Seasoned marketing executive and serial entrepreneur Mark Stouse discusses the importance of analytics and having a single source of truth for informed business decision-making. Mark explains how his platform, ProofAnalytics.ai, aids businesses in tracking progress and making strategic decisions supported by artificial intelligence. Mark also reflects on the critical differences between being data-driven and analytics-led, sharing leadership insights from his forthcoming book.
Adrian Tennant: Coming up in this episode of IN CLEAR FOCUS.
Mark Stouse: Data alone can’t forecast anything. It is a raw material. It is like crude oil, It has to be distilled into insights that create value, that make better decisions. And the refinery for data is analytics.
Adrian Tennant: You’re listening to IN CLEAR FOCUS, fresh perspectives on marketing and advertising produced weekly by Bigeye: a strategy-led, full-service creative agency, growing brands for clients globally. Hello. I’m your host, Adrian Tennant, Chief Strategy Officer. Thank you for joining us. Earlier this year, MarketingWeek reported on a study that found the average tenure of Chief Marketing Officers working at the top 100 advertisers in the US had fallen to the lowest level in a decade: 39 months, or just 3.3 years. Another study found that the average tenure for CMOs at B2B businesses is longer, at 53 months or 4.4 years. B2C marketing is perceived as more dynamic, putting CMOs under pressure to keep up with rapidly evolving consumer preferences, whereas CMOs in B2B organizations are more likely to be responsible for the go-to-market strategy – or GTM for short – which includes marketing, sales, product, and customer success functions. Our guest today is Mark Stouse, who, over the past two decades, has held senior marketing positions, including CMO and Chief Commercial Officer at large technology companies Hewlett-Packard, BMC Software, and Honeywell. Today, Mark is the Chairman and CEO of ProofAnalytics.ai, a company he founded, which is on a mission to revolutionize marketing analytics by providing advanced, AI-driven solutions. To discuss how ProofAnalytics quantifies marketing’s impact on a brand’s overall performance and to share his unique perspectives on the evolving landscape of marketing leadership, Mark is joining us today from his home in Paradise Valley, Arizona. Mark, welcome to IN CLEAR FOCUS.
Mark Stouse: Hey, thank you so much. I’ve really been looking forward to this conversation.
Adrian Tennant: Me too. Mark, as I mentioned in the introduction, you’ve held senior marketing leadership positions in large enterprises, including Honeywell and BMC. Today, you’re the founder and CEO of a software-as-a-service firm, Proof Analytics. So, what prompted you to make the leap into entrepreneurship and establish Proof Analytics?
Mark Stouse: Proof is actually my second entrepreneurial venture. My first one was when I was quite young, and it was in the 1990s in a completely different area. It was in defense technology. After I successfully exited that, I kind of really wanted to go back to my roots, which were marketing and communications and things like that. And so I was hired by Compaq as a senior director and then rapidly rose through that, particularly after the merger with HP. And it was about that time all the pressure was really mounting on marketing to prove its value. Mark Hurd came in in 2005 as the CEO of HP and really shook it up. And he was a very sales-focused CEO and very operations-focused CEO. And so he had no problem getting in our face, and it was not pleasant. And so I rapidly got to a point, as did I think a lot of my colleagues, where we realized that, you know, we had to like get out of there or something, or in my case, it had to do something to fix the problem. And that’s how I’m wired, for good, or maybe occasionally for not so good, right? That’s the way that I am. Actually, in the very early days, I remember I found my old high school and college math [books]and started to get myself reacquainted with some of the principles. And, of course, one of them is regression. And that sort of led to a rapidly escalating interest and a lot of executive support, not only at HP, but then at BMC, and ultimately probably the reason why I was hired as CMO At Honeywell Aerospace. So there was kind of this 15-year arc of going from being a marketing leader – that was probably, in many respects, just like any other marketing leader – to being one of a very rare, short list of marketing leaders, particularly in B2B, who was actively using analytics to lead decision making. When you do all that, and you get to the top of it, at that time anyway, you were doing it by brute force, right? And, so we started running into not just mathematical challenges that we had to get over and all that kind of stuff, but operational issues. You know, you start to really find out what the real issues are that are inhibiting your relevance and your effectiveness, and, on a broad basis, I think this is true for analytics in general, data science in general. Inside of businesses, the operational latency that exists, in other words, how much time passes between when the insight to make the decision is needed and when it actually arrives. That could be like, actually, way too long most of the time. It could be months later, in many cases. So you’re fighting that fight; you’re fighting the scalability fight. You know, how do I implement more models, test more things, and help more people make better decisions? When at the end of the day, all of this is being done by human beings, and there are only so many people hours in the day and all that kind of stuff. The understandability piece. If you hand an average business team a data science output from one of these