When you enable a Best next recommendation, the model analyses your order data to identify patterns in customer purchases. It builds a matrix of customers and products, grouping together those with similar buying habits. Then, it uses this data to uncover products that one customer has bought but another has not, highlighting the items that may be of interest to those customers.

This approach is especially effective for retailers with limited product data, as it mainly relies on order history rather than product attributes. Best next is quick to set up and delivers high-quality recommendations with minimal data requirements.

If your account does not have predictive recommendations enabled:

A side panel allows you to request that the feature be enabled on your account (for an additional monthly charge). We first review your data and then confirm with you that the feature is ready to use, or if there are data dependencies to be resolved first.

Predictive recommendations are available on a 30-day free trial.

Before getting started, please review the data dependencies for this product recommendation type.

This model works well even with product catalogs that have limited detail. It only considers order data for making recommendations. The more order data you have, the higher the accuracy of the recommendations.

Best next requires less data than the Lookalikes recommendation model, which uses content-based filtering. As such, it is able to generate recommendations quickly. It provides good quality recommendations for most types of retailer.