Increases Sell-Through by 15% with Enriched Product Data from Lily AI

The Problem: Millions of unique items, thousands of ways to look for them

With more than 100,000 items arriving every day from sellers’ closets, thredUP must categorize and tag products at tremendous scale. Its inventory includes more than 50,000 brands and hundreds of categories, each with dozens of attribute groups—any of which could lead a buyer to the item she wants.

thredUP associates, working in distribution centers across the United States, inspect each item that arrives from a seller’s closet and create a unique product description for it. Before thredUP began working with Lily AI, the associates were also responsible for tagging inventory items with all of the attributes that the site uses for buyer navigation, searches, facets, and filters. This process could be time-consuming and required a robust QA process to ensure a good customer experience.

Chris Homer, co-founder and CTO at thredUP, leads a team of software engineering and data science experts who work together to eliminate the friction from consignment selling. He knows that search and navigation are critical to his customers. However, he aims to keep internal resources focused on technology and algorithms that are unique to thredUP’s business.

“We need to invest in our pricing algorithms, scoring of seller merchandise, and garment routing in the distribution center—critical profitability drivers that are unique to us,” Homer says. “When it comes to personalization and recommendations, which involve customer behavior, products, and affinities that are relatively consistent across retailers, we want to find the best partners.”