Global Luxury Retailer Increases Relevant Results for Descriptive Searches By Up to 30x, Driving Incremental Revenue



training data points


long-tail searches


increase in search results


increase in online order conversions

The Problem

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 Al could help.

The Solution

Understanding that getting this right was a huge sales opportunity, the Company entered into a proof of concept with Lily Al that took a specific set of products – initially, dresses — and, using historical data sets and Al-based computer vision, appended Lily Al’s extensive customer-oriented product attribution to the retailer’s existing product data. Measuring initial success here, the relationship expanded across apparel, shoes, handbags, jewelry, and accessories, with over 75,000 products tagged in less than two weeks.

In an interview, the retailer’s SVP had this to say:

“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 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.”

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

The Results

By using Lily Al’s consumer-focused tags to augment their on-site search, the retailer increased the relevant results for descriptive searches by up to 30x. This translated to incremental revenue for the Company, which led to an expansion of Lily Al product attribution across the Company’s diverse, varied, and rapidly-growing product catalog.

Lily Al’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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