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Chapter 1: What is the main topic discussed in this episode?
Welcome to Corozant Technologies, home of the Digital Executive Podcast. Do you work in emerging tech, working on something innovative, maybe an entrepreneur? Apply to be a guest at www.corozant.com forward slash brand. Welcome to the Digital Executive. Today's guest is Mark Stoose. Mark Stoose is the chairman and CEO of Proof Causal AI.
He leads a team with the only AI-native, MASB-certified, and FASB-compliant causal AI platform that enables go-to-market teams to plan, predict, prove, and pivot their investments in real time. With more than 25 years of experience in marketing communications, customer success, and commercial strategy, Mark helps transform business performance with data-driven insights and agile decision-making.
Mark Stoos also leverages his expertise in go-to-market analytics and economics to co-chair the modern go-to-market organizational structure at the Marketing Accountability Standards Board and to serve as a member of the Brand Valuation Workgroup at the Association of National Advertisers.
In addition, Mark has been recognized as an innovator and leader in the analytics field with multiple awards, patents, and publications. He is committed to advancing the practice and standards of go-to-market accountability and optimization across industries and markets. Well, good afternoon, Mark. Welcome to the show. Hey, glad to be here. Absolutely, my friend.
Chapter 2: How does Mark Stouse define causal AI and its importance?
I appreciate it. And you're hailing out of the Phoenix area. I'll say Paradise Valley. That should be, I don't know. It seems like Phoenix was built around it, but I didn't know that. And I'm in Kansas City, so we're just a little bit apart. I just appreciate you making the time today and traversing the time zone. So, Mark, if I could, I'm going to jump into your first question.
You spent 25 years inside major corporations like HP, BMC Software, and Honeywell Aerospace as one of the first B2B chief marketing officers to actually use causal analytics to measure and calibrate go-to-market spend globally before founding Proof Causal AI. What did you see from the inside that convinced you that this was a problem worth building a company around?
well i mean anybody who has been in the go-to-market professions for any length of time knows all too well that the business is still not persuaded that the value is clearly established that they know what to invest in and what not to invest in. They don't know how to calibrate their investments at all.
And that is only been exacerbated by the volatility and high velocity change in the marketplace. which essentially means that past is not prologue.
Chapter 3: What challenges do organizations face with correlation-based analytics?
You can't look at how you've always done it and just assume that it's still gonna work. And we see that, I mean, every year we publish an annual report on Effectiveness in different parts of companies, but one of them is go-to-market effectiveness. And we just keep on seeing it sink and sink and sink. And it crossed over the 50% mark in 2025. So that was fantastic.
In the Fortune 500 or so, Fortune 1000 actually, it was over 50% of go-to-market spend. So that's sales, marketing, product, customer success, and depending on the company, other things that touch the customer. That's more than 52, 53% of it was ineffective. And for startups and scale-ups, it's a lot worse. It's like about 72% ineffective.
So it's a huge issue that everybody talks about all the time. And so even nine years ago when we started Proof, It was a big, really big deal. And so that's the basis of filling that need is the basis of, of starting the company.
Chapter 4: Why is MASB certification significant for marketing accountability?
And I would say this the, the only pivot that we've really made in that is that we, we work primarily with finance teams today.
looking at the the objective performance of different parts of the company that would include go to market but not even remotely exclusively that and we started out selling to marketers and that that was a hard sell because once you really figure out what causal inference and causal ai really represent you realize that you're looking at
something that's going to really show you the facts of the of what's going on and that can be a little scary and and so the only people at that time that weren't well they they not only weren't terrified they were eager To find out more, we're finance leaders, and that is only proven to be more and more true.
Thank you. Really appreciate that. And obviously your experience lends to what you're building at Proof Causal AI. But you talked about, I'd just like to highlight a few things is. You know, your go-to-market business, which you mentioned early on, really doesn't know how to prove that value.
And you mentioned some statistics, the ineffectiveness across a range of, let's say, Fortune 1000, over 50%. And that's kind of scary when people are spending a lot of money in this space. But what you do is you measure the companies and the go-to-market effectiveness. which includes marketing, product customer success, and some of those other areas within the company.
So I appreciate you sharing your insights. And Mark, most marketing analytics tools are built on correlation, showing what happened alongside what else happened.
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Chapter 5: How does causal AI differ from traditional marketing analytics?
Why is that fundamentally insufficient for go-to-market decision-making? And what does causal AI do differently that changes the quality of those decisions?
Well, causal AI is fundamentally about causal inference, which is not a new idea. The math on it has been around for a very long time. It is substantially more representative of reality than correlation. Correlation... is essentially, Hey, we saw that when it's sunny outside, we sell more ice cream, but clearly. The sun didn't cause the sale of more ice cream.
