The DataWeave Blog

Tag: Similar Product Matching

Own Your Product Matches: Gain The Power of Accuracy and Control at Your Fingertips
AI-powered product matching is the backbone of competitive pricing intelligence. Accurate matches help you compare prices correctly, identify meaningful assortment gaps, and optimize product content. Inaccurate matches distort every one of these insights. In some categories, a single mismatch can cause millions of dollars of lost revenue.

Retailers and brands know this problem well. Product catalogs are vast. Competitor assortments shift daily. Titles are inconsistent. Product codes are missing. Images vary by region or packaging. Basically, context matters, and AI alone often misses that context.

This is why a human-in-the-loop approach is essential. It allows product matches to be verified consistently, at scale, and with the context that only people can provide. Many retailers have also told us they want to take this a step further. They want the ability to control and define their own product matches.

Sometimes that is because they need to fix inevitable errors quickly. Other times, it is because their teams have deeper category knowledge and can make the right judgment calls when AI falls short.

To make that possible, DataWeave introduced User-Led Match Management. It combines the scale of AI with the judgment of experts within retail organizations. The platform does not just suggest matches. It gives your teams the tools to approve, reject, or refine them. This ensures your competitive intelligence reflects both machine precision and your unique business logic.

Why AI Matching Alone Falls Short

AI has changed the speed and scale of product matching. Algorithms can process millions of SKUs quickly. They can detect similarities in text, images, and metadata. But in retail, the stakes are too high to rely on AI alone.

Here is where AI sometimes falls short:
- Category complexity: Matching rules that work in electronics may fail in fashion or grocery. An electronics SKU may depend on a model number. A fashion SKU may depend on seasonality. A grocery SKU may depend on pack size or whether it is a private label.
- Data inconsistency: Titles vary. Images differ across regions. These gaps, when large, trip up algorithms.
- Business context: Should a premium product ever be compared against a budget line? Should seasonal products match year-round items? AI may not know these boundaries.
- Scale vs. accuracy: Automated systems optimize for coverage. That speed often limits accuracy for a small set of SKUs. Even a 1% error rate across millions of SKUs creates thousands of bad comparisons.
AI is critical for scale. But accuracy requires human input. DataWeave’s human-in-the-loop framework addresses this by allowing expert reviewers to validate and improve AI outputs. Our user-led match management takes this further by putting control directly into the hands of your business teams.

What DataWeave’s User-Led Match Management Delivers

With User-Led Match Management, your team is not a passive reviewer. They become active participants in shaping the accuracy of your competitive intelligence.

Your teams can:
- Approve, reject, or flag AI-suggested matches. Every suggestion comes with full visibility into why it was made. Your team can validate matches quickly, fix errors, and improve the dataset in real time.
- Define what “similar” means for your business. A retailer may want to compare multipacks against single packs. A brand may only care about comparing premium products to other premium products. With User-Led Match Management, your team sets tolerance levels that match your strategy.
- Manually add or refine matches. When AI misses edge cases, your team can add them. This ensures coverage is complete and reflects the true competitive landscape.
This approach creates a loop where AI, complemented by DataWeave’s human-in-the-loop framework does the heavy lifting, and your teams can fine-tune the results. The outcome is both scale and accuracy.

Key Features

DataWeave designed User-Led Match Management to be simple, intuitive, and scalable:
- Expert-Led Decision Making forms the heart of the system. Rather than trusting AI suggestions blindly, teams gain full visibility into matching logic and can leverage their contextual knowledge of products, categories, and retailers. When the system suggests matching a premium product against a basic alternative, human experts can reject the match and flag it for different criteria. This expertise is particularly valuable for new product launches, seasonal items, or products with complex positioning strategies.
- Business Logic Integration: Teams can define matching parameters that reflect their specific strategic needs. A premium brand might establish rules that prevent matches against budget alternatives, while a value retailer might specifically seek those comparisons. Category managers can create different matching criteria for different product lines, ensuring that seasonal items, limited editions, and promotional products are handled appropriately.
- Transparent Decision Making: Every match decision creates an audit trail capturing who made the decision, when it occurred, and the reasoning behind it. This transparency is crucial for enterprise environments where pricing decisions need to be defensible and strategies need to be consistent across teams and time periods.
- Scalable Validation: User-Led systems provide bulk operations for efficiency while maintaining oversight. Teams can upload thousands of matches for validation, use filtered views to focus on high-priority items, and leverage automated alerts for matches that fall outside established tolerance levels.
Each of these features reduces the friction between AI outputs and business-ready insights.

