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Cluster why customers actually return products — sizing, quality, expectation gaps — by product, from ticket and return language, so you can fix the root cause instead of just processing the refund. Returns are a margin killer; this finds where they start. Read-only.
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| name | returns-reduction |
| description | Cluster why customers actually return products — sizing, quality, expectation gaps — by product, from ticket and return language, so you can fix the root cause instead of just processing the refund. Returns are a margin killer; this finds where they start. Read-only. |
Returns are one of the biggest silent margin leaks in DTC, and the reason for each one is sitting in plain language in your tickets — "ran small," "looked different online," "stopped working after a week." This recipe clusters the why behind returns by product, so you can fix the source (a misleading PDP, a sizing gap, a quality issue) instead of just eating the refund again next month. Read-only.
| Variable | Example |
|---|---|
{{WINDOW}} | "the last 90 days" |
{{SCOPE}} | "all products", a category, or one SKU |
Using the Gorgias MCP, analyze why customers are returning products in {{SCOPE}} over {{WINDOW}}.
- Pull tickets and conversations about returns, exchanges, and "this wasn't what I expected" — use tags where they exist, and customer language where they don't (label those (heuristic)).
- Cluster the reasons customers give: sizing/fit, quality/defect, didn't match the description or photos, shipping/damage, changed their mind, wrong item.
- Break it down by product — which products drive the most returns, and the dominant reason for each.
- For the worst offenders, name the specific expectation gap and the root-cause fix: PDP copy, a sizing guide, better photos, a QA flag for the buying team, or a packaging change.
- Rank by volume × fixability — where will one change remove the most returns.
Nothing — read-only.
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