Those two things just move together in time. And so, if anything, the sun in that sense would be what mathematicians call a confounder. And that's a negative word, but what it really means is Something that you don't control that has an impact that you don't control, but it's not the whole thing. It's by no means right.
We're talking about a network of causes and effects that stretch out over time. The time lag is totally variable with the business and with the industry and with the market situation.
Chapter 6: What impact does agentic AI have on B2B buying behavior?
This is, you know, we're talking about something that the marketplace reality is highly variable and correlative systems are breaking right now all over the place. because of that extraordinary volatility and high velocity change. Two really primo examples of this that have nothing to do with go-to-market would be the chairman of the Fed commented about this about a month ago, and in his world,
the more correlative kinds of approaches, econometric approaches, are just completely shattering. They're no longer representing the way that the economy can be expected to move. And that's just because it's correlation within a closed analytical system, and it's diverging more and more and more from absolute reality. which it did not do that nearly as much for, say, the past 40 years.
Another really huge example is actuarial analysis in the insurance business. We've all seen the news coverage about this property insurance company or that one exiting a particular state or a particular zip code where they won't. write policies anymore. It could be because of wildfire danger. It could be because of hurricanes and climate change related stuff. But the real deal is that
Because of the increasing volatility in the macro world, their systems are struggling to accurately predict their level of risk going forward, which makes it impossible for them to price that risk. In other words, decide what they're going to charge you for your policy.
Chapter 7: How can organizations optimize their go-to-market strategies?
And so that risk is so severe that they would rather do something they don't really want to do, which is just not write any policies for people in that area. But they'll do that because to accept the bet, so to speak, could expose them to levels of risk that their modeling no longer delivers.
And so these are all reasons and illustrations of why correlation is a bust, right, in the current environment. Thank you.
Appreciate you breaking that apart for us here. You did mention a couple of things here. Causal AI is about causal inference. Your analogy about the sun and selling that ice cream, right? You explain the variability that can go into something like this and not everything can have a correlation that is going to give you that answer you're looking for. There's just too much volatility.
And I appreciate you really unpacking a lot of that here for our audience. And Mark, Proof Causal AI is the only AI native platform that's both MASB, which is I believe the Marketing Accountability Standards Board certified and FASB, Financial Accounting Standards Board compliant. For listeners who may not know those bodies, why do those standards matter?
And what does it mean for a CFO or board to finally see marketing investment treated with the same rigor as any other financial asset?
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Chapter 8: What future trends should companies prepare for in marketing analytics?
Well, I think that's exactly what finance teams have been looking for for a really long time. And to be perfectly honest with you, they have just been really frustrated. for years by the tendency of go-to-market teams to seek other ways of illustrating their value other than what you might call a truly business approach. FASB in particular is super important because
FASB is the organization that develops the accounting rules that all companies in the United States use, and particularly public companies, but it goes well beyond that. And so when they see that we are FASB compliant, they kind of relax because they understand that we understand their situation. We actually do a lot in the fiduciary duty and decision governance areas as well.
So all of that kind of dovetails together in their minds and it gives them, particularly early in the sales motion, a lot of confidence that they're talking to the right people who really understand what they're dealing with. The Masby piece is also really important for marketers.
I mean, we want marketers and other professional groups to know that we understand where they're coming from too, right? and that we're not trying to sharpen a knife at their expense, right? In the end, one of the wonderful things about causal analysis, causal AI, is that everybody benefits. Well, you could reasonably say, how? Well, it shows you what not to do more of, because it's not working.
And it also shows you where you're absolutely killing it and where you can do more and how much more until you reach a point of diminishing returns. So it's not only a kind of like good, not good kind of rubric, but it's also much more refined than that. It's saying, okay, how much should you really be doing in these areas to make sure that you get the most value without wasting money.
And today, that's really super important because money is no longer easy and it's no longer cheap to get. And if you don't spend it the right way, the first time, the opportunity cost later gets pretty severe. So you're kind of looking at a situation, maybe this is the best way to talk about it, right?
So for the last, say, four years, we're seeing a situation where CAC, customer acquisition cost, is actually underreported. It's far larger than it's commonly declared as being, and it is growing substantially. At the same time, The deal volume that the company in question is experiencing, the average deal size, the average deal velocity, these are all going the other way, right?
Which is not good. And then you have the really, arguably the worst possible compounding outcome, which is after 12 to 18 months of pursuit, the decision is, Well, we're not going to buy from anybody. So you've just wasted all that CAC on that particular customer and didn't get anything for it. And you wasted it for a long period of time.
So we are all about helping people, whether it's finance and those kinds of people or go-to-market professions who need and can benefit from better guidance in this area. We're here to help in that respect.
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