Technical Foundation

The AI foundation behind User-Led Match Management is built for precision and scale.
1. It uses multimodal AI that combines text, image, and metadata analysis to identify matches even when products are described or displayed differently across retailers.
2. Domain heuristics apply retail-specific logic, recognizing that “Large” means something different in apparel than in beverages, and that seasonal items require unique treatment.
3. Knowledge graphs link products across brands, categories, and regions to reveal true relationships even when surface attributes vary.
4. Through continuous learning, every human correction improves future AI suggestions, making the system smarter and more accurate over time.
For more information, download our whitepaper here!

Why This Matters

Pricing Intelligence

With DataWeave, accurate and reliable product matching is the standard. Advanced algorithms and built-in quality checks deliver consistently high accuracy, reducing the risk of mismatched products and unreliable insights.

In the few cases where a match needs review, User-Led Match Management gives your team the ability to validate it quickly and easily. You get full visibility and control, while DataWeave ensures the integrity of the overall matching framework.

The outcome is true apples-to-apples price comparisons that protect margins, strengthen pricing strategies, and build trust in every decision.

Assortment Analytics

Gaps and overlaps only matter when matches are accurate. To understand your true competitive landscape, you need to eliminate false gaps and phantom overlaps that distort assortment insights.

DataWeave’s advanced Match Management ensures precise product alignment across retailers, categories, and regions, giving you a clear view of your position in the market. At the same time, user-led oversight adds transparent validation, allowing your teams to confirm or refine matches based on their category knowledge.

The result is a complete and trustworthy view of category coverage that reflects reality, not noise. It helps you identify real opportunities to expand assortments, close gaps, and respond quickly to market changes.

Content Optimization

Digital shelf audits only deliver value when the comparisons are accurate. DataWeave ensures that every product is benchmarked against its true competitors so that your insights reflect the real dynamics of your category. For example, a luxury serum is never compared to a basic moisturizer, and a premium electronic device is never matched with an entry-level model.

With user-led control, your teams have transparent oversight of every match. They can review, validate, or adjust comparisons to make sure each audit aligns with your business standards. The result is a more reliable and actionable view of your digital shelf performance, helping you fine-tune content, optimize visibility, and strengthen conversion across channels.

Trust and Accountability

Leadership teams need complete confidence in the data they use to make decisions. User-Led Match Management delivers that confidence by combining the scale of AI with the assurance of human validation. Every match decision is transparent and traceable, giving teams clear visibility into how and why a product was matched.

This approach builds trust across departments, from analysts to executives. It ensures that every pricing, assortment, and content decision is backed by data that is both accurate and accountable.

Your Market, Your Rules, Your Insights

Retailers and brands today need more than fast data. They need data they can trust, shape, and act on with confidence. User-Led Match Management gives them that control. It turns product matching from a static, automated process into a dynamic, collaborative workflow that adapts to how real teams operate.

Category managers can fine-tune match rules instead of waiting on system updates. Pricing teams can validate critical SKUs in minutes, not days. Digital shelf teams can ensure their audits reflect real competitors, not algorithmic guesses. Executives gain visibility into decisions they can stand behind, supported by transparent data trails and measurable accuracy.

In short, User-Led Match Management puts control back where it belongs – in your hands. It helps every team move faster, compete smarter, and make decisions powered by data they can truly believe in.

Reach out to us to learn more!
October 21, 2025
Maximizing Competitive Match Rates: The Foundation of Effective Price Intelligence
Merchants make countless pricing decisions every day. Whether you’re a brand selling online, a traditional brick-and-mortar retailer, or another seller attempting to navigate the vast world of commerce, figuring out the most effective price intelligence strategy is essential. Having your plan in place will help you price your products in the sweet spot that enhances your price image and maximizes profits.

