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Analytics Reference

Sizing-Related Returns

Identify SKUs and categories with high sizing-driven return rates.

Sizing-related returns are the single biggest cost center for apparel and footwear stores. Industry-wide, sizing accounts for 30-50% of all apparel returns. The good news: it’s also one of the most fixable problems, because it’s usually driven by a small number of identifiable SKUs and a small number of fixable causes.

Where to find sizing data in RefundSentry

Anywhere the analytics filter to the SIZING_FIT reason cluster:

  • Reason clusters chart — Shows SIZING_FIT as one segment. Click it to filter every other view.
  • Reason × product heatmap — Shows which product types are contributing most to SIZING_FIT.
  • Product risk map — Filter to SIZING_FIT and the upper-right quadrant becomes your hit list of high-volume, high-rate sizing offenders.
  • Recommendations engine — When sizing returns hit a threshold, you’ll see a recommendation card on the dashboard linking back to this section.

The five root causes

  1. Wrong size chart — The most common. Your “Medium” is what the manufacturer calls a “Small” in their region. Fix the size chart, watch sizing returns drop within weeks.
  2. Inconsistent sizing across SKUs — Customers learn that your Medium is true to size on T-shirts but runs small on jackets, then they bracket-order to compensate. Audit your sizing across categories.
  3. Poor fit photography — Customers can’t see how the garment drapes on a real body. Add fit notes and on-body photos for at least your top 50 SKUs by volume.
  4. Missing size guides — Some products have a chart, others don’t. The ones without are 2-3× more likely to have sizing returns.
  5. Bracketing as policy — Some categories of customers deliberately order three sizes to keep one. Detected by the bracketing signal in scoring; surface them via a workflow that filters on size variant count and return reason.

The sizing audit playbook

  1. Open Reason clusters, filter to SIZING_FIT for the last 90 days.
  2. Switch to the Product risk map with the same filter applied. Note the top 10 SKUs by return volume.
  3. For each SKU, open the Shopify product page and check: does the product have a size chart? Is the chart vendor-specific or generic? Are there fit notes? Are there on-body photos?
  4. Pick the cheapest fix (size chart update) for the top 5 SKUs and ship it. Don’t batch — measure the impact of each fix individually.
  5. Re-check the chart 30 days later. SIZING_FIT for those SKUs should drop 20-40%.

Bracketing is a different problem

Bracketing — ordering 3 sizes intending to keep 1 — looks like a sizing problem in the data but it’s really a customer-behavior problem. The bracketing signal in the scoring engine catches it explicitly. If your sizing data shows a lot of returns from a small number of customers, those are bracketers. Use a workflow to require store credit (not cash refund) from customers with a bracketing flag.

Next steps