AIWorkDex
Ecommerce Task

Find and fix the causes of product returns

Connect return reasons to products and original customer comments. Choose one evidence-backed fix to test first.

“This is costing me margin and reverse-logistics work without telling me which product problem to fix.”

The result to work towards

Identify recurring causes by product, inspect the supporting customer evidence, and test one measurable intervention.

The situation

The work is costly. A clearer route can change it.

When it starts
Refunds rise, one product looks problematic, or vague reason codes stop the team from choosing a fix.
What makes it difficult
The team can see that products came back. It cannot tell which recurring cause is avoidable or worth fixing first.
What progress looks like
You have enough evidence to act on one likely cause of returns, make a change, and measure whether it works.

Next useful action

Turn return feedback into one prioritised fix

Create a reviewable cause table by product, then choose one change with traceable customer evidence and a measurement plan.

Open the practice

Boundaries

Keep the first release narrow.

  • Separate physical returns from cancellations, edits, and refunds without returned goods.
  • Keep original comments or excerpts attached to every generated label.
  • Remove direct customer identifiers before using an AI analysis tool.
  • Check AI labels against a human-reviewed sample and inspect every low-confidence row.
  • Compare rates with a valid sales denominator. Raw return counts alone can mislead.
  • Change one main factor at a time where practical. Record other changes that could affect the result.

Research basis

Inspect the material behind this Task.

The source set combines official product documentation with general AI risk guidance.

Edited by MarioReviewed 18 September 2026