Latest / Marketing Automation with Fexingo: HubSpot, Marketo, and Automated Customer Journeys / When Your Marketing Automation Ignores a Loyal Customer
Transcript
- Lucas: So Luna, I got a loyalty email this week. It was from a hotel chain I stay at maybe six times a year. And it said, 'We miss you. Here's a discount to come back.' Luna: And you were there last week, right? Lucas: Exactly. I checked out on Monday. The email went out Wednesday. They missed the fact that I had just been a guest. And honestly, that kind of thing happens all the time. Luna: It's like they're targeting the wrong version of me. I get 'we miss you' emails from brands I literally bought from yesterday. Lucas: Right. And it's not just hotel chains. Loyalty programs across retail, airlines, gyms — they all struggle with this. The automation is built on triggers that are too simplistic: 'no purchase in thirty days' equals 'at risk.' But that ignores the actual customer journey. Luna: So what's the fix? How do you write a trigger that doesn't treat a loyal customer like a lapsed one? Lucas: The fix is about data architecture. Most companies have their loyalty data in one system, their email platform in another, and their CRM in a third. And those systems don't talk to each other in real time. So the email team sends the 'we miss you' campaign based on stale data. Luna: It's like they're reading from a script that's three scenes late. Lucas: That's a great way to put it. And the deeper issue is identity resolution. When you check in at the hotel, you're a loyalty number. When you book online, you're an email address. When you pay, you're a credit card. If those don't link up instantly, you become multiple people in their database. Luna: So the data model itself is broken. It's not the campaign that's dumb, it's the data underneath. Lucas: Exactly. And that's the angle we're going to drill into today. Because the solution isn't to write better copy or more clever subject lines. It's to fix the plumbing. Luna: Let's get into it. I want to know why this keeps happening even at big brands with huge tech budgets. Lucas: Well, the first reason is that most automation platforms are built on batch processing. They update customer segments overnight or every few hours. So even if your checkout system knows you just bought, the email system doesn't see it until the next sync. Luna: So the 'we miss you' trigger fires because the data hasn't caught up. Lucas: Right. And in the old world, that was acceptable. But now, with real-time expectations, customers notice. And they get annoyed. Especially loyal ones who just gave you money. Luna: So what's the cost of that annoyance? Apart from a bad email. Lucas: It erodes trust. And trust is the whole point of a loyalty program. You're saying, 'We see you, we value you.' But when the automation says 'We miss you' the day after you visited, it tells the customer, 'We don't actually know you at all.' Luna: And that's a bigger problem than one bad email. It makes people question the whole program. Lucas: Exactly. So let's talk about what a better approach looks like. And there's a really good example from a regional grocery chain I read about recently. Luna: I'm listening. Grocery stores are interesting because they have so much data — but they often don't use it well. Lucas: This chain had a problem: customers who shopped every week were getting the same 'we miss you' emails as people who hadn't been in for six months. So they rebuilt their data model. Luna: What did they change? Lucas: They started by unifying customer identities. They took loyalty card numbers, credit card tokens, and email addresses, and built a single customer profile in real time. It was a massive project, but the result was that the moment you checked out, your profile updated instantly. Luna: So the next morning, you wouldn't get a 'we miss you' email because they know you were just there. Lucas: Right. And they also changed the trigger logic. Instead of just 'no purchase in thirty days,' they looked at purchase frequency and recency together. So a weekly shopper would have to miss three weeks before they got a 'we miss you' email. And a quarterly shopper would get one after sixty days. Luna: So the automation is actually adapting to the customer's normal behavior, not a one-size-fits-all threshold. Lucas: Exactly. And that's what I want to emphasize. The trigger isn't just a date. It's a behavior-based trigger that respects the customer's rhythm. Luna: It's like having a good friend who doesn't call you every day, but knows when something's off. Lucas: That's the ideal. And this is where the conversation about automation gets deeper than just 'set it and forget it.' It's about designing the data flow. Luna: And before we go further, I have to say, this is the kind of episode that makes me think about my own inbox. If today's conversation has given you one practical idea you can actually use, that's exactly why we keep these episodes ad-free. The way to support that is to buy me a coffee dot com slash fexingo. It's a small gesture that keeps the shows going. Lucas: Yeah, genuinely, if you've ever taken a nugget from this show and used it in your own work, that's the best compliment. And if you want to help the show stay independent, that link is the way. No pressure, just a simple way to keep going. Luna: So, back to the grocery chain. They also started using predictive analytics. They could anticipate when a customer was about to lapse, and send a relevant offer before the customer even thought about leaving. Lucas: That's the next level. Instead of reacting, you're being proactive. And that's where the real ROI is. But it requires a data infrastructure that can handle that kind of computation. Luna: So what about the small business that doesn't have a data science team? Is there hope? Lucas: Absolutely. In fact, small businesses can sometimes do this better than the giants, because they can choose simpler tools and keep their data clean from the start. And they have fewer legacy systems to integrate. Luna: Give me an example. How would a local boutique handle this? Lucas: Take a local clothing store. They have a point of sale system, an email marketing tool, and maybe a loyalty app. The key is to make sure those three are connected in real time. Some platforms like Square and Mailchimp have native integrations that sync purchases to email lists instantly. Luna: So if I buy a dress on Saturday, I don't get the 'we miss you' email on Monday. Lucas: Exactly. And they can also set up triggers based on purchase history. If someone buys a specific brand of jeans every six months, and it's been six months, send them a restock alert. That's useful, not annoying. Luna: So it's about being relevant, not just frequent. Lucas: Right. And the common thread is: know your customer's baseline. The automation should be calibrated to the individual, not the average. Luna: But I'm thinking about the data quality problem. Even if you have the right tools, if your data is messy, you're back to square one. Lucas: That's the other big piece. Data hygiene. You can have the best automation in the world, but if you have duplicate records or outdated addresses, the whole thing falls apart. So there's a discipline aspect. Luna: So the takeaway for marketers is: before you build another campaign, audit your data architecture? Lucas: That's the takeaway. It's not sexy, but it's the foundation. And for the loyalty program specifically, the question to ask is: does your automation know who your best customers are? If it doesn't, you're going to keep sending that 'we miss you' email to someone who was just there. Luna: And that's a fast way to make them feel invisible. Lucas: Exactly. And the irony is, the more you spend on those campaigns, the more you might be pushing them away. So it's worth getting right. Luna: Yeah. I'm going to check my own data sources after this. Lucas: And if you're a marketer, maybe start by mapping every touchpoint your customer has with you, and then look at what your automation actually knows. Because that's where the gaps are.