In the latest podcast episode, Mike sits down with Mark Stouse, CEO of data analytics platform Proof Analytics.
Mark discusses the difference between marketing mix modelling (MMM) and marketing resource management (MRM) and how they can demonstrate the impact of marketing activities on business bottom line.
Mark also explains why it is vital to trust and use math when making marketing decisions and why pressure from the C-suite means this is increasingly important.
Listen to the podcast now via the links below:
- Marketing B2B Technology on Apple Podcasts
- Marketing B2B Technology on Spotify
- Marketing B2B Technology on napierb2b.com
Transcript: Interview with Mark Stouse – Proof Analytics
Speakers: Mike Maynard, Mark Stouse
Mike: Thanks for listening to marketing B2B Tech, the podcast from Napier, where you can find out what really works in B2B marketing today.
Welcome to marketing B2B technology, the podcast from Napier. Today I’m joined by Mark Stouse. Mark is the CEO of Proof Analytics. Welcome to the podcast, Mark.
Mark: Hey, it’s great to be here. Thank you so much.
Mike: So Mark, tell me what happened in terms your career? How did you end up founding Proof?
Mark: You know, I started out like probably everybody else in marketing and communications, because I used to do that as well, you know, and I was beating my head against this brick wall of the inability of being able to prove the value of what we were doing, right, where everyone understood that they needed to have marketing and communications. But they saw it more in terms of tactical execution, rather than business impact. And so when there was a budget cut, the conversation was always around, well, what activities, what levels of support are we going to lose, it was never about loss of business impact. And this just seemed to me in this very kind of, at that time, very ethereal sort of way, right to be utter insanity. And so I got to a point where rather than cursing the darkness, I decided to try to strike a match. I mean, I hated math in high school. But all of a sudden, when I rediscovered it in my late 20s, early 30s, professionally, I really gravitated to it. And so I, I started with a team, I started kind of scaling the heights of this problem, and got to a very high level of maturity, not in the b2c side, which is, you know, had already done all this long before, right. But in B2B, I mean, I am probably still one of a handful of B2B CMOS, large company, B2B CMOS, who can prove that they connected everything that they were doing, and their teams were doing to various types of business impact, to the satisfaction of the C suite, and the board, which is the key phrase, right, none of us get to define our own success. Other people do that. And so, you know, I just kept I kept on gone. By 2010, I was hired to be the CMO of Honeywell aerospace, by Dave Cody, who was the CEO of Honeywell International at that time. And you know, we just incredibly complicated very long cycle very business with a lot of time lag in it. And we were able to, to put it all together and change that part of the world at least. But we it costs us like eight or $9 million a year. And so it became very obvious that automation was going to be a really important part of the next step. And that’s what took us to Proof. And so took us three years to build the platform, the way that we felt like it needed to be and we had a lot of early customers, like Intel and Oracle and people like that, who were chiming in and saying, Yeah, I really like that really hate that. Don’t do that, you know, all that kind of stuff. And so it was, it took a while to get going. But boy, you know, it’s it’s been good ever since.
Mike: And that sounds amazing, because what you’re basically saying is you can tell marketers, the impact of what they’re doing. In terms of the business bottom line. I mean, that’s kind of the holy grail for everyone, isn’t it?
Mark: Yeah, no, I mean, I think that really what, you know, most people still talk about this in terms of establishing the ROI on stuff they did in the past. And that’s certainly part of it. And regression, math will generate those multiplier numbers. That’s what they’re called, technically. But the real deal here is can you forecast into the future? So this is not prediction. Prediction is a qualitative thing. The forecasting is quantitative, right? It’s calculated as computed, you need to forecast the impact of your investments into different time horizons. And then you have to be able to recompute those models over and over and over again on a on a an appropriate interval that’s relevant to your business to say, okay, you know, what, the reality is deviating from the forecast, why is that, right, and what do we need to do about it? And if this sounds sort of similar to the way a GPS guy had you on a journey? You would be right on. Right? That is actually it’s been said by somebody a lot smarter than me that every business decision is essentially a navigation decision. When do I need to make a change? Why do I need to make a change? What do I need to change? And by how much do I need to change it? And that is, that’s navigation. And so that’s what mmm, automated modern marketing mix modelling. That’s what it does.
