Marketing Science: An Interview with Koen Pauwels, Vice Dean of Research, Northeastern University

Stephen Shaw (SS):: The application of marketing science to marketing decision making is where this really pays off, obviously. So is the outcome really here optimization?

KP:: Typically not, so, only one out of maybe 20 of my consultancy clients want me to optimize anything, and that is really important, right? So, yes, I am an econometrician by training, so I typically, you know, either analyze historical data or run field experiments to basically come up within my models, what I think you should do. And this could be increase your price by 10%, drop your advertising by 20%, bring out a new product, or stop trying to make the product perfect because consumers are not willing to pay for it. So this is the kind of things that come out as insights, I would say. But then typically in marketing practice, you have so many other limitations that you say, no, I'm not going to increase my price by 10%. If I believe Professor Powells, I'm going to increase it by 1 or 2% and see what happens. If I going to drop advertising by 20% because he says I'm going to save millions of dollars. Let me just drop it by 4% and see what happens. So in practice, you see that people are more risk averse. For instance, they care about their career too, you know? So even though it's in the best interest of your organization, it doesn't mean that it will make your career. So in reality people are more, let's call it … or if they believe what I tell them, they will move in the direction, instead of fully, doing it. The other thing that comes to do is at which decision level are you in the organization? So I can tell you what your optimal marketing budget should be. But if you have no control in a company about the budget, if you can only decide how it's allocated, right. And it may be online and offline or within online, if, you know, retail media and social media or within social media. So depending on where you are and which decisions you make, you're going to be taking some things as a given and saying, well sorry, Professor Powells, you tell me I should double my budget, but I can't convince my boss to do that. But I will try to implement your advice in terms of the things that I can control and that I have decision over.

SS:: Well, I would think you would be a wonderful ally to convince the CFO that they need to spend more money on marketing.

KP:: I typically am that way. So about half the time I'm hired by marketing, half of the time I'm hired by finance. And so a lot of my courses also has both in there, and I find the different mindsets absolutely fascinating. So marketing folks tend to be what I call promotion focused or have a growth mindset. They see life as opportunities and they always know that there's more opportunities out there and more clever ways to spend our money and we can have potentially huge payoff for that. Finance folks are much more risk averse. They call it prevention focus. They think about life more as disasters that you can either avoid or don't avoid. So they are much more like, yeah, but what is the risk involved? The marketing person may champion this shiny new thing, but, you know, it will cost this much money for sure. Probably they will run over budget, but the return that they promise is very uncertain. And I want to be a good steward of my company. I have to make sure that we don't go bankrupt or spend money unwisely. So I would feel much more comfortable if I get such a feeling from my counterpart in marketing too, that they have also at least thought about the risks. And so very often, my job involves nothing to do with data, but really kind of making sure that people speak the same language and understand each other's perspective. (10.19)

SS:: So you're an intermediary in many respects.

KP:: Yeah, very much so. I would also say, because you asked for the difference between marketing science and data science. So, in marketing science, we do use theory. That's the difference, mean, I did my PhD at UCLA, and I did all of the economics, classes, and I stopped with economics relatively soon because I couldn't live with the assumptions about everybody's rational, and managers are, you know, optimizers and they have perfect knowledge. And I'm like, have you ever talked to managers? That's as far away from my experience, and I've been the manager myself. So econometrics, I really liked, because it really just took data and you didn't have to make these very strange assumptions about rationality to come up with good insights. So, within the economics field, actually, economics see me as a theoretical. I don't use enough theory, whereas when I talk to data scientists, I'm like, oh my god, I'm so theoretical because I do start from what is known in marketing and human behaviour. I do formulate hypothesis, and sometimes I have multiple theories, or the hypothesis are in conflict with each other, which is why it's so cool to analyze it then. Whereas data scientists seems to be much more starting from, you know, we don't know anything. Let's have the data tell us what's going on, which is sometimes cool because you uncover things that you have never imagined, but very often leads to completely unactionable things that as a marketing manager, you also can't explain to your team about. Okay, why do people who buy x also buy y? And how is that actionable to us to put in a campaign? And so I find that marketing scientists are typically, just a bit better in relating it to concepts, our ways of thinkings that we do in marketing.

SS:: Well, answering the why, for sure. Clearly.

KP:: Here we go.

SS:: So how would you characterize the state of marketing science today? Do you feel - you've been doing this for a long time - has it entered the mainstream of marketing yet? Or is it still kind of viewed as this exclusive domain of marketing academics?

