarity matching retailers need. For example, categories like fashion, toys, home and kitchen work best with similarity matching based on text and images. In these highly-visual categories, consumers can quickly determine whether a product fits the design and aesthetic they are looking for. As a result, e-commerce product titles, descriptions and product images play a big role in consumers’ purchase decisions.

    By contrast, consumer electronics and furniture are categories in which consumers tend to seek specific product attributes, such as a certain level of resolution for their high-definition TV or a couch with particular dimensions so it fits their living room. For these types of products, consumer purchases are driven by product specifications, so similarity matching takes into account their specific needs as well as a degree of tolerance for exact or near-similar attributes across online competitors.

    Expect intense e-commerce rivalry in 2021

    As more consumers shop online, they are increasingly informed by online product comparison information. A wide variety of product choices means consumers can substitute similar goods with ease, especially if a particular item is out-of-stock. Perceived product differentiation, price sensitivity and private labels can also influence consumers’ purchase decisions.

    Across categories, e-commerce growth is outpacing total retail growth. When competition is this fierce, there is an increased risk that numerous and aggressive players will drive down profit margins. Leading retailers are now seizing opportunities to earn consumer loyalty. Using similarity matching helps retailers by offering in-demand products that consumers will actually buy and deliver exceptional online experiences to prevent shoppers from switching to rivals and their comparable products.

    Similarity matching lets you stay competitive

    As e-commerce traffic and rivalry increase, similarity matching helps retailers stand out and serve online shoppers more effectively.

    Retailers gain visibility into their entire competitive landscape to keep their e-commerce strategy responsive to shifts among consumers and rivals. By knowing the full scope of potential substitute products available online, retailers can keep their pricing and assortment strategies in line with rivals’ to reduce their risk of losing sales to rivals, and boost their top line, profitability and cost savings.

    The data insights give retailers the flexibility they need to align with online shoppers’ different needs across categories. As a result, retailers can use similarity matching to boost agility and gain a competitive advantage by adapting to online shoppers’ needs, winning their sales and fueling e-commerce growth.DataWeave’s similarity matching capability lets clients

    1 US Direct-to-Consumer Ecommerce Sales Will Rise to Nearly $18 Billion in 2020. eMarketer. April 2, 2020.

    2 Ochwat, Dan. Shopper study: Private brands purchased because they’re preferred. Store Brands. February 24, 2021
    3 Miranda, Leticia. Small businesses who pivoted to e-commerce saw record sales during Black Friday weekend. December 1, 2020.
    4 Charm, Tamara, Harrison Gillis, Anne Grimmelt, Grace Hua, Kelsey Robinson and Ramiro Sanchez Caballero. Survey: US consumer sentiment during the coronavirus crisis. McKinsey & Company. March 24, 2021.