Lily AI Fashion Retail Customer Placeholder Logo. Major Global Luxury Retailer Increases Relevant Results for Descriptive Searches by up to 30x, Driving $20M in Incremental Revenue


training data points


long-tail searches


increase in search results


incremental revenue


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.


Understanding that getting this right was a huge opportunity on their online properties, the company entered in a proof of concept with Lily AI that took a specific set of products – initially, dresses – and, using historical data sets and AI-based visual scanning, appended Lily AI’s extensive product attribution data to the retailer’s existing product data. Seeing initial success here, this rapidly grew across apparel, shoes, handbags, jewelry, and accessories, with over 75,000 products tagged in less than two weeks.

The company’s SVP of Customer and Revenue Growth had this to say in a 2021 webinar:

“Then we saw something rather remarkable. There were two metrics that were really interesting. The first one was, of the many people that I show these better attributed results to, how many people click into a product page? That increase was kind of mid-single digits. Super interesting to me, because again, these are like really high-intent customers, who are now finding a lot more of interest for them to click into. Now I’ve kept them on the website longer, and I am getting them to a product page; so further down my funnel. 

Even more interesting was the metric of: are they actually converting on those products, and they were converting even more. I saw that if I could show them things that were more relevant, they’re going to click on that; they’re going to spend more time on my site exploring, and they’re much more likely to convert. So this was hugely interesting, because it told me, basically, there’s a lot of lift for my overall business for any product where I could attribute them in this more reliable way. I’m engaging customers by showing them more relevant products, and I’m helping them complete their shopping journey, which is good for them and good for me and good for their value to me in the long term.” 

Having seen exceptionally strong results in their proof of concept, the retailer extended the relationship with Lily AI to leverage Lily AI’s granular and consumer-centric product attributes to enhance site search across apparel, shoes, handbags, jewelry and more.


By using Lily AI’s consumer-focused tags to augment their on-site search, the retailer has increased the relevant results for descriptive searches by up to 30x. This translates to $20 million in incremental revenue for the company, which is leading to a further expansion of Lily AI product attribution across the company’s diverse, varied and rapidly-growing product catalog.

Lily AI’s product data enrichment engine, trained on over 1 billion data points, delivers product attributes with unparalleled detail and accuracy, enabling an intuitive digital experience. The solution also enforces consistency across the entire product catalog, which is extremely valuable for large retailers such as this one, and then pushes product data to the entire e-commerce stack, creating a universal, customer-centric language that can be used to enhance search, product recommendations, SEO/SEM and demand forecasting.

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