models, they’ll look at it and go, “What the hell am I supposed to do with this?” It’s not actionable. It’s not understandable to them. And there’s a whole bunch of stuff underneath each one of these comments. And then the cost, right? The cost is probably the easiest thing to understand. At Honeywell, nobody ever complained. The output was so significant that I never heard a single leader complain about the fact that we were spending $8 or $9 million a year just on analytics in go-to-market, and we were having to spend that kind of money, a ridiculous sum because we were having to do it by brute force. We had to over-hire data analysts in order to get the latency down on the recalculations of all these models so that we would be relevant. And so it was kind of like in for a penny in for a pound. And it clearly worked, and it clearly made things a lot better, but you did not have to be a rocket scientist to figure out that the number of companies in the world, like Honeywell, who are willing to spend that kind of money on analytics – particularly for just one or two functions – that’s a pretty, pretty short list, probably. So, you know, coming out of the software industry before Honeywell, I was just sitting there going, “Man, it does not take a rocket scientist to figure out that automation is the wave of the future here.” And it’s not about lights-out automation. It’s not about taking the human data analysts or data scientists out of the equation entirely. That’s a fiction. It’s a total fiction and will remain a total fiction for quite some years to come. But by using AI, using automation, you can significantly accelerate the latency of modeling itself. The recalculation of modeling and the delivery of insights, you can reduce costs dramatically. You can increase scalability, but from a cost side perspective and from an absolute value side perspective, immensely. You can build screens that are not just really beautiful versions of data science output. You can actually interpret those kinds of data science results into stuff that real people understand and can make good decisions about. And so that’s why we built Proof.
Adrian Tennant: Mark, you’ve described proof analytics as being like a GPS. How so?
Mark Stouse: Most problems that we face, most decisions that we face in this life, and certainly in business, are really navigation questions. You know, “Where am I?” Well, most people know that. “Where do I need to go?” Okay. “What’s the best, most effective, most efficient way to get there?” So when you’re using your GPS on your phone, you plug in your destination, and what does it do? It gives you three choices. Three routes. You pick one. Each one of those is actually a forecast. It’s an optimized forecast for each route. And that’s why the times, for example, the ETA on each one, varies slightly and all this kind of stuff. And so, that’s exactly what Proof does. Proof is tracking your progress on all the stuff that you’re doing, that you control in the context of all the stuff that’s in that model that represents all the things that you don’t control that is important to that everyone has pretty well figured out is really important to the outcome. This can be stuff like competitor action, macroeconomic realities, you know, can literally be anything that’s relevant, and you’re cruising along, and all of a sudden, it’s running forecasts in Proof. So it’s maintaining the forecast in your GPS, right? But then, all of a sudden, it starts to show that reality is intruding, and maybe reality and the forecast are not syncing up anymore. Proof gives you that really fast, on an on-demand basis, based on the cadence of your business. And so you’re always up to date is what it really means. And you can see things deteriorating or getting far better in real-time. And you can make certain decisions. This would be roughly equivalent to re-routing yourself on a GPS. Or hey, this, this street that you were on, it was great. It was the best way forward, but there was an accident a mile ahead. Traffic is piling up, and it’s no longer the best way to go. And if you stay here, you’re going to be an hour late. And if you reroute, go here, go there, go here, go there, you’ll be there in eight minutes, right? That is, in essence, exactly what Proof enables businesses to do.
Adrian Tennant: You’ve explained what types of problems ProofAnalytics aims to solve, but I understand it’s available in two different versions. Could you just explain the differences between them?
Mark Stouse: Sure, the original version was built on AWS, so completely neutral, agnostic platform. It is wide open to anyone using anything, and it is priced by the number of data sets that are in your library in Proof. So it’s, I think it’s like $500 a year per data set. The other version is on Salesforce. It is part of a larger product, which is really a go-to-market ERP. It’s called an MRM tool: Marketing Resource Management tool. It is a very popular category. A lot of action in that category. If you’re a heavy Salesforce user and you want an MRM, we are absolutely totally the way to go. So we end up being in that situation, a big part of platform fights between, say, Salesforce and Adobe. That would be a pretty normal one, where we are really part of the Salesforce strategy to win a particular account. And one of the ways we do this is that in addition to all the classic ERP kind of functionality – so planning and budgeting and compliance and approvals and asset management and all this kind of stuff, right? – we have our analytics fully integrated. And so it’s a complete closed-loop system where the results of the analytics come back around and inform the next planning and budgeting cycle. It’s really been cool to see the reaction to it. So that’s the main difference.