KP:: That's a great question. So, I have been concerned about the gap between academia and practitioners and marketing for all my career, and I tried to, like, shorten them with things like writing books and blogs, and conference presentations. There's good and bad news. I think the good news is that, given, you know, somebody who uses data a lot and, you know, mathematical models and so forth, I feel that, how would you call this? The mathematical sophistication has really increased, in senior marketing decision makers. So it used to be that I would be hired by somebody, and he's like, Koen, I completely get your model, I understand it, but there's just no way I can explain this to my boss. And the higher up you went to, I always joke, like, a lot of people went into marketing because they hated math in high school and university, right? So you saw that really kind of coming true, I would say, in the last 25 years. And, of course, the Internet has a lot to do with that. You see people in the top of marketing organizations that can really ask very great questions about my model. So even though they don't, you know, run the code themselves, and they're not producers of mathematical models, they're good consumers, they ask the right critical questions. So I think that's the good news. I would say the bad news. And we typically have this, right? So you don't need marketing credentials. In finance you need to have an exam to be a chief financial officer in marketing. So anybody can call themselves a marketer at any time. And so we have this constant influx of people into the industry that think it's smart of them to rebrand as a marketer, even though they have no experience whatsoever, right? They are completely new, and they only know a little bit of, a little bit of a little bit. So, you know, growth marketers or growth hackers or digital marketing experts, very often they just don't know the good old truths that we have developed offline and that still apply to human behaviour. So there seems to be even more than before, I think, a fundamental disrespect of what came before, because every year is new and because the technology is changing, everything is different. And I'm like, no, I mean, human behaviour, you know, adapts, but it evolves very slowly. And knowing what has worked and what hasn't worked before and why, to your point, I think that's still very relevant. And a lot of today's marketeers don't really take the time to, I would say, educate themselves on these things. (15.02)

SS:: No, they're chasing fads. I mean, I think about 30% of marketers are actually trained in marketing. The rest flowed in from some other discipline. It is remarkable - I spoke at length with Brent Chater, who's the marketing transformation head at Accenture on my last podcast, and we talked about exactly, this issue, in your book, “It’s Not the Size of the Data” book. You quote one of your clients as saying, lots of data and lots of action, but no link between the two. Now, I might amend that a little bit to say lots of data and lots of insight, but no action. Meaning marketers still struggle to convert data driven insight into meaningful strategy. There seems to be still a gap there, and I'm just wondering if that's because of the reason we were just talking about, that they're not really that trained in marketing. Or is it that they're not trained to ask the right questions? Or is it risk aversion? You alluded to that earlier, and in your book, in fact, and this, again, you alluded to, are marketers, most marketers, simply not math oriented. They're just not drawn to the world of statistics or are comfortable in that world. What are the reasons it’s still a struggle for marketers to really translate data into really good strategy.

