Generic "you may also like" rows leave money on the table. CustomFit.ai personalizes recommendations to each shopper — frequently bought together, recently viewed, trending, complete-the-look — and A/B tests them on real revenue, so AOV climbs without discounting.
Each row adapts to the shopper's behavior, affinities, and what's converting for similar buyers.
Cross-sell and bundle recommendations at the moment of intent raise units per order, no discount needed.
Every block runs as an experiment with a holdout, so you keep only the recommendations that pay.
The same engine, rendered right inside your product page — personalized to each visitor and styled to match your store. This is a live "complete your routine" block on a D2C skincare PDP.
A daily vitamin-C serum for even, glowing skin — lightweight, fast-absorbing, and fragrance-free.
Every card is chosen per visitor — and the whole block runs as an experiment against a holdout, so you keep only what lifts revenue.
Product recommendations are personalized product suggestions shown to shoppers — frequently bought together, recently viewed, trending, complete-the-look — designed to help them discover relevant items and to raise average order value and units per transaction. A recommendation engine predicts what each visitor is most likely to want and surfaces it at the right moment.
A recommendation only works where it's relevant. Place the right type at each stage — and test which lifts revenue most.
Trending & recently viewed to restart the journey.
Complete-the-look & similar items to widen consideration.
Frequently bought together to lift units per order.
One-click add-ons while intent is still high.
CustomFit blends catalog relationships with live behavior — what this shopper viewed, what similar shoppers bought, what's trending right now — to pick products each visitor is genuinely likely to want, not a random "related" row.
Oak Scrub 92%Oak Balm 87%
Refill Pack 81%Hydrating Mist 64%Most recommendation widgets show a number; they can't tell you whether it added revenue or just shifted it. Because CustomFit runs every block against a holdout, you see true incremental AOV and units per order.
Surface the right products to the right shopper, everywhere.
Test which recommendations actually lift revenue.
Raise AOV without discounting — no code, no agency.
Personalized cart recommendations were the single easiest AOV win we shipped. We tested the bundle, proved it was incremental, and rolled it out the same week.
Product recommendations are one of the most reliable ways for D2C and ecommerce brands to grow revenue per visit. By surfacing the items a shopper is most likely to want — frequently bought together, recently viewed, trending among similar shoppers, or complete-the-look — recommendations help customers discover more and lift average order value and units per transaction, all without discounting.
A recommendation engine works by combining catalog data with shopper behavior. It looks at what the current visitor has viewed and carted, what similar shoppers purchased, what's trending now, and contextual signals like geo and device, then predicts the products most relevant to that individual. Placement is just as important as the algorithm: trending and recently-viewed rows fit the homepage, complete-the-look fits the PDP, frequently-bought-together fits the cart, and one-click add-ons fit post-purchase.
CustomFit.ai lets marketers place, personalize, and A/B test product recommendations on live pages with no code. Crucially, every recommendation block runs as an experiment against a holdout, so you measure true incremental AOV — proving a recommendation added revenue rather than just shifting it. It works across Shopify, WooCommerce, BigCommerce, and custom stacks, and shares one audience model with your A/B tests and personalization so insights compound across the whole store.
It analyzes catalog data and shopper behavior — views, carts, purchases, affinities — to predict which products a visitor is most likely to want, then surfaces them on the PDP, cart, and homepage in real time. CustomFit lets you place, personalize, and A/B test them with no code.
Yes. Relevant cross-sell and frequently-bought-together recommendations are among the most reliable ways to lift AOV and units per order, because they surface complementary products at the moment of intent. CustomFit measures the lift against a holdout so you know it's incremental.
Yes. Every recommendation block can run as an experiment — test placement, algorithm, and design against a control, and keep only the versions that lift revenue per visitor.
Match the type to the stage: trending/recently-viewed on the homepage, complete-the-look on the PDP, frequently-bought-together in the cart, and one-click add-ons post-purchase.
No. Marketers place and personalize recommendation blocks in a no-code editor; the AI Copilot can even scaffold one from a prompt.
Other ways teams lift conversions with CustomFit.
Personalize and A/B test product recommendations across your store — no code, live in 4 minutes.
