Latest / Marketing Automation with Fexingo: HubSpot, Marketo, and Automated Customer Journeys / Why Marketing Automation Misses Your Big Life Event
Transcript
- Lucas: So we keep coming back to this idea that marketing automation is supposed to make everything seamless, but it trips over the messiest parts of real life. Last week it was home offices. This week, I want to talk about the big one - the really big life events. New job, wedding, new baby, retirement. The moments where your whole world shifts. And yet, for most automated systems, these are just… blind spots. Luna: It's funny, because these are exactly the moments when your attention is most valuable. If you're a bank, a retailer, a travel company - I want to hear from you when I'm about to move house, not three months later when I've already switched everything over. Lucas: Exactly. And the tools have all these clever triggers for 'user clicked this link' or 'user abandoned cart'. But the only way most systems learn about a life event is if the user tells them - manually. And that's where the whole thing falls apart, because nobody updates their profile voluntarily. When was the last time you logged into your bank or your airline and updated your 'life situation' fields? Luna: Honestly? Never. And I should be the target audience - I work in marketing, I know the value of that data. But even I don't do it. It's a chore, and it feels like the burden is on me to fix their system. Lucas: Right. The burden is on the customer. And that's the fundamental design flaw. The system is asking the person with the least incentive to act to do the most work. So what happens? Your automation keeps sending the same travel deals to the couple who just had their second child, because nobody told it the family size changed. It keeps pitching the starter credit card to someone who just got promoted and is now a prime candidate for a premium card. The data is sitting there, somewhere - in their purchase history, in their browsing behavior, even in public records, if you know where to look. But the automation isn't wired to see it. Luna: And it's not like we don't have the technology. We've got machine learning, we've got AI, we've got data enrichment services that can append all kinds of firmographic and demographic attributes. So why is this still so hard? Lucas: Part of it is that the systems are built around a very narrow definition of a 'signal'. A click, a form fill, a purchase. These are easy to capture and easy to act on. But a life event is a pattern that emerges across multiple channels over time. It's subtle. Maybe the customer's IP address changes because they're browsing from a new city. Maybe their email pattern shifts - they start opening emails at three in the morning, which might suggest they're up with a newborn. Or maybe they change their last name on one platform but not another. That's the kind of thing a human marketer might notice, but it's really hard to codify into a trigger. Luna: So we're relying on humans to notice the pattern, but we've automated away most of the human touch. And that's the irony, right? In the name of efficiency, we've made the system less intelligent. Lucas: Yeah, and the cost is real. I mean, think about the inbox. How many times have you gotten an email that's just… tone-deaf? Like, you just got a promotion, and there's a credit card offer for 'young professionals just starting out'. It makes the brand look careless. And it's not a one-off, it's a systemic failure. Luna: I've seen that from the other side too. As a marketer, I've launched campaigns that I knew were going to be irrelevant to a chunk of the audience, but I had no way to filter them out. The data just wasn't there. And then you get the unsubscribes, and you know you've lost a customer for good. Lucas: Let's talk about a concrete example. Say you're a bank, and you want to market home equity loans. The obvious trigger is a mortgage record - if someone just bought a house, they might need a loan for improvements. But mortgage data is public record in most places, so you could actually buy a list of recent homebuyers. That's not creepy, that's just smart - because the signal is real. But your automation probably isn't set up to ingest that kind of external data. It's waiting for the customer to raise their hand. Luna: And the customer is never going to raise their hand, because they don't think of themselves as 'a home equity loan lead'. They're just someone who just bought a house and is now staring at a kitchen that needs remodeling. Lucas: Exactly. So the fix isn't a better trigger. The fix is rethinking what a 'trigger' even means. Instead of waiting for a single event, we should be looking at clusters of signals. A change in IP address, a new email domain, a spike in searches for moving companies - put those together, and you've got a pretty good guess that this person is relocating. And you can act on that with a relevant message that feels timely, not stalker-ish. Luna: But isn't there a risk of being too clever? I mean, if you target someone who's searching for moving trucks, and they're actually just helping a friend move, that's going to feel really intrusive. Lucas: That's a fair point. And that's why you need to be careful about the confidence level. But you can also design the message to be helpful, not assumptive. Like, 'Planning a move? Here's