Why do the best beauty retailers make the browsing experience itself do the selling? Because most shoppers explore before they decide, and the store that guides that exploration captures sales a static catalog never would. Sephora surfaces personalized recommendations as customers browse — and it shows up in average order value.
The result: Sephora’s AI-powered discovery during browsing drives a 25% increase in average order value by surfacing personalized product recommendations as customers explore categories.
The browse is where beauty baskets are built#
Beauty shopping is exploratory. A customer browsing skincare isn’t looking for one item — they’re assembling a routine, and the right serum implies a moisturizer, a cleanser, an SPF. If the store treats each product page in isolation, it leaves that natural basket-building to chance. The shopper has to think of the complements themselves, and many don’t.
Sephora’s browse AI does the thinking for them. As customers explore categories, it surfaces personalized recommendations — the complementary and coordinating products that complete a routine — tuned to each shopper’s profile. That in-the-moment relevance is what lifts average order value 25%.
How browse AI works#
Browse AI personalizes the exploration experience for shoppers who haven’t searched for a specific product. It reads what the shopper is viewing and their preferences, then surfaces relevant suggestions alongside the browse.
Three mechanics drive the AOV lift. The engine recommends in context, tied to whatever category or product the shopper is currently exploring. It personalizes to the individual, weighting suggestions by behavior and stated preferences so they feel chosen, not generic. And it builds routines, surfacing the complementary products that complete a beauty regimen, which is precisely how the basket grows.
Why complementary discovery lifts AOV#
A 25% AOV lift from browsing comes from a simple truth: shoppers buy more when the complements are surfaced for them at the right moment. A customer looking at a vitamin C serum who’s shown the compatible moisturizer and SPF doesn’t feel upsold — they feel helped toward a complete routine. The store anticipates the next product the shopper would logically want and removes the friction of finding it.
This works because it’s relevant and well-timed. Random “you may also like” rows get ignored; contextual, personalized complements get added to cart. The difference between the two is exactly the difference between browse AI and a static related-products widget.
What this means for your store#
Any store whose products work together — routines, kits, complementary items — can apply this:
- Surface personalized recommendations as shoppers browse, not just at the cart.
- Tie suggestions to what the shopper is currently exploring so they’re contextual.
- Recommend the complements that complete a set or routine to grow basket size naturally.
The browse session is where baskets get built. Guide it, and average order value rises on its own.
Bring browse AI to your store with CartAmplify#
CartAmplify brings the same kind of in-context, personalized browsing that lifts Sephora’s AOV 25% to any store — Shopify, dropshipping, or marketplace. Recommendations that complete the basket while shoppers explore.
Related reading#
- How Sephora Lifted AOV 25% with AI Product Discovery
- How Sephora Drives 3.2x Purchase Likelihood with AI
- How Zalando’s AI Browsing Reached 500K+ Shoppers
Figures cited from publicly reported Sephora AI case studies. Results vary by catalog, traffic, and implementation.
