The DataWeave Blog

Category: Counterfeits

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 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.

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 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.

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.

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.

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.

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!
September 18, 2024
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)
March 4, 2024
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

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
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

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
- 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

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!
May 16, 2022
Best Practices to avoid MAP Violations

Competition is a fundamental and healthy part of commerce that protects customers by keeping prices low and the quality of services (and choice of goods) high.

Healthy competition drives prices down, but it can harm brands and their reputation without a pricing policy. The manufacturer or brand designs MAP or Minimum Advertised Pricing policies to stipulate retailers’ lowest price point to advertise the product. It is an agreement between distributors and manufacturers about the minimum price that retailers and resellers can advertise the product for sale.

Most legitimate brands have a MAP policy, especially brands that rely heavily on brand identity. It becomes critical that they maintain price parity across retailers. When a retailer violates MAP policies, brands can penalize them under the agreed-upon terms or terminate contracts.

In this blog, you will learn about MAP policy, its benefits, and tips on tackling MAP violations.

1. What is a MAP policy?

MAP Violations

MAP stands for Minimum Advertised Price, and brands create MAP policies to ensure that retailers don’t advertise their products below the specified price. However, it only controls advertised prices, ensuring the retailers don’t display a lower price in online listings or advertisements. Since it doesn’t cover the checkout price, retailers can sell products at a lower price through promotional offers like discounts and cashback during checkout.

MAP policies ensure a price war between eCommerce platforms does not devalue products and that an even playing field is set among retailers that allow everyone to drive margins. Brands have a legal right to withdraw products if a retailer advertises products below the minimum advertised price. Brands can also restrict future sales or refuse to replenish products after the current stock has sold out if an eCommerce platform, reseller, or distributor violates MAP policies.

In the U.S., MAP policies fall under federal antitrust law since they restrict advertisement pricing rather than the last sales price. However, in the UK and the EU, violation of minimum advertised pricing is an infringement of current competition laws.

2. Why Does Having a MAP Policy Matter?

Having a MAP policy protects both brands and retailers while ensuring consumers get the best-priced items. Following are the benefits of having a MAP policy:

a. Prevent margin erosion

Although online retailers are willing to take a margin cut to attract traffic, selling products below MAP can significantly hurt a brand’s bottom line. Setting a minimum advertised price benefits both parties. It allows shoppers to purchase products at the best-valued price & also creates a balanced economy and prevents hyper-competition of products between retailers. However, manufacturers must set a realistic pricing policy that matches current market demand, ensuring eCommerce platforms implement MAP while taking care of the margins.

b. Retain brand identity

Brand Protection

Price is one of the essential indicators consumers use to determine the authenticity and value of a product. Constant price fluctuations can negatively impact a brand’s reputation. Brands need to safeguard their pricing to create a consistent price perception. Price changes often make the buying decision complex since consumers no longer have a clear reference of prices. It also shifts purchasers’ attention from the brand and product features to its price. With price fluctuations, brands that were used to be differentiated for their features can be seen as commodities.

Low prices & MAP violations on an online platform can even be a sign of counterfeit products or unauthorized sellers. However, customers might hold the brand responsible if they purchase counterfeit products from a retailer at lower prices. A negative product experience with a retailer will also reflect the brand’s reputation. An effective MAP policy that enforces consistent pricing will ensure that customers hunting for the best deals will stick with the most legitimate retailers.

Read how DataWeave helped Classic Accessories, a leading manufacturer of high-quality accessories detect counterfeits and identify unauthorized sellers.

c. Ensure price parity across retailers

Comparing prices has become an essential and common milestone in every consumer’s purchasing journey. It’s imperative that a brand ensures price parity across platforms and stores because substantial pricing variations on different platforms can make customers suspicious of a brand. Consistent pricing across eCommerce platforms ensures brands maintain their identity. MAP policies also allow retailers to maintain profit margins while avoiding price wars.

d. Combat revenue loss from illegitimate sales

While most authorized sellers or distributors comply with pricing policies, unauthorized sellers or grey market sellers have no obligation to follow a brand’s MAP pricing infrastructure. Brands can reduce risk with an authorized seller badge on retailer websites. This will help customers to verify authorized retailers and resellers of your products & help safeguard your brand equity online.

