Turning Your Marketing Into Dollars

With Peter Fader, the Wharton School marketing professor

Dr. Peter Fader is a professor at Wharton Marketing Department, researching things such as: the lifetime value of the customer, sales forecasting for new products, using behavioral data to understand and forecast shopping/purchasing activities across a wide range of industries, and managerial applications with focus on topics such as customer relationship management.

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Introduction to the episode

(00:00-01:08)

AS: Welcome to the Experience-Focused Leaders podcast. I am delighted to introduce you to Peter Fader, the legendary Wharton marketing professor. A serial entrepreneur with an exit to the likes of Nike and an inventor of the customer lifetime value calculation as we know it, he is the author of four books on customer-centricity-related, definitive set of knowledge. You want to go from a very high level to a very detailed one. I'm just honored to have him as a friend and a guest on this podcast after a few years of working together, including having Peter be one of the first backers of RELAYTO. So thank you very much, Peter, for coming on board!

PF: Alex, I appreciate all those kind words, and it's people like you and the technologies that you've built that make it possible to do all the things that you mentioned. So it goes both ways.

About Peter Fader

(01:08-02:15)

AS: Amazing. Well, Peter, I think the reason you're the first faculty member of any institution, even though we love the research that comes out of our alma mater like Wharton, is that you really are the ultimate, in my view, applier and doer, not just a researcher and a teacher.

One of the stories that I obviously know is the story of the Zodiac, a company that you founded that got venture-backed and exited very successfully to Nike, validating a lot of your research on customer lifetime value and backing that vision was a tremendous exit. And now you're a founder of Theta, which is doing the same thing for customer lifetime value-based valuations of companies and businesses.

So tell us a little bit about what drives you to combine research with going and starting your own businesses or backing businesses and being so focused on the application of your ideas.

What drives Peter to combine research & business

But I've always loved crunching numbers and forecasting things and coming up with algorithms and insights that arise from them.

(02:15-03:51)

PF: That's a couple of things. One is that I wasn't born to be a business school professor. You know, a lot of folks here, we have wonderful colleagues. So I think, when they were eight, they'd say, “Mommy, I want to be a marketing professor.” I wasn't like that. I got dragged into it when I was actually planning on just going to work in the industry. But I've always loved crunching numbers and forecasting things, and coming up with algorithms and insights that arise from them.

So it's not surprising that I went down this path to do the “publish or perish” professor thing. It did take some coaxing from some of my mentors, up at MIT, but once I was here, I said I'm not going to give up my values. Yes, it's going to be “publish or perish”. I'm just going to be judged by how many academic articles I can get in journals that no one reads. I understand that that's the day job. That's what's gonna drive my salary and professional success. But my inherent interest is still in watching people use this stuff, making better decisions, raising their quantitative literacy, and just viewing the whole field of marketing in a very different way.

So I take those parts really seriously, and I’m fortunate that the research that I do enables that industry outreach. I'm lucky that I can do both without them coming at the expense of each other.

About "Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data" paper

And what I've done is I've taken some of the work from other people, other smarter, more creative people, and just helped refine it, improve it, add some bells and whistles, make it a little bit more accessible, and motivate people to want to use it.

So the big question is, what kinds of metrics is it that companies could put out there?

(03:51-12:42)

AS: Well, I think you're too modest because I understand that one of the papers that you published got to be one of the most-read marketing papers in the history of the channel. Do you want to tell us a little bit about that and part of your latest adventure with data? I think that would be great for people to know how you're really making marketing turn into dollars.

PF: Yeah. Let me back up and give the context for that paper, which, again, it's I dislike predicting customer behavior. The ultimate behavioral metric is customer lifetime value. Do you say I invented it? Not really, I mean, the concepts have been around for a while. A lot of people put lifetime value models out there.

So I'm not gonna be too modest. Yes, I've had some really great contributions, but they just wouldn't be possible without other folks first putting the ideas out there. And for me, both academically and practically, I've always wondered how well we can do with how little. So, if I have a full transaction log from a company, I'm gonna build great models.

But suppose the data is limited in different kinds of ways. Either there's data missing, or the data is overly aggregated, there are a lot of different kinds of very practical data questions. And I've just always wondered, first of all, how can we build the same models if we have limited data? And number two, how much worse will they be? So, it's been a big theme of the research.

