Latest / The DTC Marketing Podcast with Fexingo: Direct-to-Consumer Brands, Shopify, and E-Commerce Growth / How DTC Brands Use Cohort Analysis to Cut Churn
Transcript
- Lucas: So Luna, you know how every DTC brand obsesses over that dashboard showing average order value and repeat purchase rate, but somehow churn still creeps up and nobody can explain why? Luna: Oh, absolutely. It's like looking at a blurry photo. You know something's wrong, but you can't see the details. Lucas: Right. And that's where cohort analysis comes in. It's not a new technique, but a lot of brands I talk to still aren't using it properly. Today we're going to dig into how slicing your customers by acquisition date — their cohort — can actually reveal what's driving retention and where you're losing people. Luna: Before we dive deeper — and I know this is a marketing show, but if these conversations have sparked something you've actually used in your own work, that's great. And a handful of listeners chip in monthly through buy me a coffee dot com slash fexingo, and that's literally what funds making this many of these. Lucas: Yeah, it's a small group that keeps this going, and we're grateful for every one of them. But back to cohorts — let's talk about why the aggregate view is so misleading. Luna: Walk me through it with a concrete example. Lucas: Take a skincare brand that launched a big discount campaign in March. They saw a huge spike in sales, and their average order value for that month looks incredible. But if you look at that same group of customers three months later, how many of them are still buying? That's the cohort question. Luna: So you're saying a discount-driven customer might behave completely differently than someone who came through organic search. Lucas: Exactly. And that's why comparing cohorts across different acquisition channels is tricky. If you lump everyone who joined in March together, you might miss that the paid social cohort has a 30-day retention rate of 10 percent, while the organic cohort is at 40 percent. Luna: That's a huge difference. So what's the right way to look at this data? Lucas: You want to track each cohort over time — say, every week for the first twelve weeks. You can see when the drop-off is steepest. For most DTC brands, it's in the first 30 days. If you can get someone to make a second purchase within that window, their lifetime value jumps dramatically. Luna: And that's where email flows and subscription offers come in, right? Lucas: Right. Cohort data tells you exactly when to trigger that 'we miss you' email or offer a subscription discount. You're not guessing — you're responding to actual behavior patterns. Luna: I've also seen brands use cohort analysis to decide on product development. If you see that a certain cohort keeps buying your moisturizer but not your serum, maybe it's time to reformulate. Lucas: That's a great point. The data isn't just for marketing — it can inform the product roadmap. And it's not just about whether they buy again, but when. Some categories have a natural repurchase cycle, like a 30-day supply of a supplement or a quarterly razor blade refill. Luna: So if you're not tracking cohorts, you might be sending a subscription offer too early or too late. Lucas: Exactly. And that's a common mistake — brands set a one-size-fits-all flow based on averages, but the cohort view shows you that different groups behave differently. One cohort might respond best to a 10 percent discount on their second order, while another prefers free shipping. Luna: How do you actually build these cohorts? Is it something you can do in most analytics tools, or does it require a data scientist? Lucas: You can start with something as simple as a spreadsheet, honestly. Pull your transaction data, assign each customer to a week based on their first purchase, and then track how many come back in subsequent weeks. Most e-commerce platforms like Shopify have apps that do this automatically now. Luna: That's reassuring. So what should a brand look for in their cohort chart to know if they're on the right track? Lucas: First, the shape of the curve. A healthy retention curve should flatten out after the initial drop. If it keeps declining at a steady rate, that's a red flag. Second, look at the slope between week one and week four. A steep drop there means you're losing people right after the first purchase — and that's where you should focus your energy. Luna: It's like a diagnostic tool. Instead of guessing, you know exactly where to intervene. Lucas: And it's not just about churn — it's about understanding the economics. If you know that a cohort acquired through paid ads has a lower retention rate, you can adjust your customer acquisition cost threshold accordingly. You might be willing to pay more for an organic customer who stays for six months. Luna: That's a shift in mindset. It's not just about the first sale, but the lifetime value of that cohort. Lucas: Right. And we're seeing more brands move away from last-click attribution and toward this kind of holistic view. Cohort analysis is a tool that helps you make smarter decisions about where to invest. Luna: What about seasonality? If you launch a big holiday campaign, that cohort might behave differently just because of the time of year. Lucas: That's a really important nuance. You should always compare cohorts from similar seasons or adjust for seasonality. A December cohort might have a higher return rate because of gift returns, for example. So you need to be careful when drawing conclusions. Luna: So what's the biggest mistake you see brands make when they start with cohort analysis? Lucas: They look at one cohort in isolation. You need to track multiple cohorts over time to see if your retention is actually improving. If this month's cohort is retaining better than last month's, you're on the right track. If not, you need to dig into what changed. Luna: And that's where the real power is — using this data to continuously improve. Lucas: Exactly. It's a feedback loop. You make a change to your onboarding emails, and you can see within weeks whether that cohort is retaining better. That's the kind of agility that DTC brands need to survive in this market. Luna: Speaking of the market, we're in August 2026, and with rising ad costs and privacy changes, retention is more important than ever. Lucas: Absolutely. When acquisition gets more expensive, the brands that win are the ones who can hold onto their customers. Cohort analysis gives you the visibility to do that. Luna: So if someone listening wants to start today, what's the one thing they should do? Lucas: Pull your customer data for the last three months and segment them by the week they made their first purchase. Then track how many from each group come back in the following weeks. Just looking at that chart will probably tell you a story you haven't seen before. Luna: And maybe that story will lead you to your next big improvement. Lucas: Here's hoping. And for those of you who want to support the show, you know where to find us — buy me a coffee dot com slash fexingo. It genuinely helps us keep these conversations going.