Do reviews reduce returns? What three studies found, and when reviews add returns

Why ask whether reviews reduce returns?

Every return costs you twice: the shipping and handling, and the sale you thought you had. If you sell clothes, shoes or anything a shopper has to imagine on their body or in their home, returns may be the biggest cost you do not see on the product page.

Review apps often claim that reviews cut returns, and the claim sounds reasonable: a shopper who reads that a jacket runs small orders a size up. But the numbers you see quoted usually come from shopper surveys run by the review apps themselves. Yotpo's own article on the subject, read on 6 November 2026, rests on its survey of shoppers and cites no study that measured actual returns. Surveys tell you what shoppers say they would do. For what they actually did, you need order data.

Which returns can reviews prevent?

Only the ones where the product was fine but not what the shopper expected: the wrong size, a colour that looked different on screen, a sofa bigger than the room, a cream that smelled stronger than they hoped. Reviews can't help when the warehouse sent the wrong item, the parcel arrived broken or late, or someone ordered three sizes planning to keep one. Those are fixed in packing, shipping and the returns policy.

So before you change anything on the page, pull your last 50 returns and sort them into those three groups by the reason the customer gave. If most of them are damage or wrong items, reviews will not move your return rate much. If most are “too small”, “not as pictured” or “not what I expected”, the rest of this article is for you.

To see what that is worth, multiply your monthly orders by your return rate, then by the share of returns that were about expectations, then by what one return costs you. That last figure is more than the refund: the label back, the time to inspect and restock the item, and the markdown on anything you can't sell as new.

What do studies of real orders find?

We looked for peer-reviewed studies that measured returns in a shop's own order data, and read what each one reports. Three stood out. For two of them the full paper sits behind a paywall or a bot check, so what follows comes from the authors' own abstracts and, for the sample size of the first, the press release issued when it was published.

The European study is the largest. Minnema and colleagues followed more than two years of sales at a big online retailer, almost 9 million page views and 631,063 purchases, and found that when a product's reviews looked more positive than its long-term average, more people bought it and more of them sent it back. The effect was stronger for first-time buyers and for cheaper products. How many reviews there were, and how much they disagreed, mattered for sales but had little or no effect on returns.

The North American study points the other way on volume. Sahoo, Dellarocas and Srinivasan compared different customers buying the same products while the set of reviews around them changed. More reviews, and more reviews that other shoppers had voted helpful, went with fewer returns after they controlled for the customer, the product and the context. When there were few reviews, people more often bought several similar items at once, which the authors read as a way to deal with uncertainty, and those extra items were what came back.

The third study looked only at fit. Wang, Ramachandran and Sheng used a change in one retailer's review system that separated two kinds of fit information: the verdict, such as “runs small”, and the reviewer's own size with the size they chose. They compared apparel returns before and after. A verdict alone helped only when most reviewers agreed. With the reviewer's size beside it, fit comments lowered returns, and negative fit comments helped too.

Is this cause and effect, or just correlation?

Some of each, and none of it is a controlled experiment. The European study measures how returns moved with what the reviews looked like at the moment of purchase, so products with glowing reviews may also have differed in ways the data does not show. The other two get closer to cause. Sahoo's team used a jump in how the retailer displayed its averages and found that products shown with a higher average than their true rating were returned more often. Wang's team used the date the review system changed, with a method called generalized synthetic control that estimates what returns would have been without the change.

Each study covers one retailer and a few categories: electronics and furniture, a specialty retailer, clothing. None tells you how many percent fewer returns your own shop will get, and you should be wary of any article that does. What they agree on is the direction: reviews that describe the product accurately help people buy the right thing, and reviews that make it look better than it is lead to disappointment and returns.

What should you change to get fewer returns?

Get more reviews on each product, steadily, by asking every buyer once after delivery. Thin products are where shoppers hedge by ordering two sizes.