a checklist of things to update your address for.' That works whether or not it's a personal move. It's useful either way. Luna: That's a good example. It's about being helpful, not just being smart. I think that's the key principle - the automation should be in service of the customer, not the other way around. Lucas: And that's exactly the kind of the thing we talk about on this show - which is why we're so careful about keeping it ad-free. We don't want the conversation to be shaped by a sponsor's agenda. If you like that approach, and you want to support it, there's a link that matters: buy me a coffee dot com slash fexingo. Luna: It's a small way to keep this independent and useful. And we appreciate the thought. Lucas: So back to the core problem. Even if we agree that life events are important, how do we actually get the data? I think there are three practical approaches. First, you can ask customers directly, but you need to make it easy and worth their while. Second, you can use data enrichment services to append external signals. And third, you can start using AI to infer life events from behavioral patterns - which is where things are headed. Luna: Let's talk about the first one - asking. How do you do that without making it feel like homework? Lucas: You wrap it into something they already want. For example, a bank could offer a 'life event checklist' when someone logs in, and as they check off 'I just got married', they get a coupon for a financial planning session. It's a value exchange, not a data grab. And it works because the customer gets something immediate. Luna: So the automation becomes the facilitator, not the gatekeeper. That's a nice shift. Lucas: Yeah. And for the second approach - data enrichment - there are services that can append homeownership status, marital status, even the presence of children, based on public records and purchase data. The tricky part is privacy. You have to be transparent about where the data came from, and you have to give people a way to correct it. But if you do it right, it can dramatically improve your segmentation. Luna: I've used those services, and they can be hit or miss. Sometimes the data is stale or wrong. And once you act on bad data, you've just made the problem worse. Lucas: Exactly. That's why the third approach is so promising - using AI to infer life events from the customer's actual behavior. This is something that's becoming more accessible, even for mid-sized companies. You can train a model on your own customer data to recognize patterns that precede a life event. For example, if a customer suddenly starts clicking on links about retirement planning, that's a strong signal. You don't need to know their exact age or salary. The behavior speaks for itself. Luna: But again, the privacy question comes up. If I'm a customer, I might not want a brand to know that I'm about to get divorced or lose my job. There's a fine line between being helpful and being creepy. Lucas: There is. And that's why the ethical approach is to focus on the positive life events - marriage, new baby, new home, new job. Those are things people are generally excited about, and they're more likely to appreciate relevant offers. But you also need to give customers control. Let them opt out of being tracked for these kinds of inferences. Respect their boundaries. Luna: And at the end of the day, no matter how good your data is, you still need to write good copy. If the email says 'Congratulations on your new home!' and it's clearly a template, that's worse than not sending it at all. Lucas: Right. The automation gets you to the right person at the right time, but the message still has to feel human. That's where most campaigns fall apart - they have the trigger, but the content is generic and lifeless. Luna: So what's the takeaway for our listeners? If you're a marketer, what's the one thing you should do differently starting tomorrow? Lucas: I'd say, audit your triggers. Look at every automated campaign you have and ask: 'What is the trigger that starts this? Is it a single action or a cluster of signals? Does it capture a life event?' If you're only using simple triggers, start thinking about how you can incorporate more data sources. And if you can't get the data, at least make it easy for customers to tell you about their lives - and give them a reason to do it. Luna: And for the rest of us, the consumers - what should we do when we get those tone-deaf emails? Lucas: Honestly? Just unsubscribe. The brand has already shown you they don't understand you. But if you want to give them a chance, update your profile - it's the only way they'll learn. And that's the sad state of affairs, that the burden is on the customer to educate the machine. Luna: Maybe next episode we can talk about how to design systems that don't put the burden on the customer. I feel like that's a whole other conversation. Lucas: It is. And we should have it. But for now, I think the key is to remember that automation is a tool, not a replacement for understanding. The best marketers are the ones who can see the life event behind the data point. That's the skill that's never going to be automated away. Luna: I'll drink to that. And maybe that's the silver lining - as automation gets better at the mundane stuff, we get to focus on the human stuff. Lucas: Exactly. And that's a future I'm excited about.