In the world of price optimization, a business’s ability to accurately match products across platforms directly impacts its capacity to monitor and act on price intelligence. The better an organization is at finding and tracking the right product matches, the better its analysis, and eventual product positioning will be. Further, product matching isn’t just about identifying products; it directly influences price intelligence by improving the accuracy and relevance of the products you compare, benchmark, and act upon.
In most cases, businesses start with exact matching, where identical products are compared across multiple platforms—this is straightforward. However, in a dynamic market, many companies also need to look beyond exact matches and consider like-matching or similar-matching, where products that aren’t identical but share significant attributes are compared. This approach helps businesses understand the broader competitive landscape and optimize pricing for products with similar characteristics, even if they differ slightly in terms of brand, features, or appearance.
Similarity and Like-Matching
Now, let’s explore the technical aspects of like-matching and how machine learning (ML) and artificial intelligence (AI) solve the challenge of identifying similar products. At first glance, like-matching may seem simple, but its complexity comes from defining what “similar” means for each business. Different stakeholders often have varied expectations of what should be considered similar. For example, the size of a product’s packaging may be identical, but that doesn’t necessarily make two products alike. AI models are essential for identifying more meaningful similarities, such as shared uses, designs, or features that are relevant for a specific business context.
When leveraging AI for like-matching, the goal is often to expand the data set beyond direct matches and compare products based on unstructured data—such as product descriptions, customer reviews, or specifications—that don’t always align perfectly across platforms.
Types of Product Matches in eCommerce
- Exact Matches: Products with identical attributes, like brand, model, and specifications, are matched to compare the same item across different websites.
- Like-Matches (or Similar Matches): Products that are very similar but not identical are matched, accounting for slight differences in product names or descriptions.
- Image Matches: Products are compared based on their images, which is helpful for visually-driven categories like fashion or furniture.
- Attribute Match: Products are matched by specific features, like comparing smartphones based on screen size or camera resolution.
- Category Match: Products are grouped by category, such as comparing laptops within a price range or shoes by brand and size.
Title Similarity and Adding More Attributes
The most common approach to identifying similar products is using the titles and attributes of products. For this, ML models often rely on vector representation, which turns product information into numerical values that the model can interpret. Each product is represented as a series of numbers that capture its unique characteristics. For instance, Product A might be represented as [2,1,0,5,3,8] and Product B as [1,4,7,0,0,1]. These vectors allow the system to compute cosine similarity, which measures how closely related two sets of numbers are.
Cosine similarity values range from -1 (complete opposites) to 1 (exactly alike). A score of 0.89, for example, indicates two products are highly similar but not identical. This score doesn’t explain how they differ—just that they do. In practice, businesses need to interpret those differences themselves, which can vary based on the specific attributes they are analyzing.
Common Use Cases: Recommendation Systems and eCommerce Applications
Like-matching is a key driver behind recommendation systems, which are widely used in ecommerce to suggest products to customers or assist businesses in tracking products more effectively. These systems analyze large sets of data and identify products that, while not identical, share relevant similarities that make them competitors or alternatives. For instance, a recommendation engine might suggest different models of front-load washing machines with similar energy ratings and smart features, allowing retailers to compare and adjust pricing even if the products are from different brands.
Such systems are invaluable for product discovery, helping retailers monitor competing products that may not be identical but are relevant in the same category. For example, an electronics retailer could compare smart TVs from multiple brands that offer similar screen sizes, resolutions, and smart functionalities, even if the models differ slightly in design or additional features. This allows businesses to stay competitive in pricing and inventory management.
Expanding Similarity Beyond Text and Simple Attributes
While most of the discussion around like-matching focuses on product titles, descriptions, and structured attributes, AI’s potential for similarity matching goes far beyond this. One emerging use case involves image-based matching, where AI models analyze images to detect products that look alike but may be listed differently on various platforms. This is particularly useful for industries where visual appearance is key, such as fashion or home decor, allowing businesses to find similar-looking products across different retailers. AI can scan through images and identify common patterns, materials, or designs to flag products that are comparable visually, even if their descriptions or titles are different.
Another growing area for similarity matching is in copyright infringement detection. Businesses can use AI to scan vast databases of images, searching for unauthorized uses of a product’s design or likeness. This enables companies to quickly identify potential infringements and take action to protect their intellectual property.
In the future, similarity matching could expand to other forms of media, such as video or 3D models. This would allow retailers to track products across new channels or monitor content that contains similar features, such as virtual product demonstrations, augmented reality apps, or even social media videos where products are showcased. These advancements would open up a new frontier of data analysis, enabling businesses to better understand how products are being marketed and consumed across the digital landscape.
Conclusion
Like-matching, or similar-matching, is a crucial component of price optimization in ecommerce. The ability to find and compare similar products across platforms helps businesses broaden their competitive analysis, improve pricing strategies, and capture more market share. By combining AI models with techniques like vector representation and cosine similarity, ecommerce companies can unlock a new level of precision and insight in their product comparisons, driving more informed pricing decisions and ultimately increasing sales.
As we continue to explore the power of AI, the applications of similarity matching are only growing, from improving recommendation systems to tackling copyright infringement. Businesses that invest in these technologies will find themselves better equipped to navigate the complexities of modern ecommerce and stay ahead in a rapidly evolving market.
Want to learn more? eCommerce leaders are invited to join us for a 30-minute working session to explore practical applications of ChatGPT in product classification. Attendees will gain hands-on experience with classifying their own products and how to optimize prompts to test and refine their strategies.
Register your interest for a September session below.
