In 2020, the luxury retailer began a deep look into some of its key e-commerce business metrics, and had a hypothesis that conversion from online searches was lower than both the company’s own expectations and below industry norms. In setting out to understand why, the company sought to understand precisely what the company’s shoppers were looking for. The company found that while many searches were focused on either distinct brand names or on broad categories (i.e. “dresses” or “shirts”), over 50% of searches were attribute-heavy, long-tail searches that combined materials, trends, occasions and other customer-centric attributes.
An example from Q4 2020 found that shoppers were often searching for “sequin” in the retailer’s on-site search engine, and were returned between 10 to 20 sequined dresses; and yet the company knew that they had over 200 sequined dresses that could have been returned. When the company dug into why this was happening, they came to realize that its manual, human-based, merchant-driven attribution was a clear bottleneck in terms of consistency, speed and accuracy, and in ultimately allowing for better conversion from search queries on its e-commerce properties. The company wanted to reach these high-intent customers, and knew that Lily AI could help.