AI product recommendations use purchase history, browsing behaviour and product attributes to suggest items each shopper is likely to want, on product pages, in carts, in emails and in ads. In client campaigns they work when the catalogue data is clean, the placements match the shopper’s stage, and every widget is tested against a simple baseline such as bestsellers.
How do AI product recommendations work?
Recommendation engines rely on a few families of methods. Collaborative filtering finds patterns across shoppers: people who bought a tent often also bought a sleeping mat. Content-based filtering compares product attributes such as category, colour, material and price. Modern systems combine these with machine learning models that weigh recent behaviour, session context and business rules.
Most stores do not build this themselves. Shopify offers native product recommendations and a Search & Discovery app, and many merchants add apps such as Rebuy, LimeSpot or Nosto. WooCommerce stores use plugins or connect external engines. Larger retailers use platforms such as Google Cloud’s Vertex AI Search for commerce, Amazon Personalize, Dynamic Yield or Algolia Recommend. Email platforms such as Klaviyo include recommendation blocks built from store data.
The engine is only as good as its inputs. Missing categories, inconsistent titles and products without stock information produce odd suggestions.
Where do recommendations appear in a campaign?
On-site placements
- Product page: similar items and frequently bought together.
- Cart and checkout: low-cost add-ons that complete the order.
- Homepage: recently viewed and personalised picks for returning visitors.
- Empty search results: alternatives instead of a dead end.
- Post-purchase page: complementary items or replenishment.
Email and messaging
Abandoned cart, browse abandonment and post-purchase flows perform better when they show relevant products rather than a fixed bestseller block. Klaviyo and similar tools fill these slots automatically from catalogue and event data.
Paid advertising
Dynamic product ads on Meta (Advantage+ catalogue ads) and Google’s Performance Max with a Merchant Center feed are recommendation systems in their own right: the platforms choose which products to show to whom. Feed quality and product grouping strongly affect what they pick.
What data do recommendations need?
Before any widget goes live, we check the data foundations:
- A complete product feed with accurate titles, categories, prices, images and stock status.
- Event tracking for product views, add-to-cart and purchases, sent consistently to the engine and to analytics such as GA4.
- Enough traffic and orders for patterns to form; small catalogues with few orders may do better with rule-based picks.
- Business rules: exclude out-of-stock items, respect margin floors, avoid recommending a cheaper version of what is already in the cart.
- Consent handling: personalised recommendations based on behaviour depend on the shopper’s cookie consent under GDPR or KVKK, and the fallback for non-consenting visitors must still work.
How do we test whether recommendations work?
A recommendation widget always looks helpful, which is why it needs testing. The honest comparison is against a simple alternative: bestsellers in the same category, or no widget at all.
Our usual method:
- Choose one placement, such as the cart, and one metric that matters, such as average order value.
- Run an A/B test where half the visitors see AI recommendations and half see bestsellers.
- Run it long enough to cover at least two full weekly cycles and enough orders for a meaningful read.
- Look beyond the main metric: check conversion rate, so a higher order value is not hiding fewer orders.
- Segment results by new and returning visitors, since personalisation usually helps returning shoppers more.
Many apps report their own revenue attribution, often counting any order that included a clicked recommendation. Those numbers tend to be generous. A controlled test is the only reliable read.
How Alien Road applies AI product recommendations for clients
In a typical engagement discovery starts with the store’s analytics and catalogue. We look at average order value, the share of orders with more than one item, top product pairs and drop-off points in the funnel. This shows where recommendations could add value and where they would just add clutter.
Setup covers feed clean-up, tracking checks and the placement plan. We usually begin with one or two high-traffic placements, such as product page cross-sells and cart add-ons, rather than switching on every widget the app offers. Business rules for stock, margin and category logic are configured before launch.
For measurement every new placement runs as a controlled test against a bestseller baseline, with order value, conversion rate and revenue per visitor tracked in GA4 and the store’s own reports. Email recommendation blocks are tested in flows with split variants.
Reporting compares test results honestly, including placements that made no difference. When a widget does not beat the baseline, it is removed or redesigned, because every element on a product page competes for attention.
How long before recommendations show results?
Expect a learning period. Engines that rely on behaviour need enough product views and orders to find patterns, so a new store or a newly launched catalogue may see generic suggestions for the first weeks. During that time, rule-based fallbacks such as category bestsellers keep the widget useful.
Seasonality also matters. A gift shop’s top pairs in December look nothing like its pairs in March, so review results across seasons before drawing permanent conclusions. For a mid-sized store, a quarter of data usually gives a fair picture of whether a placement earns its space on the page.
Which mistakes should you avoid?
- Recommending items that are out of stock or have long delivery times.
- Showing near-identical products that cannibalise the item already chosen.
- Overloading the page with four recommendation carousels.
- Ignoring mobile layout, where carousels can push the add-to-cart button below the fold.
- Trusting app-reported revenue without a test.
- Forgetting the fallback for visitors who decline tracking.
Where to start this week
Export your last three months of orders and find the ten most common product pairs. Check whether those pairs already appear together on your product pages. If they do not, set up a frequently-bought-together block for those products using your platform’s native tools, and run it as a split test against your current page for two weeks. That gives you a baseline before you pay for a larger recommendation engine.