Again, a lot of the papers were just to get more lines on my curriculum vitae and get that salary bump. But it just turns out that it really is a coincidence with the rise of GDPR, just overall interest in privacy with Apple pulling the rug out from the digital marketing world and just making it much harder to collect granular data.

And a lot of these models I was building anyway. And the ultimate way of limiting data is less about technology and more about financial reporting. No company is ever going to put their transaction logs or any kind of granular data out there in their reports. No company should ever say, “Here is the lifetime value of customers.” So the big question is, what kinds of metrics is it that companies could put out there?

And again, how do we then reverse engineer and run the models that we'd run if we had all the detailed data? It's something I've always wondered about. And kind of noodled around with it, but it wasn't until meeting my Ph.D. student, my co-author, and my co-founder, Dan McCarthy. This was his dissertation work.

So Dan came back here, he was an undergrad here. Didn't take my course but came back to get his Ph.D. in statistics, not even in marketing. And through a mutual acquaintance here, we met another graduate student, and it was like love at first sight because Dan had some history on Wall Street. He was a hedge fund guy. But he's a brilliant statistician. And when I started just asking these questions more from an academic standpoint, “How do we squeeze as much value out of little data as possible?” Understanding that a perfect domain for that would be customer-based corporate valuation.

We then started on a mission that involved data. Can we find companies that actually do reveal some aggregated metrics about their customers? A mission that involves math? So, you know, can we redesign our models to work on some of those kinds of limited data sets again, data that companies might and do put in their actual quarterly reports? And you know, gospel spreading, how do we get these ideas out there? Not just the techniques themselves but the insights that arise from them and the need to do this kind of stuff.

So, Dan's been just really prolific. He's just a brilliant researcher. But his dissertation, I keep it right near me here, even though these papers are a few years old. The paper that you referred to, Alex, is this one over here — “Valuing subscription-based businesses using publicly disclosed customer data”. So this is a published version, but when we first wrote it, we posted it on our website where people share their papers, a site called SSRN Social Science Research Network. This paper and then its follow-up rose right to the top of the list.

One of the reasons why they've been among these ones is still the number one most downloaded paper ever. This one is somewhere in the top 10. One of the reasons for that is that they are not only of great interest to marketing professors and marketing professionals but also to finance professors and professionals. That this idea of customer-based corporate valuation really is, it's a wonderful crossover. It doesn't only help marketers say, “Here's a nice domain where our insights, our models can help, but for finance and accounting people to say, “Huh, never thought about that before.”

Maybe we really should be looking more carefully at customer-level disclosures, the role that they could play in valuation models, and the improvements that we can see in projecting future revenue cash flow, EBITA. And that's all we're doing is basically taking the frameworks out of finance and accounting, bringing in some marketing metrics to basically show how we can squeeze value out of some of the marketing data in ways that finance and accounting would have never thought about.

But in ways that marketers have been talking about for decades, best of all worlds. And the nice thing about it is when we give credit, not only to Dan but even to our academic reviewers. When we first started writing these things up, we did it almost kind of theoretically, “Here's stuff you could do if you had these disclosures”, and their viewers said, “No, no, no, you need to name names, you need to name specific companies and tell us what they're worth in reality, relative to what Wall Street thinks they're worth.

So in this first paper, we did it because we had no interaction with the companies. We're taking public data about the Dish Network and SiriusXM Satellite Radio. And we showed the Dish that our models were pretty close to what Wall Street said. But to the extent that they were different. We showed that Dish was overvalued and Sirius XM was undervalued. And here we are, six years later, and sure enough, their stocks have moved exactly in the directions that we said they should back when we were doing the research—

AS: And you were able to capture the publicly available data in this case, in terms of their subscription numbers, movements, and so on.

PF: That's right.

AS: Here he goes. So, public service announcement of your hedge fund or private equity professional, listening to this podcast here or YouTube video, please check out the work that Peter is doing at Theta Equity Partners.

PF: We've kind of shortened it, so it's thetaclv.com.

AS: So taclv is the new brand, and its website is thetaclv.com.

PF: That's right. And there are so many examples too, and I mentioned a couple in the published papers, but we do a lot of these other things for fun. So whether it's companies going public or other companies that just happen to put a lot of good metrics out there in the public domain, we do this work commercially more on the private side, working with private equity firms and other investors. But it really is great to show off and go out on a limb and say, “A Warby Parker is actually worth as opposed to what Wall Street says.” That's a great example of how, when they went public, they were grossly overvalued. But today, they're undervalued. And we absolutely believe in what we're doing. Now, people shouldn't take stock tips from a marketing professor, it's always a bad idea. But there are, I think, a lot of really useful insights from the analyses themselves.