KP:: That's a fantastic question. So, first of all, I completely agree with you. So that, that these complaints that I had in my first book are still true today. And by the way, the reason of the first book. So the publisher approached me because they wanted to write a book about big data, and they had several books about big data. This was back in 2011 when it's supposed to be very important. And I was like, well, in my experience, and at both big and small companies and three continents, I mean, most companies don't even know how to translate a small data and insights have into action. So if you just enlarge the data, it's just going to be more problematic. And so, later on, I published several articles that actually show that the bigger the data, sometimes the more problems you have with biases in decision making and so forth. And so I would say I think at least two of the explanations that you noticed are very important, I would say number one, marketers continue to have issues convincing risk averse decision makers. So they typically don't have the finger on the pulse, and they have to convince people who don't like the marketing mumbo jumbo of awareness and mysterious brands and, you know, winning Cannes Gold Alliance. They say, well, show me the money, show me a projected return on investment and some risk assessment. So, a lot of my courses is focused on making marketers comfortable, saying these things and calibrating their assessment. I think, though, it's also, and that was one of your other explanations that really jived with me, there is this kind of fear that if a model or multitouch attribution or marketing mix model or whatever you get that is coming from a data scientist or a modeler, right? There's always this big question mark. Yeah, but will it work? Will it work for my company, in my country, in my industry? And never forget, anything that you get as an insight is built on the near past, right? So it's models run on historical data. It's an experiment that worked last month. What is the guarantee that it will work when you try to apply it now? And this is why obviously marketing is the toughest function in any company because the success of what you're going to do now depends on how potential customers react, competitors react, maybe some macro terms like whether the interest rates go up and down. So there is just a lot of uncertainty. And so, a good marketing manager kind of takes the model’s input and says, okay, this is fantastic, but I see the limitations and I do have my own experience and intuition about what has changed. A typical example, right? So, in most of my models I have competitive retaliation. So let's know, you're a big car manufacturer in the US. Let's talk about big companies. And you only have really kind of, you know, five or six main competitors in your niche. And so I have modeled every time in the past when you give a price promotion or you had a new generation or you had more advertising, what your competitors did. And so I think that's pretty valuable. The other reason the car industry is so cool is because, you know, one and a half years before your competitor comes out with a new model, what the new model is. So it's pretty predictable. So I build my model, and then of course I put that into the predicted net effect if you're going to do something. But then you may say, well, wait a minute, you know, the CEO of my main competitor was just fired and the new CEO comes in and this is a guy who really wants market share. So he's going to react way more strongly than the previous CEO. Or, you can say, oh, the person that is now in charge of my competitor, they're in bankruptcy proceedings. They are much more careful and they don't want to rock the boat, so they're not going to react. And so I always build in that you as a manager can kind of shut these things on and off, that you can say, well, in my forecast, I know that this will happen even though it's not in the model. And so I think modelers have to, you know, just appreciate this flexibility, understand that they are not the only ones who know something, right? That the managers know their industry a lot better and what's going on currently. And so, I always go back to a pretty old article that I like that says it's 50% model, 50% manager, which means that you know, you can get much better results if you combine that human intuition with something more formal. The same goes nowadays for AI and human interaction, right? I mean, it's just inconceivable to me that AI can replace us all, but I can see how it can make my decision making better. (21.02)

SS:: Sure, and we'll touch on that a little later on in this interview for sure. I want to go back to your model development, and I think that's one of the strengths of your book, by the way, that you can build a marketing dashboard, but really the secret recipe here is the engine under the hood as you describe it, which is your VAR models. And these are really ‘what if’ models that allow marketers, as you were just describing, to play with specific lead indicators to see the likely impact on sales, to project out what might happen in certain scenarios. Just explain, if you can, in as simple a language as you possibly can, why you're such a strong believer and user of VAR modeling.

KP:: So I want to go all the way back to 1980. So Chris Sims is an economist and he then later on got a Nobel Prize for this. And he basically published an article that says, look past economic models, they're way too complicated and they build too much on assumptions. In marketing you have last click, first click, right. And all of these assumptions in online economists also usually have huge assumptions about what can influence what and so forth. And he comes up with these vector auto regressive models, which seems complicated, but it's basically a very flexible way to take into account direct and indirect effects and also long term effects that we typically don't know exactly when our marketing will have an effect. And different actions like TV may take a bit longer than your retail media. So he proposed a model that I then happily used in marketing that basically allows for marketing wear in and wear out without having to specify it up front as a manager. So you don't have to say, hey, TV advertisers will take two months to work and online works right away. The data will basically give you that information. I would say the second part is that it's more than one equation. And I think online marketing is a great example. Brand equity is another one. So traditionally you would say, well, let's say my sales, if this is what you care about, depends on a whole bunch of marketing actions and maybe some stuff that your competitor is doing. One big problem there is with this pesky thing called brand equity. So with brand equity, you have the feeling that your marketing feeds into it. I think Tim Ambler calls it like little streams feeding into a reservoir of goodwill, and that your brand equity builds and builds over time and influences your sales, but at a much more long term level than changing your search marketing budget. So I have a second equation explaining brand equity with marketing actions. And so, you know, having multiple equations allow me to also model how your competitor is going to act, for instance. So instead of just explaining sales, let's say by online offline marketing and brand equity, I also explain your brand equity with certain steps. And if I have competitive data, which is not always the case, I can do that too. So why is this important? A lot of times if you put a very bottom of funnel marketing thing in there, it dominates everything else. So I worked for four and a half years at Amazon and Amazon ads has like sponsored products which are very much people browsing in your category and you stand out. Then they have sponsored brands which is you know, more like a storefront online that you can talk about your brands. Then they have, you know, of course takeovers of Prime Video, which is much more upper funnel. So if I try to explain your sales with everything, it's always the bottom funnel that dominates. It's always that. So things that build awareness, that build consideration, something that we as marketers care about completely get washed away if you just explain, let's say daily or weekly sales with these things. But now if you have a second equation explaining awareness and consideration and then you know what feeds into that one, you can distinguish these things. So you can say something like, hey, I have this brand new TV ad, I air it and yes, a few people get convinced right away to buy it and that's my immediate sales effect. But my TV ads also make people much more likely to click on my online ads and over time they increase my pricing power because I will be able to increase prices without losing too many consumers. So that's why it's a complicated system, it's kind of a web, but I think it's really useful to get to these direct and indirect effects. An so that's why I use it. (25.47)

SS:: And understanding the interdependence between the two. And there's an ongoing debate right now, obviously between long term brand building, performance marketing and it helps to address that issue.