Mike: I love that GPS analogy. So just tell us a little bit more about the company first. I mean, you’ve talked about the mmm product and marketing mix modelling, you also have another product as well.
Mark: Yes, MRM, which is marketing resource management, which is, as a category has been around for a lot longer. And there’s some very, very large players a primo and allocate it. And there’s been a lot of consolidation in the space in the last three years. It’s historically very expensive. So like, you know, if you were to buy, you know, these are general numbers, but if you were to buy 300 seats, for a primo, you’re probably looking at a million and a half and licence fees, and another million and a half and implementation costs. So your total cost, your one is not for the faint of heart, or the sleight of wallet, right? We came along and we said, look, that just doesn’t make sense anymore. And then and this was happening before the bottom fell out of the economy, which made it even more relevant. You know, SAS is supposed to I don’t care what SAS you’re talking about. SAS is supposed to make things cheaper, not more expensive, right. And so we came out with a MRM product native on Salesforce, lightning, we’re the only one that has that. So we have automatic data sync within minutes after you spin up Proof MRM. It’s automatically syncing with whatever Salesforce clouds you have. This is the tool that this is essentially an ERP for marketing, right or for go to market. It’s tracking, your planning, your budgeting, your approvals, your asset management, it’s all that stuff. And it’s a very known category. We’re just disrupting the heck out of it, both from a product point of view and a pricing point of view.
Mike: That’s amazing. I mean, how do you get down to such a low price? When your competitors you say a many times more expensive? What have you done that’s different?
Mark: Well, I think that you have to look at price. I mean, there’s a huge reason why price is one of the four P’s of marketing, right, and this is, this is something that a b2c marketer totally gets and deals with every day. But most B2B marketing teams don’t even touch pricing. So they’re trying to constantly sell value. And there’s nothing wrong with that. That’s really that’s part of the equation. That’s really important. Right? But you know, I can remember when I was 16, getting my first car, and I had to buy my own car. And I really wanted this BMW three series. And there was actually one available for low dollars, relatively speaking. And I and I told my dad about it, and he goes, Well, you know, it’s, it’s not a deal, unless you can afford it. Right. And it was a that was a really tough point. And and the same applies today to enterprise software, right? You can, you can have great value, it can be totally worth it from a value standpoint. And if you can’t stroke the check to buy it, it’s not happening. Right. So you have to price based on where the market is the reality of the market risk factors. I mean, SAS customers have never been more risk averse than they are today. And that goes back probably three years now. They’re dispensing more procurement teams are saying I’m not doing annual contracts prepaid, right. I want an annual contract that’s payable either monthly or quarterly. And I want to be able to get out at any time, right? I mean, these are major shifts in the SAS universe that you have to deal with. And so we decided, I had a great opportunity to talk to Michael Dell about it. And he’s like, man, he goes, you know, you want to be as disruptive as possible right now. Right with your pricing. And so we had the ability from a cost basis point of view, which actually exists in most software companies anyway, to go real low. Right. And so essentially, I mean, I don’t think I’m being unduly transparent here when I say this. Mr. M is our volume, it’s our it’s our generates our the volume of seats, the volume of revenue, all this kind of stuff, the margin is not as high. Okay, we get our margin out of MRM.
Mike: And typically people would want both right, they’d want the the MRM to do the planning, and then the mmm to actually model what’s going to work and what’s not not going to work. Is that really, how people use the products?
Mark: Yeah, no, that that that is an accurate statement. Although I would say that, typically, they come in that, you know, their first purchase is MRM. It’s a very straightforward, let’s call it transactional sale, right? There’s not a lot of implementation pain and suffering attached to it. Unless, unless, of course, you know, we do have some customers that insist on massive amounts of customization. And that’s a different category altogether, right. But the the main customer, the main customer type that we have in large enterprise down through the upper end of the mid range, right is, is going to be, hey, we want to buy it, we’re gonna use it initially, at least for the first year, straight out of the box, right? We want 300 seats that maybe a little bit of services for six months, going down the road, right, and then we’ll talk later if we need more customization or something, right. So basically, they they implement MRM, they get solid with that. But our mmm is fully integrated into that. And so at some point, they feel at a at the right level of maturity, or they’re getting pressure from inside or, you know, whatever, right, and they activate the mmm, portion of it, which makes it completes the loop, right? I mean, so what Salesforce says about Proof is that we’re the only fully closed loop marketing analytics offering around today, right, which is not actually true. Right. There are some others, we have competitors, but I think we are the best. And particularly if you are a Salesforce customer already, right? I mean, there’s just no reason to go anywhere else.