3. Tips on Implementing MAP policy and Tackling violations

Enforcing and tackling MAPs comes down to two things: monitoring the market for infringements and then acting on those violations. Here are a few tips for tackling MAP violations:

Implementation of MAP Policy & Tracking Violations

a. Communicate actively with retailers

To maintain a positive relationship with retailers and avoid confusion, brands should create proper communication strategies and channels to accompany the launch of the MAP policy. The policy should be easy to understand, but legal advisors are necessary to understand the jargon of the document. Brands can use checklists, videos, and well-briefed brand reps to communicate their policy clearly with retailers.

b. Reward retailers for compliance

Retailers who follow MAP guidelines can lose out to platforms that do not follow these pricing guidelines. Non-MAP following platforms undercut the price of products to drive sales and secure higher traffic. In such instances, brands can incentivize MAP following retailers to encourage them to comply with MAP guidelines while not affecting the competitive edge. It can be in the form of laxity of rules during promotion seasons like New Year, Christmas, and Black Friday sales. The laxity of rules for promotional seasons should be used as an exception to the general rule, and outlined in the guidelines.

c. Implement an AI-driven MAP monitoring

When product distribution is spread across the globe through a network of resellers and retailers, keeping a close watch on all platforms for multiple products can become difficult. With the expansion of online marketplaces, manually tracking the pricing of numerous products on multiple platforms is time-consuming and unsustainable. An automated AI-driven monitoring platform can track the pricing of all products sold across hundreds of online platforms and identify violations around the clock. Such platforms can alert brands of violations, price inconsistencies, or suspicious activities in real-time.

d. Send cease and desist to MAP violators and unauthorized dealers

Brands must enforce a MAP policy to ensure price parity among retailers and resellers. Brands must systematically monitor prices across retailers, social media, marketplaces, and price comparison websites. Whenever brands encounter a MAP violation, they should take action by sending a cease and desist letter to unauthorized sellers. For legitimate sellers, brands can notify them and outline the steps that will be taken if they don’t comply. Brands must be consistent in enforcing MAP policy violations, signaling retailers and unauthorized sellers that there will be repercussions for MAP violations.

MAP Policy

4. Conclusion

The trend towards online shopping helps businesses to cut overheads, allowing their products to be sold at a significantly reduced price. Although price appears to be the most effective consumer attraction strategy, significantly lowering product prices can devalue products and hurt brand reputation in the long term. However, including and enforcing MAP policies helps brands to manage their reputation and allows retailers to manage their margins.

Want to see first-hand how DataWeave can help brands track MAP Violations, Counterfeit products, and identify unauthorized sellers? Sign up for a demo with our Digital Shelf experts to know more.

January 29, 2022
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%.

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
- 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

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 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

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

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.
December 20, 2021
Top 10 Retail Analytics that You Must Know

Customers expect personalization. Unless they have a seamless experience on your online channels, they’ll leave for a different retailer. Retail analytics can solve these problems for merchants looking to increase customer satisfaction and sales. It provides insights into inventory, sales, customers, and other essential aspects crucial for decision-making. Retail analytics also encompasses several granular fields to create a broad picture of a retail business’s health and sales, along with improvement areas.

Big data analytics in the retail market

Big data analytics in the retail market is expected to reach USD 13.26 billion by the end of 2026, registering a CAGR of 21.20% during the forecast period (2021-2026). The growth of analytics in retail depicts how it can help companies run businesses more efficiently, make data-backed choices, and deliver improved customer service.

In this blog, we’ll discuss the top 10 analytics that retailers are using to gain a competitive advantage in accurately evaluating business & market performance.

Top 10 of Retail Analytics You Must Know

1. Assortment

Assortment planning allows retailers to choose the right breadth (product categories) and depth (product variation within each category) for their retail or online stores. Assortment management has grown beyond simple performance metrics like total sales or rotation numbers. Instead, retail analytics offers a comprehensive analysis of product merchandise and an estimated number of units at the push of a button. Retailers that effectively apply assortment analytics can enjoy increased gross margins and prevent significant losses from overstocks sold at discounted prices or out-of-stock inventory leading their customers to buy from competitors.

It also helps retailers gain insights into the trendy and discoverable brands and products on all e-commerce websites across the globe. They can boost sales by making sure they have an in-demand product assortment. They can also track pricing information and attributes common across popular products to drive their pricing and promotion strategies.

2. Inventory Management

An inadequately maintained inventory is every retailer’s worst nightmare. It represents a poor indicator of inadequate demand for a product and leads to a loss in sales. Data can help companies answer issues like what to store and what to discard. It’s beneficial to discard or increase offers on products that are not generating sales and keep replenished stocks of popular items.

