Latest / The DTC Marketing Podcast with Fexingo: Direct-to-Consumer Brands, Shopify, and E-Commerce Growth / How DTC Brands Use Returns Data to Improve Products
Transcript
- Lucas: So we've talked a lot on this show about driving the first sale—acquisition, conversion, all of that. But today I want to flip the script and talk about something most DTC founders treat as a pure cost: returns. Luna: Oh, that's a good one. Returns are the enemy in most ecommerce rooms. But you're going to tell me they're actually a goldmine? Lucas: I mean, not a goldmine in the literal sense—you're still losing margin on every box that comes back. But the data inside those returns is genuinely underused. Most brands collect return reasons, but they treat them as a customer service formality, not as product intelligence. Luna: So what does the data actually tell you? I imagine it's not just 'it didn't fit' and 'changed my mind.' Lucas: Exactly. And that's the first problem—the reason codes are often too vague. 'Fit' can mean too big, too small, or the cut is wrong. But when you actually break down the data, you might find that one specific SKU has a returns rate three times the rest of your catalog. That's a signal. Luna: So you're saying the returns data can point you to a broken product, not just a broken process. Lucas: Right. And here's a concrete example. I was talking to a mid-sized athleisure brand—they do maybe twenty million in annual revenue—and they noticed their high-waisted leggings, the bestseller, had a returns rate of 14 percent versus the catalog average of 8. The reason code? Mostly 'fit.' Luna: But 'fit' is so generic. How did they dig deeper? Lucas: They started asking customers to leave a quick note at the return step, optional, but they got maybe half the customers to write something. And the pattern was clear: the waistband ran small. Women who normally wore a size medium were ordering the medium and finding it dug in. Luna: So they had a sizing flaw in their hero product. What did they do? Lucas: They made a small tweak to the pattern—added a bit of stretch in the waistband—and then they tested it with a group of their repeat customers before rolling it out widely. Within a couple of months, the returns rate on that SKU dropped to 6 percent. Luna: That's a massive improvement. And I bet the repeat purchase rate went up too, because people finally got a pair that fit. Lucas: Exactly. They saw an 18 percent increase in repeat purchase rate from customers who had previously returned that SKU. Because those customers weren't lost—they just wanted the product to work. Once it did, they came back. Luna: That's a powerful story. But I guess there's a risk of overcorrecting. What if you change the fit and then your original loyal customers, the ones who loved the old fit, feel left out? Lucas: That's the cautionary tale. I recall a DTC shoe brand that had a similar issue—one of their sneakers had a returns rate of 12 percent for 'too narrow.' So they widened the last, the foot form, and the returns dropped. But then they started getting complaints from existing customers who said the shoe felt sloppy. They'd alienated their core fan. Luna: Ouch. So it's not just 'fix the product'—you need to be careful not to ruin what already works for a segment. Lucas: Right. The fix isn't always universal. Sometimes you need to offer a wide version as an option, not change the whole line. That's where returns data gets you to segment-level insights rather than just a blunt average. Luna: I love that. So how does a small brand actually start doing this without a data science team? I'm thinking of the founder who's just getting off the ground and can barely keep up with support emails. Lucas: You don't need fancy tools. Start by making your return reason codes more specific. Instead of 'fit,' have options like 'too small,' 'too large,' 'cut is unflattering.' Even that small change gives you signal. Then, once a month, pull the report and look for any SKU that's above your average return rate by, say, 50 percent or more. Luna: And then what? You can't just read the numbers and know what to do. Lucas: That's when you add the qualitative layer. Reach out to a few customers who returned that product, ask them a couple of open-ended questions. You're looking for a pattern in their language. If five out of ten say 'the sleeves are too tight,' that's your signal. Luna: And that doesn't have to be a huge effort. Maybe a few emails a week. Lucas: Exactly. And there's a secondary benefit: customers who get asked for feedback often feel more valued. Some brands have seen a bump in customer satisfaction scores just from that outreach. Luna: So it's not just about reducing returns—it's about building a better product over time. Lucas: Yes. And that's