Mike: Yeah, absolutely. I’m, that’s such a good endorsement from Salesforce. So let’s step back a bit. And for people who maybe don’t fully understand and print maybe I don’t as well have not having worked in a huge marketing organisation. Can you just explain what MMA is, what the process of using it is, and how it helps you plan more effectively?
Mark: Sure, I mean, mmm, is nothing but the application of multivariable regression math. So this is the same math that used to answer about 85% of the world’s questions. You know, if you look at the science behind climate change, if you look at the science behind epidemiology, you look at I mean, you just run through all of these major things, right? The analytics are fundamentally rooted in two things, multivariable regression, and then machine learning to establish patterns, right, repeating patterns. And so and they’re very complimentary, they work together, right? So we have automated the regression part, which is the only way and this is one of the laws of gravity here. You know, if you don’t like it, I’m really sorry, it’s not my rule, right? It regression is the only way to get to causality. The only way period, right? And so that’s what we’ve automated. And so essentially, the way it all starts, if we kind of frame this through and the way we onboard a customer, we sit down with them, we say, okay, what are your top 20? Top 50 questions, whatever it happens to be, that you really need answers to right to support decisions that you’re having to make on a regular basis. Usually, formulating that list is not hard for people. Right? It’s particularly, you know, one of the groups that we talked to is we talked to the C suite about marketing. And so we get all of their questions. And these questions are now extremely predictable, right? I mean, like, seriously, there’s like we actually have codified the 50 most common questions right about marketing and marketing impact on go to market, right, the overall go to market sequence.
So we, we we start there, each one of those has parameters to the question, right? Because the way the question is being asked, it starts to suggest the different factors that are important to At. And so we we list that, we start to create a model framework or we are assisting in some cases, the customer to do it themselves. And then those model frameworks become models when they are armed with the right kinds of data. We have brought agile as a methodology into the analytics and into the modelling process, because historically, the way that analytics teams have approached this is to create a giant mega model that’s designed to pretty much explain everything in one model. And it’s just not the way life actually operates. It’s very, very hard to communicate that with the business leaders that need to get value from it. So we exploded it and use you know, we, we created the idea of a minimum viable model, which is something that’s now gone really viral and mainstream in the data science community, it allows you to spin up a very focused, targeted model, you know, work on it in a very discreet very tight way with whoever the business leader is that’s supposed to benefit. get to a point, you know, and say, a week or two, where that business leader is saying, Yeah, you know, what, that answers my question that gives me real value that helps me out big time. At that point, it goes, the model goes into production.
And what that means is it starts to get hooked up to automatic data flows, API’s, right? At which point it becomes largely autonomous, is automatically recalculating that model, every time new data is presented to the model. So this is why this system actually does literally work like a GPS, because you are throwing out a forecast, right? So this would be in GPS terms, this would be your route to your destination, right. And then as, as you move forward, and you have to adjust and bad things happen, or good things happen that get in the way, or, you know, they either hinder what you’re trying to accomplish, or they make it even more effective. You’re having to make changes, right? Just you’re ultimately like going back to the GPS, GPS and saying, Hey, tonnes of traffic ahead, if you stay on this route, it’s going to totally suck, you’re going to be an hour late, right to dinner, or whatever. But if we reroute you, if you go right, left, right, left, right, you’ll only be 10 minutes late. It’ll all be good. Right. And that is, I mean, one one cmo recently, I actually, I guess it was earlier this year, so not all that recent. But he said, you know, the thing I really love about prove is that I’m never really wrong. And I kinda kinda like, didn’t know quite what to do with that, right? And then all of a sudden, it clicked, right? And it’s just like, with a GPS on your phone, you’re never not getting there. You always ultimately get to your destination, it’s changing the way you get to your destination. Whereas if you were using an old fashioned map that was printed 10 years before, right, you you could very easily actually be wrong. Right? You could fail to arrive. Right? And, and I guess probably all of us have a certain age have actually experienced that, right? So that’s really what he meant is that the GPS means you’re never wrong. Also means and if you’re a guy, you really understand this, you never have to ask for directions, which is something that men, whatever reason really hate to do. It’s a universal construct, right? And GPS made it possible so that we’d never have to do that anymore.
