Latest / The Retention Marketing Podcast with Fexingo: Email, SMS, and Loyalty for Repeat Customers / How a Sneaker Brand Uses Email to Personalise Repeat Offers
Transcript
- Lucas: You know that feeling when you browse a pair of sneakers, don't buy, and then your inbox gets flooded with the exact same shoe from six different brands? Generic, right? Luna: Yeah, and usually it's the wrong size or color I already dismissed. Lucas: Exactly. Today we're looking at a direct to consumer sneaker brand — let's call them Stride Collective — that took the opposite approach. Instead of blasting the same offer to everyone, they built an email system that uses real-time browsing data to send each customer a personalized feed of products they actually might want. Luna: So, not just 'we saw you looking at running shoes' — but actually showing me the ones in my size, in a color I've bought before? Lucas: Right. And they tied it directly to repeat purchase behavior. Their thesis was: generic email campaigns might drive one-off sales, but personalized product discovery in the inbox could build a habit. They tested it for six months, and here's the headline number — repeat purchase frequency increased by 35 percent. Luna: That's a big jump. How did they pull it off technically? I imagine it's not just adding a first name merge tag. Lucas: No, it's a lot more involved. They integrated their email service provider — they use Klaviyo — directly with their product catalog and their real-time behavioral tracking. So every time a customer views a product, adds something to their cart, or even just hovers on a size selector, that event feeds into a customer profile. Then the email system pulls from that profile to assemble a dynamic product block. Luna: Dynamic how? Like, live at send time? Lucas: Exactly. At the moment the email is opened, it queries the product feed for items that match that customer's preferences: size, category, price range, and even past purchase history. So if you bought a pair of low-top canvas sneakers in white last month, the email might show you the same model in navy, or a new colorway that just dropped. They also included restock alerts — if you looked at a shoe that sold out in your size, the email triggers automatically when it's back. Luna: That restock piece is smart. I've definitely bought from a brand just because they emailed me when my size was available. So, did they A/B test this against their old batch and blast campaigns? Lucas: They did. They ran a three-month A/B test with a holdout group that continued receiving the generic promotional emails — weekly new arrivals, site-wide sales, that sort of thing. The personalized group received a maximum of two emails per week, but each one was tailored. The result: the personalized group had a 42 percent higher click-through rate and, as I mentioned, a 35 percent higher repeat purchase rate. The generic group had slightly higher open rates on sale emails, but those were mostly one-time buyers. Luna: So the personalization didn't just drive more clicks — it drove actual repeat behavior. But I'm wondering, is this something a smaller brand with a smaller engineering team could pull off? Because integrating real-time data feeds sounds heavy. Lucas: It's a fair question. Stride Collective has a dedicated engineering team of three people working on marketing automation. But the key components are available off the shelf. Klaviyo, for instance, has a product feed API that can be set up with a few hundred lines of code if you have a developer. The bigger lift is cleaning up your product data: making sure every SKU has accurate size, color, category, and inventory status. Without that, the dynamic blocks break. Luna: So data hygiene is the real barrier, not the tech itself. And I guess that's something even a small team can invest in upfront. Lucas: Exactly. Another interesting angle: they also experimented with what they called 'abandoned browse' emails — not just abandoned cart. If someone spent more than 30 seconds on a product page but didn't add to cart, they'd get an email the next day with that product plus two similar alternatives. That alone accounted for 12 percent of their repeat orders during the test. Luna: Huh, because most brands only target cart abandoners. Browsing without carting is a huge missed opportunity. But there's a fine line between helpful and creepy. How did they handle privacy? With all this real-time data collection, I'd worry about creep factor. Lucas: They were very deliberate about it. First, they only use behavioral data that customers have explicitly consented to through their cookie and tracking preferences — they're fully compliant with GDPR and CCPA. Second, they don't use any third-party data; it's all first-party based on interactions on their own site. And third, they give customers a clear way to opt out of personalization in their email preferences. In practice, less than 2 percent of subscribers opted out. Luna: That's a really low opt-out rate. It suggests that when personalization is done well, people actually appreciate it. Now, one thing I'm curious about: did they use any human curation in these emails, or was it fully automated? Lucas: It's mostly automated, but they do have a human-in-the-loop for the weekly 'personalized lookbook' email. A stylist reviews the top 20 product combinations that the algorithm suggests for each segment and approves or tweaks them. They found that purely algorithmic recommendations sometimes showed products that were technically a match but aesthetically clashed — like putting a neon running shoe next to a formal loafer. The human touch smoothed that out. Luna: I love that balance. Automation at scale, but a human eye for taste. And it's a reminder that personalization isn't just about data — it's about taste and context. Lucas: Speaking of context, you know, these kinds of marketing conversations — if they've sparked something you've actually used in your own work or just found interesting — we keep this show ad-free and listener-supported. It's a small thing, but if you'd like to help, you can buy us a coffee at buymeacoffee.com/fexingo. Luna: Yeah, every bit of support goes straight into keeping the production running and the topics relevant. No pressure, just if you find value in what we do. Lucas: Alright, back to Stride Collective. So after the initial six-month test, they rolled out personalization to their entire email list. They also started using the same data to personalize SMS — though that's a topic for another episode. The key takeaway is that they shifted from thinking of email as a broadcast channel to thinking of it as a personalized product discovery feed. Luna: And that shift is exactly what drives repeat purchases. It's not about convincing someone to buy again — it's about showing them something relevant so they want to buy again. Lucas: Right. The brand also saw a 20 percent increase in average order value from the personalized emails, because the recommendations often included higher-margin accessories that matched the primary product. So it wasn't just more orders — it was better orders. Luna: That's a double win. So for a brand thinking about implementing this, what's the single most important piece of advice you'd give based on Stride Collective's experience? Lucas: Start with your product data. Before you even connect any APIs, make sure every product has clean, consistent attributes: size, color, category, season, style. If that's a mess, the personalization will fail. Then run a small test with a segment of your most engaged subscribers — maybe 5,000 people — and measure repeat purchase rate over 90 days. That's enough to see if the approach works for your audience. Luna: And what about the creative side? Did they use specific subject lines or preheaders that performed well? Lucas: They tested a few approaches. The highest-performing subject lines were specific without being presumptuous: things like 'Your size is back in stock' or 'New colors you haven't seen'. They avoided 'We thought you might like...' because it felt generic. Preheaders often included the product name or category to give context. Luna: Makes sense. So the lesson is: be specific, be relevant, and let the data do the heavy lifting — but always keep a human in the loop for taste. Lucas: Exactly. And that's the sweet spot for retention marketing today. Thanks for listening, and we'll be back next time with another deep dive.