Many GTM teams continue to rely on correlation to justify decisions. It’s an ongoing problem. In this kickoff to our Causal CMO Series, Mark Stouse and I cover why marketing attribution continues to fail, how internal data keeps getting “engineered”, and how new legal rulings will put GTM leaders directly in the line of fire.
Mark Stouse and I kicked off the first session with a direct conversation about how marketing attribution models still hold GTM teams back from reporting real-world buyer behavior. Too many are still stuck in correlation, and it’s costing them.
Marketers look for patterns. It’s what we've been trained to do. But in complex, time-lagged buying cycles like in B2B tech, correlation doesn’t tell you anything useful, like what actually caused the deal to close (or not).
Mark made a great point: correlation is binary. It either exists or it doesn’t. That makes it easy for humans to understand, which is why we gravitate toward it. But it’s not how markets work. It’s not how buyers behave. It’s not what happens in the real world.
Causality tells you what actually led to the outcome. It’s multidimensional. It accounts for context, time lag, headwinds and tailwinds, and everything else correlation ignores. That’s why it’s harder to fake and why it’s so valuable.
Many GTM teams still lean on correlation because it’s faster and easier to defend, especially under pressure. As Mark pointed out, correlation-based patterns are easy to manipulate. If the data doesn’t match the story you need to tell, you can tweak the inputs until it does. That’s why attribution charts and dashboards persist: they give the illusion of precision without exposing the actual drivers. It looks clean. It feels controllable. But it’s a shortcut that hides the real story.
An intuitive decision is either really right or really wrong. No leadership team can afford that kind of spread.
Mark shared a case where a fraud detection tool scoured over 14 years of CRM records. More than two-thirds of the data was found to be “engineered.”
It wasn’t one bad actor. It was many people over many years, under tremendous pressure to prove their seat at the table, while slowly reshaping the story to fit what they needed to show.
People use data to defend themselves, not to learn.
This kind of manipulation is hard to catch with correlative tools. Causal systems, on the other hand, make it obvious when something doesn’t add up. It’s unavoidable.
Unlike Sarbanes-Oxley, which took years to pass and gave leadership time to prepare, the Delaware rulings came quickly and without warning. Mark called it one of the biggest blind spots in recent corporate memory.
The 2023 McDonald’s case expanded fiduciary oversight beyond the boardroom. Now every officer (CMOs included) is legally accountable for the quality and integrity of business data.
The writing’s on the wall. If you’re not governing your data, you’re exposed.
Data quality is now the number one trigger for shareholder lawsuits. CRM systems are full of data manipulation and missing governance. Lawyers know it’s an easy audit. If your GTM data can’t hold up under causal scrutiny, you’re exposed.
First-touch. Last-touch. MTA. Even Bayesian models. They’re all correlative. They’re all easy to manipulate. And they all fall apart under scrutiny.
Mark told the story of a meeting where a CIO changed the MTA weightings mid-call, then had someone else change them again. Marketing freaked out, but had no causal rationale to defend their weightings. The numbers were all made up.
If your model changes when you tweak the sliders, it’s not a model. It’s a toy.
Jon Miller, founder of Marketo and Engagio, recently said himself that Attribution is BS.
Respect to Jon for saying it out loud. That’s a bit like the first step in the 12-step program: admitting you were wrong. And that’s where every CMO still holding onto attribution logic has to start.
Mark followed up with his own take on why causality is quickly becoming the standard.
Both are worth reading.
Causal models don’t promise certainty. They help you understand what likely led to an outcome, accounting for what you can and cannot control.
Mark compared it to throwing a rock off a building. Gravity ensures it will hit the ground every time, but where it lands and what it hits is a different question, especially when you consider things like time of day, weather, etc.
It’s the same with marketing. You know your efforts will have an effect. What you need to model is the direction, magnitude, and time lag.
The shift to causality has nothing to do with better math. As Mark said, if a vendor’s pitch is built on “new math,” you should run. The math already exists, and it works.
What matters is asking the right questions. Don’t start with your data. Start with the outcome you care about.
Start with the outcome. Work backwards. See if the data supports it.
That shift exposes where the real drivers are. And it resets expectations for performance.
Mark compared it to baseball. If you hit .350 in the majors, you’re a Hall of Famer, even though you failed two-thirds of the time. Baseball is full of external variables players can’t control.
In academia, it’s the opposite. Most of your GPA is in your control.
GTM should be judged like baseball but it’s not. Markets are messy. Nothing is certain. Causal modeling reflects that uncertainty by showing you what data you’re missing or misreading.
But traditional marketing metrics like attribution expect certainty. Marketers are held to a similar standard as academia. This makes zero sense given how uncertain markets are.
And therein lies the problem, and it is a critical insight for GTM teams. We’re trying to make sense of uncertainty using tools that assume predictability. The classic square peg through a round hole.
Attribution tools weren’t designed for complexity or context. They were built to assign credit. They don’t help GTM teams. They polarize them.
Mark explained a few key things every GTM team needs to plan for, including how correlation fails, why causality matters, what legal risk CMOs now face, and how to move beyond attribution logic in B2B GTM.
In Part 2, we’ll get into the mechanics. What causal models actually look like. How to map time lag and external variables. And how to build something your CFO will trust.
Missed the session? Watch it on LinkedIn here.
In the meantime, here’s a quick FAQ to clarify the core ideas, especially if you're new to the conversation or want to share this with your team.
If your dashboard still runs on attribution logic, this is your chance to change it.
If you like this content, here are some more ways I can help:
Cheers!
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