Mike: And presumably, because you’ve got this model, you don’t just need, you don’t just have to feed it real values, you can create scenarios. Yeah, you know, maybe you change your marketing mix. And you’re almost saying, Well, if I did this, where will I end up? Is that is that kind of the way it works?
Mark: That is exactly how it works. In fact, that is the single most popular part of the tool, right? Because when things start to change, and that shows up in the way that everything is represented to the user, so it’s very intuitive in that sense. Then how do you know how to reroute right what is what are your options? You’re gonna you’re gonna have to respond and experiment with different scenarios to get back on track. And the you know, with every model and every model has its own screen, right for you to do this, you can play around and you can say, Okay, this is the best choice. I mean, like one of the things that I loved, I mean, we were doing it the old fashioned way, this is pre Proof. But at Honeywell, we would be sitting in a meeting with finance and the CFO who was a big believer, and all this would say, you know, so what would happen if we gave you an additional $20 million to spend in the back half of the year? Right? How, what would that look like in terms of impact, timed impact, all this kind of stuff. And we could say, Okay, we’re going to take that money, and we are going to, because you have to make certain assumptions on something like that, we’re going to assume that it will be allocated according to the current allocations in the system. And, and then we would run the model right there in the meeting, right, and it would show what what happened right?
Now, what was really interesting is that there’s, you know, what you’re really trying to do is you’re trying to optimise spend in light of results. And the results are often time lag well into the future. So all of that has to be computed. And it all has to kind of be packaged into a single answer like that. And what that means to is that, you when you’re optimised, that can mean, that can also mean that you are past the point of diminishing returns. So it can mean actually, if we continue spending more and more and more money in this particular area, the amount of goodness we’re gonna get back is is not worth it, we kind of have maxed it out under the current market situation. And so don’t spend any more money in that area right now, because you won’t get any additional value. The really, the really super, excuse me compelling scenario is when it shows that you’re low on the S curve low on the optimization curve, but you’re killing it at that point. So that means if they spend more money, they’re gonna get even more good stuff up to a point, right. And so if you’re a business, and you can afford to do it, so this is where affordability is always part of the equation. But if you can afford to do it, you would be insane not to do it. Particularly since you have analytics that are totally governing it right. So it’s never going to not be transparent, what’s happening. So this is really where it is. And I think that five years from now, particularly if, if the what happens in the macro continues to get really rugged for two or three years, this is going to be the only way that people do it, right? Because it is actually the only mathematically viable way.
Mike: I’m really interested by by the fact you say it’s the only way people can do it, because we still have a bit of that Mad Men, you know, kind of mentality and marketing where people want to go for what they like and what they feel should work rather than necessarily trusting the maths. So do you think the push towards a more analytic approach is going to come from marketing? Or is it going to come from the C suite demanding, you know, more predictability and more value from marketing?
Mark: I think I think right now, at least it’s overwhelmingly the latter. It’s coming from the C suite who are just basically saying, not doing this anymore. You know, we were talking about before we started, right? If you look at the MAR tech stack, in the average company, this is all about economies of scale. This is all about being able to do more, touch customers more, all that kind of stuff, right? But there’s no governance, there’s no it’s the Headless Horseman, right. It’s, it’s, there’s no economies of learning being applied to the economies of scale. And the prima facie evidence for this is when when martec portunity, marketing automation and things like that really took hold. Most marketers just went crazy with it. And the law of unintended consequences has been awesome, right? Because you have GDPR you have California doing its thing. All these laws are getting more, they’re getting tighter and tighter and tighter and they’re not softening at all. And by not being able to calibrate and govern what they were doing. They actually killed the goose that laid the golden egg. Right, they didn’t do it intentionally. Right, but they still did it. And so this is about saying, You know what, there has to be a brain, there has to be a way. And I’m not, I’m not saying that marketers are not a brain. But let’s just look at real life science here for a second. The unaided human brain can’t process more than three or four variables at a given time. And if one of them is one or more of them is extensively time lagged, and its relationship to effects, right, you’re screwed, you’re just totally screwed you are, the human brain is not going to be able to intuit its way to the truth. So you have to have math.