What is the myth about CLV and Customer-Centricity

And so what I'm trying to do is not only come up with the world's best lifetime value models but the world's simplest. Because I want to make them broadly applicable. I wanna basically knock down all the excuses. Why do you think this one wouldn't apply to you, or why you couldn't implement it yourself? It's that just right balance between state-of-the-art in terms of math and computation. But also, why wouldn't we give this a try?

(12:42-16:51)

AS: Well, this is beyond the stock tips, we're basically creating alpha here. And I think what I love about this is the perennial challenge, and the perception of marketers and CMOs is that they're the sort of fluffy, you know, vague, non-quantifiable things. They're kind of very lower level things like the leads that we get, and none of the focus on either the customer dimension of it, not as sophisticated about the averaging that.

So let's back up a little bit and dive into what is the myth about customer lifetime value and what's the reality. And broadly, customer centricity is a course component of this.

Because when people say, “Hey, I'm customer-centric, the assumption is, “I just follow around my customers and do everything they say.” Which is actually exactly the opposite of your work, right?

PF: Yes, that is right.

AS: Your customers are created equal, and you need to respect them, and respect your business, including your finance organizations, because the only way you will support it as a marketer or sales organization is by understanding that difference. Please, dive into that.

PF: Hearing you embrace a lot of these ideas is a great validation right there that people are taking this stuff seriously.

To answer your question, myths and misconceptions. First, about CLV. A lot of companies out there say, “That's all well and good, but our company is different. Our customers are different. Our market is different, the kinds of headwinds that we're facing in the marketplace or nonsense.”

We can come up with some fairly standardized ways of looking at lifetime value. Now what is going to be different for a subscription business versus a more discretionary business like a LEGO retailer? Well, of different lifetime value models. So about four, five, or six different ones for different kinds of business settings. That's number one. It can apply very broadly.

And number two, there's not just one-lifetime value formula. Too many people write these white papers and say, “Here is the formula.” Now, it depends on the nature of the business, you can't use the same approach for a subscription business as you would for a non-contract discretionary business.

So there's myth number two. Myth number three is there are a lot of folks who believe that we can't. They actually like the idea of forecasting customer behavior, but they don't believe we can do it over a lifetime. They don't believe that we can do it over really long horizons because, once again, they think that things are going to change a lot.

There's a lot of stuff about customer behavior or just human behavior. That's incredibly predictable over long horizons. And it goes back to what I was doing as an undergrad at MIT before becoming a marketing professor. I was on the path to becoming an actuary.

If you look at the ways that insurance companies come up with their rates, you know, they don't know exactly when you're going to die, Alex, but they can look at people who share similar behavioral characteristics to you and say what percent of you are going to live to be 80 years old.

So, by having those long runs—

AS: I hope that it's 100%.

About the "Customer Centricity" book

Let's not, you know, upset anyone, let's not fire customers. But let's not pretend that we're going to be everybody's best friend, either. And figure out how to find that balance, how to allocate resources, and how to measure the impact of those actions. That's customer-centricity.

(16:51-18:51)

PF: But, just looking, I know some of your good, clean habits; I don't see any cigars or motorcycles behind you. So that's the perspective is to take very actual wearable viewpoints to be able to make long-run horizon forecasts. And to validate them, to show that we're going to take your data and come up with a long holdout period that sometimes is going to be even longer than the amount of data we use to run the models and show that it works. It works in the aggregate, it works at a granular level, and then we build our bridge to customer centricity.

There's a whole lot of big “so what's” that emerge from it. It's not just a good forecast, but it's a forecast that's going to make you go, “Whoa, we ought to be running our business differently”. Again, I'm a forecasting guy. I like the data, I like the models, but

I looked at the implications of the models and started saying, “Wait a minute, this goes against the grain of a lot of conventional marketing thinking.” And this is too important to ignore. That's why I started writing the books.

And that's why I started— maybe badly, but that's why I started talking about this idea.

Actually, you said it perfectly.

These words, customer centricity, are easy to misinterpret, it makes it sound like we love every customer; I can't sleep at night until the least happy customers are satisfied, not “Oh, that's not what we're talking about!” Really, it's a matter of which customers we want to be centered around. Or, as the subtitle says, let's focus on the right customers. For strategic advantage. Let's not, you know, upset anyone, let's not fire customers. But let's not pretend that we're going to be everybody's best friend, either. And figure out how to find that balance, how to allocate resources, and how to measure the impact of those actions. That's customer-centricity.

