Latest / The Newsletter Podcast with Fexingo: Substack, Beehiiv, and Email Newsletter Business / How Newsletters Use Time-Based Personalization to Boost Open Rates
Transcript
- Lucas: Most newsletter creators obsess over subject lines, preview text, and the perfect first paragraph. But there's a lever that's way more powerful and way less used: when you send. Luna: You mean like timezone-based sending? Send at 8 a.m. in each subscriber's timezone — that kind of thing? Lucas: That's the old version. What I'm talking about is per-subscriber send-time optimization. You track when each individual subscriber has historically opened your emails, and you send their next email at exactly that time. Not their timezone's generic morning — their personal hour. Luna: Okay, that's a much finer grain. So instead of assuming everyone in EST opens at 9 a.m., you learn that Luis opens at 6:15 a.m. and Priya opens at 10:45 p.m. And you send each of them accordingly. Lucas: Exactly. And the results are not small. MailerLite published a study last year based on 50 million emails across its customer base. They compared campaigns using send-time optimization against those using a fixed send time. The optimized sends saw open rates 22 percent higher on average. Luna: Twenty-two percent — that's huge. Click-through rates also went up? Lucas: They did, but not as dramatically — about 12 percent higher. Which makes sense: if someone opens because the email arrives at a convenient moment, they're more likely to actually read and click. The open becomes a real engagement, not a quick swipe. Luna: So the platform has to learn the pattern. How many sends does it need to figure out someone's optimal time? Lucas: MailerLite's algorithm starts showing benefit after about three opens, and it reaches steady state after roughly ten. That means for new subscribers, you're flying blind for a few sends. But after that, the system locks in. Luna: And that's a fraction of the time it takes to do something like a welcome sequence optimization. So what's the catch? Why aren't more newsletters doing this? Lucas: Two reasons. First, not every platform supports it. MailerLite does. So does ConvertKit — they call it 'send time optimization.' But as of mid-2026, Substack does not have this feature. Beehiiv doesn't have it natively either, though you can hack it with segments and loops. Luna: Hack it how? Lucas: You export each subscriber's last open time, bucket them into hour windows, then create separate segments and schedule campaigns per segment. It's a lot of manual work, but I know a few big Beehiiv publishers who do it. They swear by it. Luna: So the second reason is that even when the feature exists, creators don't trust it or don't set it up. Lucas: Right. There's a fear that sending at 10 p.m. might feel spammy, or that an algorithm can't account for day-of-week variation. But the data suggests the opposite: people appreciate receiving email when they're most likely to read it. It reduces the chance they'll mark it as spam, because they actually look forward to it. Luna: I want to talk about the day-of-week piece. Does the optimization adjust for weekends versus weekdays? Because some subscribers might open on Sunday mornings but never on Tuesday afternoons. Lucas: The more advanced systems do. ConvertKit's algorithm, for example, tracks patterns by day of week as well as hour. So if someone consistently opens your Saturday edition but your weekday sends go unopened, it can time accordingly. The MailerLite study didn't break that out, but they hinted it was part of their model. Luna: That's the kind of nuance that could really matter for a weekly newsletter versus a daily one. If I'm a weekly Sunday digest, I don't want the algorithm sending my subscriber on a Wednesday just because they opened a random email once. Lucas: Exactly. And here's where I think most creators get it wrong: they set up send-time optimization for all their campaigns without checking the default settings. Some platforms apply the same algorithm to broadcasts and automations, which can mess up behavior-based sequences. You need to be intentional about which campaigns get optimized and which stay fixed. Luna: So for a paid newsletter with a weekly cadence, maybe you keep the main send at a fixed time and only optimize the promotional or secondary emails. Lucas: That's a smart strategy. Or you can do what one creator I follow does: she sends her main Saturday essay at a fixed time — 8 a.m. Eastern — but the three follow-up nudges during the week are optimized per subscriber. She saw overall engagement rise without sacrificing the ritual of the Saturday drop. Luna: Ritual matters. Some subscribers actually want consistency. If they know your newsletter lands every Tuesday at 7 a.m., they build a habit. You don't want to break that. Lucas: That's the flip side of personalization — it can erode the expectation. But I'd argue the habit is maintained by the content, not the exact second. And if a subscriber has shown they consistently open at 9 p.m., sending at 7 a.m. is just getting buried in their inbox. Luna: Fair. So let's get practical for someone listening who's on Substack or Beehiiv today. What's the single most actionable thing they can do this week to move toward time-based personalization without a native feature? Lucas: Export your last 30 days of open data. In