Turning returns into a data flywheel
Most stores collect return reasons as free text. It's unsearchable, unaggregatable, and effectively thrown away. The opportunity is to treat each return as a labeled data point.
Direction × area, not free text
A reason like 'too small at the shoulder' encodes a direction (too small) and a body area (shoulder). Captured as a machine code — FIT_TOO_SMALL_SHOULDER — it becomes aggregatable across thousands of orders.
The reason behind a return is the most honest feedback a store gets. Structure it, and it compounds.
The flywheel
Structured reasons feed analytics that pinpoint which products and which fit issues drive returns. For apparel sellers, the same data can power storefront size guidance — closing the loop so the next order is better than the last.
- Capture: structured reason at the point of return
- Resolve: exchange-first, keeping revenue
- Learn: analytics + optional fit guidance
- Repeat: each loop sharpens the next decision
See it on your own returns.
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