Latest / The DTC Marketing Podcast with Fexingo: Direct-to-Consumer Brands, Shopify, and E-Commerce Growth / How DTC Brands Use RFM Analysis for Segmentation
Transcript
- Lucas: So Luna, we talk a lot about segmentation on this show — demographic, behavioral, psychographic. But there's one classic framework that a surprising number of DTC brands still underutilize. Luna: Let me guess — RFM analysis? Recency, frequency, monetary value? Lucas: Exactly. It's been around since the direct mail catalog days, but it maps perfectly onto modern e-commerce data. And when I say underutilized, I mean — I looked at a study last month from a marketing analytics firm, and only about 22 percent of DTC brands with a Shopify store have any kind of RFM scoring in place. Luna: That's surprisingly low. Especially when you consider how easy it is to pull that data from a platform like Shopify or Klaviyo. Lucas: Right. The data is literally sitting there — transaction timestamps, order frequency, average order value. The hard part is actually structuring it into a usable score. But once you do, you can segment your customer base into tiers like champions, loyal customers, at-risk, and lost. And then tailor your marketing spend accordingly. Luna: Let's break down the three components. Recency — how recently did they purchase? Frequency — how often do they buy? Monetary — how much have they spent total? You assign a score from one to five for each, right? Lucas: Exactly. A score of five means best in class — most recent, most frequent, highest spend. Then you combine the three digits into a single RFM code, like 5-5-5 for your absolute best customers, or 1-1-1 for someone who bought once a year ago and never returned. The beauty is you can slice and dice those codes into meaningful segments. Luna: And I love that it's not just about who spent the most. A customer who buys a $20 item every two weeks could be more valuable than someone who dropped $500 once and never came back. Lucas: That's the key insight. In fact, one DTC brand I've been following — a subscription-based intimate apparel company called Adore Me — they use RFM heavily. They found that customers in their 'champions' segment, which is high recency, high frequency, high monetary value, had a lifetime value over four times higher than their average customer. So they started giving that segment early access to new collections and exclusive discounts. Luna: And that's smart because those champions also tend to be your biggest word of mouth drivers. They're the ones leaving reviews, posting on social media. Lucas: Right. Meanwhile, for the 'at-risk' segment — customers who used to buy frequently but haven't purchased in 90 days — they might send a reactivation email with a personalized discount. The RFM score tells you exactly which customers to prioritize for those campaigns. Luna: I want to talk about implementation for a second. A lot of smaller DTC brands might think they need a data scientist to do this. But there are tools that automate RFM scoring right out of the box. Lucas: Absolutely. Klaviyo has built-in RFM segmentation in their premium plans. There's also a Shopify app called Retentioneering that does it. And if you're using a customer data platform like Segment or mParticle, you can set up RFM as a trait. The basic math is simple — you just need to define your scoring criteria based on your business. For example, what counts as 'recent' for a furniture brand is very different from what counts as 'recent' for a snack subscription box. Luna: That's a good point. Recency windows need to reflect your purchase cycle. A monthly subscription box might consider anything under 30 days as 'recent', while a furniture brand might use 90 days. Lucas: Exactly. And frequency thresholds too. For a low-cost consumable brand, a customer who buys every 30 days is a five. For a high-ticket item, buying once a year might be a five. You have to calibrate it to your own data. Luna: One thing I've seen brands struggle with is keeping RFM scores dynamic. If you only calculate it quarterly, you might miss a shift in behavior. A champion could slip into at-risk territory within weeks. Lucas: Yeah, the best practice is to update RFM scores nightly or at least weekly. Most automation tools handle that. And then you can set up triggers — if a customer's recency score drops from five to three, automatically enroll them in a re-engagement flow. Luna: I want to bring up a real example. I spoke with the head of marketing at a DTC skincare brand last month. They implemented RFM scoring six months ago. They identified a segment of customers with high frequency but low monetary — meaning they bought often but only the cheapest items. So they created a 'bundle upsell' campaign targeting that segment, offering a discounted starter kit of full-size products. The conversion rate was 14 percent on that campaign. Lucas: That's a great use case. It shows RFM isn't just about retention — it can drive upsells and cross-sells too. And it's all based on actual behavior, not assumptions. Luna: Another finding from that brand: they used RFM to identify 'lost' customers — ones with a recency score of one or two and a frequency score of three or higher. These were people who used to buy regularly but stopped. They sent a 'we miss you' email with a 20 percent discount and saw an 18 percent reactivation rate within 30 days. Lucas: That's impressive. And the cost of that campaign was essentially zero compared to acquiring a new customer. New customer acquisition cost for DTC brands has gone up something like 60 percent over the last three years. So using RFM to retain and re-engage is becoming a necessity, not a nice to have. Luna: Are there any pitfalls to watch out for? I've heard that RFM can be misleading if you don't account for seasonality. Lucas: Big one. If you're a DTC brand that sells holiday decorations, your recency scores in January are going to be terrible for everyone. So you need to normalize for seasonality or use trailing windows. Another pitfall is ignoring customer lifetime value in favor of just monetary score. A customer with a high monetary score from one big purchase might not be as valuable long-term as a low monetary but high frequency customer. Luna: So RFM should be one input, not the only input, into your segmentation strategy. Lucas: Exactly. Combine it with demographic data, product affinity, channel preference. But as a starting point, it's incredibly powerful. And it forces you to actually look at your transaction data and understand your customers' behavior patterns. Luna: If these marketing conversations have sparked something you've actually used in your own business, or even just a new way of thinking about your customers, that's exactly why we do this show. And if you've gotten value from it, listener support is what keeps these episodes ad-free and focused on practical insights. Lucas: Yeah, it's a small way to make a big difference — and it's what allows us to keep digging into topics like this without any sponsored interruptions. If that resonates, you can find us at buy me a coffee dot com slash fexingo. Luna: Alright, back to RFM. One last thing I want to touch on — how do you see RFM evolving with AI and machine learning? Lucas: I think the next step is predictive RFM — where instead of just scoring past behavior, you use machine learning to predict future recency, frequency, and monetary value. That way you can identify which customers are likely to churn before their recency score actually drops. Some CDPs are already incorporating that. Luna: So it's almost like RFM on steroids. But the core concept — measuring actual purchase behavior — remains the same. Lucas: Exactly. And that's why it's stood the test of time. It's simple, it's data-driven, and it works. If you're a DTC brand and you're not using RFM yet, start this week. Pull your last 12 months of transactions, assign scores, and see what segments emerge. You'll be surprised what you find. Luna: Great advice. And if you've been a champion listener — thanks for sticking with us. See you next time. Lucas: Take care, everyone.