How to acquire the right customers

But they're not looking at the quality of the customers that are being acquired, just the sheer quantity. As you pointed out, they're not looking very carefully at the cost of making those acquisitions. And so it's just an ugly, wild west out there about the ways that the companies do and report these customer acquisition activities.

(18:51-23:08)

AS: What's interesting is, well, I think a lot of your work is with existing customers, right? We at RELAYTO focus on helping you acquire new customers, but that's really connected, right? And sometimes people miss this part. If you do a good job of analyzing who the new customers are that are succeeding the most with your product, fit, or service, who are easy to serve or cost cost-effective to serve, who go on and spread your word, spread your message.

For example, folks like yourself, we want him to have more customers like that, right? And so then, when we go out into the world and build our demand generation, or demand capture, strategies and tactics, we have this insight. And I'm kind of worried that a lot of marketers are so disconnected from that actual operational customer, who is the successful customer, that’s the ideal customer profile in reality. They don't have some hypothesis that they go out and do a bunch of email marketing malpractice that costs money and causes problems downstream. And so you end up having a bubble bursting with all these software, businesses, and AI in our universe that just kind of acquired crazily, the wrong type of customers wasting investors' money, and so on and so forth.

Why haven't those folks listened to your work? They just had different incentives? What are some of the issues that can affect people?

PF: That's a big part of it. Where do we begin? So first of all, a lot of it's organizational; there's just a real disconnect between the kind of folks who are out there, hunting and gathering customers versus the kind of folks who are working on the care and feeding of existing ones, very often completely from people that don't talk to each other.

In fact, they're sometimes at odds with each other: different metrics, different tactics, and a complete lack of alignment within the organization.

So we kind of start there, we can also talk externally that there are too many companies out of those software companies. Even though digitally native retailers, all they were rewarded for was top-line growth, and sometimes it's the VCs, who are a little bit naive, a little bit shortsighted, and all they see are these hockey stick curves.

Again, for me, it's been just an amazing lesson, learning from Dan McCarthy, not only in terms of forecasting, the number of customers and the revenue from them, and stuff I've been doing all along, but the cost of doing so. To be just as rigorous about CAC as we are about the revenue we get from them, holding companies accountable, finding inconsistencies and the things they're doing that require reporting, and so on. I'm not an accountant. And that's kind of on thin ice when I even talk about this kind of stuff. But man, is it important. So it's internal, it's external, and the fix is, on the one hand, pretty easy — to create the right kind of alignment, to put out the right kinds of metrics, to have the right kinds of scrutiny from external stakeholders.

They're also really hard, just because old habits die hard. It's one of those I love working with, for instance, companies outside the US, companies in Europe and Asia and so on, where they don't have as many bad VC practices kind of built-in, so maybe we could start with the good stuff and watch companies flourish. So I'm always looking for clean slates like that, as much as I want to help online companies improve. It's just harder, and it's going to take longer. And it's nice to get some quick wins of the right kind.

About different types of CMOs

So I want people who are leading the CMO, not just the marketing position with a CMO, who's leading with the technical and really understands the metrics and the models, the interplay of acquisition and retention, and the importance of winning over finance.

(23:08-25:54)

AS: So, on that note, as a marketing faculty leader at Wharton, you teach executive education; you teach MBAs. Describe to us who you envision as a Renaissance CMO. What does she look like? What does he look like? How are they able to balance the analytics with other directions in terms of creativity?

PF: It's going to start with the analytics. That's the key, and we're seeing more and more of that these days as folks who are common to the marketing function, and maybe even the CMO position, from a technical background, for most companies, a CMO is someone who just did the purely creative stuff. And by the way, I'm not criticizing the importance of creativity, but it's got to be much more than that.

There are too many CMOs I talked to, and I'll try to raise some of the analytic things. And they'll say, “Oh, yeah, you fully embrace that there's someone who works for someone who works.” I mean, they do all that kind of stuff, and you could talk to him if you wanted to, so they'll kind of implicitly downgrade the importance of it.

We're seeing more and more people like that, again, it's a generational shift. We can't just snap our fingers and make it happen. But it's just been amazing to see. And it makes me feel really good about the future of the field of marketing.

