Latest / The DTC Marketing Podcast with Fexingo: Direct-to-Consumer Brands, Shopify, and E-Commerce Growth / How DTC Brands Use Returns Data to Improve Product Design
Transcript
- Lucas: Returns. For most DTC brands, it's a dirty word. The cost of processing, the hit to margin, the logistics headache — and if you're selling on Shopify, you're probably eating the return shipping too. Luna: Right. Returns are basically a tax on customer acquisition. You spend to get them, then you spend again when they send it back. Lucas: Exactly. But here's the thing — a small but growing group of DTC brands have started treating returns not as a cost center, but as a data source. They're using return reasons to inform product design. And I mean specific, granular changes. Luna: I love this. So instead of just tracking 'return rate,' they're actually reading the reason codes and letting them drive the roadmap? Lucas: Exactly. Let me give you a concrete example. There's a luggage startup — I won't name them, but they sell hard-shell carry-ons. They noticed about 40 percent of their returns cited 'wheels too loud.' Not broken, not stuck — loud. So they didn't just swap suppliers. They redesigned the wheel assembly with softer polyurethane treads and a different bearing system. Return rate on that model dropped by more than half in the next quarter. Luna: Forty percent. That's a screaming signal. And it's the kind of thing you'd never catch in a survey because customers don't think 'noise' is a defect — they just feel annoyed and send it back. Lucas: Right. And that's the opportunity — the return reason is often a better indicator of product-market fit than the star rating. A four-star review might say 'great bag but a little heavy.' A return reason says 'this literally doesn't work for me.' Luna: So what's the practical step? How does a brand actually set this up? Lucas: First, you need a taxonomy. A structured list of return reasons that maps to product attributes — not just vague categories like 'defective' or 'changed mind.' I've seen brands with twenty or thirty codes: 'zipper jammed,' 'color different from site,' 'snap closure too stiff,' 'sizing too tall.' Then you track those codes over time and look for clusters. Luna: Clusters. So if suddenly 15 percent of returns for a new shoe style say 'heel slip,' you know there's a last-shape issue. Lucas: Exactly. And the best brands feed that back into design in real time. One mattress-in-a-box company I spoke with — they have a return-reason dashboard that the product team checks weekly. They noticed a spike in 'edge support too soft' on a new model. Within two months, they'd updated the foam density in the perimeter. That's faster than most traditional retailers can even process a return. Luna: It's basically turning returns into a free focus group. But there's a tension here — if returns are expensive, isn't there a disincentive to collect more data? Like, you don't want more returns just to learn. Lucas: You're right — you don't want to increase returns for the sake of data. But one cosmetics brand I know actually took the opposite approach. They intentionally loosened their return policy to gather more feedback. They figured the data from returns was more valuable than the margin hit. And they redesigned their packaging based on complaints about pump mechanisms failing. Their return rate went up for two months, then dropped below the original level because the product got better. Luna: That's a gutsy move. Because most CFOs see returns as pure cost. It takes a product-obsessed culture to say 'let's invite more returns so we can learn.' Lucas: It does. And I think that mindset is exactly what separates DTC brands that treat their supply chain as a competitive advantage from ones that just see it as logistics. You're collecting data at every touchpoint — returns are the most honest feedback you'll ever get because the customer already decided they didn't want it. There's no social desirability bias. Luna: No 'I'll say it's fine and never buy again.' They just send it back. It's a revealed preference. Lucas: Exactly. And if these marketing conversations have sparked something you've actually used — a new way to look at your own return data, a new category on your reason code list — that's exactly the kind of thing that keeps this show ad-free. Listener support at buy me a coffee dot com slash fexingo helps us keep digging into these niche operational angles. Luna: Yeah, it really does. And honestly, this returns as data idea is one of those things that sounds obvious once you hear it, but almost no one does it well. Lucas: So let's talk about another dimension — sustainability. Because returns are also an environmental nightmare. The reverse logistics carbon footprint, the fact that many returned items end up in landfills. Luna: Especially for apparel. There's data that says something like 25 percent of returned clothing is never resold. It's just trashed. Lucas: Right. So when you use return data to improve design, you're also reducing waste. Fewer returns means fewer truck miles, less packaging, less product destruction. One outdoor gear brand I looked at used return reasons to identify that their tent rainfly seam sealing was failing after three uses. They changed the adhesive and the seam construction. Return rate on that tent dropped from 12 percent to under 4 percent. And the carbon savings from fewer replacement shipments was significant. Luna: So return data becomes part of the sustainability playbook. That's a nice alignment: better product, lower costs, less waste. Lucas: Exactly. And I think the next frontier is using AI to analyze free-text return reasons. Instead of forcing customers to pick from a dropdown, you let them type what's wrong, and an NLP model classifies it into your taxonomy. One brand I know does that and they're surfacing issues like 'the strap adjuster pinches my neck' — something they'd never have thought to put in a dropdown. Luna: That's brilliant. Because the customer's language often reveals the real pain point better than a preset code. 'Pinches my neck' is actionable. 'Defective' is not. Lucas: Exactly. So the takeaway is: don't just minimize returns. Mine them. Build the taxonomy, close the loop with product teams, and treat each return as a free design consultation. It's one of the highest roi changes a DTC brand can make — and it costs almost nothing to start. Luna: And it's a perfect example of using what you already have. The data is sitting in your Shopify orders or your return portal. You just have to read it. Lucas: Right. So whether you're selling luggage or lipstick, your returns are telling you something. The question is whether you're listening.