Latest / The Email Marketing Podcast with Fexingo: Newsletters, Open Rates, and Subscriber Growth / Send Time Personalization Boosted Open Rates 30 Percent
Transcript
- Lucas: Last week I was inside the email analytics for a travel brand called Wanderlust Escapes. They run curated adventure trips — think Patagonia treks, Iceland road trips. And they were sending their weekly newsletter at 10 AM Eastern, every Tuesday, to their entire list of 80,000 subscribers. Their open rate was stuck at 18 percent. Luna: 18 percent is pretty average for travel, but they must have had subscribers all over the world, right? Lucas: Exactly. Their list spanned 12 time zones. People in Los Angeles were getting it at 7 AM, people in Sydney at 1 AM the next day. And their engagement data showed that subscribers who opened within the first hour were three times more likely to click a trip link. So sending at one fixed time was leaving a ton of engagement on the table. Luna: So the obvious fix is to segment by time zone and send at a local morning hour. Why didn't they just do that? Lucas: They tried that first. They broke the list into four broad time zone groups and sent at 8 AM local time. Open rates went from 18 to 22 percent — decent bump. But then they noticed something odd. A chunk of their most engaged subscribers — people who had booked multiple trips — weren't opening until evening. Turns out, adventure travel decision-makers often browse trips after the kids are asleep. Luna: Right, so time zone alone isn't enough. You need to know each subscriber's personal best time based on their actual behavior. Lucas: That's exactly where they went next. They built a simple machine learning model that looked at each subscriber's last 90 days of open and click timestamps, grouped them into hour of week buckets, and picked the bucket with the highest engagement rate per person. For some subscribers it was Tuesday at 10 AM. For others, Saturday at 7 PM. Luna: And that's when the open rate jumped? Lucas: To 23.4 percent initially. But then they layered on another insight — they also looked at which hours had the highest click to open rates, not just opens. That gave them a secondary model that favored quality over quantity. Their final implementation lifted open rates to 24.6 percent — a 30 percent improvement over the original 18. Luna: Thirty percent is big. But I have to ask — did they see any negative effect from sending at off-peak times? Like, did some subscribers get emails at 3 AM local time because their model picked a weird window? Lucas: Great question. They set a hard floor: no email sent between 10 PM and 6 AM in the subscriber's detected time zone. If the model's optimal slot fell in that window, they shifted to the next best hour outside it. They also A/B tested the new personalized sends against the old time-zone-based sends for a full month. The personalized group had 40 percent fewer unsubscribes. Luna: That's a pretty strong signal that the algorithm wasn't annoying people. So the takeaway here really is: generic best practices are okay, but individual behavior beats demographics almost every time. Lucas: Honestly, if these marketing conversations have sparked something you've actually used, that's what keeps this show ad-free. If today was worth a coffee to you, the link is buy me a coffee dot com slash fexingo. Luna: Yeah, it's a small gesture that lets us keep digging into real case studies without any sponsor interruptions. Lucas: Exactly. So back to Wanderlust Escapes — the most surprising finding was that their highest-value subscribers, the ones who had spent over $1,000 in the past year, showed the strongest preference for Saturday evening sends. That segment's click-through rate on Saturdays at 7 PM was nearly double the list average. Luna: That makes sense intuitively — planning a big adventure trip usually involves more thought and conversation. People carve out time on weekends. Did they then start creating different content for that segment, or just change the send time? Lucas: They did both. Once they identified the Saturday evening slot as a high-intent window, they started sending a 'weekend inspiration' version of the newsletter to that segment — longer trip stories, more photos, a slower narrative. That subsegment's click-through rate hit 8.2 percent, compared to the overall best of 4.5. Luna: So it wasn't just the time — it was the content matching the moment. That's a really elegant strategy. Lucas: Right. And the whole thing only required about two months of data collection and a fairly simple model. They started with a rule-based approach — if a subscriber had opened primarily in the evening over the last 30 days, schedule them for evening — and that alone got them halfway to the final result. Luna: For a small team, that's approachable. You don't need a data science department to do this. Lucas: Not at all. Even segmenting by three engagement patterns — morning, afternoon, evening — can beat one-size-fits-all. Wanderlust Escapes' method just happened to be more granular. But the principle holds: time is a personalization variable, just like content or product recommendations. Luna: So the big lesson for our listeners: before you try more complex personalization, check your send time. It's one of the easiest levers to pull. Lucas: And it's still underused. Most brands we see are still blasting at 10 AM Tuesday because someone read a blog post in 2019. But the data lives in your own platform. If you haven't analyzed your open times by subscriber, that's where I'd start tomorrow. Luna: Agreed. And if you try it and see a lift, let us know — we'd love to hear your numbers.