You know, a lot of people talk about the CMO as this kind of revolving door position. There's a reason why, and I think we're making great strides to change that. Some companies, which means actually changing the name of the position, don't even call it a CMO anymore. It has all that baggage. So it's called a Chief Customer Officer or a Chief Revenue Officer. I love that.

It's a matter of balancing the creative with the quantitative, but I'll say the quantitative analytical parts are kind of harder to become experts at. They're harder to get the other marketers to understand that. So that's the stuff you really need to lead with.

And then it's enough to say, “Hey, you know, the people who work for me are just astonishingly good at being creative.” And we value that to just shifting the balance a little bit.

The role of technology in marketing

It's the same thing every time, which is that we just throw a ton of money at the technology. It's just, “Let's build the thing, and then, you know, money will come raining down from the sky.” It never happens that way. It never has happened, and it never will happen.

(25:54-28:37)

AS: Interestingly, one of the things that is not very well known about you is that you were recognized by Advertising Age as one of the top 25 marketing technologists, and you were the only one with an academic credential in that esteemed group. I'm curious as to what your take on the role of technology is, because increasingly, when you look at software as a service, people say that CMOs own more of the budget than the CEOs in some cases in some organizations. How are you looking at that and the role of the technologies in helping? Is it sometimes misleading like any other tool? What’s your perspective?

PF: It's just the same old song that I've been singing for my 30 or so years on the Wharton faculty. In the beginning, it was scanner data. Oh, when we put scanners in stores, we could start to track who was buying what. Then we started getting a 360-degree view of the customer CRM systems back in the early 90s, and then it turned into big data. And now it's AI.

And again, a lot of the CRM stuff from the early 90s, a lament that continues just as much today, where we built all these systems, and we keep all this money to the CEO, CTO. I built this thing. But why are we building? What are we going to do with it? And it's kind of important to have real, crisp, clear answers to those questions before you build it. To be able to give strong priorities to those folks, say, “Here's the sequence of things we should build in terms of the ROI we're going to get from them, not in terms of the ease or cost of building them.”

So again, we see it all the time. I'm just trying to get companies to think first and, again, prioritize through the customer data, even if we haven't collected it yet.

From what we've learned from other companies, just anticipating what it is that we're going to see or what it is that we need to see, let's build that stuff out first, even if it's not the sexiest or the cheapest. So there really needs to be a bona fide partnership between marketing and technology.

Building a bridge between marketing and finance

(28:37-29:23)

I gotta tell you, as hard as it's been to build that bridge in marketing and finance, I think we've done a great job of it with some of this work on customer-based corporate valuation. That's not to say every company has embraced it; it's a generational thing.

With the marketing technology thing, there's still a lot of tension, a lot of misunderstanding, a lot of turf. It's still a long way to go because technology keeps changing. So we keep coming up with new technologies. We're kind of figuring out the old one. That's a really tough thing. And it's just hard for a CMO, even for a CEO, to step up and say, “Wait a minute, we're gonna walk before we run with this thing. It's very hard to do that when all your competitors are running.

About the gaps in behavioral analysis

“Okay, here's the data we have, what can we do with it?” And they're not being scientific about it.

(29:23-32:30)

AS: Yeah. And it feels like one of the fundamental challenges is the attribution model, right? Like, who touches what, where the leads come from, and the big surprise to us, at least in the running of the digital body language of how people consume marketing collateral, or proposals, is the overall level of ignorance by the marketing professionals and design professionals who are putting a lot of creativity, a lot of sweat, blood, and tears into this content, that it's very disconnected from “Do people actually read it? What do they read? Where do they click? Where do they spend their time.”

And it's not some vague little cloud of pretend screenshots of screen time. It's the actual clicks.

So discovering that just opens people's eyes in terms of what's working, what's not, and where they could allocate the resources, which is another big part of marketing spend, is actually content creation and then driving people to that content. If the content that's created is not blocking people from engaging with your ideas, that's clearly an unlocking opportunity.

So what's your take on having gone through three years of actual behavior analysis? Where are the gaps that you see? We are probably more exposed to B2B businesses. In both B2B and B2C businesses where people are a little bit blinded by either too much data not getting to the essence or not enough data, kind of missing the obvious.

PF: Yeah, it's a really important question. And it goes back to some of the work I was talking about earlier with respect to lifetime value, which is that there are too many companies out there that say, “Okay, here's the data we have, what can we do with it?” And they're not being scientific about it.

