Latest / The Newsletter Podcast with Fexingo: Substack, Beehiiv, and Email Newsletter Business / How Newsletters Use Hyper-Personalized Product Recommendations to Boost Revenue
Transcript
- Lucas: You open a newsletter you read regularly, and right there in the middle, there's a product recommendation that feels almost too relevant — like, 'Oh, I was just thinking about buying a new mechanical keyboard.' Luna: Yeah, and you wonder, did they just get lucky? Or is this newsletter actually tracking what I click? Lucas: It's the latter, increasingly. We're talking about hyper-personalized product recommendations inside email newsletters — a strategy that started with e-commerce giants like Amazon, but has quietly become one of the fastest-growing revenue drivers for newsletter operators on platforms like Beehiiv and Substack. Luna: So the newsletter itself becomes a storefront, but a storefront that rearranges itself for every single reader. Lucas: Exactly. And the numbers are compelling. I was looking at a case study from early 2025 — a mid-size tech newsletter with about 80,000 subscribers. They implemented personalized product recommendations using a simple click-tracking and segmentation tool. Within three months, their affiliate revenue grew by 34 percent, and click-through rates on product links jumped 22 percent. Luna: That's a massive lift. But how granular does the personalization get? Are we talking about recommending different categories of products, or specific brands and models? Lucas: Both, depending on the data they have. At the basic level, they segment readers by which topics they engage with most — say, someone who clicks on every article about cloud computing versus someone who always reads the cybersecurity section. Then they serve product recommendations aligned with those interests. At a more advanced level, they use past purchase data or survey responses to recommend specific products. Luna: And what about the tech stack? Because most newsletter writers aren't engineers. Lucas: Right. And this is where the platforms have stepped in. Beehiiv, for example, now offers a native product recommendation engine that lets you tag products to specific audience segments. You don't need to write custom code. You set up rules — if a subscriber has clicked on three or more posts about productivity tools, serve them a curated list of the top-rated apps and gadgets. Luna: So it's almost like a dynamic content block that swaps out based on the reader's behavior. Lucas: Precisely. And the really interesting part is that this approach changes the economics of newsletter monetization. Instead of relying solely on sponsorship deals or paid subscriptions, you can build a steady affiliate revenue stream that scales with your audience. The newsletter becomes a recommendation engine, not just a content product. Luna: But there's a tension here. If I'm a subscriber and I start feeling like every link is a paid recommendation, doesn't that erode trust? Lucas: It absolutely can. That's the line operators have to walk. The most successful newsletters are transparent about affiliate relationships — they'll include a disclosure like 'We may earn a commission if you buy through this link' — but more importantly, they only recommend products they genuinely believe in. The personalization should make the recommendations more relevant, not more predatory. Luna: So the authenticity is still the foundation. Personalization just makes the right product find the right reader. Lucas: Exactly. And there's data to back that up. A 2024 survey from the Email Marketing Institute found that 68 percent of subscribers said they found personalized product recommendations helpful, as long as they were clearly marked as affiliate links. Only 12 percent said they felt manipulated. The key is relevance. Luna: So what's the practical first step for an independent newsletter writer who wants to try this? They can't afford a full-time data analyst. Lucas: Start manual. Pick one product category that aligns with your newsletter's core topic. Create three or four reader personas based on the most common questions or comments you get. Then, in your next broadcast, manually insert a product recommendation that fits each persona — but only send each version to the appropriate segment. You can do this with most email platforms using simple tagging. Luna: Manual segmentation and manual product selection. That's not scalable, but it proves the concept. Lucas: Right. Once you see that the personalized version outperforms a generic control, you can invest in automation. And the lift is usually significant enough to justify the upgrade. One writer I spoke with — she runs a newsletter about sustainable living with 12,000 subscribers — she started by recommending reusable kitchen products to readers who clicked on her zero-waste posts. Her affiliate revenue went from about $200 a month to $800 a month in four months. Luna: That's a four-times increase. And she didn't have to grow her subscriber base to do it. Lucas: Exactly. That's the beauty of personalization — it's a revenue lever you pull on your existing audience. And because the recommendations are more relevant, your click-through rates go up, which in turn makes your newsletter more attractive to sponsors. It's a virtuous cycle. Luna: So we're seeing a shift from newsletters as pure editorial products to newsletters as curated shopping experiences. But doesn't that change the relationship with the reader? Lucas: It does, but I'd argue it can deepen the relationship if done well. Think about it — if you trust a writer's taste, you actually want their recommendations. That's why so many newsletters now have a 'what I'm reading' or 'gear I use' section. Personalization just makes those sections more relevant to each individual reader. Luna: Speaking of trust and relevance, I want to come back to something