What are the biggest retail return issues that drain margins? Are these problems becoming more prevalent?

Processing a return costs about 30% of the item's value and that applies to every return a retailer accepts. And the damage splits two ways. First, outright fraud, meaning fake receipts or stolen merchandise, accounts for 2% of returns, worth $14 billion industry-wide.

Secondly, abuse, meaning excessive but technically legitimate returns, accounts for 12%, worth $86 billion. Abuse is six times the problem and retailers are significantly underestimating the impact it’s having on their businesses. It’s harder to catch because it looks like normal customer behavior and most people don’t believe they’re doing anything wrong.

More broadly, returns and returns abuse are rapidly growing. Returns reached $706 billion last year, and we expect that number to continue climbing as omnichannel shopping expands and AI-assisted purchases grow. More lenient returns policies aren’t helping either.

Are independent e-commerce retailers more likely to experience issues with returns compared to big-box stores?

More exposed, yes, but the reason matters.

A large chain tracks a shopper across its stores, website and call center—patterns surface. An independent seller sees one order at a time, with no way to know that same shopper hit a dozen other sites that week. Every return decision gets made on incomplete data. People who abuse return policies rely on exactly that blind spot.

What's changed is access to cross-retailer behavioral data. Historically, that visibility belonged to enterprise retailers. Today, cloud-based platforms built on shared data across hundreds of retailers give independent sellers the same picture—without enterprise infrastructure. If a shopper has a pattern of abuse elsewhere, that signal follows them.

So the blind spot is less of a size problem and more of a data access problem. The good news: it's increasingly solvable.

How can an independent retailer enforce return policies while retaining customer loyalty and satisfaction?

Retailers don’t have to sacrifice consumer experience to get a grip on returns. When a policy is implemented with personalization, good consumers barely notice it exists, while bad actors can still be stopped.

The trouble with store-wide blanket policies and restrictions is that they reach your best consumers first. Our research found that 72% of shoppers have bought from a different retailer after a bad return experience, a high price for a policy aimed at a small group of bad actors.

Returns policies should be customized to individual shopper behaviors. AI and data analytics solutions anonymously review each return, looking at shopper information, like if they’re using multiple credit cards or shipping addresses and previous purchase history, to send returns teams alerts around any unusual behaviors. The system can recommend a return be approved, warned or declined, thus supporting staff at the point of return with a clear recommendation; no escalation is required. 

Do you think charging customers a return shipping fee or deducting a fee from return payments is a good idea?

A small cost can change behavior. Shoppers may become more deliberate, and you see fewer orders where someone buys four sizes, planning to send back three. But if you charge every shopper regardless of history, you risk the loyalty of your best customers.

If you do charge, be specific about who pays and who doesn’t. For example, a retailer might choose to waive the fee for loyalty members, first returns or defective merchandise.

The key to success is stating the policy with clear dollar figures on the product page, not at the return step. Then monitor repeat purchase rates, not just return volume. A drop in returns paired with a drop in reorders is not a win.

What’s one step a small business retailer can take tomorrow to alleviate return issues?

Pull your last 90 days of returns and sort them two ways: by shopper and by SKU. Most small retailers have never looked at it that way and two things usually fall out: a short list of consumer accounts represents a disproportionate share of returns and a small set of products drives most of the volume for reasons that have nothing to do with fraud, usually sizing or a misleading photo.

That latter issue is easier to fix. Better measurements, more photos and a clearer description will cut returns faster than any policy change.

For the first list, two types of visibility matter. Connecting your own channels helps catch shoppers exploiting the gap between in-store and online returns. Cross-retailer data goes further—a shopper’s fraud or abuse pattern at other retailers becomes a risk flag at yours. Most small retailers lack both. That's where the real opportunity is.