Do not inflate the rating. Publish the lukewarm and negative reviews along with the rest, show the average as a number next to the stars, and avoid anything that tilts the mix toward five stars, such as asking only happy customers or rewarding only good reviews. Our article on the average star rating explains why a rounded-up picture misleads, and the European study is the reason it costs you money as well as trust.

For anything with a size, ask about fit with a reference point. A scale from runs small to runs large is the start; add the size the reviewer bought and their usual size, so a shopper can find someone like them. Our guide to custom review form questions covers how to set these up without making the form long.

Do the same for other categories, with the question that matches why people send that kind of product back. For furniture, ask which room it went in and how hard the assembly was. For electronics, ask what the reviewer used it with and how the setup went. For beauty, ask about skin type and whether the shade matched the photo. For anything that wears, ask how it has held up after a few weeks. A blank text box gets you “love it”; a specific question gets an answer the next buyer can use.

Ask for photos. Colour and size are the two things words describe worst, and a customer's photo in ordinary light shows the product as it arrives rather than as the studio lit it. None of the three studies measured photos separately, so treat this as common sense, not a proven effect.

Let shoppers vote reviews helpful and sort by it. In the North American study, the share of helpful reviews mattered, not only their number.

Put the reviews where the decision is made. A review only prevents a return if it is read before the order. A line such as “most reviewers say it runs small” belongs next to the size picker, customer photos belong beside the product photos, and the default sort should not push the critical reviews out of sight.

Read the reviews from people who returned things. Sahoo's team found that customers who send a product back are more likely to write a review, and that those reviews are more negative. Reply to them, and count what they complain about; review tagging shows one way to sort complaints by cause.

Read your return reasons too. Once a quarter, take the most common reason for each of your most-returned products and check whether the product page answers it. “Too small” on one item means a sizing note on that page. “Not as pictured” means your photos flatter it. The same fault in many reviews, such as a zip that sticks, is a product problem for your supplier, and no page copy will fix it.

How do you know if it worked?

Not from your store-wide return rate, which moves with the season, the sales you run and the mix of products people buy. Measure product by product:

1. Pick 6 to 10 products with high return rates and write down each one's return rate and number of reviews today.
2. Make the changes above on half of them and leave the other half as they are.
3. Wait at least one full return window after the change, plus a few weeks for returns to arrive and be processed. With a 30-day policy, a result in week two means nothing.
4. Compare the two groups over the same weeks, not each product with its own past.
5. Look at the return reasons as well as the rate. If “too small” falls and “arrived damaged” rises, the reviews did their part and something else changed.

Expect a modest result. Reviews work on one kind of return, and a few points off the rate of your worst products is a good outcome.

How does Formgong help with this?

Formgong's customer questions let you add up to five questions to every review form, as a choice or a scale, on every plan including Free. A fit setup could be a scale question with Runs small, True to size and Runs large, a choice question for the size bought, and one for the reviewer's usual size. Answers show on each review card, the scale gets an average bar above the reviews, and shoppers can filter the list by an answer, such as everyone who usually wears a medium. The questions on reviews docs show the steps.

Customers can attach photos and a video to their review on every plan, and the files go public only together with an approved review; once there are five approved reviews and one of them has a photo, the widget's toolbar shows a “With photos” filter. If you switch on Ask the author, a shopper can email a question to a reviewer who agreed to it, such as what size they normally wear, without either one seeing the other's address. There is no separate Q&A where you answer product questions yourself, so put those answers in the product description.

You can also turn on a Helpful button on each review and let shoppers sort by most helpful. Reviews are shown newest first by default, and the widget counts only approved reviews and says so, so nothing on the page claims more than it holds.

We cannot show you a returns figure from our own customers. Formgong does not see your orders' returns, and on 6 November 2026 none of the 16 review sites on Formgong had set up customer questions yet, so there is no fit data to compare. If you try it, we would like to hear how it goes.

Have reviews changed your returns?

If you have numbers from your own shop, before and after reviews or fit questions, or a study we missed, write to us at support@formgong.com. We will add anything we can check.