Personalization that converts
a direct-to-consumer retail brand
revenue per visit
higher add-to-cart rate
more catalog discovered
Every shopper saw the same merchandising and generic recommendations. The catalog was deep, but discovery was shallow, so visitors bounced before finding what fit them. Manual merchandising could not adapt to individual intent at scale. The brand needed personalization that lifted revenue without feeling intrusive or creepy to customers.
How we approached it
Built a unified behavioral profile from browsing, purchase, and catalog signals.
Developed recommendation and ranking models tuned to each shopper's intent.
Personalized merchandising and search results across the storefront in real time.
Ran controlled experiments to confirm revenue lift before full rollout.
“The store finally feels built for each shopper. Revenue per visit jumped, and customers tell us they're actually finding what they came for.”
Personalization was validated through controlled experiments against a baseline, so the increase in revenue per visit is a measured result rather than an estimate.
No. It works from first-party browsing, purchase, and catalog signals the brand already owns, focused on relevance rather than invasive tracking.
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