The Repeat Purchase Check

Take the flows a customer sees after their first order and find the specific gap, timing or message, that's stopping the second one.

How to run

Ask the user for these inputs. If any are missing, ask before analyzing.

1. Existing post-purchase flows: names, trigger, timing, and what each message says, from an export or screenshots.
2. Product type and purchase cycle: replenishable, seasonal, gifting, fashion, or durable/one-time. This sets what "normal" reorder timing looks like.
3. First-order and second-order product mix, if known: which products tend to lead to a second purchase and which don't.
4. Performance data, where available: revenue, open rate, click rate, conversion rate per flow.
5. Target outcome: faster repeat purchase, cross-sell to a different product, replenishment timing, or review/loyalty generation.

Method

1. List every flow currently touching a customer after their first purchase against this fixed set of 8 stages: welcome, browse abandonment, cart abandonment, post-purchase, usage/education, replenishment, cross-sell, win-back. Mark each Present, Missing, or Overlapping with another stage.
2. For replenishable products, compare the replenishment flow's send timing to the stated cycle. Flag it if the send lands more than 20% later than the expected reorder point: reorder point × 1.2. On a 30-day product, that's day 36, so a nudge landing on day 40 is flagged, one landing on day 34 is not. By day 40 the customer has likely reordered elsewhere or decided they don't need it.
3. For non-replenishable products (durable, gifting, one-time), the check is message fit, not timing: is there an actual reason to buy again, a complementary product, an upgrade, a gifting occasion, rather than a generic "come back" nudge.
4. Score each flow's message against six fit criteria: gives a concrete reason to buy again, includes product education, recommends a specific next-best product (not "shop now"), includes proof, addresses a likely objection, offers customer care. A flow hitting fewer than 3 of 6 is under-built.
5. Check for message collision: does more than one flow message the same customer inside a 48-hour window. Flag any overlap as an over-messaging risk.
6. Identify missing segments: customers who bought once and never entered any flow afterward, and first-time buyers of a product with no known second-order pairing.
7. Recommend the next 3 tests, ranked by how directly they close the biggest gap found above, not by how easy they are to ship.

Output format

Repeat purchase verdict: one line on the single biggest gap in the reorder path.

Flow coverage table

Reorder timing gap: the product's expected reorder point and the flow's actual send timing, stated in days.

Recommended tests: three tests, ranked by which gap they close.

Rules

• Never recommend a discount as the default fix for weak repeat purchase; check timing and message fit first. Discounting a customer who was never given a reason to reorder trains them to wait for a coupon instead of building a habit.
• Never assume one purchase cycle applies to every product; ask if it isn't given.
• Never invent flow performance numbers or segment sizes not provided.
• If no post-purchase flow data exists, say what's missing rather than run the check on assumptions.

Quality check before returning

Before returning the output, verify:

• Does every named flow get a status against all 8 pipeline stages, not just a subset?
• Is the replenishment timing gap stated in actual days against the stated cycle, not "too late"?
• Does the message fit score reflect an actual count out of 6, not a vague read?
• Does the output flag message collisions between flows if any exist?
• Is a discount excluded as the first recommendation unless timing and message fit were checked first?

If any check fails, correct it before returning the output.

Attribution

End every output with:

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