Mark Stouse Podcast Transcript

Mark Stouse Podcast Transcript

Mark Stouse joins host Brian Thomas on The Digital Executive Podcast.

Brian Thomas: Welcome to The Digital Executive. Today’s guest is Mark Stouse. Mark Stouse 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 Stouse 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 work group 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. 

Mark Stouse: Hey, gra- glad to be here.  

Brian Thomas: Absolutely, my friend. I appreciate it. And you’re hailing out of the Phoenix area. I’ll say Paradise Valley.  A trivia I didn’t know. It was 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 of apart. I just appreciate you making the time today and traversing the time zone. 

So, Mark, if I could, I’m gonna 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 Casual, Proof Causal AI. 

What did you see from the inside that convinced you that this was a problem worth building a company around?  

Mark Stouse: 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 has only been exacerbated by the volatility and high velocity change in the marketplace, which essentially means that past is not prologue. 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, we just keep on seeing it sink and sink and sink, and it crossed over the fifty percent mark in twenty twenty-five. So that meant that was in the Fortune five hundred or so, Fortune one thousand, actually. It was over fifty percent of go-to-market spend, so that’s sales, marketing, product, customer success, and, and depending on the company, other things that touch the customer. 

That’s more than, fifty-two, fifty-three percent of it was ineffective. And for startups and scale-ups, it’s a lot worse. It’s like about seventy-two percent 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. 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. And 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 ’cause 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, 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, were finance leaders, and that has only proven to be more and more true  

Brian Thomas: Thank you. Really appreciate that, and obviously your experience lends to what you’re building at Prove 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 s-statistics, the ineffectiveness across the, a range of, let’s say, Fortune Fortune one thousand over fifty percent. 

And, and that’s, 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-marketness go-to-market effectiveness, which includes marketing, product, customer sus-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. 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?  

Mark Stouse: Well, Causal AI is, 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, 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, what mathematicians call a confounder. It, it… and, 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, with the market situation 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 it– 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 forty years. Another re-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, in the macro world the– 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. 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 i-is, is a bust right in the current environment.  

Brian Thomas: Thank you. Appreciate you breaking that apart for us here. You did mention a couple things here. Causal AI is about cau-causal inference. Your analogy about the sun and, and selling that ice cream, right? 

You explained the variability that can go into all something like this, and not everything can have a correlation that is gonna give you the, 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 the, 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?  

Mark Stouse: Well, I think that’s exactly what finance teams have been looking for a really long time, and, and to be perfectly honest with you, they have just been really frustrated for years by the tendency of go-to-market teams to, ooh 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, is the organization that develops the accounting rules that all companies in the United States use pr– and, particularly public companies, but it goes well beyond that. 

And so when you– when they see that we are FASB compliant, th-they kind of relax ’cause 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, and 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, what they’re dealing with. 

The, the MASB piece is also really important for marketers. I mean, we want, we want we want marketers and other professional groups to know that we understand where they’re coming from too, right? And that we are not, 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 ’cause 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, kinda 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, and today that is, that’s really super important because there’s… 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, you’re kind of looking at a situation maybe this is the best way to talk about it, right? At the… 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, 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, twelve to eighteen months of pursuit, the, the the decision is, well, we’re not gonna buy from anybody, okay? 

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, 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.  

Brian Thomas: Thank you. And again, I appreciate you really unpacking a lot of this for our audience. But just again, I like to highlight a few things. You know, you talked about early on the finance teams, the boards, they’ve been really frustrated with the way things have been reported in the past. 

And, and there’s a lot of, strategies where we throw a lot of money down, after something that, that, that isn’t provide a good ROI. But they, they’re frustrated, and they, they wanna find alternatives ways to demonstrate their, their value in these areas. But you did mention being MASB certified and having that FASB compliance helps build that trust and helps really at the end of the day, people believe in, in that they’re doing something that has some rigor behind it, some, some, whether it’s a certification or some sort of compliance is always a good thing. 

And then you talked about, of course, the CAC, the customer acquisition costs that are continually rising, and there’s a lot of frustration in this area as well ’cause we throw a lot of money after this. So, I appreciate that. And Mark, the last question of the day, you’ve written about how AI is shifting B to– B2B buying behavior and putting buyers more in control. 

As agentic AI begins to act on behalf of buyers, researching, evaluating, and even initiating purchases, how does that transform go-to-market strategy, and what does it mean for demand generation as we know it?  

Mark Stouse: That’s a great question. It has very far-reaching implications and consequences. For the first time in, in kind of like in go-to-market history, we have a situation where customers are actively deploying capabilities using AI to filter and otherwise defeat the, the outbound marketing and sales activity of vendors That is– that’s really important for a lot of reasons, not the least of which is it demonstrates that they’re pretty fed up with the last ten to fifteen years of, of go-to-market activity, constant bombardment of, of marketing automation and things like that. 

I mean, you see this also in the EU legislation for the past decade. I mean, how many, how many laws have you know, in their preamble say basically that, that the abusive marketing tactics of businesses is the reason why this law exists? I mean, wow, right? I mean, talk about an indictment. That is pretty bad. 

And so, what we see today is enormous filtration of email and text m-messages so that the person that, that is the intended recipient doesn’t even see it. It’s intercepted at the server level and, And you, you talk to IT teams, they’re seeing after they implement those kinds of tools, they’re seeing, really staggering reductions in email traffic ’cause they’re getting back to the actual non-go-to-market kinds of email, right? 

And most of… Well, just for those that are kind of wondering about this common question is, well, just how much of your email is go-to-market? It’s generally, in a lot of businesses that I talk to and that Gardner has written about and all that kind of stuff, it’s in the vicinity of two-thirds to three-quarters. 

That is a lot of go-to-market email. Text is just as bad. And I think anybody who is in business today can attest to that in their own experience, right? So that is a– That we’re seeing I think a fundamental move by customers to gain control of the process, the so-called funnel. And the funnel actually is now a filter and one of the things that we did on our homepage, we have a kinda looks like a chatbot attached to an LLM, but it’s more than that, and it’s geared to talk to machines. 

So, bots, right? And we have a lot of bots that hit our website and interrogate our, our LLM, which is probably the most comprehensive LLM about causal AI that I’ve seen and, and also about proof, clearly. And so it is– these are customers who are deciding who they’re going to buy from. At, at the, at an absolute minimum, they’re creating their shortlist from, from all of that work. 

And that is, that’s, that’s great. I mean, that’s that’s the way it should be  

Brian Thomas: Thank you. Appreciate that. Really do. You know, you talked about AI in this space. We’re looking down the road a few years and like many verticals, it’s a double-edged sword right now, and we’re exploring a lot, and we’re seeing AI just constantly leapfrog in some of the technologies. 

But your example of some folks using AI to circumvent that, those traditional go-to-market strategies be- out of frustration. And of course, out of all this mess, we start to see legislation come out, starting to crack down, limit some go-to-market strategies. You mentioned an example in the EU and, and they do have a lot just like California. 

But really to add more chaos to all this, then you got all these bots. You talked about that quite a bit and there’s just, there’s just a lot to navigate, and I’m glad that we have experts like you out here helping us find our way in this crazy go-to-market strategy space. So, thank you. 

And Mark, it was such a pleasure having you on today, and I look forward to speaking with you real soon.  

Mark Stouse: All right. Thank you so much, Brian.  

Brian Thomas: Bye for now. 

Mark Stouse Podcast Transcript. Listen to the audio on the guest’s Podcast Page.