How Decathlon's Recommendation AI Lifted RPU 224%

Why is revenue per user the metric that best captures recommendation AI’s value? Because it rolls conversion, basket size, and relevance into one number — how much total value each shopper generates. Decathlon, the global sporting-goods retailer, moved that number dramatically with AI personalization.

The result: Decathlon’s AI personalization achieved a 224% uplift in average revenue per user by recommending products based on browsing behavior and sports preferences.

Matching gear to the athlete#

Decathlon’s catalog spans dozens of sports, and a runner has almost nothing in common with a climber or a cyclist. Generic recommendations waste that diversity. Decathlon’s AI instead reads each shopper’s sports preferences and behavior, recommending the gear and accessories relevant to their activity. A shopper into trail running sees running-and-outdoor recommendations; a swimmer sees swim gear. That precise matching is what lifted revenue per user 224%.

The key is understanding the athlete, not just the shopper — what sport they do shapes everything about what’s relevant.

How recommendation AI works#

Recommendation AI predicts what each shopper wants from their behavior and surfaces it. Decathlon’s version weights sports preference heavily.

Three mechanics drive the result. The engine infers sports interest from behavior, building a profile of each shopper’s activities. It recommends activity-relevant gear, surfacing the equipment and accessories that fit the shopper’s sport. And it grows the basket, surfacing the complementary items that complete a kit for that activity.

Why revenue per user captures the full effect#

A 224% RPU uplift is so large because RPU reflects multiple wins at once: more shoppers convert (relevant recommendations get bought), each spends more (complementary gear completes the kit), and the relevance keeps them engaged. Tracking conversion alone would miss the basket-growth effect; tracking AOV alone would miss the conversion effect. RPU captures both, which is why it’s the truest measure of recommendation AI’s impact — and why Decathlon’s number is so striking.

What this means for your store#

Any store whose shoppers have distinct use cases can apply this:

Infer the shopper’s use case or activity, not just their browsing, and recommend accordingly.
Surface complementary items that complete the kit for their need.
Track revenue per user to capture the full value of better recommendations.

Understanding what a shopper is for — their sport, project, or use case — is what makes recommendations land. RPU shows the payoff.

Bring recommendation AI to your store with CartAmplify#

CartAmplify brings use-case-aware recommendation AI to any store — Shopify, dropshipping, or marketplace. Match shoppers to what fits their need and grow revenue per user, the way Decathlon lifted RPU 224%.

Try CartAmplify free →

The +224% RPU figure is as reported in Decathlon AI case studies. Results vary by catalog, traffic, and implementation.