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

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

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

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

The Match Rate Challenge

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

Let’s put it into focus. Imagine you’re trying to price a 12-pack of Coca-Cola. This is a well-known product that, hypothetically, should be easy to identify across the web. However, every retailer uses their own description in their listing. Some examples include:
- Retailer A lists it as “Coca-Cola 12 Fl. Oz 12 Pack”
- Retailer B shows “Coca Cola Classic Soda Pop Fridge Pack, 12 Fl. Oz Cans, 12-Pack”
- Retailer C has “Coca-Cola Soda – 12pk/12 fl oz Cans”
While a human can easily deduce that these are the same product, the automated system you probably have in place right now is most likely struggling. It cannot tell the difference between the retailers’ unique naming conventions, including brand name, description, bundle, unit count, special characters, or sizing.

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

Why Match Rates Matter

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

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

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

The Business Impact

Besides the bottom-line savings, accurately matching competitor products for pricing intelligence has other business impacts that can help your business. Adding technology to your workflow to improve match rates can help identify blind spots, improve decision quality, and improve operational efficiency.
- Pricing Blind Spots
  - Missing competitor prices on key products
  - Inability to detect competitive threats
  - Delayed response to market changes
- Decision Quality
  - Incomplete competitive coverage leads to suboptimal pricing
  - Risk of pricing decisions based on wrong product comparisons
- Operational Efficiency
  - Manual verification costs
  - Time spent reconciling mismatched products
  - Resources needed to maintain price position
Current Industry Challenges

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

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

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

The AI Advantage

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

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

Implementation Strategy

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

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

Key Takeaways

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

Starting this process to add AI to your pricing strategy can be overwhelming. At DataWeave, we work with you to make the change easy. Talk to us today to know more.
February 5, 2025
Redefining Product Attribute Tagging With AI-Powered Retail Domain Language Models
In online retail, success hinges on more than just offering quality products at competitive prices. As eCommerce catalogs expand and consumer expectations soar, businesses face an increasingly complex challenge: How do you effectively organize, categorize, and present your vast product assortments in a way that enhances discoverability and drives sales?

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

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

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

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

Why Product Attribute Tagging is Important in eCommerce

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

Taxonomy Comparison and Assortment Gap Analysis

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

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

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

Assortment Depth Analysis

Another big challenge in eCommerce is the lack of standardization in retailer taxonomy. This inconsistency makes it difficult to compare the depth of product assortments across different platforms effectively. For instance, to categorize smartphones,
- Retailer A might organize it under “Electronics > Mobile Phones > Smartphones”
- Retailer B could use “Technology > Phones & Accessories > Cell Phones”
- Retailer C might opt for “Consumer Electronics > Smartphones & Tablets”
Inconsistent nomenclature and grouping create a significant hurdle for businesses trying to gain a competitive edge through assortment analysis. The challenge is exacerbated if you want to do an in-depth assortment depth analysis for one or more product attributes. For instance, look at the image below to get an idea of the several attribute variations for “Desks” on Amazon and Staples.

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

Enhancing Product Matching Capabilities

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

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

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

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

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

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

Elevating the Search Experience

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

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

Pitfalls of Conventional Product Tagging Methods

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

Scalability

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

Inconsistencies and Errors

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

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

Speed

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

How DataWeave’s Advanced AI Capabilities Revolutionize Product Tagging

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

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

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

RLMs for Enhanced Semantic Understanding

Semantic Understanding of Product Descriptions

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

Attribute Extraction

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

Identifying Implicit Relationships

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

Synonym Recognition in Product Descriptions

Synonym Matching with Context

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

Overcoming Brand-Specific Terminology

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

Dealing with Ambiguities

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

Contextual Understanding for Improved Accuracy and Precision

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

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

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

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

Case Study: Niche Jewelry Attributes

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

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

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

Unparalleled Scalability

DataWeave can rapidly scale tagging for new categories. The solution is built to handle the demands of even the largest eCommerce catalogs enabling:
- Effortless management of extensive product catalogs: We can process and tag millions of products without compromising on speed or accuracy, allowing businesses to scale without limitations.
- Automated bulk tagging: New product lines or entire categories can be tagged automatically, significantly reducing the time and resources required for catalog expansion.
Normalizing Size and Color in Fashion

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

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

Continuous Adaptation and Learning

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

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

Stay Ahead of the Competition With Accurate Attribute Tagging

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

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

To learn more, talk to us today!
November 14, 2024