In 2020, the estimated value for out-of-stock items ($1.14 trillion) was double that of overstock items ($626 billion). A similar trend was especially prominent in grocery stores, where out-of-stock items were worth five times more than overstock items.

Unavailability of high-selling products can lead to reduced sales, ultimately generating incorrect data for future forecasting and producing skewed demand and supply insights. Retailers can now use analytics to identify which products are in demand, which are moving slowly, and which ones contribute to dead stock. They can know in real-time if a high-demand product is unavailable at a specific location and take action to increase the stock. Retailers can use this historical data to predict what to stock, at what place, time, and cost to maintain and optimize revenue. It helps satisfy consumer needs, prevents loss of sales, reduces inventory cost, and streamlines the complete supply chain.

3. Competitive Intelligence

Market intelligence & Competitive Insights

The ability to accurately predict trends after the global pandemic and with an unknown economic future is becoming the cornerstone for successful retailers. Smart retailers know how important it is to Pandemic-Proof their retail strategy with Market Intelligence & Competitive Insights

With 90% of Fortune 500 companies using competitive intelligence, it’s an essential tool to gain an advantage over industry competitors. Competitive Intelligence allows you to gather and analyze information about your competitors and understand the market–providing valuable insights that you can apply to your own business. A more strategic competitor analysis will explain brand affinities and provide insights on what to keep in stock and when to start promotions. Customer movement data will also give you access to where your customers are shopping.

4. Fraud Detection

Fraud Detection

Retailers have been in a constant struggle with fraud detection and prevention since time immemorial. Fraudulent products lead to substantial financial losses and damage the reputation of both brands and retailers. Every $1 of fraud now costs U.S. retail and eCommerce merchants $3.60, a 15% growth since the pre-Covid study in 2019, which was $3.13. Retail Analytics acts as a guardian against fraudsters by constantly monitoring, identifying, and flagging fraud products and sellers.

5. Campaign Management

Some of the challenges of the retail industry are that it’s seasonal, promotion-based, highly competitive, and fast-moving. In today’s competitive marketplace, consumers compare prices and expect personalized shopping experiences. Campaign management allows marketing teams to plan, track, and analyze marketing strategies for promoting products and attracting audiences. Retail analytics can help businesses predict consumer behavior, improve decision-making across the company, and determine the ROI of their marketing efforts.

According to Invesp, 64% of marketing executives “strongly agree” that data-driven marketing is crucial in the economy. Retail analytics can help businesses analyze their data to learn about their customers with target precision. With predictive analysis, retailers can design campaigns that encourage consumers to interact with the brand, move down the sales funnel, and ultimately convert.

6. Behavioral Analytics

Retail firms often look to improve customer conversion rates, personalize marketing campaigns to increase revenue, predict and avoid customer churn, and lower customer acquisition costs. Data-driven insights on customer shopping behaviors can help companies tackle these challenges. However, several interaction points like social media, mobile, e-commerce sites, stores, and more, cause a substantial increase in the complexity and diversity of data to accumulate and analyze.

Insider Intelligence forecasts that m-eCommerce volume will rise at 25.5% (CAGR) until 2024, hitting $488 billion in sales, or 44% of all e-commerce transactions.

Data can provide valuable insights, for example, recognizing your high-value customers, their motives behind the purchase, their buying patterns, behaviors, and which are the best channels to market to them and when. Having these detailed insights increases the probability of customer acquisition and perhaps drives their loyalty towards you.

7. Pricing

Competitive pricing in retail

Market trends fluctuate at an unprecedented pace, and pricing has become as competitive as it’s ever been. The only way to keep up with competitive pricing in retail is to use retail analytics that enables retailers to drive more revenue & margin by pricing products competitively.

A report from Inside Big Data found companies experience anywhere from 0.5% up to 17.1% in margin loss purely because of pricing errors. Pricing analytics provides companies with the tools and methods to perceive better, interpret and predict pricing that matches consumer behavior. Appropriate pricing power comes from understanding what your consumers want, which offers they respond to, how and where they shop, and how much they will pay for your products.

In 2021, the price optimization segment is anticipated to own the largest share of the overall retail analytics market. Retailers can identify gaps and set alerts to track changes across crucial SKUs or products with pricing analytics. Knowing your customer’s price perception will increase sales and also allow you to design promotions that’ll attract customers. Pricing analytics also accounts for factors like demographics, weather forecasting, inventory levels, real-time sales data, product movement, purchase history, and much more to arrive at an excellent price.

