Latest / The Email Marketing Podcast with Fexingo: Newsletters, Open Rates, and Subscriber Growth / How Email List Lifecycle Scoring Predicts Churn
Transcript
- Lucas: Luna, you know how every email marketer talks about list churn—subscribers going cold, opens dropping, eventually they stop engaging altogether? Luna: Yeah, it's that slow fade that feels almost impossible to catch until it's too late. Lucas: Right. Well, there's a method that's been quietly gaining traction in more sophisticated shops: email lifecycle scoring. It's a predictive model that scores each subscriber based on recency, frequency, and engagement behavior—basically an RFM score for email. Luna: RFM—recency, frequency, monetary—usually used for sales, right? How does that translate to email when you don't have direct revenue per subscriber? Lucas: Great question. Instead of monetary, you substitute engagement intensity. So you score recency—when they last opened or clicked. Frequency—how many times they've engaged in a set period. And then a composite engagement score that might weight clicks higher than opens, or account for email types. Luna: So you're basically building a health score for each subscriber. What does that actually look like in practice? Lucas: Let me give you a concrete example from the outdoor retailer REI. They've been doing lifecycle segmentation for years, but they refined it around 2023 into five segments: new subscribers, active, lapsing, dormant, and churned. The scoring triggers different automations. For instance, someone who's been active for 90 days but then goes 30 days without opening gets moved to lapsing—and they get a different re-engagement sequence than someone who's dormant for 60 days. Luna: I like that—lapsing vs. dormant. Most people lump them together and send the same 'we miss you' email. Lucas: Exactly. And that's where the scoring matters. A lapsing subscriber might just need a reminder of what they're missing—a top-performing recent email, or a survey about their preferences. A dormant subscriber who hasn't opened in 60 days might need a stronger incentive, like a discount or exclusive content. Luna: But how do you set the thresholds? Thirty days without an open might be normal for a weekly newsletter, but for a daily deal site, that's a long time. Lucas: That's the art part. You have to calibrate based on your send frequency and your audience's typical behavior. For a B2B SaaS company that sends a weekly newsletter, 30 days of no opens might be lapsing, while 90 days is dormant. For a daily e-commerce brand, 14 days of no clicks might be lapsing. The key is to use your own data—look at the median time between engagements for active subscribers and then set flags at 1.5x and 2x that interval. Luna: So you're saying the scoring is dynamic—it's based on your own list's behavior, not some industry average. Lucas: Exactly. And once you have that scoring, you can build automated workflows. For example, when a subscriber moves from active to lapsing, you can trigger a 'we noticed you haven't opened lately' email with a link to update preferences or choose a different frequency. If they move to dormant, you might send a 'is this still relevant?' email with a clear unsubscribe option. Luna: I've seen data that letting subscribers self-select frequency actually reduces overall churn—like, people who choose a lower frequency stay engaged longer than those who get hammered. Lucas: Right, that's backed by research. A 2024 study by the Email Sender and Provider Coalition found that subscribers who were offered a frequency preference option had a 28 percent lower churn rate over six months compared to those who weren't. And lifecycle scoring makes that offer timely—you're not asking everyone, you're asking the ones showing signs of fatigue. Luna: What about small lists? If you're a solopreneur with 500 subscribers, can you still do this? Lucas: Absolutely. You don't need a machine learning model. You can do it with simple rules in any ESP. For instance, create a custom field called 'engagement score' that adds a point for every open in the last 30 days, subtracts a point for every day without an open after day 7. Then set up automations based on that score. I've seen a boutique skincare brand with 1,200 subscribers do exactly this and recover 15 percent of lapsing subscribers within two weeks. Luna: That's a solid ROI. Let's talk about the monetary side—even if you're not tracking revenue per email, you can still use purchase data if you have it. Lucas: Yes, and that's where it gets really powerful. If you have e-commerce data, you can add a monetary component: how much they've spent in the last 90 days. That lets you prioritize high-value lapsing subscribers—someone who spent $200 last quarter but hasn't opened in 30 days might get a personal outreach, while a never-buyer who hasn't opened gets a simple re-engagement. Luna: So it's not just about saving subscribers—it's about saving the right ones. That makes sense