Latest / Marketing Automation with Fexingo: HubSpot, Marketo, and Automated Customer Journeys / Why Marketing Automation Forgets Your Favorite Brand
Transcript
- Lucas: You know what I keep circling back to, Luna? Marketing automation is brilliant at tracking what people do, but it's pretty terrible at understanding why they do it. Luna: That's a big statement. You mean like, we see a click, we see a purchase, but we don't know the motivation behind it? Lucas: Exactly. And the gap gets really obvious when you look at brand affinity. Think about all the data we capture—page views, email opens, cart abandons—none of it directly measures the emotional pull that makes someone choose one brand over another, even if it's pricier or less convenient. Luna: So we're missing the 'why' behind the 'what'. That feels like a huge blind spot. Lucas: It is. And honestly, this is a good moment to mention something real—this show, and the whole Fexingo network, is only possible because a small group of listeners chips in monthly through buy me a coffee dot com slash fexingo. No ads, no sponsors, just listener support. It's what keeps these conversations honest. Luna: Yeah, and we really appreciate that. It lets us dig into topics like this without any corporate agenda. Lucas: Right. So back to brand affinity. I've been looking at a 2025 study from a consumer insights firm—I think it was called the Loyalty Gap Report—and it found that 68 percent of consumers say they stick with a favorite brand even when a cheaper alternative is available. But here's the thing: most automation platforms would treat that person as a price-sensitive buyer based on their past behavior, completely missing the loyalty signal. Luna: So the data says one thing, but the reality is something else entirely. How do we capture that emotional attachment? Lucas: Well, let's start with what we have. Loyalty programs are the obvious proxy, but they're flawed. They measure points and purchases, not feelings. I mean, I've been a member of my grocery store's loyalty program for years, but I don't have any emotional connection to that store. Luna: Right, it's a transactional relationship. But what about something like Apple? People camp out for new iPhones. That's not just about specs. Lucas: Exactly. Apple has a brand affinity that's almost tribal. Their automation doesn't just track your upgrade cycle—it plays into the identity of being an Apple person. But a lot of their marketing still relies on behavioral triggers: 'your device is two years old, here's a trade-in offer.' That's fine, but it's not tapping into the deeper attachment. Luna: So even a brand with massive affinity is underusing it in automation. What about companies that do it better? Any examples? Lucas: Patagonia comes to mind. They've built a brand around environmental values, and their customers are fiercely loyal. But their automation doesn't just track gear purchases—it engages with people who attend their repair workshops or sign up for advocacy emails. Those are signals of affinity, and they use them to tailor messages that reinforce the values, not just push products. Luna: So they're looking at community signals, not just transactional ones. That's a smart move. Lucas: It is, and it's still rare. Most companies are stuck with the classic behavior-based model: if a customer opens an email, they get more emails; if they buy, they get more offers. But affinity is a feeling, and feelings don't always map neatly to actions. Luna: Can we actually measure affinity, though? Or are we just guessing? Lucas: There are ways. Sentiment analysis on social media, reviews, and even customer support interactions can give signals. But it's messy. And most automation platforms don't integrate that kind of unstructured data well. You need a customer data platform, or a CDP, to pull it together, and even then, it's not straightforward. Luna: So it's a data integration problem as much as a strategy problem. Lucas: Exactly. And I think there's another layer: the 'why' behind a purchase often comes down to identity. People buy brands that reflect who they are, or who they want to be. That's a deeply personal thing, and it's hard to automate around. Luna: I remember reading about a study where people chose a Tesla partly because it made them feel like early adopters, innovative. That's identity, not just utility. Lucas: Right. And Tesla's automation famously doesn't do a lot of traditional marketing, but they've built a community of evangelists. That's affinity at scale. But can you bottle that in an automated journey? Probably not fully. Luna: So what's the practical takeaway for marketers listening? How do we start incorporating affinity into our automation? Lucas: I'd say start by identifying your superfans. Look for people who engage beyond purchases—they attend events, they comment on your blog, they share your content. Tag them as high-affinity, and treat them differently. Don't just send them the same discount codes as everyone else. Luna: So segment by affinity, not just by behavior. Lucas: Yes. And then, for those high-affinity customers, shift the message from 'buy more' to 'be part of something.' Invite them to a loyalty community, give them early access, or feature their stories. That reinforces the emotional connection. Luna: That makes sense. But what about the other side—people who buy only once? How do you know if they have latent affinity you can nurture? Lucas: That's the harder question. You can look at signals like time spent on your content, engagement with your brand story, even how they found you. If someone came via a word of mouth referral, that's a hint they're already predisposed to like you. But it's still probabilistic. Luna: So we're back to the limitation: automation is built for probabilities, not certainties. Lucas: Right. And I think that's the core insight here. Marketing automation is incredibly good at responding to what people do, but it's fundamentally reactive. Brand affinity is proactive—it's about creating a relationship before the next purchase, not just after a click. Luna: That's a nice way to put it. So what's the future? Will we ever get to a point where automation truly understands the 'why'? Lucas: Maybe. As AI gets better at natural language understanding, we might be able to analyze customer conversations at scale—support chats, social posts, even voice interactions—to infer sentiment and values. But it's a long way from being a plug and play feature in your marketing platform. Luna: So for now, it's a human-led strategy with automation as a support, not the driver. Lucas: Exactly. And that's a good thing. It means marketers still have a role that can't be fully automated—understanding what makes people tick. And that's what keeps this job interesting. Luna: Definitely. So, Lucas, if a listener wants to start acting on this today, what's the one thing they should do? Lucas: Look at your customer segments and ask yourself: which ones are defined by more than just purchase history? If you don't have a 'brand advocates' segment, create one, even if it's small. Start there. Luna: That's a concrete first step. I think we've given people a lot to chew on. Lucas: Yeah, and I think the big takeaway is: don't let your automation reduce your customers to a set of behaviors. The best marketers see the person behind the data, and that's a skill no algorithm can replace yet.