Case Study: Global Luxury Retailer increases relevant search results by up to 30x with Lily AI

Major Global Luxury Retailer Increases Relevant Results for Descriptive Searches by up to 30x, Driving $20M in Incremental Revenue

Global Luxury Retailer wanted to improve conversion from on-site search by better serving the more than 50% of shoppers using long-tail, attribute-heavy queries. Their manual, merchant-driven product attribution was inconsistent and too slow, leaving many high-intent searches—such as “sequin”—with too few relevant results. Lily AI helped the retailer address this challenge by adding more accurate, consumer-centric product attributes to its catalog.

Using Lily AI’s AI-based product attribution and computer vision, the retailer first tagged dresses and then expanded to apparel, shoes, handbags, jewelry, and accessories, with over 75,000 products enriched in less than two weeks. Lily AI increased relevant results for descriptive searches by up to 30x, helping drive incremental revenue and a 3.5% increase in online order conversions, while also improving product-page clicks and overall search relevance.