Return Reason Miner

Problem

When return notes are fragmented, colloquial, and mixed with customer sentiment, counting return volume alone rarely tells a team whether the issue is product, fit, packaging, fulfillment, or service. Product, operations, and CX owners need a way to classify evidence before deciding what is fixable through process changes versus what needs deeper product investigation.

How it works

return-reason-miner is a heuristic analysis skill. It works from user-supplied return/refund notes, product context, and operational changes, then produces:
- Reason clustering: separates quality, fit, expectation, shipping, and fulfillment drivers
- Root-cause hypotheses: maps repeated issues to likely causes, clearly marked as hypotheses
- Fix priorities: ranks actions by recurrence, severity, and controllability
- Cross-functional actions: tells product, operations, CX, and merchandising what to review next

Inputs may include apparel, beauty, electronics, home goods, or food context, plus promo periods, warehouse shifts, new vendors, or policy changes. The output is a Markdown brief with a taxonomy table, root-cause hypotheses, fix priorities, action plan, and assumptions and limits.

Limits and caveats

It does not connect to live order, warehouse, review, or return systems, and it does not execute refunds. It fits weekly return reviews or launch-period triage, but not regulated QA investigations or precise financial modeling. Percentages, defect rates, and financial impacts should not be treated as facts unless supplied by the user or verified by systems.