Reference · 2026
Shopify return rates by category, 2026 edition
An honest reference table of average ecommerce return rates by fashion and adjacent categories, with the methodology, caveats, and what to do about your number.
This is a reference table of typical ecommerce return rates by category, sourced from publicly reported industry data (NRF annual returns surveys, Shopify and BigCommerce merchant benchmarks, and a handful of third-party reports referenced throughout). It’s designed to be the page you can bookmark and send to your team when someone asks “is our 28% return rate bad?”
Short answer: for most apparel categories, the average ecommerce return rate runs roughly 2–3× the average for in-store retail. If your Shopify return rate is under 20%, you’re doing well in fashion. Above 30%, there’s meaningful operational money on the table.
Average return rates by category
All numbers below are for online sales. In-store return rates are roughly a third of these. “Typical” is the midpoint of the range we see most often in merchant benchmark reports.
| Category | Range | Typical | Main driver |
|---|---|---|---|
| Apparel — tops | 18–28% | 23% | Fit, fabric weight, color accuracy |
| Apparel — dresses | 25–35% | 29% | Length, drape, fit in the bodice |
| Apparel — bottoms (denim) | 28–40% | 34% | Inseam, rise, stretch |
| Outerwear | 20–30% | 25% | Shoulder fit, sleeve length, warmth expectation |
| Knitwear & sweaters | 22–32% | 26% | Fabric pilling expectation, fit across shoulders |
| Swimwear | 30–45% | 38% | Bust/hip fit, coverage, color in water |
| Bridal & occasion wear | 35–50% | 42% | Bracketing, alterations needs, lighting in venue |
| Loungewear | 12–20% | 16% | Generally sized loose, fewer fit-driven returns |
| Shoes | 25–35% | 30% | Width, half sizes, arch support |
| Accessories (non-jewelry) | 8–15% | 11% | Smaller fit surface, fewer expectations to fail |
| Jewelry | 5–12% | 8% | Sizing rings, expectation vs. photograph |
| Beauty & cosmetics | 3–8% | 5% | Color match, shade selection |
How to read this table
A few important caveats before you benchmark yourself against these numbers:
- These are averages across the segment. Brands at the low end of the range typically have either an unusually strong sizing system, restrictive return policies, or lower-bracketing customers. Brands at the high end usually have a more generous policy, broader sizing, or a customer base that bracket-buys on purpose.
- Region matters. EU return rates run higher than US, sometimes substantially — partly because of consumer protection law, partly because Germany has the highest consumer return rate in the developed world.
- Price point matters. Higher-AOV items get returned at higher rates, in part because shoppers bracket-buy them more readily.
- Channel matters. Returns from paid social traffic tend to run 3–5 percentage points above returns from organic search traffic in the same category.
What “good” looks like
If your Shopify store is below the “typical” column for your category, you’re ahead. If you’re within a few percentage points, you’re normal. If you’re above the range, you have a real operational opportunity — and the math is almost always larger than founders expect:
- A dress brand at 35% returns, with $1M monthly online revenue, is processing about $350k of merchandise in the wrong direction every month.
- At an all-in cost of $20–25 per returned item (shipping + labor + write-down), that’s a $70–87k monthly direct cost line.
- Cutting that to 28% — a realistic 7-point improvement with the levers we describe in our return-reduction playbook — frees up roughly $14–17k per month in direct return costs alone, before counting recovered margin or reduced working capital.
Methodology
Numbers in the table above are synthesized from:
- NRF annual returns survey (most recent publicly available data points).
- Shopify Plus merchant benchmark reports (where category breakdowns are published).
- Public statements from large fashion DTC brands in their investor decks and press.
- Third-party returns-software vendor benchmark reports (Loop, Returnly, Narvar) — used directionally because each is biased toward its own customer base.
- Aggregated, anonymized data from the Voilae merchant cohort (used only to validate that the public ranges match what we see on the ground).
Where sources disagreed, we used the midpoint and flagged it in the “range” column.
What to do with this number
If your category sits in the high-return-rate cohort (swimwear, bridal, dresses, bottoms), the highest-leverage move available today is letting shoppers see the garment on themselves before they buy. That’s the thesis behind virtual try-on: cut the fit and appearance categories of returns, which combined represent 70%+ of fashion returns by reason code.
For a deeper read on the levers — photography, sizing, virtual try-on, and policy design — see how to reduce fashion returns on Shopify.
If you want to see whether AI try-on can move your specific category numbers, Voilae has a permanent free tier — run it on your top 20 SKUs and compare the return cohort.
License & attribution
This page and table are free to cite. If you’re writing a piece that uses the numbers above, please link back to voilae.app/blog/shopify-return-rates-by-category.