So I want to really understand the science of behavior. That's my job.

And then start with, “Okay, well, what kind of data do I need to bring that to life to answer those scientific questions, to build that forecasting model?” Instead of just knowing what kind of data we have, what can we do with it? And you're absolutely right. You mentioned attribution models, all that stuff is just an addition because we're trying to make attributions with very limited touch points that are kind of being measured badly; they're disconnected from each other. There's nothing holistic about it.

AS: Basically, to echo that, it's like the funnel-based view of customers, right? Especially, as you know, in complex journeys, that is almost a model; it's probably better than nothing. But it's definitely not a reality of how people go down the journey, right, especially in larger organizations?

What does the data really tell us

It goes back to my point about our people letting technology lead the insights instead of the other way around, and it's hard to get people past that.

(32:30-34:20)

PF: Exactly right. We impose the structure on it without saying, “Well, what does the data really tell us?” And then what other data do we need to take our bona fide story and bring it to life? And so I think it's really important for us to think first: let's do the science; let's understand what kind of data we need. Let's ask ourselves, “What kind of decisions would we make on the basis of it?” And then design and invest from there.

Of course, right underneath the question you asked, my answer to it is exactly the technology that you built is fabulous. The very fact that we're not only investors in your firm but also big-time users of your technology is because we really believe in the quality of the data, the ability to really know who's doing what, and again, in a very comprehensive, holistic way, where we can really fit it all together, tell just a much more complete, accurate, actionable story about customer behavior, that interaction with, what bits of content are they reading, as well as what we can understand about the other kinds of actions that they've taken, the sequence of things that they've clicked on, and so on.

It's a no-brainer. But because it's, first of all, an emerging technology. It's not somewhat unconventional. So other people would say, “Well, that doesn't fit into our tech stack.” I mean, well, “Fine, your tech stack is wrong, so fix it.” It goes back to my point about our people letting technology lead the insights instead of the other way around, and it's hard to get people past that.

The culture of valuing analytics insights

(34:20-38:47)

AS: I think this also relates to why Nike bought your previous firm, Zodiac, right? They love the data, but they clearly wanted to do something proprietary with that data. I think when we talk about interactions and capturing the engagement data and deep granular analytics from those interactions, they didn't hear that how you want to apply your insights is that this is not just for the data capture, but maybe some analytics. Ultimately, if a company gets a hold of this information, they are going to change how they do business, how they engage with their customers, when they're going to engage with their customers, and they're going to have a much more sophisticated array of ways to drive the deals or transactions, but more importantly, the relationship forward.

I think you've mentioned a little bit of different teams sometimes right in the organization, but the customer success team is probably very far from the customer analytics team in some ways. What do you think we could do to create a culture where people take those insights seriously? Whether it's from the models that you're building, and your students are building off of a platform like ours, and actually incorporating that into a loop? Where do you start changing your behavior toward your customers?

PF: Okay, so let's talk about top-down and bottom-up to answer that question. First, from the top down, it's great to have a visionary CEO who gets this. You mentioned Nike buying my previous company, Zodiac. Let's give a lot of credit to Mark Parker, their previous CEO. And by the way, they did this from a position of strength. Nike was doing great, but they knew they could be doing better. They wanted to connect all the different data sources that they can tag, track their customers, draw insights from it, and figure out what actions to take. That's why they kind of looked around on their own and figured out what kinds of technologies they needed instead of just saying what technologies were sexy and what our competitors were doing.

Again, it was just very strategic. “Here's what we need to know. Here's the technology we need to enable that.” That's exactly where Zodiac came in. It was just great to see the work that went into not even buying the firm but the way they then integrated it within the organization. So, you know, we'll see those kinds of top-down examples. Again, I wish there were more of them. From a generational standpoint, I think we will see more of them.

Then there's the bottom-up. So what I want to do, again, is self-serve. I've got these models that are really good; they can drive a lot of different decisions, whether it's about marketing, finance, customer experience, or even supply chain talent management, or, you know, M&A. That’s really more about what goes on in book number two. So how do we implement a winning strategy based on lifetime value? How do you come up with a use case for each part of the organization?

Instead of trying to force the people in R&D, or accounting to say, “Here are our models, you must use them to say, “Here's why you want to use our models; here's why you're going to find greater success and be a better internal hero; and hey, we can work together.” So that's on me to basically come up with those seemingly unrelated use cases that relate to the models.