8. Sales and Demand Forecasting

Sales and demand forecasting allow retailers to plan for levels of granularity—monthly, weekly, daily, or even hourly—and use the insights in their marketing campaigns and business decisions. The benefits of a granular forecast are apparent since retailers don’t have to bank on historical data of previous clients and customers to predict revenues. Retailers can plan their strategies and promotions that suit their customer’s demands.

With sales and demand forecasting, retailers can also consider the most recent, historical, and real-time data to predict potential future revenue. Sales and demand analytics can predict buying patterns and market trends based on socioeconomic and demographic conditions.

9. Customer Service and Experience

With the development of eCommerce, more and more customers prefer to browse and interact with the product before purchasing online. They look for better deals and discounts across stores and platforms. 3 out of 5 consumers say retail’s investment in technology is improving their online and in-store shopping experiences. To enhance merchandising and marketing strategies, retailers can gather data on customer buying journeys to understand their in-store and online experiences.

Retailers can run test campaigns to know the impact on sales and use historical data to predict consumers’ needs based on their demographics, buying patterns, and interests. Retail analytics help retailers to bring more efficiency in promotions and drive impulsive purchases and cross-selling.

10. Promotion

Analyze Competitors’ Promotions

Promotions are potent sales drivers and need to be cleverly targeted towards specific customers with precise deals to generate outstanding sales. Retail analytics allows companies to study their customers and competitors to a vastly elevated level.

To be an industry leader, retail companies not only have to understand their customers, but they must also analyze competitors’ promotions to improve their marketing strategies. Analyzing your competitor’s promotional banners, ads, and marketing campaigns are no more associated with imitation.

With data analytics and AI, retailers can watch their competitors’ commercialization strategies. It can uncover vital information about their target audience, sales volume fluctuations, popular seasonal product types, product attributes of popular items, and significant industry trends. Knowing exactly which products and brands are popular among your competitor’s campaigns can help retailers improve their promotional strategies.

Conclusion

The benefits of retail analytics are spread across various verticals, from merchandising, assortment, inventory management, and marketing to reducing losses. The need for analytics has become even more apparent considering the growing eCommerce platforms, changing customer buying journeys, and the complexity of the industry. Understanding which products sell best among which customers will help retailers to deliver an optimized shopping experience.

Want to drive profitable growth by making smarter pricing, promotions, and product merchandising decisions using real-time retail insights? DataWeave’s AI-powered Competitive Intelligence can help! Reach out to our Retail Analytics experts to know more.

November 19, 2021
Prioritizing Brand Protection Before the Holiday Rush

Counterfeits pose a dangerous threat to any retail brand. Since every single sale is a pivotal branding opportunity, especially for young, burgeoning eCommerce brands, an online marketplace flooded with counterfeits can be particularly dangerous. One in five customers will boycott a brand after mistakenly purchasing a counterfeit product, and that’s not the kind of ratio that any retailer –– from the smallest Direct-to-Consumer (DTC) site to the behemoths like Amazon –– can afford to ignore.

In the age of online reviews, it’s especially dangerous to have counterfeits floating around. Customers that have a bad experience with a counterfeit can take to the internet to disparage your brand without ever actually interacting with your company or trying your product. That’s why consistent and thorough content audits are paramount to ensuring your brand’s authentic products are highly discoverable, and brand protection and governance processes are in place to safeguard brand integrity across all applicable eCommerce websites.

The Holiday Counterfeit Boom

The holidays are a time when customers search for gifts for their friends and family, which means exploring brands outside of their usual fare. Many consumers will be exposed to your brand’s Digital Shelf for the first time over the holiday season, creating an opportunity for brand growth. But if you don’t have eCommerce brand protection initiatives in place, the holidays can be detrimental to brand positioning, customer trust, and your bottom line.

As consumers boost their online spending and web traffic increases over the holidays, so does the likelihood of them purchasing counterfeit goods online. eMarketer predicts that retail eCommerce sales will comprise almost 20 percent of total holiday retail sales this year. As such, there will also be a surge in counterfeit inventory. So, this is an ideal time to invest in a brand protection solution to help you stay ahead of unauthorized sellers entering the marketplace.

Brand Integrity Helps Suppliers Save

Implementing a solution to mitigate the risks of counterfeit products should be at the top of every retailer’s “To-Do” list this year. However, for many retailers, this means manually reviewing numerous websites and third-party marketplaces for violations. Not only is manually reviewing content, images, and seller authenticity a time-consuming process, but it also leaves a lot of room for human error – making it possible for counterfeits to slip through the cracks and into the hands of unsuspecting customers. Not to mention your time should be spent fulfilling orders and increasing customer satisfaction during the high-traffic holiday season, not distracted by monitoring counterfeits.

