Add frequently bought together suggestions to product and cart pages

Frequently bought together is a block on product and cart pages that suggests items other customers bought alongside the one being viewed. It often lets shoppers add all of them in one click. When the suggestions come from real purchase patterns, they feel like helpful advice and raise items per order. It suits stores with a range of complementary products, such as cameras and memory cards, or skincare steps that are used together.

Why it works

Shoppers often forget the second thing they need until it is too late, such as batteries for a toy or a case for a phone. A well-placed suggestion reminds them at the right moment and saves a return visit. The line “customers also bought” adds a light layer of social proof, since it signals that other people found the pairing useful.

For the store, each added item costs almost nothing to sell. The customer is already checking out, and the extra product ships in the same box. Because the suggestions come from real orders, they also improve as your store grows, so the block keeps getting more accurate with very little upkeep.

How to do it

1. Find real pairs in your orders. Export order lines and count how often each pair of products appears in the same order. Rank pairs by how often they occur relative to how often the main product sells.
2. Sanity-check the top pairs. Remove pairs that only appear together because of a past promotion or a bundle. Keep pairs that make sense to a shopper.
3. Choose two or three suggestions per product. Prioritise items that complete the main product, have healthy margins and are reliably in stock.
4. Place the block near the buy button. On the product page, show the suggestions with small images, prices and a combined “Add all to cart” option.
5. Repeat in the cart. Show a short “Don’t forget” row based on what is already in the cart. Keep it below the order summary so it does not distract from checkout.
6. Use automation for the long tail. Once you have enough orders, use your platform’s recommendation feature or an app to cover products you did not set by hand.
7. Test the block against no block. Run an A/B test on a group of product pages and compare items per order and conversion.
8. Refresh pairings regularly. Re-run the analysis each quarter or after big catalogue changes, since new products and seasons shift buying patterns.

Example

A hypothetical home electronics store sees that buyers of a particular webcam often buy a ring light and a USB hub within the same order. The team adds a block under the webcam’s add-to-cart button showing those two items with an “Add all three” button. In the cart, anyone with the webcam sees a smaller reminder for the hub. After a month, they compare against a holdout set of product pages without the block and keep it on the pages where items per order rose. On a few pages the block made no difference, usually where the paired items were expensive. There, the team swaps in cheaper accessories and runs the test again.

Metrics to track

• Items per order for sessions that viewed a product with the block. This should rise compared with the holdout.
• Click and add rate on suggestions. Low rates point to weak pairings or poor placement.
• Average order value, alongside conversion rate. You want order value up without conversion falling.
• Return rate on suggested items. Frequent returns suggest the pairing promised more than it delivered.
• Page load time on product pages. Recommendation scripts can slow pages, which hurts every visitor.

When it backfires

• Out-of-stock suggestions. Showing items that cannot ship frustrates shoppers. Filter by stock automatically.
• Generic bestsellers everywhere. A row of the same popular items on every page stops being useful and gets ignored.
• Distracting from the main purchase. A large, flashy block can pull attention away from the product the shopper came for.
• Stale pairings. Suggestions based on a discontinued range or a past sale make the store look neglected.

Questions people ask

Should I use an algorithm or pick pairings by hand?

Start by hand for your top sellers, using order data and common sense. Once you have enough orders, an automated recommendation app can cover the long tail, and you can still override weak suggestions.

How many suggestions should I show?

Two or three items is usually enough. A long row of products looks like an ad and slows the page, while a short set reads as helpful advice.

Where should the block go on the product page?

Close to the add-to-cart button, where shoppers are already deciding. Placing it far down the page means most visitors never see it.