Steven Barta1 Answers
Dani Avitz Staff answered 4 years ago
One of the most significant benefits online stores like Amazon, eBay, and Walmart have provided is a lower barrier to entry for even small businesses to start selling their products. That’s why you see millions of products and sellers on these large eCommerce sites.
However, this change has also resulted in a new problem for marketplaces: there’s bound to be multiple sellers offering the same product now and again, and most users don’t want to see duplicates in their search results. A new solution has arisen in response, known as product matching.
By detecting potential duplicate entries in their catalogs, these businesses can make a single page for every instance of a product from multiple sellers. From there, customers can choose to buy from individual providers.
This process ultimately improves the quality of the catalog, making it easier to find the products you want and removing duplicate listings that waste networking resources. For consumers, matching provides better accuracy in search results by showing them not only the product they’re interested in but also similar products they might not have known about yet. Performing product matching manually would be tedious, so it makes sense to automate it. An AI can analyze product attributes and distinct features to make determinations based on the data at hand.
Another issue is that many products are incredibly similar. The same item could come in variations and different conditions, while others may be used or refurbished. Any product matching system that you implement should have the capability to identify these minor variations.
And finally, matching is challenging mostly thanks to the sheer volume of data the algorithms have to deal with. Manual product matching isn’t cost-effective or possible for companies that have extensive product catalogs.
In these situations, a company will require a comprehensive product matching AI that can improve the way products are matched based on a user’s search query.
However, this change has also resulted in a new problem for marketplaces: there’s bound to be multiple sellers offering the same product now and again, and most users don’t want to see duplicates in their search results. A new solution has arisen in response, known as product matching.
Why Is Product Matching Important?
The user experience dominates the digital world. Customers today demand flawless interactions with the largest retailers. To improve the user experience, sites like Amazon, eBay, and many smaller stores use a system of product matching. That is, they use artificial intelligence to identify product matches and similarities.By detecting potential duplicate entries in their catalogs, these businesses can make a single page for every instance of a product from multiple sellers. From there, customers can choose to buy from individual providers.
This process ultimately improves the quality of the catalog, making it easier to find the products you want and removing duplicate listings that waste networking resources. For consumers, matching provides better accuracy in search results by showing them not only the product they’re interested in but also similar products they might not have known about yet. Performing product matching manually would be tedious, so it makes sense to automate it. An AI can analyze product attributes and distinct features to make determinations based on the data at hand.
How Product Matching Works
Product matching AI looks at a variety of data, including:- Detailed specifications
- Unique product codes (UPC, GTIN, ASIN, etc.)
- Titles
- Descriptions
- Brand
- Color
- Size
The Challenge of Product Matching
Product matching must be accurate. The grouping must be comprehensive enough to prevent duplicates but also precise enough that unrelated products don’t end up on the same page. An unoptimized system can damage the user experience, which may increase drop off.Another issue is that many products are incredibly similar. The same item could come in variations and different conditions, while others may be used or refurbished. Any product matching system that you implement should have the capability to identify these minor variations.
And finally, matching is challenging mostly thanks to the sheer volume of data the algorithms have to deal with. Manual product matching isn’t cost-effective or possible for companies that have extensive product catalogs.
In these situations, a company will require a comprehensive product matching AI that can improve the way products are matched based on a user’s search query.
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