Fortunately, that’s not the only way to identify counterfeits and protect your brand online. An effective content auditing tool can help you monitor, detect, and determine systems to identify and act on identified violations, saving time and labor hours normally spent on manual auditing processes. Content audit software also often contains helpful features to help you run your business more strategically by monitoring online hygiene factors like product titles and description. It works across all online channels by highlighting content gaps, which can then be remedied to improve product visibility and conversions. Through online content optimization, you can save money (in unnecessary labor costs), improve your Share of Search, and increase sales and share, with a modest up-front investment.

Brand Value Protection Boosts Consumer Confidence

Brand image protection doesn’t just protect retailers, it also protects customers from unintentionally buying dangerous counterfeit goods. Counterfeiting has skyrocketed during the pandemic. The International Chamber of Commerce reports that, by 2022, counterfeit goods will be a $4.2 trillion industry, and global damage from counterfeit goods is projected to exceed $323 billion. Studies show one in four customers has unknowingly purchased a counterfeit item online.

As counterfeits increase in number, so does the risk of counterfeit consumption by unwitting consumers. Counterfeit goods are as dangerous as they are ubiquitous. Customs and Border Patrol has found ingredients such as cadmium, arsenic, lead, and cyanide inside of counterfeit cosmetics. Consumers are aware of these risks. So, as a retailer, you need to be able to reassure customers that they can trust the authenticity of the goods they are purchasing at your online store.

A counterfeit detection tool can help you identify fakes and image replicas across multiple online marketplaces, so you can get fake products delisted. Automated counterfeit solutions can increase customer satisfaction in their purchasing experience, since they know they’re getting an authentic product right off the bat. This type of online brand protection creates increased brand loyalty over time, as well as more positive first-time product interactions.

Making a Measurable Impact: A Counterfeit Detection Case Study

Classic Accessories is a leading manufacturer of high-quality furnishings and accessories. The company’s investment in a counterfeit detection tool paid off in spades for their organization. After noticing a surge in counterfeit versions of their goods being sold via online, global marketplaces, they decided they needed to change their manual counterfeit and image violation detection process to an automated one to proactively respond to concerned activity in a timely manner.

Their goal was to achieve streamlined, actionable insights across all retail websites to account for varied violation submission processes, and to reduce the timespan in which insights were generated, ultimately eliminating the need to conduct daily, manual audits. They partnered with DataWeave, who built out a fully customized program to automate Classic Accessories’ content inventory management process, and identified SKU-level violations by matching names and images in diverse online marketplaces.

During the first three months of onboarding, Classic Accessories was able to detect more than 25,000 violations, submitting notices to each marketplace, and even achieved a 100% removal rate across all Amazon sources. Additionally, they also achieved their goal of saving time (22 hours per week) in automation processes, translating to a $68,000 savings opportunity in labor costs.

Closing Thoughts

Prioritizing your online brand protection strategy is imperative to growing your online presence and achieving customer satisfaction and brand loyalty. Fortunately, there are options like DataWeave’s brand protection tool available to help curate your online content, provide consistency across online channels, and improve consumer confidence by addressing and removing counterfeit violations. Implementing the right solution can help find counterfeit products in real-time to keep your brand safe –– and your reputation intact –– throughout the 2021 holiday season. The right brand protection software will provide both Brand Protection and Content Audits, so your brand is optimized from every possible angle for truly competitive results.

October 29, 2021
Coronavirus Outbreak: Impact on E-Commerce Retailers and Consumer Brands

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

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

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

The Inadvertent E-commerce Wave

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

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

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

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

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

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

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

A Logistical Nightmare

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

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

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

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

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

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

Keeping Up With The Online Surge

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

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

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

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

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

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

DataWeave Offers Support

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

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

Reach out to us for further details.

March 6, 2020
Study of Brand Inconsistency in Furniture eCommerce

From initially lagging well behind early high-penetration categories such as consumer electronics, books, and apparel, furniture is now emerging as a key growth category.

Online furniture purchases are growing at a rapid clip, estimated to currently be around 14 percent rate annually and is anticipated to reach 7.6 percent of total category sales in 2018.

Savvy furniture brands are becoming increasingly aware of this shift in consumer shopping patterns and are taking steps to embrace the importance of creating a seamless online customer experience consistent across all eCommerce websites.

