The opportunity
Products that appear together frequently are not automatically strong bundle candidates. Popular items co-occur naturally, repeat-purchase windows may be incomplete, and a bundle can reduce margin or consume inventory needed for full-price orders.
BizSidekick compares product affinity against a baseline, follows complete first-to-second purchase paths, and adds margin, refund, and inventory safeguards before proposing a test.
How it works
- Build complete cohorts — Confirm customer identity rules, order states, markets, cohort dates, and enough follow-up time for repeat purchase.
- Read customer order paths — Pull ordered products, variants, quantities, discounts, refunds, order timing, channel, and customer history.
- Measure repeat behavior — Compare repeat rate, time to next order, first-to-second product paths, and cohort value without counting immature cohorts as failures.
- Calculate product affinity — Measure lift and confidence against product popularity rather than ranking raw co-occurrence alone.
- Apply commercial safeguards — Check contribution margin, refund concentration, inventory cover, product compatibility, and discount overlap.
- Design controlled tests — Return the segment, product set, timing, offer hypothesis, holdout, guardrails, and success metric.
Safety boundary
The output is an opportunity analysis, not a causal claim or guaranteed forecast. BizSidekick does not create a bundle, discount, audience, message, or Shopify product.
Expected outcome
A repeat-purchase opportunity map with customer paths, bundle candidates, timing, and safeguards
Try this prompt
Paste it into ChatGPT to start this use case.
@BizSidekick Analyze complete Shopify customer order histories to find repeat-purchase and bundle opportunities. Compare cohorts, first-to-second purchase paths, product affinity, reorder timing, margin, refunds, and inventory, then propose controlled tests without creating offers.