KP:: Yeah, exactly.

SS:: So I want to also talk about, you were referring to your work with finance folks and the budgeting process, which always, marketers struggle with, and partly because they aren't doing the sort of complex performance analysis you're describing. They struggle with credible forecasts, with making the exact kinds of trade-offs that you were describing. Where do marketers go wrong here with finance? Is it that they're not speaking the language of finance? Why does it remain such a trial, such a battle every year over the annual budgeting process?

KP:: Well, I think language is the first thing, but language you can learn. And so I think there's lots of sources that marketers can find also, just talk to your finance counterpart. Right? So I think the most important thing is to give finance folks that comfort, that you will be a good steward of the company's resources. So the complaint of finance is typically the following. Every year, the marketing department comes to me with a new funding request for a shiny new thing that we absolutely have to be on, right? And then, of course, they can't prove it's going to be effective because it's new. So you can't look at past ROI. But then I ask, so what can we cut? And they say, nothing. Everything is absolutely 100% necessary. And so that is the kind of ask every year that gets finance people kind of on the defensive and on the back of their heels, and they say, no. So what I would like to see, what really builds trust, is that you as a marketer are more proactive. And you say, hey, this is something that we had to spend a lot of money on, right? Let's say search marketing ten years ago, but now we have hit diminishing returns. So now every dollar we spend on search marketing is not worth it. Maybe we can even cut down a bit, because our brand is now so notorious that we can get most of these consumers for free. So instead of typing the direct link, they just out of habit, because, you know, their opening screen is a search engine, they click on our ads, but we don't really need it so much anymore. And so, for instance, very famously, eBay figured this out, right? So that if they just cut all of their Google spend, they lost hardly any customers. And of course, eBay is a very kind of famous company, and it doesn't work for everybody. But, so kind of doing these small experiments and saying to finance, you know, I really want you to fund this one or that one. And this is why it's going to fit with our company's advantage and why it's going to be very important that we're there first. But then at the same time, these are two or three things that you can cut something from. And it doesn't have to be fully compensating. Right. You can still argue for an increase in your overall budget. But just once telling finance, hey, we can get less funding for this one channel is just going to be very much appreciated. And so in my course, also in the taking action one, I confront the student with a case study where the CEO says, cut your budget with 20%. And then you know, you have some data, you do the modeling and you figure out that you have been completely misallocating. This is between advertising and salesforce. And of course you should have given more to advertising in the example here, some students say, okay, we have now completely misallocated. Let's allocate it correctly and let's follow the CEO in cutting the budget with 20%. And other students say, no, I mean, the reason you wanted to cut it, why it's been going down overall, is because we misallocated. And now, thanks to the model and the insights, thanks to the better allocation, I think we should actually increase the budget with 10%. And this is what I think based on past success, right? This is what I think we would get. And they do so very effectively. So just saying, okay, we can cut on one channel to increase more on the other one, I think is something that would go a very long way to have finance see people not just as championing for the new thing and very bubbly, right? This is kind of the stereotype, but also as people who think about the resources of the company and are good stewards of them. (30.05)

SS:: Well, and the ability to draw a line between those expenditures and its impact on the KPIs that the C-suite really cares about. I think that's one of the challenges, isn't it?

KP:: It is. And so, there's some old research that says, and I always teach it in the beginning of my course, right. I'm like, look, at some point we're going to dig deep in data and that may not be your cup of tea, but the research has showed that because I ultimately go towards a dashboard. What's the subtitle of the book that you read? The research has shown, I think this is by Ambler and Clark, that just alignment of marketing with business goals is already 50% of the whole battle. So if you can just, and ah, this is just talking to the CEO and the CFO, right? If you're the CMO, just ensuring that you as the marketing department are aligned with the business goal. So, one year it may be that the business really wants to get a lot of new customers. So customer acquisition is the big thing. Another year, they're like, well, we should try to get more out of our existing customers and cross-selling is much more important. Knowing what the business really wants you to do and then, of course, as a marketing expert, you can translate that in marketing KPIs, but they should ultimately indeed fold into that overall business.

