Quantum Leap Forward

Collecting and categorizing return reasons effectively

The foundation of insight is clean, structured data. Shopify’s default return system captures basic info, but most valuable details live in customer comments and reason codes.

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• Enable detailed reason dropdowns — Use apps like Return Prime, Loop, or AfterSell to offer specific options: “Too small”, “Too large”, “Doesn’t match description”, “Poor quality / damaged”, “Changed my mind”, “Wrong item sent”, “Color not as expected”, etc.
• Capture free-text comments — Always allow an optional comment field. Many customers write exactly what the issue is (“sleeves feel scratchy”, “waistband rolls down”, “zipper broke on first use”).
• Tag returns automatically where possible — Set rules so certain keywords auto-tag items (e.g., “small”, “tight”, “runs small” → sizing issue).

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Measuring impact and closing the feedback loop

Stores that consistently mine return data for product insights often see not only lower return costs, but higher average order values, better customer satisfaction scores, and stronger organic word-of-mouth. Returns stop being a problem and start becoming your secret product research department.

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Start small: pick your highest-returning product this week, export the last 100 returns, categorize the reasons, and decide on one concrete change. The data is already there — it’s time to listen to it.

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Let me know if you’d like any of the other posts expanded in the same way (with 3 sections) or further tweaks to this one!