Frequently Bought Together
An automated recommendation feature that displays products other customers have purchased together with the product the user is currently viewing, based on real co-purchase data.
Definition
Definition
The system analyzes order history to identify products that are frequently purchased together with statistically significant frequency. It uses collaborative filtering: recommendations are based not on product similarity, but on the actual purchasing behavior of thousands of users. If many buyers of Camera A also purchase Battery B and Case C, that association emerges automatically without anyone having to configure it.
The co-purchase signal is stronger than the co-view signal, but slower to accumulate because it requires transaction history. The system identifies product pairs or groups that occur together more often than chance would predict and generates a list ranked by purchase affinity.
From a business perspective, this type of recommendation has an advantage over traditional upselling: it does not require convincing the user to spend more on the same product, but rather to complete their purchase with items they are likely to need. As a result, it creates less perceived resistance and delivers more tangible value.
What it's used for
What it's used for
Increases average order value (AOV) organically without requiring manual product-by-product configuration. It is particularly effective in categories where products naturally complement one another, such as electronics and accessories, apparel and add-ons, or food and related items. Because the recommendations reflect actual purchasing patterns, they are generally perceived as helpful rather than intrusive.
Doofinder
Doofinder includes a recommendations module that allows merchants to display Frequently Bought Together blocks on product pages. The system learns from the store's own transaction data to identify the most relevant co-purchase relationships, eliminating the need for manual setup on a product-by-product basis.
Merchandising rules can also be used to prioritize or exclude specific items from recommendations, for example, to promote slower-moving inventory or maintain minimum margin requirements.
Example
Case study
A photography equipment store adds a Frequently Bought Together block to the page of an entry-level mirrorless camera.
The algorithm identifies that 34% of customers who purchased the camera also bought an extra battery, while 28% included a carrying strap in the same order. Both products are displayed as preselected recommendations within the block, and the combined price of all three items is calculated automatically.
References