How does the Frequently Bought Together algorithm work?

The algorithm uses data from past customer transactions to identify products commonly bought together. It then suggests these complementary products to customers, either on product pages or at checkout, to encourage additional purchases.

What factors influence the product recommendations in the Frequently Bought Together algorithm?

The algorithm considers factors like past customer purchase behavior, item popularity, seasonality, customer browsing patterns, and similar product characteristics to make accurate recommendations.

Can I manually adjust the product recommendations?

Yes, many Frequently Bought Together apps allow you to manually curate product recommendations, giving you control over which items are suggested to customers alongside their purchases.

Does the algorithm work for all product types?

Yes, the algorithm can be applied to both individual products and product variants. It works effectively across a wide range of categories, from fashion to electronics, as long as there is sufficient transaction data.

Can the Frequently Bought Together algorithm increase sales?

Absolutely! By suggesting relevant products at key moments, such as checkout, the algorithm encourages customers to add more items to their cart, directly boosting Average Order Value (AOV) and overall sales.