And and if we look at B2B go to market, we’re talking about every model has 50 factors in it, there abouts, more or less, two thirds of which represent things you don’t control. It’s the wave that you’re trying to serve in the model. Right? That’s two thirds of the model. So I mean, I, you know, I just honestly, I, what I say to most people is, which seems to be resonate very clearly with everybody is, if you look at your bets in 2019 2020 2021, and 2022, if you basically made the same bet every year, for those four years, your way out, even even if they were all killing it in 2019, and 2020. In 2021, they were like, tanking, right. Field Marketing is a great example of this, but there are many others, right? And then you look at what’s working today versus a year ago, at this time, it’s totally different as well. And so how are you going to keep up with that, you short of using an analytic. And remember, it’s not just a data thing, data is critical, but data is like crude oil. If you try and put crude oil into your car to run it, you will have destroyed your engine. Right? It has to be refined into something that can be combusted in your car and add value to you. Right. Analytics is the refinery for data is the thing that generates the final output that has meaning. Well, why is that? Because data by itself is only about the past. And it has no ability to forecast anything by itself, right? And we live in a multivariable world. It’s all about the relationships between things, not about single measurements of different things. So this is all like, I mean, this is not me, obviously, I you know, I’m the CEO of Proof. And I want you to buy great stuff from Proof. Right? But this, what I’m saying right now transcends anybody’s product. It’s just fact. Right? It’s like a law of gravity you and you can’t change it, it is what it is.
Mike: I’m fascinated about what this change is going to do to marketing. I mean, if you were talking to a young person today thinking about a marketing career? I mean, do you think that the ability to use this data is going to make marketing a more exciting and interesting career? Or do you think actually marketers are going to be governed by the data and have less influence? I mean, where do you think things are going?
Mark: So the, I think there’s a real answer to that question is that, unfortunately, all of us as human beings, we tend to be people have extremes, before we hit a point of balance. So marketing for as long as I’ve been a marketer, has been skewed creatively. A lot of B2B marketers believe that we’ve already put too much science into it just because there’s a martech stack, which is sort of scary. I mean, to be really honest, because it’s there’s no science in it at all yet. So I think that what will happen, largely because of what is kind of the mindset of a lot of C suites that I meet with, is that they’re going to swing the pendulum hard in the other direction. And so creativity will be redefined as problem solving, you’re gonna have to be able to prove it with the numbers. Now, what I also really believe and really no, because it’s throughout history, that this has been proven over and over and over again, is that creativity in the way that marketers define that term? It only gets better and better and better, with more and more and more information. I mean, can we think of somebody who’s more creative? Again, using the marketing definition of creativity? more creative than Leonardo da Vinci? Probably not. Right? And yet, why was he so creative? It’s because he knew so much about so many different things. And he would cross pollinate. And he would bring data into art, he would bring math into art, right? And make the art better, make it more compelling, right, make it more beautiful. So and that and that’s a, you read the latest biography of Leonardo, that is talked about explicitly, as they translate his own diaries, right? He’s talking about it. Which is really surreal. Right? When you when you think about how long ago he lived, actually, the same is true for Aristotle. Aristotle also talked about this, that’s even further back. Right. But it’s, it’s when you read what they’re talking about, it reads just like today. Another kind of example of this real fast, right, is that there’s a lot of tension between marketers and business people, right? Same kind of tension actually exists between business people and data scientists. They define things differently. If you look at the letters between Leonardo da Vinci and meta Qi, his patron, it is surreal, it really is to see them having the same arguments, right, that we’re all so familiar with today, right? I mean, meta cheese basically going, Look, man, I’m at war with Venice, and I need those war machines that I hired you to build for me. Otherwise, I’m gonna lose. If you do that, I’ll buy so much marble for your sculptures that you won’t ever be able to use it all. Okay, but dammit, can we please focus on what’s really important right here first? I mean, you just kind of sit here and go, Wow, you know, human nature hasn’t changed at all.
