Wayfair Uses AI to Catch and Correct Bad Product Listings

Wayfair Uses AI to Catch and Correct Bad Product Listings

Put simply, Wayfair has a lot of furniture.

Its catalog spans about 30 million items across nearly 1,000 product types. Roughly 47,000 attributes cover details such as color, material and dimensions. Those details determine how products show up in search results and recommendations. Before employing artificial intelligence (AI), fixing bad listings depended mostly on suppliers and shoppers flagging errors. Manual review couldn’t keep up with a catalog that size.

Through a partnership with OpenAI, Wayfair has corrected 2.5 million product attribute tags and now automatically handles 41,000 supplier support requests a month, the AI startup shared in a March post. The work covers two jobs that used to depend on people: merchandising staff checking product listings for accuracy and supplier support teams sorting through requests one at a time.

Wayfair built a system that reviews product listings against a description of what each detail is supposed to mean, then flags or corrects mistakes. If a coffee table is listed as walnut but the photo and description point to pine, the system catches it.

The system is running on more than 1 million products so far, OpenAI added. A test on corrected listings showed they got more clicks and ranked higher in search than before, according to OpenAI.

Wayfair didn’t let the system make changes unchecked. Staff manually inspect samples of the corrected listings. When the system is confident in a fix, it updates the listing and tells the supplier. When it isn’t, it asks the supplier to confirm the change first. Wayfair said it expects to expand the program to cover four times as many products within six months.

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PYMNTS reported that Wayfair’s use of Google’s AI models to categorize products had already cut the time needed to curate listings by 67% and lifted some conversion rates by 2%.

Routine Supplier Requests Get Handled Without Staff

Wayfair works with tens of thousands of suppliers, and the company’s support team used to read every incoming request, figure out what the supplier needed, and send it to the right internal group. Requests cover hundreds of different issue types, more than any one staff member could keep track of, according to Wayfair’s supplier support team.

Now a system reads each request, pulls in missing details from Wayfair’s internal records, and sends it to the right team on its own.

For some teams, the system goes further. One example covers replacement part requests, where it reviews the history of a case and drafts a suggested response for a staff member to review before sending. Wayfair checks how often these suggestions match what staff would have done anyway. For tasks where that match rate is consistently high, the system can act without a staff member reviewing it first. Across these efforts, Wayfair said it now handles up to 70% of ticket volume automatically in some workflows.

Catalog Cleanup Becomes a Bigger Target for AI

Wayfair’s approach reflects a broader shift in how retailers are applying AI: cleaning up product data and back-office workflows, not just adding chatbots for shoppers. Microsoft in January introduced a tool for retailers that pulls product details from photos and fixes catalog errors automatically, with the apparel brand Guess as an early user.

Wayfair has also made its product data available to third-party AI tools that shoppers use to search and compare products. Those tools, including ChatGPT, can pull directly from a retailer’s product feed to answer shopping questions or surface items in response to a search. That means listings corrected through Wayfair’s internal system can also show up more accurately when a shopper asks an AI assistant for product recommendations.

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