you hinted at earlier — the idea that this strategy works best when it's built on a foundation of genuine authority. If a newsletter is just a thin wrapper for affiliate links, personalization won't save it. Lucas: Absolutely. The content has to come first. The product recommendations are a service to the reader, not the reason the reader subscribes. In fact, the best-performing personalized recommendations I've seen are the ones that feel like a natural extension of the editorial voice — like the writer is personally handing you something they think you'll love. Luna: You know, that reminds me of a conversation I had with a friend who runs a fitness newsletter. He told me that his most successful personalized recommendations came from a simple survey he sent out once. He asked subscribers what their biggest fitness goal was — weight loss, muscle building, endurance, or flexibility. Then he created four separate recommendation lists for gear and supplements. His open rates on those emails were 45 percent, compared to his usual 32 percent. Lucas: That's a perfect example. The survey gave him the segmentation data with zero technical complexity. And because the subscriber self-selected, the relevance was even higher. Luna: And the survey itself also signals that the writer cares about the reader's individual needs. It's a trust-building move. Lucas: Right. So we're really talking about a strategy that sits at the intersection of data, content, and relationship. And it's accessible to anyone, regardless of technical skill. The barrier is not technology — it's the willingness to get to know your audience deeply enough to serve them well. Luna: If today's conversation gave you a concrete idea you might actually use — a new way to think about serving your readers or a first step to try — that's exactly what we hope happens here. This show stays ad-free because of listeners who find value in it and choose to support it at buy me a coffee dot com slash fexingo. Lucas: Yeah, and we mean that genuinely. If something we talked about today sparks a change in your newsletter, that's the best outcome. The link is buy me a coffee dot com slash fexingo. No pressure, ever — just a way to keep these conversations going. Luna: So let's pivot to something I've been curious about. How does personalization for product recommendations differ on Substack versus Beehiiv? Because the platforms have different philosophies. Lucas: Great question. Beehiiv is very much built for growth and monetization out of the box — they have native affiliate link management, audience segmentation, and even an AI recommendation engine that suggests products based on your content. Substack, on the other hand, has been slower to add those features. They focus more on the writing experience and paid subscriptions. So on Substack, you'd likely need to use a third-party tool like SparkLoop or a custom integration. Luna: So the choice of platform can dictate how easily you can implement this strategy. Lucas: Exactly. But interestingly, some of the most creative implementations I've seen are on Substack, precisely because writers have to be more resourceful. One writer I follow — she runs a media criticism newsletter — she uses a simple Google Sheet to track which subscribers clicked which links, then manually curates a 'recommended for you' section in each edition. It's labor-intensive, but her subscribers love it because it feels bespoke. Luna: So the manual approach can actually be an advantage in terms of perceived authenticity. Lucas: Yes, because the reader can sense the human curation behind it. The risk of automation is that it can feel robotic if the recommendations are off. A human editor can catch those misses. Luna: Let's zoom out for a second. Where do you see this going in the next year or two? Are we heading toward fully ai generated newsletter content with embedded personalized shopping? Lucas: I think we'll see more ai assisted personalization, but the editorial voice will remain central. The newsletters that succeed will be the ones that use data to enhance the human connection, not replace it. So rather than an AI writing the newsletter, you'll have an AI suggesting which products to recommend to which readers based on behavior, but the writer still decides whether to include it and how to frame it. Luna: So the writer becomes a curator, not just a content creator. Lucas: Exactly. And that's a more sustainable role. Because curation has value — it saves the reader time and effort. A good newsletter writer who can recommend the right product at the right moment is providing a genuine service. And readers are willing to pay for that, either through affiliate revenue or through premium subscriptions that include exclusive product guides. Luna: It's a shift from 'I write about things' to 'I help you find the things you need.' Lucas: Right. And that's a much more valuable relationship. It's also harder to replicate with automation alone, because it's built on trust and taste. Luna: Alright, so to wrap up — if someone listening wants to start implementing hyper-personalized product recommendations today, what's the single most important action they should take? Lucas: Send a one-question survey to your subscribers. Ask them: 'What's the one area of your work or life where you're looking for the best tool or product right now?' Use the answers to create three to five segments, then in your next newsletter, include a product recommendation tailored to each segment. Track the click-through rate on those links versus a generic link in the same email. That experiment will tell you everything you need to know about whether this strategy will work for your audience. Luna: And the beauty is, that experiment costs nothing but a little time. Lucas: Exactly. And the data you get back is pure gold. From there, you can decide how far you want to take it.