Latest / The Email Marketing Podcast with Fexingo: Newsletters, Open Rates, and Subscriber Growth / How Email List Hyperpersonalization Boosts Conversions
Transcript
- Lucas: So we talk a lot on this show about personalization — first names, maybe a birthday offer, a segment based on whether someone opened your last campaign. But I think most email marketers are leaving a huge amount of revenue on the table because they stop short of what's actually possible. Luna: You're talking about hyperpersonalization — using every data point you have, not just the obvious ones. Lucas: Exactly. There's a study from McKinsey that says personalization can lift revenue by ten to fifteen percent, but when you move to what they call 'individual-level personalization,' that lift can be closer to twenty-five percent. And the gap between what brands do and what consumers expect is widening. Luna: I've definitely opened emails that felt like they were written just for me — and I've also opened plenty that clearly used the same template for everyone. The difference in engagement is night and day. Lucas: Let's look at a concrete example. There's an outdoor gear retailer — I'll call them Trailblazer Outfitters — who segmented their email list not by demographics or even by past purchase category, but by the specific activity their customers were interested in: hiking, climbing, camping, trail running. They used a combination of purchase history and browse behavior to assign each subscriber a primary and secondary activity. Luna: So instead of sending a generic 'Spring Gear Sale' to everyone, they could send 'New Hiking Boots Arrived' to the hiking segment and 'Ultralight Tents' to the backpackers? Lucas: Right. And the results were dramatic. Their click-through rates on the activity-specific emails were about three times higher than their usual segmented campaigns. But the really interesting number was the conversion rate — up forty-two percent compared to their previous best performing segment. That's not a small bump. Luna: Forty-two percent is huge. How did they assign those activity labels without making customers fill out a survey? Lucas: They used a combination of signals. First, past purchase data — if you bought climbing shoes and chalk, you're probably a climber. Then they looked at browse behavior on the site: which category pages you visited, which product detail pages you spent time on. They also used a preference center during the welcome sequence where subscribers could select their primary activity. But only about twenty percent of people actually filled that out, so the behavioral data did most of the heavy lifting. Luna: So the key is connecting your email platform with your website analytics and your purchase data. That's a technical integration, but it's not rocket science. Lucas: No, it's not. Most email service providers like Klaviyo, ActiveCampaign, or Mailchimp have the ability to pull in custom events from your site. The challenge is often data hygiene — making sure you're tracking the right events and that your segments are actually accurate. Trailblazer Outfitters spent about three weeks cleaning up their data before they launched the hyperpersonalized campaigns. Luna: And did they see any downside? Like, what if someone gets tagged as a climber but they're actually a hiker who bought climbing gear as a gift? Lucas: That's a great point, and it's one of the risks. Their solution was to use a confidence score. If a subscriber's behavior strongly indicated one activity — say, three purchases in climbing plus five product views on climbing gear — they got that primary label. If the signals were weaker, they got a secondary label or were put into a 'general outdoors' segment until more data accumulated. They also set up a preference center link in every email so people could correct their label anytime. Luna: I like that. It's not just about using data — it's about being smart with it and giving people control. What about privacy? Some subscribers might feel creeped out if they get an email about something they only looked at once. Lucas: It's a balance. The general rule is: use first-party data that the subscriber has implicitly or explicitly shared with you. Don't bring in third-party data. And be transparent about why they're seeing what they see. In their welcome email, Trailblazer included a line like 'We'll send you gear recommendations based on your interests — you can update these anytime.' That sets expectations. Luna: I think the bigger obstacle for most businesses isn't tech or privacy — it's that they don't even know what data they already have. They have purchase history, but they've never looked at it as a segmentation opportunity. Lucas: Exactly. So let's talk about a simpler version that any email marketer can implement this week. Instead of a full behavioral model, start with one piece of data you already have — like the category of the last product a customer bought. If you're a bookstore, you know whether someone last bought fiction, nonfiction, or a specific genre. Send them a recommendation based on that one data point. Luna: That's a low-effort win. And you can test it against your usual send to see if it moves the needle. Lucas: Right. And then layer on another signal — maybe browse abandonment on a specific category. If someone looked at cookbooks but didn't buy, send them a cookbook recommendation with a limited-time discount. That's hyperpersonalization at its simplest: using two data points instead of one. Luna: It's almost like the email becomes a personalized shopping assistant. And I think that's the bar consumers have now. They expect brands to know what they like. Lucas: Especially after years of Amazon and Netflix setting expectations. If a brand sends you a generic blast, it feels lazy. If they send you something relevant, it feels thoughtful. And that thoughtfulness drives loyalty — not just one-off purchases. Luna: You mentioned Netflix — they're the gold standard for this. Their recommendation engine is hyperpersonalized to the individual, and it keeps people subscribed. Email marketers can learn from that. Lucas: Absolutely. And the tools exist now to do it. You don't need a data science team. You need a clear strategy, clean data, and a willingness to test. Even a small step — like sending a different version of your newsletter to customers vs. prospects — can improve engagement by double digits. Luna: One thing I've noticed: brands that do hyperpersonalization well also tend to have higher customer lifetime value. It makes sense — if every email feels useful, you're less likely to unsubscribe. Lucas: That's the flywheel. Better segmentation leads to better engagement, which leads to more data, which leads to even better personalization. The hardest part is starting. Once you have the first segment working, you can iterate. Luna: I want to ask about tools — do marketers need a customer data platform to do this, or can they get by with their email service provider's built-in features? Lucas: For most small to mid-sized businesses, the ESP's native segmentation is enough — especially if you're using something like Klaviyo or ActiveCampaign that allows custom events. A CDP becomes necessary when you have data coming from multiple sources — online and offline — and you need a unified view. But for the example we gave — purchase category and browse behavior — a good ESP can handle that. Luna: So the barrier is really just time and intention. Which brings me back to something you said — why don't more marketers do this? Lucas: I think it's a mix of inertia and fear of complexity. Many marketers set up their email automation once and never revisit it. They have a welcome sequence, a post-purchase sequence, maybe a re-engagement sequence, and they call it done. But the real lever for growth is continually refining those sequences based on behavior. Luna: If these marketing conversations have sparked something you've actually used in your own work — a new segment, a smarter test, a campaign that finally worked — that's exactly why we do this show. We deliberately don't run ads on these episodes, and that's a choice. If you want to support that choice, the link is buy me a coffee dot com slash fexingo. Lucas: And it genuinely helps keep the whole podcast network ad-free. Appreciate anyone who chips in. Now, back to hyperpersonalization — one more data point I want to share. A study by Epsilon found that eighty percent of consumers are more likely to purchase from a brand that provides personalized experiences. But most brands still send batch and blast emails. That's the opportunity. Luna: The gap between what consumers want and what brands deliver is the competitive advantage for anyone willing to do the work. Lucas: Exactly. And it doesn't have to be perfect from day one. Start with one segment, one personalized recommendation, and measure the lift. That proof of concept will sell the rest of the organization on investing more. Luna: Alright, I think we have our homework: pick one data point in our own email lists and build a hyperpersonalized campaign this week. Lucas: I'm doing that. And I'll report back on what happens. Thanks for the conversation, Luna. Luna: Likewise, Lucas. See you next time.