AI Brain for Commerce
Use case 37BustlyUpdated

Explore bundles from repeat-purchase behavior

Look for complementary products in actual repeat orders before proposing bundles.

The context

The operator wants to design bundles and follow-up messages around what customers actually buy next. Aggregate best-seller rankings do not show how a first purchase leads to a second, or which product combinations recur among returning customers.

The Bustly workflow

Bustly matches first and subsequent orders within the requested customer cohort, identifies recurring product combinations and suggests bundle or cross-sell ideas. It states the cohort and product-grouping rules so the operator can assess whether the patterns are suitable for a repeat-purchase campaign.

A prompt to adapt

Analyze repeat customers from the last 90 days. Compare their first and second purchases, identify recurring product combinations and suggest bundles, cross-sells or follow-up emails. State the cohort definition and any limits in the sample.

Available actions depend on connected tools and permissions. Confirm the scope and authorization before publishing, sending messages or changing spend.

Adapted from real operating tasks. Customer identifiers and sensitive business details are omitted. The workflows are examples, not promises of business results.