In the highly competitive $245B resale market, Beni, a secondhand shopping tool, partnered with Refuel to tackle its data normalization challenge across a 200M+ item catalog. Leveraging Refuel's custom LLMs, Beni dramatically enhanced the accuracy of normalized size values from 46% to 87% with only one day of team effort, surpassing their target of 80%. This improvement led to notable operational efficiencies, including up to a 99% reduction in data quality issues for some reseller partners and a significant 245% increase in Gross Merchandise Value (GMV) for a major partner, highlighting the transformative impact of AI-driven solutions in large-scale retail data management.
Beni is a secondhand shopping tool that helps users find the best resale deals from across 40+ resale sites in a matter of seconds. Beni was started by an impressive leadership team, has raised more than $5M to date, and is going after the $245B resale market.
One of the key challenges for Beni, is that data across their partner sites is messy and not normalized. In their words:
“We often find differences in the way that products are categorized amongst all of our resale partners, especially as it relates to product category, gender, color, brand, and size. If we are unable to appropriately classify products into the right bins, it makes it almost impossible to surface these listings to you in the right situations.”
This is an exceptionally tough problem to solve since the number of product listings in their database is greater than 200 million. A key product attribute is “size”, and the Beni team had identified that size not being normalized was leading to a sub-par search experience for their users.
Their existing solution was an in-house ML model that was trained on data labeled by their team, with a combination of heuristics and manual effort. The goal was to improve accuracy to more than 80% in order to deliver the desired experience.
“Beni has a product catalog of 200M+ items and ensuring clean, structured data is an ongoing challenge. Using Refuel’s customized LLMs, we were able to label millions of items and improve accuracy on a key attribute from 46% to 87%. What would have taken us months, only took a few days with Refuel.”
CTO – Beni
The Beni team is continuing to integrate AI and LLMs into their stack to improve data quality and search experiences for their users.
If you’re interested in learning more about their secondhand shopping tool, you can find more information here: https://www.joinbeni.com/
If you’re interested in how LLMs can help your marketplace operations and improve data quality, sign up here to get a demo of Refuel: https://www.refuel.ai/get-started