Latest / The Operator's Manual with Fexingo: Daily Decisions, Tactical Wins, and Business Execution / How One Retailer Cut Returns 60 Percent With One Fit Tool
Transcript
- Lucas: Returns are one of those silent margin killers in retail. The numbers are staggering — global online returns hit about $800 billion last year, and a lot of that lands on the brands themselves. Luna: And clothing is especially brutal. I think some categories see return rates above 30 percent, sometimes even 40 percent for online-only shoppers. Lucas: Right, and the cost isn't just the refund. You've got shipping both ways, restocking, inspection, potential damage, and then the item often can't be sold as new. One estimate I saw said every return costs the retailer about 15 to 20 percent of the original sale price in operational costs alone. Luna: So the holy grail would be reducing returns without hurting conversion. And that's exactly what this one brand did — a uk based online fashion retailer called ASOS, actually, but the same principle applies to a lot of companies. Lucas: Yeah, and actually the case I want to dig into is a smaller brand — a us based online retailer called ModCloth. They were acquired by Walmart in 2017, but before that, they had a return rate problem that was typical for vintage-inspired clothing: size inconsistency. Luna: Because vintage sizing is notoriously different from modern sizing. A vintage size 12 might fit like a modern size 6. So customers were ordering multiple sizes and sending back what didn't fit. Lucas: Exactly. So ModCloth implemented a digital fit tool called Fit Analytics — it's a small widget on the product page where you enter your height, weight, and fit preferences, and it tells you which size to buy. That's it. No AI body scanning, no smartphone camera. Just a few inputs. Luna: And the results were dramatic? I've heard about this. They cut returns by something like 60 percent for items that used the tool. Lucas: That's the number — 60 percent reduction in return rate for items where the tool was used. And about 15 percent of shoppers actually used it, which is decent for an optional tool. But if you scale that across a larger catalog, the savings add up fast. Luna: And the tool also increased conversion. Because if you're unsure about size, you might not buy at all. So you get a double win: more sales and fewer returns. Lucas: Right, and that's the key operational insight here. The fix wasn't about changing the product or the packaging or the return policy. It was about giving the customer better information at the point of decision. And it's a low-cost change — the tool is basically a software integration, a few lines of JavaScript. Luna: So the cost to implement is almost negligible compared to the savings. The average return costs a retailer, say, $15 to $25 in logistics and markdowns. If you cut returns by 60 percent on a $50 item, that's maybe $10 saved per original sale. Lucas: And ModCloth was doing around $150 million in revenue around that time. If even a quarter of their sales used the tool, you're talking millions in savings. Plus the environmental impact — fewer shipments, less waste, fewer items ending up in landfills. Luna: It's interesting because a lot of retailers focus on making returns easier — free shipping, prepaid labels, no questions asked. But that can actually increase return rates. The smarter play is to prevent the return in the first place. Lucas: And that's where tools like fit analytics, augmented reality try-ons, and better product descriptions come in. There's a great example from a company called True Fit — they power fit recommendations for brands like Adidas and Nordstrom — and they claim a 20 to 30 percent reduction in returns on average. Luna: So the takeaway isn't just about returns. It's about identifying the root cause of a problem and applying a targeted, low-cost fix. For ModCloth, the root cause was size uncertainty. The fix was a fit tool. And it worked because it addressed the customer's actual pain point. Lucas: And I think that's a model for any business. Instead of layering on more processes or policies, ask: what's the bottleneck that causes the most friction? For a law firm, it might be discovery. For a hospital, it might be room turnover. For a retailer, it's often fit. Luna: We've covered similar ideas in other episodes — like when Kaiser Permanente cut missed appointments with a simple text reminder. Same principle: find the friction point, apply a simple tool, measure the impact. Lucas: And that's really the operator's mindset. You don't need a massive digital transformation. You need one well-chosen change that removes a specific source of waste. Luna: So for anyone listening who runs an e-commerce business or works in operations, the question is: what's your version of the fit tool? What's the one piece of information your customers don't have but would make them more confident in their decision? Lucas: Yeah, and it doesn't have to be complex. A lot of these episodes prove that. One change, big result. Luna: And speaking of small changes that make a difference, if these episodes have helped you think about your own operations or just made a commute more interesting, a couple of dollars a month genuinely keeps this show ad-free and independent. It's buy me a coffee dot com slash fexingo — no pressure, just if you've gotten something out of it. Lucas: Yeah, and it's listeners like that who let us keep drilling into these specific cases instead of chasing clicks. So thank you if you've ever contributed. Luna: Alright, back to returns. So one thing that's interesting is how this scales. If you're a smaller brand, a tool like Fit Analytics probably costs a few thousand dollars a month. But the savings from reduced returns can pay for it quickly. Lucas: And there's a network effect too. The more customers use the tool, the more data the algorithm has, and the better it gets at recommending sizes. So the return rate keeps dropping over time. Luna: It's a virtuous cycle. And it also builds customer trust. If you buy something that fits perfectly the first time, you're more likely to come back. Lucas: Exactly. So the next time you're looking at a high return rate, don't just assume it's an inevitability. Ask: what information would my customer need to get it right the first time? The answer might be simpler than you think. Luna: And that's where we'll leave it. Thanks for listening, and we'll see you next time.