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Analytics Reference

Reason × Product Heatmap

Cross-reference return reasons with product types to find systematic issues.

The Reason × Product heatmap is the most useful chart in the analytics hub for one specific job: figuring out which products are driving which kinds of return reasons. It’s a 2-D matrix you scan for hot spots.

How to read it

Rows are product types (or categories — toggle in the chart header). Columns are reason clusters (SIZING_FIT, QUALITY, EXPECTATIONS, etc. — see Reason clusters). Each cell’s color intensity is the return rate for that combination.

  • White / pale — Few or no returns for that combination. Healthy.
  • Amber — Notable concentration. Worth investigating.
  • Red — Significantly above the all-product average. Almost certainly a problem.

What you’re looking for

A healthy heatmap is mostly pale with a few amber cells. An unhealthy heatmap has one or two clear red cells that stand out from the rest. Those red cells are your action items.

The most common red-cell patterns:

  • SIZING_FIT × Apparel — Almost universal. The question is which apparel SKUs. Click into the cell to drill down.
  • QUALITY × Footwear — Often a supplier issue concentrated in a single style or batch.
  • EXPECTATIONS × Home Goods — Usually a photography problem. The product looks different in person.
  • WRONG_ITEM × any product type — Warehouse picking error. Check with your fulfilment team.

Drill-down

Click any cell to filter the dashboard to just the returns in that combination. You’ll see the actual returns, the actual customer reasons (in the original free-text), and the specific SKUs involved. This is usually enough to tell you whether it’s a product issue, a supplier issue, or a marketing/photography issue.

What it doesn’t show

The heatmap is a rate view, not a volume view. A small product line with 3 returns out of 5 sales will show as red even though the absolute count is tiny. Cross-reference with Returns by product type for volume context before you take action — you don’t want to ditch a profitable SKU based on 5 data points.

Next steps