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Improve cart suggestions by showing a small number of genuinely useful products that complement what a shopper is buying, explaining why each appears, and measuring what happens from recommendation view through checkout. A recommendation widget is not a guaranteed sales lift: relevance, placement, and evidence from your own store matter more than filling every available slot.

What makes a cart suggestion useful?

A useful suggestion helps a shopper complete or get more value from the purchase they already intend to make. It should fit the items in the cart, the shopper’s likely need, and the moment in the buying journey. For example, a compatible case for a selected device may be helpful; a loosely related product shown only because other customers bought it may not be.

That distinction matters near checkout. Complementary products work with or add to a cart item. Alternatives are other versions of a product the shopper is already considering. Baymard Institute advises prioritizing complements in the cart: presenting alternatives at this stage can prompt a shopper to reconsider a decision and potentially leave checkout. Baymard Institute’s cart cross-sell guidance discusses this risk.

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Build recommendations around relevance

Use the cart and product context

Combine information about the current cart and session with product relationships and broader behavior. Purchase history, browsing activity, customer profiles, and category relationships can all help, but “customers also bought” data alone does not establish that a product is appropriate for this shopper. Shopify describes recommendation methods including collaborative filtering, content-based recommendations, and hybrid approaches that combine signals. The useful question is whether the method reflects the shopper’s original intent, not simply whether it can produce a list. Shopify’s overview of product recommendations explains these approaches.

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Prefer a few strong matches to a fixed quota

Do not fill every recommendation slot just because the design allows it. If only one or two items are clearly suitable, show those. A weak match can make the whole recommendation area—and future suggestions—seem less credible.

The scale of the relevance problem is illustrated by a dated benchmark, not a current universal rate: in a 2021 review, Baymard Institute found that 52% of the desktop sites it benchmarked showed cart or added-to-cart cross-sells that were completely irrelevant or based only on what other customers had bought. The finding is specific to that benchmark and should not be read as an estimate for all ecommerce stores today. Baymard’s benchmark and recommendations describe the result.

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Explain the reason and disclose fit

Use a concise, accurate label such as “Pairs well with” for a complement or “Frequently bought together” when the selection is genuinely based on that relationship. A browsing-history label should appear only when browsing history actually influenced the suggestion. Where fit or compatibility matters, state it clearly so shoppers can assess the match before adding the item.

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Match theme and promotion to the purchase

Products of the same theme or use case are easier to understand as a group. Give priority to compatibility-dependent items when they help the shopper avoid a mismatch. If a suggestion includes a promotion, make sure the offer and its presentation fit the shopper’s context rather than interrupting the task of reviewing the cart. These principles align with Baymard’s guidance on cart cross-sells.

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Choose an implementation that fits your store

Native platform features, manual curation, and third-party applications can all support cart suggestions. Choose based on the quality of the product logic and the control you need—not on an assumed sales lift. Compare options using the same practical criteria:

  • Recommendation logic: Can it identify actual complements and compatibility, or does it mainly surface co-purchased items?
  • Merchant controls: Can you curate products, exclude poor matches, set the number shown, and control labels or offer context?
  • Placement: Does it support the surface you need, such as a cart page, cart drawer, add-to-cart confirmation, checkout, or post-purchase page?
  • Theme integration and performance: Does the component work with your store’s theme and cart experience without slowing or disrupting them?
  • Measurement: Can you track views, clicks, cart additions, purchases, and outcomes outside the recommendation component?

Available platform features and app capabilities vary, and marketplace listings change. A listed feature is not evidence that it improves outcomes for your store; test the experience and confirm analytics coverage before relying on it.

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Shopify’s native recommendation route

For Shopify storefronts, the Storefront API’s productRecommendations query returns recommendations for a specified product and accepts a recommendation intent. Shopify says it automatically generates related recommendations using sales data, product descriptions, and collection relationships. It can return up to ten Product objects. Complementary recommendations require manual configuration through Shopify Search & Discovery. See the Storefront API reference and Shopify’s product-recommendation documentation.

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Shopify’s guide to complementary products describes them as items often bought in addition to a selected product. It recommends showing two to three by default and paginating additional items. That is guidance for product-page recommendations; do not assume the same component or behavior works in a cart page or drawer without checking your theme and implementation. Shopify also notes that its complementary-product results exclude out-of-stock items, zero-price products, gift cards, and products already in the visitor’s cart. Read the complementary-products theme guide.

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Measure whether the change helps shoppers

Track the recommendation funnel, then assess the broader cart and checkout outcomes. Shopify’s Behavior Reports documentation describes measures for sessions with recommendations, sessions with clicks, sessions with add-to-carts, and sessions with purchases, as well as click, add-to-cart, and purchase rates. These are platform measures, not proof that a recommendation caused a purchase. Shopify’s Behavior Reports documentation explains the available reporting.

  1. Establish a baseline. Record cart value and checkout progression or completion before changing the experience. Include relevant user-experience guardrails, such as recommendation dismissals or complaints.
  2. Change one material element at a time where feasible. For example, test a better-matched complement or a clearer label while holding other major changes steady.
  3. Compare a control with the changed experience. Use a controlled test when possible, and examine recommendation views, clicks, add-to-carts, and purchases alongside total cart value and checkout completion.
  4. Check tracking coverage before interpreting results. Shopify states that recommendations must use the URL format specified by its API to be tracked. Its recommendation report excludes products added to cart or purchased through Buy Button or a third-party app, and engagement from the preceding 24 hours may not yet appear. Attribute revenue cautiously if those interactions are part of your store’s buying journey.

Shopify’s low-engagement recommendation report uses data from the last 30 days and changes daily, so it is a diagnostic view rather than a real-time verdict. Interpret any reported movement in light of reporting delays, excluded sales paths, and the other changes shoppers encountered during the test.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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