AI product recommendations help e-commerce brands show each shopper the items they are most likely to buy next, based on browsing, purchase history and what similar customers chose. The most practical uses are related products on product pages, add-ons in the cart, personalised email blocks and smarter on-site search. They work best when the catalogue data is clean and each placement has one clear job.
Most platforms now include some form of recommendation engine. Shopify offers a built-in product recommendations API and its Search and Discovery app, WooCommerce relies on extensions, and dedicated tools such as Nosto, Klevu, Algolia Recommend, Dynamic Yield and Klaviyo’s product feeds cover more advanced needs. The engine matters less than where and how you use it.
How do AI recommendation engines decide what to show?
Most systems combine a few methods. Collaborative filtering looks at behaviour: shoppers who viewed or bought A also bought B. Content-based filtering compares product attributes such as category, colour, material and price. Many modern engines add real-time session data, so a shopper who has clicked three running shoes in two minutes sees running gear rather than their purchase history from last winter.
Business rules sit on top. You can usually exclude out-of-stock items, boost high-margin products, hide items from a different gender category or keep accessories within a sensible price range compared with the main item.
Which placements work best on a product page?
The product page is where most stores start. Two widgets tend to do different jobs, and mixing them up is a common mistake:
- Similar items: alternatives for shoppers who are not convinced by the current product. Useful for fashion, furniture and anything with taste-driven choices.
- Frequently bought together: complements such as a case for a phone or a refill for a device. Useful for raising average order value.
- Recently viewed: a simple memory aid that helps shoppers compare.
- Complete the look: curated or AI-generated sets, common in apparel and home decor.
Place alternatives high enough to catch people about to leave, and complements near the add-to-cart button. Test one change at a time.
How can the cart and checkout use recommendations?
The cart is the moment of highest intent, so recommendations here should be small, cheap and obviously relevant. A store selling coffee machines might suggest descaling tablets or a milk jug, not a second machine. Many stores tie this to a free-shipping threshold: “Add one more item to get free delivery,” followed by items that close the gap.
Checkout itself should stay clean. Most teams keep recommendations out of the payment step, because distractions there risk abandoned orders. A post-purchase page or the order confirmation email is a better place for a second offer.
Do recommendations help in email and search?
Yes, and these are often the most profitable placements because they reach people who are not on the site. Email platforms such as Klaviyo and Omnisend can insert product blocks that change per recipient: replenishment reminders for consumables, items similar to an abandoned cart, or new arrivals in a favourite category.
On-site search is the other big one. A shopper who types a query has told you exactly what they want. AI search tools rank results by likely relevance and popularity, correct misspellings and understand synonyms, so “sneakers” finds products labelled “trainers.” Zero-result searches are lost sales, and better search reduces them.
Use cases by store type
- Fashion: size-aware alternatives and outfit sets.
- Beauty: replenishment emails timed to typical usage.
- Electronics: compatible accessories filtered by model.
- Grocery and pet: reorder lists and subscription prompts.
- Home goods: room-based bundles with price balance.
What data do you need before switching it on?
Recommendation quality reflects catalogue quality. If half your products have no category, inconsistent colour names or missing images, the engine cannot compare them well. A store with few orders also gives collaborative filtering little to learn from; in that case content-based and rule-based methods carry more weight at first.
Check these basics: complete titles and categories, structured attributes such as size and material, accurate stock levels synced in near real time, and tracking events for product views, add-to-cart and purchases. Consent matters too. In regions covered by GDPR, personalisation based on tracking usually depends on the shopper’s cookie choices, so plan for visitors who decline.
What mistakes make recommendations backfire?
The most common mistake is showing too much. Four carousels on one product page compete with the main product and push the add-to-cart button down on mobile. Another is recommending items the shopper just bought, such as a second identical vacuum cleaner a day after purchase. Most engines can exclude recently purchased products; make sure that setting is on.
Price balance also matters. Suggesting a 200-euro accessory next to a 40-euro product rarely works. Cold-start problems hit new products too, because they have no behaviour data yet, so they never get recommended unless you add a rule that boosts new arrivals for a set period. Finally, watch for widgets that only ever show bestsellers. That looks like personalisation, but it is just a popularity list, and it hides the long tail of your catalogue.
How Alien Road applies AI recommendations for online stores
In a typical engagement we start with an audit of catalogue data and analytics events, because broken tracking makes any recommendation test meaningless. We then map the customer journey and pick two or three placements with the clearest job, usually product page complements, cart add-ons and one email flow.
Setup uses the client’s existing platform features where possible before adding a paid tool. We define rules with the merchandising team, such as margin floors and excluded categories, and run A/B tests where traffic allows. Reporting focuses on revenue per visitor, average order value and click-to-purchase rate for each widget, reviewed monthly. Widgets that do not earn their space get removed; a cleaner page often beats a crowded one.
Where to start this week
Open your store’s analytics and find your top twenty products by views. Check whether each has a complete category, attributes and at least one sensible complement. Then add or fix a single “frequently bought together” block on those pages, using your platform’s built-in feature. Measure average order value for four weeks against the previous period, and only then decide whether a dedicated tool is worth the subscription.