I've been very proud of what I've been able to accomplish. A lot of it is because of really smart, clever people in other organizations who have done some of the stuff on their own. And I'm saying, I'm going to steal that idea and share it with other companies. It's been great to see that breadth of use cases, kind of elevating the importance of customer measurement, lifetime value, and all that across the organization.

About bringing your full self into teaching

So when I'm teaching this stuff, it's just great to see the light bulbs go off. It's great to see people who are either math-phobic in my life or who love math but have never found the right application for it and to get them to see it. This is what it's all about.

(38:47-42:35)

AS: I love hearing, Peter, how you take ownership of distributing your ideas, your research, and your insights. Anybody who's watching this or listening to this can't help but feel the energy and excitement that emanate from you and the passion for your work. I'm not surprised that your students want to go into business with you, do companies together and apply the research. Can you tell me a little bit about how you personally will continue? You've been teaching for nearly 35 years, right? If my math is not correct, correct me.

PF: It's more than that, but I like being younger.

AS: There you go. You've got the passion. You've got fans, students, collaborators? What do you think about, besides the model and the analytics, in terms of bringing your full self into how you communicate? And how do you create these changes for other people and other organizations that you're impacting with your content?

PF: So it's a combination of things. Number one, I just love this stuff. I just find it inherently interesting. For a lot of faculty, it's like, “Oh, no, I've got to go teach. I'm going to turn my brain off because I've got to go into the classroom.” And now for me, I teach my research, I commercialize, and it just all fits together; it's really, really nice. It's kind of seamless, in a way. It's really fun.

Number two, not a lot of people do it. Not just the lifetime value thing, but even these actuarial models they refer to are not very common. For every one of us doing these probability models, there are 10,000 other machine-learning people out there. And that's powerful stuff; I'm not knocking it, but it's a part of it. It’s kind of like I'm on a mission from God; there's a lot of gospel spreading just to try to get some of these methods out there because they're really good.

So if I'm not going to do it, who is? And finding the interplay between them and other kinds of methods, so that's number two. This is going to sound really weird, but remember, I wasn't born to do this stuff, and a lot of the models that I'm using now that I didn't even understand couldn't have done the math when I first started as a professor here.

There are a lot smarter people out there, so I came up with all kinds of tricks and gimmicks to try to learn a lot of the stuff myself. And I just find that, you know, “Hey, if it worked for me, it's going to work for other people.”

I just loved the teaching part of it — even if it doesn't lead to great research or great commercial applications, it's just a really fun three hours to kind of talk to people about this stuff. And get them to think about the world a little bit differently. Even if it's just entertainment, I don't want to say that because I'm doing integrals and nasty things like that. But even if it's just stuff for them to forget about the rest of the world and just think about different stuff for a while, even if they never, ever make use of it, that's still good with me.

What changed in the way business schools operate

If you look at the enrollments in our courses, finance is still the big kahuna, let's not deny that. But marketing statistics operations are much fairer than they used to be, so that's one big part of it. They're also going to be much more distributed across different industries.

(42:35-46:05)

AS: That's amazing that you are able to combine your passion with your scholarship application. What do you think has changed in the way the business schools operate? Especially as other alternatives for transferring just knowledge alone come about in the startup world or accelerator programs. I've mentored in one and then joined it because I found some value in this. You know, you've seen me work for quite a few decades now. How's it evolving? And how's it adapting to the new world that we live in? Where is the knowledge of businesses more distributed?

PF: Yeah, you said it exactly right. Let's talk about that distributed theme in a couple of different ways. Number one, it's more distributed across the different business disciplines than it used to be. A lot of people still call it the Wharton School of Finance, right?

When I first started, I couldn't even spell the word entrepreneurship. I still have a hard time with it. So yeah, a lot of other areas of business, entrepreneurship, marketing, just so many areas I would have never thought of taking courses in when I started all those years ago. Analytics is probably the most prominent example of that. So there's just much broader interest across the disciplines and much more of a balance.

So again, everyone is ready to rush to Wall Street. So even if you're doing finance or these other areas, retail is much cooler than media and entertainment.

Many people are going into things with much more, let's say, an ESG flavor to them. Or this whole new center on climate change, and so on. So besides that, the skills that people have and the areas that they go into are just much more distributed than ever before. And the third party, again, it's corny, but I believe in it, is the whole global thing, which is every example used to be just us. Part of it was ignorance because that's all we know. Part of it was arrogance because we're the best, and so we're going to tell the rest of the world how to operate.

