AI Product Recommendations

AI Recommendations Every Shopper Wants

Behavior and session signals surface the right product for each shopper, in the moment.

AI Product Recommendations learns from browsing, purchase history, and live session behavior to surface trending items, similar picks, cross sells, and curated suggestions. Every recommendation adapts in real time, driving higher engagement, lower bounce rates, more pages viewed, stronger conversion, and bigger orders.

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Recommendations that learn what sells

Every shopper wants something different, and their intent shifts by the minute. Athos Commerce reads those signals, predicts what each visitor wants next, and puts the right product in front of them.

Capitalize on Demand

Surface trending items and best sellers automatically, so shoppers see what is moving right now and you convert high intent traffic without extra work.

Match Shopper Intent

Behavior and session signals guide every recommendation, so each shopper sees products that fit what they came for, not a generic list built for everyone.

Surface the Next Best Thing

When a product is out of stock or not quite right, AI recommends similar options and complementary picks that keep shoppers moving toward checkout.

Three ways AI enriches your catalog

Every capability turns unstructured catalog data into shopper ready content. No manual tagging. No synonym lists. No rewrites. Just enrichment that works in the background.

Behavior Based Recommendations Session Based Recommendations Relevant Recommendations

Behavior Based Recommendations

Learns from every browse and every purchase.

Track browsing and purchase patterns across every visit and update recommendations continuously. Shoppers see products aligned with their real preferences, not stale suggestions from weeks ago.

Session Based Recommendations

Adapts to what each shopper is browsing, right now.

Every click, view, and add to cart action reshapes what appears next. Session signals capture intent as it forms, so even first time visitors get relevant picks tuned to the moment.

Relevant Recommendations

Every recommendation type your team needs, powered by one engine.

Cross Sell, Cart Cross Sell, Similar, Personal, Session, Recently Viewed, Trending, Handpicked, and Custom recommendations, all from one engine. Mix and match by placement to keep shoppers engaged.

Real results, real retailers

How RUDIS lifted AOV 12% with personalized recommendations

+12%

AOV increase with personalized recommendations

most-used merchandising feature for the team

We lean on Athos so much for merchandising. It is the number 1 feature we use and we have had such success using it.

Emily Riggan

Ecommerce Experience Designer

RUDIS

View Case Study

Frequently Asked Questions

What data does Athos Commerce use to power AI product recommendations?

Recommendations use three signal sources working together: catalog data like attributes, availability, and categories; historical shopper behavior like browsing paths and purchases; and live session activity like clicks, cart additions, and search terms. No personal identifiers are required. The engine combines these signals in real time so every recommendation reflects both shopper intent and what your catalog can deliver.

Can my merchandisers override the AI or pin specific products?

Yes. AI does the heavy lifting, but merchandisers stay in control. Pin specific products, exclude items you do not want surfaced, boost promotions, and set rules by category or segment. The engine learns from every override, so recommendations keep improving without giving up brand judgment.

Where can I place AI recommendations across my site?

Homepage, product detail pages, category pages, cart, search results, checkout, and post purchase confirmation. You can also feed the same recommendations into email and SMS through marketing automation integrations. Each placement uses signals relevant to that moment, so what shoppers see fits where they are in the journey.

How quickly will we see results after going live?

Session based recommendations start working from the first shopper visit, so lift shows up immediately. Behavior based recommendations sharpen as catalog and audience data build, usually within the first four to six weeks. Most customers see measurable AOV and conversion gains inside the first two months. Athos can also accept historical sales data to prime the pump and improve results even faster.

How is this different from basic recommendation apps or platform native features?

Basic apps recommend from last viewed or bestseller lists. Athos Commerce reads live intent, purchase history, catalog context, and real time trends together, then adapts every placement per shopper. You also get merchandiser controls, cross channel exports to email and SMS campaigns, and analytics that tie recommendations to revenue.