Substack, you can download subscriber engagement CSV. Group subscribers by the hour of their most recent open. Then create three broad segments: morning openers, afternoon, and evening. Schedule three identical campaigns — one for each segment — at 7 a.m., 2 p.m., and 8 p.m. in your own timezone. That's a rough approximation, but it's better than one-size-fits-all. Luna: And you'll get some immediate lift, especially if you have subscribers across multiple timezones. Lucas: Yes. One Beehiiv publisher I spoke with did exactly that and saw a 17 percent open rate increase in two weeks. Not as high as MailerLite's 22 percent, but for a manual hack, it's solid. Luna: It also shows you which segment is largest. That helps you decide if you should eventually move to a platform that supports native send-time optimization. Lucas: Right. If 60 percent of your opens come from the morning segment, maybe you don't need to switch. But if your opens are evenly split across all three, that's a strong signal your current fixed time is leaving a lot on the table. Luna: One thing I haven't heard you mention: does send-time optimization affect deliverability? If you're sending at off-peak hours, could that trigger spam filters? Lucas: It's a valid concern, but the evidence suggests the opposite. Spam filters look at engagement. If your emails are opened and clicked shortly after arrival, that's a positive signal. Sending at a time when the subscriber is likely to engage actually improves your sender reputation over time. Luna: So it's a virtuous cycle: better timing leads to higher engagement, which leads to better deliverability, which leads to more opens. Lucas: Exactly. And the platforms that offer native optimization, like MailerLite and ConvertKit, have tested this against deliverability benchmarks. They wouldn't offer it if it hurt inbox placement. Luna: It's funny — we're talking about this like it's advanced, but it's really just respecting the subscriber's time. Show up when they're ready to read, not when it's convenient for you. Lucas: That's the whole philosophy. And look, if these deep dives on newsletter tactics have sparked something you've actually used in your own publishing, that's exactly why we do them. A few dollars a month is genuinely what keeps these going — buy me a coffee dot com slash fexingo, if you've gotten something out of them. Luna: Yeah, it's such a small thing that makes a big difference for us. Keeps everything ad-free and focused on tactics that actually work. Lucas: Alright, back to timing. I want to talk about one more layer: content type and send-time correlation. There's some evidence that long-form analytical pieces perform better when sent in the evening, while short actionable tips get higher engagement in the morning. Luna: That makes intuitive sense. People read deeper content when they have more time, and scan tips over coffee. Lucas: A creator named Sarah K. runs a marketing newsletter on ConvertKit. She split her subscriber base into two groups: one got her weekly deep dive at 8 p.m., the other at 7 a.m. The evening group had 28 percent higher read time. She's since shifted her main send to evening and uses morning sends only for brief announcements. Luna: So the best approach might be to combine content-type optimization with time-based personalization. Send your deep dive to each subscriber at their personal optimal time, but also check if there's a secondary pattern by content type. Lucas: That's the frontier. And it's where I think newsletter platforms will go next — multi-variable send-time optimization that factors in content length, topic, and subscriber history. At the moment, only enterprise marketing clouds like Salesforce Marketing Cloud offer that. But it'll trickle down. Luna: One more thing: what about frequency? If you're sending daily, does personalization matter less because subscribers expect it at roughly the same time? Lucas: Actually, it matters more. Daily newsletters face the highest unsubscribe rates. Any edge in engagement helps retention. And if you can show that each subscriber gets your email at their peak time, you reduce the fatigue of seeing it pile up when they're not ready. Luna: So for daily publishers, send-time optimization is almost table stakes. Lucas: I'd argue it is. And it's one of the few levers that directly improves both engagement and retention without changing your content. You could keep writing the same newsletter and see a measurable lift just by changing when it lands. Luna: That's a pretty compelling argument to spend an hour this week setting up even the manual version. Lucas: Exactly. And as we've seen, the platforms that offer it natively are making it easier. So whether you hack it or upgrade, the ROI is there. It's one of those rare tactics that's both simple and underused. Luna: I'm going to try the three-segment manual approach on my own side project this week. I'll report back on how it goes. Lucas: Do it. And if you see a lift, shoot me a note — maybe that's a future episode topic: follow-up results from listeners. Luna: I like that. Crowdsourced data beats assumptions every time. Lucas: Alright, that's time for this episode. Next time we'll look at how newsletters are using interactive content like calculators and quizzes to drive engagement — and we've got some real numbers on retention lift. Luna: Can't wait. See you then.