SS:: So, this is a corollary subject area and Mark Ritson has said, and I listened to a recent podcast where he was being interviewed about this is, and he's referring here specifically to the US., I'm presuming North America generally, that there is a quote/unquote, “ignorance of effectiveness”. You're on record is agreeing with that statement. What do you think accounts for this blind spot here, relative to other regions, such as Europe and certainly Australia?

KP:: Well, I definitely think there's more of a short term pressure, and that may come from, you know, a lot of companies listed on Wall Street and Wall Street having the pressure for quarterly results. So when I work with companies that have the longer term perspective, it's typically easier to take the time and to understand effectiveness and to use it. I think the other thing is also that in the States, you know, jobs kind of evolve very quickly, so you're not typically in a position to see the benefits to yourself. In Europe, you know, people tend to hold positions longer, and they do get actually rewarded by their companies for doing things that are, in the long term, in the best interest of the company, I think. Since I give you one kind of quick anecdote and a pretty funny example. So when I was writing the book, I was in Istanbul in Turkey and that was one of the reasons that I couldn't go on big U.S. tours and so forth. But I went to Ülker, which is a huge manufacturer of mostly chocolate and goods. They had just bought the Belgian Godiva, and they were in all of these countries, and they spent about $100 million advertising just in Turkey. And I have other stories about their other countries, right? And so, I analyzed some of it, and I went to this Chief Marketing Officer, which is a very clever individual, and I'm like, look, I calculated that half of it is ineffective, and I can tell you exactly which half, right? This is the old John Wannamaker, so I can literally save you $50 million. And he never questioned that I could do that. He just said to me, look Koen, if I do what you say, my company gets $50 million. I don't get one cent of that. But if I lose half a percentage market share, I will get fired. He made me understand that his career goals may be very different from the company. And so I think, you know, that may be one of the reasons that I don't think that a lot of U.S. market or managers are incentivized, based on what is really effective. And you know, they get so bombarded with short term things that it's just very hard to take the time and say, oh, I get so many consultancy companies promising me the latest in effectiveness, what is really the truth? And you do need to kind of, you know, take the time to reflect a bit and ask the tough questions, to figure this out.

SS:: Well, and too, isn't the fact that a lot of businesses pray at the altar of growth, right. It's expand market share and grow, grow, grow.

KP:: That is true. Profitable growth has always been my objective. And yes, that sometimes, you know, means making some interesting trade offs.

SS:: Right? So I'm going to jump into, some controversial topics, controversial within the marketing science community, that is, and the ongoing debates that we see. And I'm going to raise the name of Byron Sharp, and he's quite possibly one of the few marketing scientists, maybe the only one, to really - you may be another one - but to have broken through the walls of academia and earned a, you know, certain widespread notoriety amongst a lot of marketers. And his book, “How Brands Grow”, was a bestseller. It really resonated with a lot of marketers at the time. Why do you think he was able to do that? Why do you think his book, which is really marketing science, resonated with so many marketers at the time, and still does? (35.32)

KP:: I think there's two good reasons, right? I think number one is that he was, very understandably going against some of the more, obscure, or esoteric things that marketing has come to believe. And I completely agree with him on that one. So one of the reasons, by the way, where very few academics know Byron Sharp is know he doesn't publish in top journals. And I think the major reason for that is that academics typically, I wouldn't call it ivory tower, but most academics are far away from what is actually going on in marketing. And so once in a while we pick up on something, right? So things like Net Promoter Score, or Reichheld saying that you should focus much more on retaining a customer versus thing. These are wonderful hypotheses that we love to debunk, which we did. But I think in the eighties and nineties, there was this whole kind of, going very deep in marketing mumbo jumbo, as Byron might call it, right, that brands have to be sensual and mysterious. And marketing, also because of that, got very far away from financial goals and kind of basic generalizations about how customers act. So I think one of the reasons he was so successful, because he went against something that was a bit of an aberration, and people kind of very understandably picked up on that one. I think the second reason he was so successful. So marketing science and marketing seems to be a little bit like mathematics and physics, right, for engineering, in the sense that, Andrew Ehrenberg's work on double jeopardy and all of the generalizations that Byron put in his book is from the 1950s and 60s, I saw it in my PhD, starting from empirics is something that I love to do, and then saying, hey, isn't this interesting? Right? So double jeopardy is basically saying big brands are different from small brands. Big brands have a lot more penetration, a lot more consumers bought you once, and the people that do buy you more often than the buyers of small brands. And it is really interesting, and I mean, I didn't quite explain that, but if you are, for instance, a pretty niche coffee brand or local brand, you may have your very loyal followers, right? And typically, marketers say you should go for a niche and you should try to get lots of heavy buyers and so forth, and then they will spread the word. But the problem is that even if I love your coffee or local brand, I also have to buy for my wife and kids and for visitors that come by. So I will buy Nescafé once in a while. And so you see that small brands stay small for several reasons. Not enough people have tried them once, but also the people who tried them and even liked them and don't necessarily spread the word. And then, of course, I would add the third one, that retailers, in the shelf space, they give really favour big brands. These are very interesting insights. And so, for some reason, that kind of science and behaviour hadn't really made it in the mainstream yet. So he went against something that was really seen as weird by a lot of people already in marketing, and he popularized things that basically, were already well researched in several data sets across several countries.