Adrian Tennant: Mark, are your customers typically marketing consumer brands, or would you say there’s a higher proportion of business-to-business marketers using the platform?
Mark Stouse: It’s both, for sure. I think that if there is a skew, it is towards B2B. And that could just as easily be a function of me and where I have tended to move the business, so to speak because I’m heavy B2B in my own career. But no, it’s both. The math doesn’t care. You know, the math has no concept of B2B or B2C!
Adrian Tennant: What kind of ROI or other business effects do your customers typically see from deploying Proof Analytics?
Mark Stouse: ROI is certainly one of them, right? But one thing you have to really say about ROI is that ROI is something that is only known about the past. The cool thing about multivariable linear and nonlinear regression is that it gives you a historical analysis without a doubt, right? It says, look, here’s a stack rank, everything that you did and everything that you didn’t control and its relative weight, against this particular outcome that is your dependent variable and time lags, which is a key part of this whole thing. All that is there. Then, [it] forecasts forward, not on an extrapolation, but on a real computed forecast, what the ongoing potential ROI of something might be. And where this becomes really important is that it saves you from spending a lot of money in places that aren’t giving you anything and won’t be giving you much in the future either. So, this is a form of optimization. And it allows you to just constantly – constantly means different things to different people, by the way – but regularly, routinely, it allows you to reoptimize your spend against a particular outcome net of stuff that’s going on in the environment. So a real-life example: Right now, out there in the marketplace, number one, it is a cardinal rule of thumb in data science that two-thirds of any model is externalities. We live in a probabilistic world. We are seeking to surf waves that we don’t control. And anyone who thinks that it’s mainly about them has got some rethinking to do. So we’re helping people focus on what those external factors are and figure out, okay, so the volatility in the marketplace, the cost of money. The rapid changing, flipping around in different areas, your competitor actions, all this kind of stuff. These are creating a headwind of this kind of power, and it is lessening the effectiveness of whatever it is that you’re doing. So now you have some choices to make that are not just marketing or go-to-market choices. They’re actually business choices. So we can then model, or more accurately, our customers can model in the tool, you know, scenarios, right? What is it going to take to make progress despite these headwinds? What is it going to take to overcome these headwinds and make net new progress, regardless of the headwinds? And then that becomes a business decision. Do we have the money to spend on that? If we do have the money, is that the best way that we want to spend it? What’s the opportunity cost of that? What is going to have to suffer, if you will, from a funding perspective? If anything, in order to fund this go-to-market effort, that’s going to be strong enough to punch through the conditions in the marketplace. There’s just all kinds of stuff like that. And then, we don’t really get into this very often cause we’re a scale-up, and so we have to stay focused, but ProofAnalytics is an agnostic platform, so there’s nothing about it that says we are marketing or go-to-market only type software, right? So we have customers that figure this out and start running computed forecasts and regression analytics and all that kind of stuff, a whole bunch of different things having nothing to do with go-to-market.
Adrian Tennant: Let’s take a short break. We’ll be right back after this message.
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Adrian Tennant: Welcome back. I’m talking with Mark Stouse, the founder, Chairman, and CEO of ProofAnalytics. ai, an AI-driven platform that enables businesses to measure and optimize their marketing investments. I opened this podcast with some stats reflecting the average tenure of Chief Marketing Officers. Now, Mark, in a video you posted on LinkedIn, you made the point that great marketing drives revenue, margin, and cash flow, so what are the key skills or competencies you believe modern CMOs need to have – or at least focus on developing – to lead the marketing function successfully and gain the trust of other members of the C-suite?
