Hi everyone, quick question for those of you in fashion or apparel.
Sizing returns have been on my mind lately… mostly because I had a customer recently return a jacket with the note “it fit great, just not how I imagined it would.” Which, honestly, I didn’t even know how to process.
So now I’m wondering how much of a real problem this is for other store owners. Size charts are the obvious answer, but how effective are they actually?
How much of your return rate is sizing-related, and what are you doing about it?
But the thing I really want to know… do customers genuinely use size charts… like, are people actually pulling out a tape measure, or are they just picking their usual size ?
That “it fit great, just not how I imagined it would” return is probably the trickiest kind because it’s not really about sizing at all. That’s an expectation gap, and no size chart in the world is going to fix it.
From what I’ve seen working with fashion stores, sizing returns break down into a few different things.
First there’s the actual wrong-size orders. Size charts help in theory, but most shoppers don’t measure themselves. They pick their usual size and hope for the best. What works way better is a quick fit note right on the product page. Something like “runs small, size up one” or “relaxed fit, order your usual.” People actually read those because it takes two seconds.
Then there’s the expectation thing, like your jacket. That’s almost always a photography problem. Flat lays on white backgrounds don’t show how something drapes or moves on a body. On-body shots from different angles (front, side, detail) cut these returns way more than any chart ever will.
And then bracketing, where people order 2-3 sizes to try at home. This got way more common once free returns became standard. Some stores push store credit instead of full refund to slow it down, or add a small restocking fee for non-defective returns.
On the size chart question specifically, I’ve seen numbers suggesting under 15% of shoppers actually look at them. What converts better is comparison language. “Our M fits like a Zara L” or “similar cut to Nike Dri-FIT.” People already know their size in brands they buy regularly.
What kind of fashion are you selling? The return dynamics are pretty different between structured pieces like blazers and stretchy stuff like athleisure.
A big driver is that “size” isn’t standardized across brands, so customers order based on habit and not your actual garment measurements. Then add inconsistent product photos, fabric stretch differences, and missing fit context like model height/weight, and returns go up fast.
Best lever is reducing uncertainty before purchase: clear garment measurements, fit notes (“runs small/oversized”), multiple model references, and reviews that mention height/weight + purchased size. Sites like Amazon have trended in this direction and I imagine it’s had a positive effect on return rates.
Sizing returns are part and parcel of apparel, usually 20 to 40 percent depending on the category. Most customers don’t measure, they just buy their usual size. What helps are model sizing information, fit descriptions, customer reviews with height and weight as well as simple fit advice such as runs small or very large which closes expectation gaps and reduces returns.
+1 on the on-body shots point, that’s the biggest one imo. we sell physical products too and the moment we started showing items on actual people from multiple angles, the “not what i expected” returns dropped noticeably. a flat lay of a jacket tells you nothing about how it drapes or fits across the shoulders.
the tricky part is most small brands can’t afford to reshoot with models every time they add new skus. we’ve been using ai-generated on-model photos to fill that gap (we built prodofoto for this) and it’s been solid for giving customers that “what does this actually look like on a person” context without booking a full shoot. but even if you just get one friend to try stuff on and take iphone photos from 3 angles, that alone will cut returns more than any size chart ever will.
The “it fit great, just not how I imagined it would” return is the one that kills me. That’s not a sizing problem, that’s a product page expectation gap. In my experience the biggest return drivers break into three buckets: actual wrong size (fixable with fit notes like “runs small, size up”), expectation mismatch (fixable with better on-body photos from multiple angles), and bracketing where people order 2-3 sizes intentionally. Size charts help less than people think because most shoppers don’t measure themselves. What actually moves the needle is comparison language on the product page. “Our M fits like a Zara L” converts way better than a measurement table nobody reads. The real unlock is connecting your return reason data back to specific products and looking for patterns. If one SKU is getting returned 3x more than everything else, the product page is almost always the problem.