SS:: Well, and probably a lot of the information you were sharing, the double jeopardy law, etcetera, were unknown to the general marketer anyway. And so that was an education for them. And on top of that, he was iconoclastic about a lot of things. And one of the - there's a few still very controversial areas here pertaining to some of his provocative claims - and one of them certainly is this whole debate over the importance of differentiation versus distinctiveness. And his argument, obviously, that distinctiveness should be at the center of brand strategy. And I think I may be misinterpreting your position on this, but I think your answer is, well, it all depends. What is your response to that debate?

KP:: It does. So I think what the Ehrenberg-Bass Institute and lots of research has shown over the years is that creating and maintaining differentiation is hard. That's just true. The point we disagree with it is that, yeah, but it's not because it's hard that you shouldn't try to do it. And because it is hard, I believe that there's huge benefits. You have so much more pricing power and if you differentiate it. But they have a great point that it's just very hard to create it. And so many things have to go right. Whereas distinctiveness is really interesting. Distinctiveness is like McDonald's Golden Arches, right? It's not specific to the product and you're not going to like, you know, their nuggets or their fries more, but it really reminds you of the brand and it allows them to be very subtle, out of home and everywhere. They don't have to state their product and their price positioning every time. They can just show you the Golden Arches. And so what happened in practice, again, is that a lot of marketing managers, when they came new into a company, they tried to change a lot. And distinctiveness tells you, no, there's a huge benefit in having people remember you with the Golden Arches or an icon or a certain logo. Unless you are really, really, really, tanking, keep it, because otherwise you're throwing everything overboard. And so I think distinctiveness is something that is, intellectually very easy. I mean, it's relatively easy to say, yeah, we should remain distinctive, and we should be consistent, but in practice, it's very hard to maintain. And so because of their wonderful practice focus, I think they put a lot of attention to that one. And I think distinctiveness is absolutely key in the kind of big, fast moving consumer goods that they analyze. So typically their data comes from fast moving consumer goods in developed markets and relatively big brands. And for those brands, yes, I mean, your competitors have by this time negated your points of differentiation. Very often it's very hard to come up with new stuff that you can actually say, hey, we have much better coverage. So what you're left with is very often distinctiveness. Whereas I think for smaller brands that really want to grow a lot, also in emerging markets, I see that. And then in things like devices and technology, you know, getting and maintaining a point of differentiation is just both very possible and very rewarding if you can do it. (41.48)

SS:: So, so there's that issue of differentiation versus distinctiveness. The other one that seems to get people's backup is his preference for mass marketing over what I call segmentation targeting, segmentation marketing, whatever term you want to use. His argument that reach trumps frequency and his argument that marketers should try to attract as many light buyers as possible and even seems to dismiss the relative importance of heavy buyers. Now that, as a longtime marketer, just doesn't seem logical. But is he right?