Mark Stouse: I think that right there is the question, right? Everything else hangs off that question. I think that what we’re seeing today in marketing and sales and other go-to-market functions is something that most professions go through at some point in their history. And that is, it becomes urgently necessary for the leaders in that profession to become more and more T-shaped. Now, what does that mean? That’s a classic recruiter’s term, HR term. What does that really mean? It means that right now, the average functional specialist, no matter how exalted in rank, is an ‘I’-shaped person. That means, you know, they think about it function first; they talk in functional language, jargon, and things like that. Even with people who don’t understand that jargon, they tend to be fairly provincial within the context of the. profession that they’re in. Some of them have realized that maybe they don’t need to show up that way quite as much. And so they have adopted a veneer of being T-shaped. They throw around certain business words and all this kind of stuff. And they play a business person on TV, so to speak, right? But to be truly T-shaped means that you know a lot about the business of the business, and you understand what’s really important and what really needs to happen for the business, and you are looking at your function, whatever your specialization is, through the lens of that horizontal part of the T, right? All that contextual understanding about the business. And you are not looking at the top of the T through the limbs of the ‘I”, the vertical part of the T. That stops happening. Some hallmarks of this would be a CFO is a business leader who happens to specialize in finance. A CIO today – it didn’t used to be this way, but today – he or she is a business leader who happens to specialize in enterprise IT. And so when they are sitting around the table with the C-suite, they can talk about their area using entirely business terms and business concepts and business impacts. Zero functional jargon at all. They can then also turn around and talk to their functional brethren and sisters about what’s going on in the business and what needs to happen in the business using a mixture of business terms and functional terms. So they are the middleware layer; they are the translation layer between these two groups, which actually probably have never been further apart in perspective than they are today. And so, T-shaped leadership has never been more important. It’s always been important. But it’s never been more important than it is right now.
Adrian Tennant: I’ve read that you believe there’s a significant difference between being data-driven and being analytics-led. Can you unpack this for us?
Mark Stouse: Sure. I think we all understand at least part of the popularity of the phrase “data-driven.” I mean, the alliteration alone makes it very attractive, right? It rolls off the tongue. It was an instant signal that you could make to people that says, “I’m a modern leader,” right? “I’m sophisticated,” or whatever. The problem is – and I think that this is just the stone-cold reality intruding on the beauty of language, right? – so, first and foremost, data is always and only about the past. Period, no exceptions. The fact that it’s measured means it’s already happened or whatever that was. It’s already happened. That’s how it got measured, right? Data alone can’t forecast anything. You can extrapolate off data. But as we have seen, that is a fool’s errand, and particularly in times of great volatility, you can get caught out easily with that. So that’s not a good idea. It is a raw material. It is like crude oil, right? It has to be distilled into something of utility and value. That in this case, data has to be distilled into insights that create value, that make better decisions. And the refinery for data is analytics. Analytics is not about siloed data. Analytics is about the relationships that exist or don’t exist between all kinds of things that ultimately produce given outcomes. If you operate on a data-driven basis, the analog here would be that you are driving a car forward but only looking in the review mirror the entire time. That’s probably not going to work out super well. Analytics-led says the past is kind of interesting. It fuels our understanding of the future, done correctly. And so, analytics are like the headlights that shine far out in front of your car as you’re moving along the highway at night, And, as we’ve all seen, we’ve all experienced this as drivers, right? I mean, there’s a limit to your headlights; they kind of hit a wall at some point. But it’s usually a pretty good distance ahead of your car. And that’s the equivalent of the forecast. Any forecast will lose accuracy and fidelity if you stretch it far enough out. It’s the way that time works. It’s another law of gravity kind of thing, but, like the headlights on your car, it’s renewing on a rolling basis, literally. Because let’s say that your headlights extend 50 yards ahead of your car. And you’re driving along, right? And so your ability to see what’s coming is constantly being revealed 50 yards ahead of your car, right? It doesn’t mean that if you went back two miles, you could see what you can see right now, but it does mean that given where you are right now, you’ve always got a jump, if you will, of around 50 yards to, make decisions, make adjustments, whatever. That’s why being analytics-led is so crucial and why being data-driven is a bust.
Adrian Tennant: Going back to the C-suite for a second, establishing the value of marketing spend as an investment rather than a cost has traditionally been challenging because the Generally Accepted Accounting Principles, which accountants have to know and follow, usually classify marketing expenses as a cost. However, a recent study from the UK-based Institute of Practitioners in Advertising reveals that nearly 90 percent of investment analysts now see marketing as an investment in future growth, similar to technology R&D, where investments are capitalized and then amortized over time. So Mark, I’m curious: do you think there’s potential here for CMOs to make the case for rewriting or at least reinterpreting those accounting rules?
Mark Stouse: Yes. I’m actually on a committee that’s just starting right now with the Association of National Advertisers, the ANA, that’s all about that. It’s all about brand valuation. But certainly, the goal is to co-create a model in concert with finance people and accounting people that brings new insight, very similar to what you were just describing, into the mix and say, “Hey, we’re probably not talking about a situation where all of go-to-market spend ends up in CapEx suddenly after decades of OpEx assignment. But it’s totally legit to begin to put some of – maybe even more than 50 percent – of go-to-market spend into CapEx, capitalize it, and then spend it out over however many years.” So I’ve had this conversation with probably four or five CFOs, in the context of a larger conversation, and all of them have been very open to it. Very interested in it. Go-to-market – which is, collectively, marketing, sales, product, and customer success functions – it’s a lot of money being spent there. If it’s a publicly traded company, there’s a lot of EPS impact up and down on that money. And legitimately, putting a lot of it into CapEx is going to significantly benefit the company while acknowledging the fact that time lag alone makes it an incredibly compelling case for the fact that a lot of this go-to-market investment needs to be recognized outside of the current quarter.