because not all churn is equal. Lucas: Exactly. And this is where lifecycle scoring connects to broader business metrics. A B2B SaaS company I consulted for implemented a lifecycle scoring model that reduced their list decay—the percentage of subscribers going inactive—by 22 percent year-over-year. They did it by sending targeted re-engagement to lapsing subscribers, and for dormant subscribers they set a three-strike rule: after three re-engagement emails with no response, they moved them to a 'suppressed' list that didn't count against deliverability. Luna: That's smart—you're not deleting them, just quarantining them so they don't hurt your sender reputation. Lucas: Right. And then after 180 days of suppression, they'd send a final 'last chance' email. If no response, they'd remove them. That cleanup actually improved their overall open rates by 8 percent because the list was healthier. Luna: So the scoring model itself can be iterative—you test, refine thresholds, and measure impact on key metrics like open rate, click rate, and list decay. Lucas: Yes. And there's an emerging trend of using AI to make these scores predictive. Instead of reactive scoring based on past behavior, some platforms are now using machine learning to predict which subscribers are likely to churn in the next 30 days based on patterns like time of day they open, device type, or even subject line preferences. Luna: That's fascinating—it moves from 'they haven't opened' to 'they're about to stop opening'. How accessible is that for a typical marketer? Lucas: It's still mostly in enterprise ESPs like Salesforce Marketing Cloud and HubSpot's advanced tier. But I expect that within two years, predictive churn scoring will be a standard feature in mid-tier ESPs. Klaviyo already has a predictive analytics add-on that does something similar for e-commerce. Luna: I want to circle back to something you mentioned earlier—the 'we noticed you haven't opened' email. I've seen that backfire sometimes. It can feel creepy or pushy. Lucas: It can, if it's not done thoughtfully. The key is framing. Instead of 'we noticed you haven't opened', try 'we're trying to send you less email—tell us what you like.' That shifts from surveillance to service. Also, timing matters. Send it when they're in the lapsing stage, not dormant. And always include a clear opt-down or unsubscribe option. Luna: So it's about respect. You're giving them control, not guilt-tripping them. Lucas: Exactly. And that's the philosophy behind lifecycle scoring: it's not about tricking people into staying; it's about recognizing where they are and offering them the right next step. Some people will unsubscribe, and that's fine—better to have a smaller engaged list than a large dead one. Luna: Speaking of engaged lists, I want to mention something that our listeners might appreciate. If you've been enjoying these episodes and applying some of these strategies, it's worth noting that this podcast is ad-free and listener-supported. If you find value in the show, a small contribution at buy me a coffee dot com slash fexingo helps us keep it that way. Lucas: Yeah, that's a good point. It's a low-key way to support the work we do. And honestly, even a few dollars helps cover hosting and research. We don't do sponsors, we don't do ads—just the content, because that's what we'd want as listeners. Luna: Exactly. And it means we can keep diving into topics like lifecycle scoring without any bias. So, back to that—Lucas, what's one thing a marketer can do this week to start lifecycle scoring? Lucas: Start with your ESP's segmentation. Create a segment of subscribers who haven't opened in the last 30 days but have opened in the previous 60. That's your lapsing group. Then create a segment of those who haven't opened in 60 days but have opened before—that's dormant. Set up an automation for each. For lapsing, send a preference center link. For dormant, send a re-engagement offer. Measure the results in 30 days. That's your pilot. Luna: Simple, concrete, actionable. I love it. Lucas: And from there, you can refine. Add frequency scores, test different re-engagement creatives, and eventually build a full RFM model. The list will thank you. Luna: I'm curious—do you see a future where lifecycle scoring becomes fully automated, where AI adjusts thresholds in real time without manual intervention? Lucas: Absolutely. In fact, I think within five years, most ESPs will offer a 'subscriber health score' that updates daily and suggests actions automatically. The challenge will be making sure the AI doesn't over-optimize for engagement at the expense of subscriber experience. But the core idea—knowing where each subscriber is in their lifecycle—will only become more central. Luna: It's a shift from batch and blast to truly personalized lifecycle management. Thanks for breaking it down, Lucas. Lucas: My pleasure. Next time, let's talk about how to use lifecycle scoring to inform content strategy—what to send to each segment.