KP:: So there's what the data shows and there's assumptions, right. So one of the key assumptions in his work is that you can't really change people's habits. He's a behavioralist, right? So the consumers are what they are. So if you're a medium buyer of my brand, and I want you to develop into a heavy buyer, he is basically assuming in his work and in his things that that's almost impossible. So you buy in the category a certain amount of time, and so it's virtually impossible for me to get you to buy more, right? And whether that jives with you really depends, I guess, on your brand and industry, because sometimes it may be the case. So given that, he says, look, yes, your customer retention, you know why would you focus a lot of resources on that one? Because it's a leaky bucket. Some people buy your brand because they felt good or bad a day. So it's not something to worry about unless you're losing way too many consumers then predicted by his model, right? So if you want to grow, and this is why it's called “how brands grow”, you should really focus on getting new people to try your brand every time you need new people. And these will typically be the light buyers because heavy buyers, they are very invested in the category, and they already know the brands. And so your marketing will get to them anyway because they pay attention to it. So it's the people who only very occasionally buy in the category. These are the people that you want to focus on to get them to pay attention, and you want to get these folks. And so I think there's definitely something to say for that one. Where I think it gets a bit too extreme is, for instance, in reach versus frequency, right? So, I think his point of view is you should always just maximize reach because for the first exposure, you get the most benefit. And so if you have a million GRPs, spend them all on reach, spend everybody wants. And I'm like, well, if I introduce a new Pauwel’s chewing gum maybe that's correct. If I'm at the right retailers, I only have to basically touch, use that phone once for you to notice it in the checkout and maybe buy it. If I want to convince you to join my new Pauwel’s Bank and to have a mortgage with me or to buy a device, then I will have to reach you a lot more times. And so there's a lot of good marketing research that talks about, hey, for certain products, you have to have a higher frequency and, you know, ideally both offline and online. I should have a nice bank building. I should have out of home, maybe a TV ad. I should also be very prominent online before anybody would consider. So I think that really depends on the category. And now with online, right, there's fascinating things. Like, so, for instance, on Amazon, instead of being passively exposed, most people are actively exploring or buying new products. So should we now have more or less frequency? You could say we should have more frequency because you're actually in the mode for buying. So you actually like ads more instead of them interrupting your TV program. Or you would say, no, you will get annoyed much faster because, you're paying attention, and you don't have to see it four times. Maybe once or twice is enough. And then you can check the reviews. So these are fascinating research questions that I have that I would love to do. And it's a priority unclear to me which way it's going to go. So I think that there's also diminishing returns to reach, just like their frequency. I believe in segmentation and targeting. I believe that if you know, you have limited resources for smaller brands, you should first try to get, you know, market share. Selling nuts to squirrels. You should try to identify people who have a very strong need and are willing to pay for your product. And yes, you should first of all, go after them. And then if you have money left or if you want to really grow beyond that group, then you can go for higher reach. So I think a lot of nuance is lost sometimes in what you see on social media about what everybody should do in marketing. (46.32)

SS:: So I have a couple more questions. Well, I have many more questions, but I only have time right now for two more, given your schedule. Let me just touch on one other debatable subject. And again, I'm going back to Byron Sharp and his belief that - this is one that is so counterintuitive to me - but he says that attitudes follow behaviour, not the other way around. And his belief is that customers are polygamous. You referred to this just now, and he doesn't really believe in the idea of a loyal customer. Again, to me quite counterintuitive. Fred Reichheld would certainly disagree. Maybe Tim Cook would as well. What do you think about the importance of loyalty? And I'm coming from the perspective of sort, ah, of a lifetime database CRM guy, where the whole objective is to increase the value of a customer over time by increasing largely their commitment to the brand and to other products within the brand. So that's the whole point. Yet if you dismiss the whole idea of loyalty, that, in his view anyway, is not a valid assumption. What's your perspective on that?