Adrian Tennant: Great. Mark, you mentioned you’re working with the ANA, and of course, you’re also a regular contributor to LinkedIn, where you’ve shared recently that you’re now in the process of writing a book. So could you tell us a little bit more about that?
Mark Stouse: Yeah, you want to talk about a journey into all kinds of things, right? I think you learn a lot about yourself when you sit down to write a book. And I’m – among other things – I’m really a professional writer. My first job was as a reporter at a newspaper, and my second job was as a reporter for Newsweek. So, I’m not exactly a slacker in the writing department, but there is a definite difference between writing even longish short form and writing a book. This book is not – we don’t want it to be a tome. It’s not meant to be a monument to me, and it’s certainly not meant to be something that gets published, and no one reads. But I want it to be something that helps people. If it’s just sitting there, it’s no good to anybody. So we have capped it at 150, 160 pages, somewhere in there. And, you know, it is a lot harder to write a short book than a long book. There are so many paradoxes in the whole process that I can’t even begin to cover them all here. We are writing about a lot of what we’ve just talked about. There’s a go-to-market revolution that’s just starting right now. It’s being led by the C-suites and a lot of Fortune 1000 companies. I’ve interviewed a lot of CEOs and CFOs. I may have, at the moment, anyway, the clearest understanding of where they are in their heads on this, as a group, of anyone. It’s quite a tall statement to make, but I think that’s probably a defensible statement. It’s going to be, I think, a really good book. We’re going to talk about T-shaped. leadership, which is really where it’s all at. What enables that? What are the connectors that take the top part of the T and connect it to the bottom part of your T, your team’s T, and your company’s T? What does that look like? One of them has to be analytics, right? There’s got to be a single source of truth. And it’s not a data visualization page. This is all about relationships and dynamism. There are causes and effects. There’s a lag time between causes and effects. What does that mean? How does that increase our risk, right? All that kind of stuff is part of it. Fluency in multiple languages. I don’t mean French and German and Spanish and Chinese. You have got to be able to talk about your specialization. You have got to be able to read financials and really understand them, understand what they mean, be able to discuss them, be able to connect the dots between what you do and those financial outcomes. The people who can do that will be very highly in demand over the next, say 10 to 20 years, and so I just think that is really where the action is.
Adrian Tennant: Well, it certainly sounds like it’s going to be a tight 160 pages. That’s a lot of information right there, Mark! Do you have a working title for your book?
Mark Stouse: Right now, the working title is GTM Revolution. Something tells me that probably won’t be the actual title. You know, publishers have the final say on all that. And I’m sure they’ll do a good job.
Adrian Tennant: I’m sure there will. Mark, if IN CLEAR FOCUS listeners are interested in learning more about ProofAnalytics.ai, what’s the best way to get in touch with you?
Mark Stouse: You can always use email, although that’s probably the least desirable. That’s Mark.Stouse@ProofAnalytics.ai. Probably the best way, actually, is I’m extremely active on LinkedIn, as you’ve alluded to, so very easy to find, and either send me a private message on LinkedIn or comment under one of my comments and just say, “Hey, we’d love to talk to you, can you contact me by private message?” And I’ll do that, and we’ll set something up. That’s probably the best way to do it right there.
Adrian Tennant: Perfect. And any idea when we might see the publication of your book?
Mark Stouse: Well, so right now, the kind of the word on the street is that it’s going to be in May. Hopefully, it is in time for poolside reading or beach reading! But I’ve learned that there are many machinations in the publishing business that are utterly outside of my control as the author. And so, that’s what we’re guiding towards right now. But we’ll see.
Adrian Tennant: Excellent. Mark, thank you very much for being our guest on IN CLEAR FOCUS.
Mark Stouse: Thank you so much. I really enjoyed it.
Adrian Tennant: Thanks again to my guest this week, Mark Stouse, the founder and CEO of ProofAnalytics.ai. As always, you’ll find a full transcript of our conversation, along with links to the resources we discussed, on the Bigeye website at bigeyeagency.com. Just select ‘podcast’ from the menu. Thanks for listening to IN CLEAR FOCUS, produced by Bigeye. I’ve been your host, Adrian Tennant. Until next week, goodbye.