That's all wrong. This is going to be much more interchange with students from other countries and with companies from other countries, and we will bring that into our research. So again, let's think about data structures and so on that might exist elsewhere but not here in the US. And how I can build my lifetime value models around them, making that both an intellectual exercise as well as one that can have a lot of practical applicability for a much broader array of companies and geographies than the ones I might have been talking to back in the late 80s.

About "Customer-Based Audit" book

First of all, in terms of just their overall goodness, are we acquiring better and better customers? Or, more frequently, slightly worse customers? And then let's do average on that basis as well. Why are they worse? Is it because they don't buy as often, don't spend as much, and don't stay as long, so the average ripples down?

(46:05-49:56)

AS: That's fascinating. Let's touch back on the international theme a little bit. So lots of companies, when they expand to new markets, take the model that works in their core markets. They kind of forget that took quite a few years to get to the market, that there were early adopter segments, and then later, they move more to the mainstream. They plunk that model into their emerging markets and then have some disappointing results until they restart, almost treating it as a new venture with some benefits of lessons learned but not one-to-one comparison. How does your research help companies avoid making these mistakes and try to understand the moments in the market? And how do you change it overnight?

PF: Alex, you're so good at setting up these questions. I love that it gives me a chance to kind of wave around book number three.

So let's abstract away from purely the international market entry piece to ask the broader question: as we acquire different cohorts of customers, even if we're doing it purely domestically, let's understand how each of those groups of customers is different from each other, how we can anticipate what the next group is going to look like and build our business in a way that's forward-looking instead of saying, “Well, what did the customers look like?”

A really big part of my research and a really big part of book number three, our customer base audit, is the idea of really understanding customer cohorts. So when we're analyzing the customer base, we're not just analyzing the customer base; we don't want the average. Please don't. And that's exactly the point. So the first way we're going to do average is to break it down into cohorts. Let's look at the tranches of customers that we've acquired, usually at different points in time, although it could be different geographies, different channels, or whatever. But let's focus on points in time. And just understand how they're different from each other.

The idea of doing analysis on a cohort-by-cohort basis to connect the dots across those cohorts to understand what the next one is going to look like to make sure that we're in a position not to be surprised when we acquire those customers and, B, to find success with them.

It's kind of obvious to talk about, but companies tend not to do it really well. One of the reasons why is that they've never really thought about it. It's one thing to think about the company. So we're going to start with the US, go to the UK, then go to Germany, and then underneath all that would be the customers.

When we started thinking about the customer cohorts and even asking ourselves, "How will the geographic distribution of those cohorts shift?" it was going to be so much more insightful than just pretending that the next one was going to be the same as the last one, which was not true. Or that we'll figure it out when we get there. This is such a cohort-level analysis; it's so obvious and easy, but it's not nearly as common as it should be.

Where to find Peter's books

(49:56-52:12)

AS: Well, a brilliant way to wrap up. Where can people find these not-obvious insights or obvious insights that are not getting done, Peter? Where can they find you? Where can they find your books and your company? Guide us a little bit on that.

PF: I love it. So, well, first of all, if they want to find me, I don't hide very well. You know, Google my name, and you'll find everything. Again, the three books are easy to find; if you just search “Fader” or “Customer centricity”, it all comes up.

I do highly recommend folks take a look at thetaclv.com. Not just because it's the business, and I'm trying to drum up stuff here. But there's a lot of really cool content there that we've done. And yourself have dealt with Tara Hepa, our wonderful marketing person; she's kind of my mouthpiece to kind of take a lot of these ideas out of these methods and make them very accessible and interesting.

Theta has been a wonderful outlet, whether it's formal analyses and corporate evaluations or just thinking about blog posts and so on. There's lots of great content there. Again, a lot of it is powered by your own wonderful company, and it's just good to have this stuff out there. I really do hope that if people do find any of it interesting, even if they don't necessarily agree with it, they want to find out more and learn more.

Connect with me on LinkedIn. Follow me on Twitter; I was going to say X. And you've already mentioned my passion for the topic. It's a conversation that I look forward to keeping going with you and with anyone else.

AS: Brilliant. Well, I couldn't recommend this more to my audience. Tara is wonderful. Peter is wonderful. Theta is wonderful. The research is going to change the way you run your business. Thank you very much, Peter, for joining us at Experience-Focused Leaders.

PF: Absolutely. My pleasure, Alex. Keep up the good work!

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