KP:: So, I always make the distinction between behavioural attitude and the loyalty. So behavioural loyalty is basically shown that people buy more and more of you over time. And so, at one of the banks that I worked for, it was really interesting. So they put all of this information online and they thought they were specifically targeting people who never banked with them before. And most of the new accounts opens where people already banked with them and just didn't know that they also offered this particular financial product. So it's kind of, you know, marketers sometimes have the assumption that their customers know as much about the brand that they do. In CLV indeed, you're trying to constantly remind customers, seeing what next product they may be interested in and so forth. So I completely agree that you can increase behavioural loyalty and there may be huge benefits to your company of doing so. What I agree with Byron Sharp is attitudinal loyalty is extremely rare. So yes, I like your bank, and I've been a customer for a while, but if another bank offers me a much better deal, are you as a salesperson moved to that other bank? Then maybe I will switch. So I do agree that very few consumers, for very few brands have this absolute love for the brand, right. I think Heineken in their KPIs, they check whether you're in love with the brand, engaged or married to the brand. They somehow assume that marriage is the highest level there. So they check this one, and I'm like, yes, I think this is a bit too exaggerated. People do feel it about some brands, right? Apple, Harley Davidson, and so there's a few brands that people do feel a very strong attitude and loyalty. But I agree with Byron, it's very hard to achieve, it's very rare. So it's probably not a good kind of objective to spend lots of resources on for the typical brand. On attitudes and behaviours, as I said, Byron Sharp is a behavioralist. He believes that asking people is completely useless because you just make it up on the spot. And he has some point about that. I think, you know, people don't think what they say and don't say what they do. So he says no, first you change behaviour and then the attitudes come. So if I ask you, what do you think about Pepsi Cola? And I can ask you a hundred questions, do you like it? Blah, blah, blah, you probably just think, well, have I drunk or bought Pepsi for the last month? And if you haven't, you're going to make your behaviour consistent with your response and you're going to say, no, Pepsi sucks, Coke is fantastic, and vice versa. Whereas there's lots of instances, by the way, so my methodology is perfect to show that kind of causality which comes first, which is really cool, I find. So, ah, there are some cases where I find this. In other cases though, Byron is just completely wrong, and attitudes change before behaviour. And you can see that in the data. One of the reasons that Jeff Bezos finally allowed Amazon to advertise is because they got into devices, right, Echo and Alexa, and he figured out, know people first have to kind of be aware of Echo and consider it and kind of think through all of the things. And so their attitude has to change before they're going to buy it. And so it's going back to the old low involvement versus high involvement decision making that we know very well as marketers. (51.07)

SS:: And the other aspect of this is if you provide a superior experience, you can even overcome a shaky value proposition just by the fact that you're treating people right.

KP:: Exactly. Yeah. And that is something really important that is, yeah, completely not in “How Brands Grow”. And I think there's a big difference between saying, hey, you shouldn't spend 80% of your company resources to retain customers and saying you should completely ignore it. Because if you mess up in being nice to people and in your service, then you can really go south, yeah.

SS:: So I have one final question, and thank you for being so generous with your time today. I mean, literally, I could talk to you for hours.

KP:: Likewise

SS:: Well, that's kind of you to say. So, there's, where marketing science has been, where it is today, AI is on the doorstep, maybe beyond the doorstep, frankly. And obviously will be a huge advantage as you build out models going forward, considering the number of variables that you have to deal with, their interdependencies, all of those things. Where do you see marketing science going in the next five years? I get the sense that it is going to become the mainstream discipline I referred to at the top of this conversation. It may be outside the mainstream now, but it will enter the mainstream because just about everything will become data driven. What's your perspective on that?

KP:: I do agree with that, right. So I also see a lot more what we call practical academics and thoughtful practitioners. So I see a lot more people in both sides of the divide really working very well together and also understanding each other's language and timelines and so forth. And I think, yes, I think generative AI was so kind of new and so kind of bizarre that we're still finding fantastic applications for that. Yeah, so I'm speaking in March at the Microsoft Innovation summit about what is the future of AI and marketing? So that's a whole different topic. But I do agree with you. There's so much interesting data, there's so much need also for marketing expertise to add the human touch to it and to make sure that the algorithms don't completely go haywire. So I'm extremely optimistic about the future of marketing science and marketing as a profession. I think that new technology adds a quiver in our arsenal, right? Or an arrow that we can use, but that doesn't mean that everything is completely different and that we have to throw out what we know before. So I think it really adds to our ongoing knowledge, creation and put in practical use in marketing.

SS:: Yes. It will be, transformational. Absolutely. I just want to say how this conversation just flew by. I've been working with data my whole life, and to hear you talk about the application of simulation models, et cetera, and cuts to the heart of, I think, why marketing has struggled. And the only way it can advance and become a mature discipline and become an adult in the room is if they follow sort of the very same practices that you're advocating for. So I just want to thank you so much for this conversation.

KP:: Thank you so much. These were the best questions I've had in a very long time, Steve, so thank you so much.

That concludes my interview with Koen Pauwels. As we learned, the use of quantitative methods and multi-variate statistical modelling to support marketing decision making is a mix of sociology and data science – longitudinal analysis contextualized by an understanding of how people tend to behave in the marketplace. For too long marketers have relied on certain immutable laws of marketing as a crutch in place of more disciplined analysis. And they could get away with it, because, well, no one really knew any better. But the world is so much more complicated today compared to when many of those laws were first formulated. And with so much more data now available to explore, marketers must take a more scientific approach to credibly answer the question of effectiveness.