thredUP is transforming resale with the mission to inspire a new generation of consumers to think secondhand first. By making it easy to buy and sell secondhand, thredUP has become one of the world’s largest resale platforms for women’s and kids’ apparel, shoes, and accessories. thredUP is extending the life cycle of clothing, changing the way consumers shop, and ushering in a more sustainable future for the fashion industry.
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.”
While the company had conducted pilots of technology-based tagging solutions, it had not yet found a solution that met its goals for accuracy or reliability until it began working with Lily AI.
Homer’s team evaluated Lily AI Product Intelligence through a pilot with women’s tops, a product category containing a wide variety of styles and used for diverse occasions. They wanted to understand the quality that Lily AI algorithms could deliver as well as how the solution would improve the efficiency of human taggers.
The company’ early split test with Lily AI showed a sell-through lift of four to 15 percent. Based on these results, Homer’s team made the decision to expand Lily AI’s deep tagging across all of its product categories.
With Lily AI Product Intelligence, thredUP customers now have two to three times as many attributes to search and explore as they look for the perfect outfit. On average, Lily AI tags about 275,000 images each week. This has resulted in a 15% lift in sell-through; a 2%+ conversion rate lift for customers who’ve purchased >2 times, and a 2.8%+ conversion rate lift on iOS.
Favorites per user increased by 2.1% as well, with favoriting being an important feature on thredUP due to the many single SKUs on offer.
Operational efficiency has been another important benefit for thredUP. Lily AI Product Intelligence has reduced the training requirements for thredUP associates and reduced QA for newly added products, so they reach customers faster. “It’s like removing extra weight from the system, and that makes a bigger and bigger difference as we scale up,” Homer adds.
Furthermore, thredUP is able to use extended attribute data from Lily AI in its pricing algorithms, which are critical to sell-through. The algorithms now have more signals to assess the value of and expected demand for a particular garment that a seller has consigned.
Today, thredUP is using Lily AI tags as inputs for targeting and segmentation capabilities that Homer’s team has built in-house. 16% of all free text search queries on the site now contain these tags, with the most popular filters being colors, pattern, and sleeve length.
The two companies have collaborated to co-develop an innovative “Thrift the Look” feature that allows thredUP customers to shop by outfit, utilizing curated lifestyle images from Instagram to find similar product recommendations for clothing, shoes, and accessories - all powered by Lily AI attributes to generate similar product recommendations.
It all needs to start with the customer. The context of what she is trying to accomplish—her progress in refreshing her closet or finding a specific piece for an event—does not come from segmentation alone. Lily AI’s customer intent platform understands what our customer is trying to do and offers the right recommendations to her, not products she doesn’t actually want.