INTERVIEW
Value, Disclosable Metrics, and Data to Guide Change
With Peter Fader – Professor, University of Pennsylvania and Daniel McCarthy – Assistant Professor of Marketing, Emory University
Peter Fader and Daniel McCarthy’s entrepreneurial and academic pursuits are quite intertwined in very interesting ways. On the entrepreneurial side, they cofounded a predictive analytics firm called Zodiac that they sold to Nike in 2018. And now they’re cofounders and directors of Theta, a predictive customer value analytics company.
The common ground between what we do at OCX Cognition and these two thinkers is a conviction that customers are a key driving force in value creation for any business. So, it was great to get these two collaborators and partners talking about the ins and outs of how customers drive value and what businesses should do to maximize that value. Some areas to listen for. Well, fundamentally, customers are assets that can be measured and managed, so an embrace of customer behavioral data and the insights it offers should lead to something of a sea change in how companies think about building their business.
Also, finding the right set of metrics that provide a clear fair valuation of companies is critical but is complicated, and there’s a need for a balance between internally and externally disclosed measures.
Richard Owen
Gentlemen, thank you for joining me today. One of the most, I think one of the most important reasons we wanted to talk in the first place is because there’s a shared conviction, I know between the two of you and myself, that at the end of the day, customers are one of the big driving forces for value creation and business. You’ve really taken that a long way down the road of understanding how that helps companies and the conviction you have has been reflected your work on customer lifetime value. Perhaps you could start and maybe I’ll pick on you, Peter, to kick off, just to give us an overview of your fundamental thesis if you like.
Peter Fader
Sure, well, so before we even get into all the customer-based corporate valuation, which is where we really want to focus, just the kind of mere idea of customer lifetime value, the idea of being able to project future profitability over long horizons at a granular level. It’s catching on more and more, but there’s still a lot of skepticism out there. There’s still a lot of people who say, well, it doesn’t apply to my business, to my customers, or, well, we can do it over a short horizon, but, you know, everything is going to change.
There’s a lot of people who just refuse to acknowledge that there’s some amazing statistical regularity about customer behavior, about what they do, about how they differ from each other, about how they change over time, and we can capture that. And that’s what I’ve been doing for 20 plus years in the academic work, is coming up with models that can project how long is this relationship going to last? How often will this individual transact with us, do other value-generating activities, maybe make referrals and so on, how much will they spend when they do, and pull all that together to come up with a highly validated metric to say this is what this customer or this micro segment of customers will be worth.
That’s where it all starts, and then it kind of goes off in two different directions. One would be from a purely strategic standpoint; how do we leverage it? Once we understand these differences across customers, how do we develop strategies and tactics and organizational structures and corporate cultures that let us take full advantage of it. And number two is how do we let it trickle into this other domain: finance, Dan’s gonna talk a lot more about that. To fundamentally change the way, we do corporate valuation and just overall investing practices that put lifetime value with these kinds of predictive analytics front and center.
Richard Owen
Dan, do you want to add to that or certainly add to the finance perspective perhaps?
Daniel McCarthy
And certainly, that’s kind of where my, my ears had perked up back when I was a PhD student that, you know, someone had recommended I go up and speak with Pete and we were talking about predicting customer purchase behavior. And there’d been some early work that kind of was proof of concept about this idea of using that to better understand overall corporate health and I had spent about six years at a hedge fund before coming back to do my PhD and it’s just an area that I know and love It just made a whole lot of sense that if you had the ability to predict what customers are going to do very well, yeah, maybe that could tell you something. There’s kind of signal there that may not be evident to the traditional finance major coming out of Wharton. And so that was kind of the basic idea. I think the fundamental, we call it just basically an accounting identity, is revenue has to come from customers making purchases who had to be acquired.
So, we kind of use exactly the same models that you’ve described, but just use them to more accurately predict what future revenue is going to be. That can provide you with a more accurate, estimate of overall revenue. And you kind of get for free all the other sort of insights that we spend a lot more time thinking about within the marketing community, like customer lifetime value and other related measures. So really great synergy there. And I think it helps elevate the role of the marketing department within the organization.
There’s just a lot more investors who have been asking very pointed questions to management teams about what’s your retention, what’s going on with the cohorts. And so suddenly the CEO needs answers to those questions. And so, I think that could be a wonderful opportunity to have the CMO fill that role. The CMO is in charge of customers. The CMO can help kind of give the talking points as to what it is that’s going on and why
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Peter Fader
And, Richard, I if I can go back to your very initial point where you said customers are the driving force, etcetera, etcetera. The point is customers are assets. And even though accounting rules don’t really recognize that, oh, they’re intangible assets. No. They’re tangible assets, and they really can be measured and managed just like we measure other tangible assets. And we need to do that.
Well, we need to stop just saying, oh, it’s a bunch of fluff, oh, it’s about the brand, oh, it’s about the experience, and so on. Not to say that stuff isn’t important, but when it comes to customers doing things over time, we can measure, we can compare, we can come up with very, very specific standards and guidelines about what we write down and what we hold ourselves accountable to. That’s all. Once we agree on that, everything follows naturally.