Selling furniture online remains logistically complex. It requires the disciplined coordination across an ecosystem teeming with bricks and mortar stores, salespeople, warehouses merchants, and a network of delivery systems.

All this complexity poses challenges for brands looking to deliver a consistent brand experience for consumers across multiple eCommerce websites.

One frequent outcome of this complex ecosystem is the emergence of white labeling.

The Invasion of White Labeling in the Furniture Category

A white label product is one that is manufactured by one company only to be bundled and sold by other online merchants using different brand names. The end product is positioned as having been manufactured by the brand marketer.

These white label products are frequently sold at a significant discount, compared to more mainstream name brands in the category.

Electronics brands have often been victims of this phenomenon. Typical electronic white label products now commonplace range from radios and DVD players to computer mice and keyboards, through to TV remote controls.

Increasingly, the furniture vertical is no longer a stranger to white label packaging and marketing as well.

At DataWeave, using our proprietary data aggregation and analysis platform, we analyzed a range of factors of the furniture vertical, specifically the emerging phenomenon of white labeling.

Our analysis spanned a sample set of over 20,000 products that we tracked across the websites of two of our eCommerce customers (whom we don’t wish to name) that have a large assortment of furniture products. Let’s call these eCommerce companies Retailer A and Retailer B.

We identified white labeled products as being those that featured the exact same image between the two retailers but were sold under different brand names. Here, our AI-powered advanced image analytics platform matched the images of various products at an accuracy of more than 95%.

The following infographic summarizes our analysis.

Clearly, not only is white labeling quite prevalent here, but in almost every instance, we identified price variation. Some of the white labeled products were sold by lesser-known brands with significantly lower price points. This pricing strategy could potentially damage the customer experience for well-established consumer brand franchises in several ways.

The shopper sees through the branding exercise where the same product is repackaged and presented as having been “produced” by a different brand, potentially eroding brand loyalty.

As some 71 percent of the products studied were identified as white labeled products, this exposes the category as a whole to this risk.

The shopper may be confused by the price difference as well, undermining the brand’s carefully constructed pricing perception. The average spread of 21 percent between competing white labeled products is potentially a major source of consumer dissonance and confusion.

A Closer Look at Pricing

While the inconsistent experience potentially created by widespread white labeling is almost characteristic of the furniture vertical, other eCommerce areas such as pricing and promotion have also been demonstrated as being key influencers of the shopping experience.

Today, brands have little control over how their products are priced on eCommerce websites and are susceptible to pricing decisions taken by either the merchant selling the product or retailers themselves. Here, price change decisions have little to do with providing a consistent brand experience, as it’s not really a priority for merchants and retailers.

In a hyper-competitive retail environment, retailers often discount heavily or change prices frequently to drive sales and margins. The following infographic summarizes the differences in pricing approaches between the two retailers we analyzed.

Both retailers demonstrated quite divergent approaches in their pricing strategies. The key point of difference appeared to be Retailer B’s discount execution, which proved more aggressive than Retailer A’s, routinely exceeding the latter by five percent or more.

This discounting strategy is focused on the 40+ percentile (by price, with 100 percentile being the most expensive product), and above price bands, while both retailers displaying similar strategies to their Top 20 and Top 20 to 40 percentile ranges.

We also observe how Retailer B is more inclined to offer higher discounts on products with higher review ratings, compared to Retailer B’s strategy — a play on developing a “low price” perception among shopper.

The Consumer Experience Matters

Today, consumers expect a truly seamless shopping experience right across a brand’s entire integrated retail community, regardless of whether it is physical or digital. Consumers have evolved beyond being merely time poor and have emerged as a group of impatient shoppers, unforgiving of inconsistencies in their experience.

With retail evolving to embrace multiple consumer touch points with a brand, the practice of white labelling represents a dangerous source of potential confusion and disillusionment. This raises the degree of difficulty involved in converting website visitors into buyers. Further, inconsistent pricing between eCommerce websites, due to dissimilar pricing strategies adopted by each website, only compounds the problem for furniture brands.

Technologies like DataWeave’s Competitive Intelligence as a Service, that can provide furniture brands with timely insights on white labelled products, unauthorized merchants, and price disparity between ecommerce websites, can assist furniture brands in their efforts to better manage their online channel.

Visit our website to find out more on how we help consumer brands protect their brand equity and optimize the experience delivered to their customers on eCommerce websites!

March 29, 2018