8.6% Additional Revenue from AI Personalization Recommendations

Client: Apparel Retailer

The Challenge

After observing a pickup in online retail during the height of the pandemic, a major apparel retailer wanted to capitalize on the trend and grow their ecommerce revenue.

Previously, they used merchandiser intuition to make product recommendations, but their ecommerce revenue wasn’t growing. To significantly drive revenue, they needed to change their strategy.


Sophelle first analyzed all ecommerce product recommendation algorithms, then compared the performance of AI recommendations with those manually entered by merchandisers.

We then ran A/B tests to match manual merchandising rules head-to-head against AI algorithms prioritizing consumer browsing and purchasing behavior.


The A/B tests showed that AI algorithms outperformed merchandiser intuition by a landslide. Sophelle then implemented this new strategy on the home and product detail pages.

Altering the approach to prioritize data-driven recommendations using real consumer behavior drove an 8.6% lift in revenue.

After achieving success with this new personalization strategy, the client leveraged more AI-based algorithms across their site.

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