why this is such a great topic for our 150th episode. We've covered so many acquisition tactics and growth hacks, but this is about using the data you already have to improve the core product. It's a more sustainable edge. Luna: Totally. And it's a nice contrast to all the fancy AI personalization stuff we've talked about. Sometimes the smartest thing is just to listen to your customers when they tell you why they're sending it back. Lucas: And that's the beauty of DTC: you have that direct line. You don't have to go through a retailer to get feedback. But it only works if you actually use it. Luna: Right. So if you're a DTC founder, where should you start? Any immediate step? Lucas: Start by auditing your current return reason codes. If they're all 'doesn't fit' or 'changed my mind,' you're blind. Make them specific, even if it's just a dropdown with ten options. Then commit to reviewing the data monthly. That's it. Luna: And if you have a hero product, give it special attention. Even a small improvement can have a big ROI. Lucas: Yeah, and that's the kind of thing that makes me excited about this space. It's not always about the next new channel or the viral hack. Sometimes it's about fixing the leaks in your bucket. Luna: And we've been pouring a lot of effort into this show for 150 episodes now. It's a good moment to step back and say that we're able to keep doing this, ad-free, thanks to a small group of listeners who chip in through buy me a coffee dot com slash fexingo. That's literally what funds the editing and the hosting so we can put out this many conversations. Lucas: Yeah, it's a listener-supported show. We don't run ads, and that's a choice we made. If these marketing conversations have sparked something you've actually used in your own work, and you want to help keep it going, that link is the way. No pressure, but it genuinely matters. Luna: And it’s a nice way to keep the show independent. But back to returns data… I want to talk about another example, this time from a beauty brand. They used return reasons to reformulate a moisturizer that was causing breakouts for some customers. They didn't change the whole formula—they made a version for sensitive skin. That's a great segment-level move. Lucas: That's a perfect example. They saw in the data that 'skin irritation' was the reason for a subset of returns, but not the majority. So instead of scrapping the product, they created a variant. That's using returns data to segment your product line, not just fix a bug. Luna: And that also opens up a new revenue stream. A separate SKU for sensitive skin, and you might even upsell existing customers who had that issue. Lucas: Exactly. And that's the difference between seeing returns as a cost and seeing returns as a strategic asset. The cost mindset says 'how do we make returns harder,' which is a terrible idea. The strategic mindset says 'how do we learn from this and improve.' Luna: And it's not just about the product. Returns data can also inform your sizing charts, your product descriptions, even your photography. If people say 'the color looks different in person,' that's a signal for your product page. Lucas: Great point. Returns data is really customer feedback that they've already paid for the privilege of collecting. You're literally getting a free survey every time someone initiates a return. Luna: And you're not even using it? That's like throwing away money. Lucas: Yeah, and most brands do. That's why I love this topic. It's low-hanging fruit for smaller players. You don't need a huge data team to start. You just need to ask better questions and actually look at the answers. Luna: So what's the one metric you'd advise a founder to track in their returns dashboard? Something they can look at every week. Lucas: I'd say return rate by SKU, but more importantly, return rate by reason for each SKU. That way you can spot a specific problem like 'the large is too small' rather than just 'the large has a high return rate.' The reason code is where the insight lives. Luna: That makes sense. And you'd want to see that trend over time. If a reason code starts climbing, something changed—maybe a supplier, maybe a URL link, maybe a new batch. Lucas: Exactly. That's how you catch problems early. A few weeks of data can save you from a whole season of bad inventory. Luna: So for our 150th, I think we've given our listeners a solid, actionable playbook. What's the one takeaway you want them to remember? Lucas: That a return is not a lost customer—it's a second chance to get it right. As long as you're learning from it, you're not just losing money, you're buying information. Luna: That's a great way to put it. And we're already thinking about the next 150. Lucas: Yeah, but let's take it one return at a time.