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 could be improved to meet internal standards and outpace industry norms. In setting out to understand areas for improvement, the team sought to understand the precise searches for all site visitors. They 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 they had over 200 sequined dresses in their assortment. When they analyzed why this was happening, they came to realize that their manual, human-based, merchant-driven attribution was a bottleneck in terms of consistency, speed and accuracy. The Company wanted to reach these high-intent customers, and knew that Lily AI could help.