Why can a recommendation engine move average order value so dramatically, so fast? Because the right suggestion at the right stage of the journey changes what — and how much — a shopper buys. Adidas saw it happen in a single month after deploying AI recommendations.
The result: Adidas achieved a 259% increase in average order value, a 13% conversion lift, and an 18.5% rise in revenue per user within one month of deploying AI-powered recommendations.
Matching the recommendation to the moment#
The power of Adidas’s implementation was stage-aware recommendation: showing new users best-sellers to build trust, giving frequent browsers personalized suggestions based on their behavior, and presenting recent buyers with frequently-bought-together items. Each shopper got the recommendation that fit their point in the journey — and that precision is what drove a 259% AOV lift.
A generic “recommended for you” row treats everyone the same. Adidas’s engine recognized that a first-time visitor and a returning browser need different nudges, and tailored accordingly.
How recommendation AI works#
Recommendation AI predicts what each shopper wants and surfaces it — and the best implementations adapt to where the shopper is in their journey.
Three mechanics drove Adidas’s result. The engine personalizes by stage, showing best-sellers to new users and behavior-based picks to frequent browsers. It drives complementary discovery, suggesting frequently-bought-together items that grow the basket. And it optimizes placement, surfacing the right recommendation type in the right context.
Why stage-aware recommendations lift AOV so much#
A 259% AOV lift comes from recommending not just relevant products, but the right kind of product for the moment. Frequently-bought-together suggestions to a buyer grow the basket; personalized picks to a browser deepen engagement; best-sellers to a newcomer build confidence. Treating the journey as stages — rather than showing one recommendation row to everyone — multiplies the impact, because each suggestion lands when it’s most likely to be acted on.
What this means for your store#
Any store can apply stage-aware recommendations:
- Tailor recommendations to journey stage — best-sellers for new visitors, personalized picks for browsers, complements for buyers.
- Use frequently-bought-together suggestions to grow average order value.
- Place each recommendation type where it’s most relevant.
The same engine, made stage-aware, converts far better. Match the recommendation to the moment.
Bring recommendation AI to your store with CartAmplify#
CartAmplify brings stage-aware recommendation AI to any store — Shopify, dropshipping, or marketplace. The same approach that lifted Adidas’s AOV 259% in a month.
Related reading#
- How Adidas Used AI Search to Lift Conversion 13%
- How Adidas’s AI Discovery Surfaces Intent to Lift AOV 259%
- How Wildberries Pairs Recommendation AI with Logistics
Figures cited from the publicly reported Adidas / Insider case study. Results vary by catalog, traffic, and implementation.
