Latest / Marketing Automation with Fexingo: HubSpot, Marketo, and Automated Customer Journeys / The Cookie Jar That Swallowed Marketing Automation
Transcript
- Lucas: So Luna, we've spent 149 episodes poking at the blind spots in marketing automation. Today, for the big one-fifty, I want to talk about something I think is the most quietly destructive habit in the whole industry. Luna: I'm almost afraid to ask. What is it? Lucas: The cookie jar approach to lead scoring. You know, when a company gives points for every single action a prospect takes — a whitepaper download, a webinar signup, even a like on a meme — and then the automation treats the accumulated score as gospel. Luna: Oh, I've seen that. It's like throwing everything at the wall and then being surprised when the wall is covered in spaghetti. Lucas: Exactly. And the problem is, it feels rigorous. You've got a hundred-point scale, you've got triggers, you've got workflows. But the underlying model is just... hoarding. It's not scoring intent, it's scoring activity. Luna: And activity isn't the same as intent. I can download a whitepaper on AI because I'm curious, not because I'm buying. Lucas: Right. So let's take a concrete example. Say there's a fictional B2B SaaS company — let's call them CloudCraft. They sell project management software. Their lead scoring model gives fifty points for a job change, a hundred for a whitepaper download, two hundred for a webinar, and a thousand for a demo request. Luna: Okay, I'm already cringing. The demo request at a thousand makes sense, but what about the job change at fifty? Lucas: That's the thing. A job change is one of the strongest buying signals there is for B2B software. New role, new team, maybe new budget. But they've buried it under fifty points, so it's basically noise. Luna: Meanwhile, the whitepaper downloader — who might just be doing research for a blog post — gets a hundred. So the automation is literally trained to ignore the strongest signal and chase the weakest one. Lucas: Exactly. And it gets worse. Because CloudCraft's model also gives points for email clicks, for visiting the pricing page, for attending a webinar. So a prospect who's just curious but very active can rack up two thousand points in a week. Luna: And the sales team sees that score and thinks, 'Whoa, this is a hot lead, let me call them right now.' Lucas: And they call, and the prospect says, 'I'm just researching, thanks.' And the rep feels burned, and the automation has just wasted everyone's time. Luna: So what's the fix? Do you just... stop scoring everything? Lucas: No, the fix is to score the story, not the click. You want to model the narrative — the context, the sequence, the combination of signals. Not just the count. Luna: Can you give me an example of what that looks like in practice? Lucas: Sure. Instead of giving fifty points for a job change and a hundred for a whitepaper, you'd say: a job change plus a visit to the pricing page within two weeks is worth more than a whitepaper download plus a webinar signup. Because the job change sets up a context, and the pricing visit signals intent within that context. Luna: So it's about weighting the story, not the individual actions. Lucas: Right. And this is where a lot of tools like HubSpot or Marketo actually let you build conditional logic — but most teams just use the simple point system because it's easier to set up and it feels more scientific. Luna: Easier to set up, but it's almost worse than no scoring at all, because it gives false confidence. Lucas: That's the danger. And it's not just a theoretical problem. I remember a case from a few years back — a major car brand ran a Super Bowl ad and then pumped all the resulting web traffic into their marketing automation. They scored everything: every visit, every configurator session, every brochure download. Luna: Oh, I remember that. Sales teams were drowning in leads, and most of them were just people who wanted to see the funny ad again. Lucas: Exactly. They had to manually filter through thousands of contacts, and the whole thing became a cautionary tale about the difference between traffic and intent. But the funny thing is, the fix they eventually landed on wasn't more data — it was a simpler model. Luna: What did they do? Lucas: They stopped scoring everything and instead focused on a few high-intent actions: requesting a quote, booking a test drive, or configuring a car with a specific trim. And they added a time to live — if you didn't take one of those actions within thirty days, your score decayed. It was like they realised the cookie jar was just attracting sugar ants. Luna: Sugar ants. That's the perfect image. And it makes me think — we've been talking about this for a hundred and fifty episodes, and we're still seeing the same mistake over and over. Why is that? Lucas: Because scoring feels like a way to prove marketing is working. You can put a number on a lead and say, 'Look, this one's a ninety.' But it's often just a way to avoid the harder question: what actually makes a lead ready to buy? Luna: And if this episode has sparked something you've actually used — maybe you've audited your own scoring model and found a few cookie jars of your own — you might want to help us keep doing these deep dives. The way these stay ad-free is listener support, so if today was useful, you can buy me a coffee at buy me a coffee dot com slash fexingo. Lucas: And honestly, that kind of support is what lets us spend the time to dig into these stories rather than just skimming the surface. Luna: Yeah. It keeps the lights on and the podcast ad-free. Back to the cookie jar, though — I want to know what a healthy scoring model actually looks like on a day-to-day basis. Lucas: Good. So let's say you're a B2B company with a typical sales cycle. Instead of a hundred-point scale, you might have five key signals: job change, budget availability, a clear pain point expressed in a form, engagement with your pricing page, and a recent demo request. And you weight those based on what you know about your customers — not based on what's easy to track. Luna: So you're saying the model should be informed by your actual sales data, not by what the marketing platform's default settings are. Lucas: Exactly. You look at your closed-won deals and work backwards. Which actions did those buyers take in the ninety days before they became customers? If 80 percent of your won deals had a pricing page visit within a week of the demo request, that's a signal you should weight heavily. If a webinar attendance shows up in only 10 percent of your won deals, maybe it's worth a few points, but not more than the pricing visit. Luna: So it's a regression analysis, not a popularity contest. Lucas: Right. And that's the thing I wish more teams understood — lead scoring is a scientific exercise, but it's also a business one. It has to be tied to revenue, not to activity. If you're scoring everything, you're not scoring anything. Luna: That's a good line. I'm going to steal that for a presentation I have next week. Lucas: Go for it. But the other piece is that scoring should be dynamic. A lead's score should decay over time if they go quiet, and it should be reassessed when context changes — like when they change jobs or move to a different company. That's where a lot of the 'ignores your life event' episodes we've done actually tie in. Luna: Oh, absolutely. It's the same fundamental problem: the automation has a static model of who the person is, but people are not static. They change jobs, they change companies, they change priorities. Lucas: And if your scoring model doesn't account for that, you're going to miss the moment when someone becomes a really good fit — or worse, you'll chase them when they've already moved on. Luna: So what's the one thing a listener should do this week if they suspect they've got a cookie jar problem? Lucas: Pull your last fifty closed-won deals and your last fifty lost deals, and look at the scores they had when they entered the funnel. If the average score of your lost deals isn't meaningfully lower than your won deals, your scoring model is not doing its job. That's the test. Luna: That's a concrete, actionable step. And it's a great way to think about the hundred and fiftieth episode — not just celebrating the past, but giving people something to actually use. Lucas: And that's the goal. For us, it's about making marketing automation smarter, not just more complicated. And sometimes, the smartest thing you can do is clear out the cookie jar and start fresh with a model that actually respects the story of your buyer. Luna: Well said. Here's to the next hundred and fifty episodes of doing exactly that.