Mike: I love that. And I think it’s actually a really optimistic point to to end, the discussion is that we can all be Leonardo and make our marketing, you know, a little bit more beautiful. I think that’s a great thought. Is there anything you feel that we should have covered in the discussion that we haven’t?
Mark: No, I think it’s been awesome. You know, I mean, there’s so many different things about this topic, to discuss that you can’t possibly do it in one podcast. Right. But I just I do think is very hopeful, right. I mean, you know, and let me just also say this to kind of pile hope upon hope, right? Because the there’s that old saying that hope is not a strategy. But let me tell you, I hope is really super important. Okay, so most marketers are scared of analytics, because they are scared that it will prove them wrong. That it will mean that marketing really isn’t as important to the business, as they’ve always been saying. I can tell you categorically that the analytics do not agree with that assessment. Marketing was created. Modern Marketing was created as a multiplier, a non linear time lag, asynchronous multiplier of the rest of the business, which is largely linear sales is linear. Right? What I mean by that, if you get a bigger sales quota, if your CRO and you get a bigger sales quota, how are you going to meet that quota? Well, you’re going to hire more sales, guys, because you know, that every single sales guy, or most of them will hit their quota, right, and it will all add up, right? But that’s not how marketing works. Marketing is a multiplier marketing is getting huge leverage across time and space.
The mission of marketing is to help sales sell more stuff to more customers as revenue faster. That’s cash flow impact and more profitably, that’s margin than sales could do by itself. That’s the whole ball of wax right there. And so if you can prove that in the math, and you will, because if you’re running a competently run solid marketing effort, then you’re generating these multipliers, including brand brand is a huge multiplier on stuff that really matters. It’s not a theory at all. All, anybody who said that brand is soft, he can’t measure it can’t understand it. It’s all kind of like metaphysical and all it literally doesn’t know what they’re talking about. So this is all really, really great stuff for marketing, if marketers will grab a hold of this math, this approach, whether it’s you buy Proof, or you buy somebody else’s product, right really doesn’t matter from that standpoint, right? You will be more successful, and you will have a better career and you will enjoy yourself exponentially more than you currently are. You have the best damn job in any company, except for one thing, and that is you can’t prove your impact. And so you get sucked into these really debilitating conversations with the business that end up in budget cuts and recriminations and arguments and all this kind of stuff. And psychically, it’s just terrible. Right? So let’s fix that. Right? Let’s stop doing this crazy shit that we’ve been doing. And let’s use the math that’s been there to solve the problem. And it’ll all be good. Trust me. You’re really well.
Mike: That’s such a positive way to end. I love that, Mark. I mean, just one last question. You know, if people want to follow up this interview, or find out more about Proof Analytics, how can they get ahold of you?
Mark: So I’ve, you know, my big channel is LinkedIn. So I’m very easy to find on LinkedIn. That would be choice number one. DM me on Twitter. That’s another good one. I’m still there. I’m kind of weighing it back and forth, right now, but I’m still there. And then, you know, our URL on the website is Proof. analytics.ai. Don’t try and email me. It’s like, I’m, you know, I’m 56. But I kind of operate like a 26 or 27 year old, right? I don’t really use email very much anymore. So you’re, you’re gonna get almost immediate responses from me on LinkedIn mail, and we’ll go from there.
Mike: That’s awesome, man. It’s been a great discussion. Thank you so much for being on the podcast.
Mark: Hey, thank you for having me. I really enjoyed it.
Mike: Thanks so much for listening to marketing B2B Tech. We hope you enjoyed the episode. And if you did, please make sure you subscribe on iTunes, or on your favourite podcast application. If you’d like to know more, please visit our website at Napier B2B dot com or contact me directly on LinkedIn.
