Lily Al Recommendations Increase RPV for Large Multi-Brand Retailer by +0.65%, Leading to Additional $20-30M in Incremental Demand

The challenge of matching shoppers to the products they actually want via recommendations is a constant across the retail e-commerce landscape. A large, multi-brand specialty retailer came to Lily AI with a product recommendation engine that they had been working with, concerned that this engine was not able to take advantage of granular product attribute data to ensure that consumers were being presented with items that truly matched their personal style and preferences.

This retailer turned to Lily AI and its product Attributes platform for help.

Highlights

• 0.6% increase in AOS
• 0.65% increase in RPV
• 0.62% increase in overall demand
• $20-30M increase in annual revenue

The Problem

The large multi-brand retailer had been looking to offer the same benefits of in-store shopping that might come from a stylist/sales associate showing a customer similar product recommendations to what they are looking for, yet in an online platform.

For instance, a customer might be looking at an eyelet top, and wants to see other options that are similar. The recommendation engine the retailer had been using could not identify eyelets or other details of the viewed product, but they surmised that Lily AI could.

In the initial proof-of-value test, Lily AI was able to provide more granular product information, which in turn provided more context and relevant product recommendations that could then be input into the existing recommendation engine. This allowed the retailer to develop a more hyper-personalized product set, thereby increasing the number of relevant products shown to a customer. Lily AI’s output for the recommendations test was deployed into Certona, the company’s product recommendation engine.

KPIs that this retailer sought to measure included RPV (revenue per visit), AOS (average order size) and overall demand—all of which were hypothesized to increase with an increase in relevant product recommendations and views.

One interesting discovery during the retailer’s proof-of-value tests was that visits, orders, CVR, and AUR (average unite retail) all remained constant, which indicated that the basket size increase was what was driving the demand. Customers were adding more “quality” products to their shopping bags, based on seeing more relevant product recommendations.

Results

With Epsilon Research finding that 80% of consumers are more likely to make a purchase when brands offer personalized experiences, delivering robust product recommendations that convert is core to any retailer or brand’s e-commerce strategy.

By layering on granular product attribution data to their existing recommendations solution, Certona, this retailer saw an overall increase in RPV, AOS, and overall demand thanks to Lily AI, driven by a substantial increase in product views. They saw an increase in AOS of 0.6%, a 0.65% increase in RPV, and a total increase in overall demand of 0.62%, for this retailer’s size, this projects to an additional $20-30M in incremental demand.