Latest / The Email Marketing Podcast with Fexingo: Newsletters, Open Rates, and Subscriber Growth / How Email List Scoring Reveals Your Best Subscribers
Transcript
- Lucas: So if you've been building an email list for a while — maybe a year or two — you probably have thousands of subscribers, and a vague sense that some are more valuable than others. But do you actually know which ones are your best subscribers? Luna: I think most people just kind of guess. Or they look at the last open date and call it good. Lucas: Right, and that's the problem. Today I want to talk about email list scoring — a systematic way to rank every subscriber on a numeric scale based on engagement, recency, and purchase behavior. It's not new — sales teams have used lead scoring for decades — but most email marketers still don't apply it to their own subscriber base. Luna: And I love that you brought that up because I think there's a misconception that scoring is only for B2B sales funnels. But this applies to any list. Lucas: Exactly. Let me give you a concrete example. Earlier this year, a DTC home-goods brand — I won't name them, but they sell kitchen tools and linens — they had a list of about 120,000 subscribers. They were sending three emails a week, their open rate was around 18 percent, and their click-through rate was hovering near 1.5 percent. Pretty mediocre. They knew they had dead weight, but they didn't know how much. Luna: And if you're at 18 percent open rate, that's not terrible, but it's not great either. It's the kind of number that says you have too many people who just aren't interested anymore. Lucas: So they built a simple scoring model. Every subscriber gets a score from 0 to 100. They assigned points for: one point for every email opened in the last 90 days, capped at 30; three points for every click in the last 90 days, capped at 30; and then a recency bonus — if they opened or clicked within the last 7 days, they get 20 points, within 30 days gets 10 points, within 60 days gets 5 points. Then they added 20 points if the subscriber had made a purchase in the last year. Luna: So the maximum is 30 from opens, 30 from clicks, 20 from recency, 20 from purchase — that's exactly 100. Luna: I like how they kept it simple. You can overcomplicate this with 15 different metrics. Lucas: Absolutely. Anyway, they ran the scores and found that roughly 40 percent of their list scored below 15. That's essentially a zero — no recent engagement, no purchase. So they did something bold: they removed all subscribers with a score below 15. In one go, their list dropped from 120,000 to about 72,000. And within two weeks, their open rate jumped from 18 percent to 34 percent, and their click-through rate went from 1.5 percent to 3.2 percent. Luna: That's more than double the click-through rate. And because the list was smaller, they saved on email-sending costs too. Lucas: Right. And here's the kicker — their revenue from email actually went up. Because they were now sending to people who actually wanted to hear from them. The dead weight was dragging down deliverability and diluting their engagement metrics. By cutting it, they improved sender reputation. Luna: I think a lot of people are afraid to cut that much. They think, 'I spent years building this list, I can't just delete 40 percent of it.' Lucas: And I understand that fear. But if that 40 percent never opens, never clicks, and never buys, what are they actually contributing? They're just a number. And worse, they're hurting your deliverability because internet service providers see low engagement and start routing your emails to spam. Luna: So the scoring model essentially gave them permission to clean their list with confidence, because they had data to back it up. Lucas: Exactly. Now, one objection I hear is: 'But what about people who just like to read in the email client and never click?' That's a real concern. That's why you weight opens. If someone opens every email for six months but never clicks, they still get a decent score from opens alone. But if they haven't opened anything in six months — zero score — that's a clear signal. Luna: Right. And the recency bonus is smart because it rewards subscribers who are currently engaged, not just historically active. Someone who opened 30 times a year ago but nothing recently — they get a low recency score. Lucas: You nailed it. The recency component is the most predictive part of any scoring model. In fact, there's a simpler version called recency-only scoring, where you just measure days since last open or click and bucket them. Less than 30 days is an A, 31 to 90 is a B, 91 to 180 is a C, and over 180 is a D. Then you focus your sends on A and B, maybe try a re-engagement campaign for C, and consider removing D. Luna: That's much easier to implement. You could do it in a spreadsheet if your email platform doesn't support scoring natively. Lucas: Let's talk about what metrics you should weight and how to decide. For an e-commerce brand, purchase history is huge. One purchase in the last year is worth maybe 30 points. For a content publisher or newsletter, there's no purchase, so you rely entirely on engagement metrics — opens, clicks, time spent reading if you can track it, maybe shares. Luna: And for a B2B company, you might include job title relevance, company size, or whether they've attended a webinar. So it's really about aligning the scoring with your business model. Lucas: Let me give you another example — a SaaS company. They scored subscribers based on email engagement and product usage. If someone opens their onboarding emails and also logs into the product, their score jumps. If they stop logging in and stop opening emails, the score drops. Then they use the score to trigger different email sequences. High scores get feature announcements and upsell emails. Low scores get re-engagement or a last-chance email before they're suppressed. Luna: So it's not just about cleaning — it's about routing the right message to the right person at the right stage. Lucas: Exactly. And let me add one more nuance: scoring can also be used to identify your power subscribers. The top 5 percent of your list by score — these are