What this page is, and isn't
This is a dated snapshot of what named external sources reported about return rates, as of September 2026 — not a target, and not a substitute for the Benchmark Library framework this page is built to feed. That framework page deliberately doesn't publish specific numbers because a number without a current, category-matched source becomes misleading as soon as conditions shift. This page is the "go find 2-3 credible dated sources" step done for you — pull it into Step 2 of that framework, then apply Steps 3-4 (record the date, use it as a sanity check, not a target) yourself.
Verify the live number yourself before treating anything below as current. Return-rate reporting is inconsistent across sources — definitions of "return" vary (refunded vs. exchanged vs. simply initiated), some figures are order-level and others are revenue-level, and compiled/aggregator sources blend multiple underlying studies. Treat everything here as directional order-of-magnitude, not a precise figure to hold anyone accountable to.
The headline numbers
The most citable primary figure is the National Retail Federation's 2025 Retail Returns Landscape report (published October 15, 2025, in partnership with Appriss Retail): retailers projected an overall return rate of 15.8% of total retail sales (about $849.9 billion), down slightly from 16.9% the prior year — but returns from online purchases specifically ran at 19.3%, meaningfully higher than the blended in-store-plus-online average. That online/overall gap (roughly 4 points) is itself a useful number: it's the clearest evidence that "ecommerce return rate" and "retail return rate" are not the same question, and any source that doesn't specify which one it's citing should be treated with suspicion.
Return rate by category (compiled, dated September 2026)
The table below compiles category-level ranges as reported by Richpanel's 2026 return-rate roundup and corroborated directionally by ShipNetwork's and Eightx's 2026 category breakdowns. These are ranges compiled across multiple underlying sources and store types, not a single controlled study — treat the width of each range as itself informative (a wide range means real variation by sub-category, price point, and sizing/fit complexity within that category, not sloppy reporting).
| Category | Reported return-rate range | Why |
|---|---|---|
| Apparel | 20-40% | Sizing/fit uncertainty is the dominant driver; multi-size "bracketed" ordering (buying 2-3 sizes, keeping one) inflates this further in categories that tolerate it. |
| Footwear | 17-30% | Same fit-driven pattern as apparel, slightly narrower because sizing is somewhat more standardized. |
| Home & furniture | 15-23% | Large-item damage-in-transit and "doesn't fit the space" returns; return logistics cost is also structurally higher here (freight, not parcel). |
| Auto parts | ~19% | Frequently fit/compatibility mismatches (wrong part for the exact make/model/year) rather than quality issues. |
| Accessories & jewelry | 12-15% | Lower fit risk than apparel/footwear but still a physical-appearance/fit category. |
| Electronics | 8-15% | Buyer's-remorse and compatibility issues rather than fit; "no fault found" returns are a known sub-pattern here. |
| Beauty & personal care | 4-12% | Structurally low — low price point, low fit risk, and in many marketplaces used/opened beauty products are simply non-returnable. |
Overall online average across sources: roughly 19-20%, consistent with the NRF's 19.3% figure above.
Where sources disagree, and why that's worth noting rather than papering over
Different compilations put "apparel" anywhere from about 20% up to 40%+, and the overall online figure ranges from Eightx's "19% overall / 14% DTC-specific" split to NRF's single 19.3% online figure that doesn't separate DTC from marketplace. Two real reasons for this spread, not just noisy data: (1) marketplace return policies and buyer expectations differ meaningfully from a single DTC brand's own policy and audience, so blending them produces a wider range than either alone; (2) "return rate" is sometimes measured as a share of orders and sometimes as a share of revenue, and a category with a few high-value returns can look very different by each measure. When you pull your own category's number, check which of these two things a source is actually reporting before comparing it to your own data.
How to use this
- Identify where your product sits on fit/sizing risk and price point — the two dimensions the Benchmark Library calls out as explaining most of the variation.
- Compare your own trailing 12-month return rate to your own history first, exactly as that framework recommends — a rate that's stable relative to your own baseline is a different situation than one that just moved sharply, even if both land inside the "normal for category" range above.
- Use the ranges above only to sanity-check whether your number is wildly outside plausible territory for your category — if your electronics SKU is returning at 35%, that's worth investigating for a real cause (a listing-accuracy problem, a defect, or a compatibility-description gap) even though 35% would be unremarkable in apparel.
- If you need a number for a board deck, a supplier negotiation, or a pricing model, re-verify the specific source above is still current — this page is reviewed quarterly, but a source it cites could update on its own schedule in between.
FAQ
Why does this page give ranges instead of a single number per category? Because the underlying sources themselves disagree by double digits in some categories (see above), and presenting a single point figure would hide that real uncertainty rather than reflect it honestly — a range communicates the actual state of public knowledge here.
Should I use these numbers to set a return-rate target for my own products? No — use your own trailing 12-month rate as your baseline and target, per the Benchmark Library framework. These figures exist to flag a number that's implausibly far outside normal territory for your category, not to set a goal.
Does a high return rate always mean a product problem? Not necessarily — see the category table above: a 25% return rate is unremarkable for apparel and would be a red flag for beauty. Match the comparison to the category before drawing a conclusion, and see How to Handle Returns, Refunds, and Cancellations Well for the operational side of managing returns once they happen.