Product recommendation ROI calculator
Most product pages are dead ends — one item, then the back button. See how much revenue you'd recover by surfacing the right products to every shopper.
Estimate your recommendations upside
See the added revenue from bigger baskets and better discovery once shoppers see items chosen for them. Adjust the inputs to match your store.
Estimates only — built to show the mechanics of recommendations, not a performance guarantee. Ranges are grounded in published benchmarks (recommendations drive ~10–30% conversion and ~10–21% AOV lift). Repeat-purchase and lifetime-value gains are extra upside we leave out to stay conservative.
A dead-end product page vs. a path to more
Without recommendations, a shopper sees one product and leaves. Surface items chosen for them and two things happen: baskets get bigger, and undecided shoppers find something to buy.
Cross-sell and upsell raise average order value — most when shoppers already buy items together.
Surfacing relevant items lifts conversion — especially with a large catalog or high product-page bounce.
The biggest gains come from starting from none or upgrading basic “related products” to personalized recs.
Turn browsing into bigger baskets
Personyze's recommendation engine reads each visitor's behavior, your catalog, and unified visitor data to surface the right products in real time — on product pages, in the cart, on the homepage, and in email. No data team required.
- ✓Algorithmic recommendations — personalized, trending, recently viewed, and frequently-bought-together.
- ✓Powered by unified visitor data and your live product catalog.
- ✓Place recs across product pages, cart, homepage, and open-time email.
- ✓A/B test placements and strategies so the lift is measured, not assumed.
Recommendations on every page they visit
Personyze surfaces the right products wherever the decision happens — discovery on the homepage, cross-sell on the product page, and order-boosters in the cart.
Model the rest of your personalization ROI
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Explore A/B testing → EmailEmail personalization
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Explore email →Frequently asked questions
How is product recommendation ROI calculated?
We estimate two effects from your inputs: a lift in average order value from cross-sell and upsell, scaled by how often your shoppers already buy multiple items, and a lift in conversion from better product discovery, scaled by catalog size, product-page bounce, and traffic type. Both are reduced by how mature your current recommendations are, then applied to your current revenue.
What revenue lift can I expect from recommendations?
It varies widely. Stores with large catalogs, high product-page bounce, and shoppers who buy in bundles have the most to gain. The calculator caps each effect at a realistic ceiling and lets you lower the captured-lift assumption to match your own data.
Do I need a large catalog for recommendations to pay off?
No, but catalog size changes where the value comes from. Large catalogs gain most from discovery, since shoppers can't find everything on their own. Smaller catalogs still gain from cross-sell and upsell on the products you do carry.
How long does it take to add recommendations?
With Personyze you connect your product catalog, choose where recommendations appear, and pick a strategy in a visual editor — typically live in hours, with no developer or data team required.
Every product page without recommendations is a dead end.
You already paid to get the shopper there. Recommendations give them somewhere to go next — bigger baskets, more discovery, and a reason to come back.