your brand advocates. They open everything, click often, and buy every time. You should treat them differently. Send them exclusive previews, ask for feedback, maybe give them a loyalty discount. They are the ones driving your email revenue. Luna: That's the 80/20 rule — 80 percent of your revenue comes from 20 percent of your subscribers. Scoring helps you find that 20 percent. Lucas: And once you find them, you can model lookalike audiences for acquisition. If your top-scored subscribers share certain traits — say, they all came from a specific lead magnet or they all opened a particular welcome email — you can use that data to create more effective campaigns to attract similar people. Luna: So scoring feeds back into list growth. It's a virtuous cycle. Lucas: Now, a warning. Scoring only works if you're consistent. You need to recalculate scores regularly — ideally weekly or at least monthly. If you score once and never update, six months later your top scorers could be ghosts. The recency component decays quickly. Luna: And that means you need your email platform or CRM to support automated scoring. Most major platforms — Klaviyo, HubSpot, Mailchimp — have some form of lead scoring or custom fields you can update via integration. Lucas: If those marketing conversations have sparked something you've actually used — maybe a strategy you tried after listening — and you want to help keep this show ad-free and sustainable, listeners can support us at buy me a coffee dot com slash fexingo. It's a simple way to chip in, and it genuinely helps us keep putting out episodes like this without sponsors. Luna: Yeah, we don't do ads, so listener support is what makes this possible. And we really appreciate it. Lucas: Alright, back to scoring. I want to talk about a common pitfall: over-scoring. I've seen people build models with 10 or 15 different metrics — email opens, clicks, forwards, social shares, website visits, form submissions, support tickets — and the model becomes impossible to maintain and interpret. Simpler is better. Three to five metrics max. And they should be directly tied to revenue or engagement outcome. Luna: What about negative scoring? Like deducting points for unsubscribes or spam complaints? Lucas: That's an interesting question. Personally, I don't recommend negative scoring for unsubscribes because, by definition, they're no longer on your list. For spam complaints, that's a deliverability risk. You could apply a penalty — say, minus 50 points if someone marks you as spam — but honestly, if someone marks you as spam, they're probably gone already. Better to focus on the positives. Luna: Makes sense. I've also seen people use scoring to decide who gets their best content. For example, a high-scoring segment gets the deep-dive newsletter, and low-scoring segments get a lighter digest. Lucas: Yes, that's a smart application. You're essentially using the score as a content gate. And it can also inform your send frequency. High scorers might get daily emails because they love your content. Low scorers might get weekly to avoid overwhelming them and causing unsubscribes. Luna: That's a good way to reduce churn — you're matching frequency to appetite. Lucas: Let me give you one more example to show the ROI. A mid-sized retailer I know — they sell outdoor gear — implemented scoring six months ago. They had 200,000 subscribers. After scoring and removing the bottom 35 percent, their list dropped to 130,000. But their monthly email revenue stayed the same, and their cost per email went down because they were sending fewer emails overall. So their ROI per email actually increased. Luna: That's the dream — same revenue, lower cost, higher deliverability. Lucas: Right. And they also started seeing higher click to open rates because the people remaining were genuinely interested. They went from 25 percent click to open to 38 percent. Luna: So the scoring model essentially paid for itself within a few months. Lucas: I'd say within weeks. The cost of implementation is just the time to set up the model. The ongoing cost is negligible. And the benefits are immediate — better metrics, cleaner list, higher revenue per send. Luna: For someone listening who wants to start tomorrow, what's the first step? Lucas: First, export your last 90 days of engagement data — opens and clicks per subscriber. Then, in a spreadsheet, assign points for each open and click, plus a recency bonus. Add a purchase column if you have e-commerce data. Sum the points, sort descending, and look at the distribution. You'll quickly see where the drop-off happens. Then pick a threshold — say, bottom 20 percent — and decide what to do with them: suppress, re-engage, or just send less frequently. Luna: That's doable in an afternoon. I think the hardest part is just deciding to do it. Lucas: One thing I want to emphasize: scoring isn't a one-time project. It's a discipline. You need to keep the model updated and revisit the weights periodically. What works today might not work six months from now if your audience changes. Luna: So it's an ongoing process, not a set-it-and-forget-it. Lucas: Exactly. And as your list grows, you can get more sophisticated — maybe introduce a decay function, or use machine learning to predict lifetime value. But start simple. A basic score is infinitely better than no score. Luna: I also think scoring forces you to think about what you really value in a subscriber. Is it opens? Clicks? Purchases? That clarity helps you align your email strategy. Lucas: That's a great point. The act of building a scoring model is itself a strategic exercise. It forces you to articulate your definition of a good subscriber. And once you have that, you can optimize everything — from content to frequency to list growth campaigns. Luna: Alright, so to wrap up: scoring helps you clean your list, improve engagement, increase revenue per send, and find your best subscribers. Not bad for a spreadsheet. Lucas: Not bad at all. And as we head into the second half of 2026, with inbox competition only getting fiercer, scoring might be one of the highest roi changes you can make to your email program. I'm curious to see how many more brands adopt it.