Richard Owen (05:50)
Yeah, I mean, there’s always been this disconnect around, you know, accounting, which is a slice of time or even backward-looking set of measures. And the fact that nothing exists on the balance sheet that really talks about futures for the business. And as customers of an asset, an intangible asset that, you know, we’re used to almost 100-year-old accounting standards, let’s worry about tractors and farm machinery on the balance sheet. And you can make the same argument for data, right? I think a lot of people who say data is a core asset isn’t reflected on the balance sheet. So maybe we’re just, our accounting methods haven’t come even close to catching up with the way in which companies need to really understand their values today.
Daniel McCarthy (06:34)
And that’s going to be very hard. We’ve talked in the past about how, if we could put it on the balance sheet, would the CFO even want it on the balance sheet? And I think that’s a whole separate question. In some sense, the CFO may want to keep, if the CFO wants the highest net present value as their tax shield, they may want to keep current profitability down naturally. And so, to the extent that they’re putting this asset, they’re not kind of recognizing all that expense upfront on some level that could be a negative. I think holding all that aside, I think that fundamental point is very much true. And in the same way that when you buy a building, you pay for everything upfront, but then you receive the benefit of a multiple future periods. Well, there you go. I mean, that’s exactly how we would describe investing in a customer relationship.
Richard Owen (07:17)
Yeah. Well, it’s funny what you say about companies may not want it. There’s no shortage of companies who are willing to create alternative ways of calculating profit if they feel it presents their business in a better light. Right? So, you know, I don’t think people hold back if there’s ways to create shadow P&Ls and balance sheets, but it raises an interesting question. So almost every innovation I’ve seen, and I might, you know, I’m more narrowed down to what I’ve seen in business-to-business SaaS over the last 20 years because that’s the field I’ve been in. There’s always been this interesting interplay between the investors and the companies. And it’s a chicken and egg situation. Often the investors start to say, listen, this is how we want to view the universe. And naturally, the companies then say, well, in that case, we’ll track it. So about probably, I want to say seven, eight, nine years ago, you started to see private equity and venture funds wake up and say, net revenue retention is suddenly important. And 15 years ago, it wasn’t important, right? Acquisition costs had started to matter, but people weren’t thinking about upsell, cross-sell. Maybe they were thinking about gross retention numbers. But then everyone started waking up to the idea that revenue retention was potentially predictive of long-term profitability. And I wonder if in general this is the cycle we should be expecting. Finance organizations decide this is a great predictor of value, a great insight and they drive companies to do it.
Peter Fader (08:55)
A lot of this kind of you know, shiny object mentality that, that, you know, somebody will say, here’s the metric that you should be thinking about, whether it is net revenue retention, whether it is LTV to CAC ratio, or, you know, or NPS, as a lot of people might talk about. And we’re pushing for not just that kind of, you know, kind of magic bullet metric, we’re pushing for a more ongoing fundamental understanding of all the facets of customer behavior, at least the relevant facets of custom behavior. So, we should be tracking different aspects of acquisition and retention and repeat purchase and spend and it shouldn’t be very context dependent. I mean sure it should be a little different in a SaaS setting versus discretionary in our non-subscription setting. But for the companies in one bucket or the other, it should be pretty much the same set of metrics and it should be the same set of metrics over time, not just here’s the cool one that the people are focusing on today. I think once we get into this kind of metric obsession, we lose sight of the reason why we even use that metric in the first place. Very few people can even talk about what the origins of Net Promoter Score are. And so, it’s important for us to, we want to think a level lower about the customer behavior we’re trying to capture and again, why we came up with those metrics initially.
Daniel McCarthy (10:18)
That’s why I think customer-based corporate valuation is kind of nice in that way, that you can kind of game any one single metric in the key ways to keep your cap down, but maybe not necessarily the best for the overall valuation of the business, but ultimately, what matters the most is shareholder value. And so, if you have a framework, I think you’re going to actually get to fair value. Well, that’s something that can serve as a North Star for any company. I think, for Net Revenue Retention, it’s kind of also another analogy is to same-store sales growth. And back in the day, people didn’t keep track of it. And then suddenly someone had found that it was valuable. And one thing led to another. And it became an informal norm that all these companies that had stores, they would actually disclose it every single month. And I love that. But I think that too, it’s an example of a number that in theory, it’s good, but you would really want to be keeping track of that in conjunction with a whole bunch of other relevant auditable measures and that those can allow you to have that 360 view that can actually understand how things are changing in terms of overall valuation.
Richard Owen (11:34)
Well, I think we’d agree that there’s no silver bullet single metric for anything, right? And I think what you’re advocating for, correct me if I’m wrong here is, at the end of the day, companies need to get much more sophisticated in their ability to build data, model data, analyze things from multiple different aspects, and in some ways bring together a whole series of viewpoints that can help them inform how they’re going forward. Now, that might be a different objective than being able to report to investors a simplified and comparable set of data points. There’s what you do for show and there’s what you do for dough. And at some level, coming up with metrics that are, and hopefully not just for vanity purposes, but are legitimately ways of communicating outside the company performance, which requires standardization and simplification.
On the other hand, within the business, we should be getting a lot more sophistication. We should be, certainly in larger enterprises, be able to look at this from multiple dimensions, make sophisticated trade-offs. And if I’m hearing you right, that’s what you’re suggesting. Don’t oversimplify this either. Get in there and fully understand what’s going on.
Daniel McCarthy (12:51)
It’s very fair to say, I mean, I think perfect is the enemy of good enough. And we certainly would rather a company disclose active customer count over time than to not disclose it. And so, there is this whole ball of wax, this Pandora’s box that opens up when we start making recommendations as to, you know, what disclosure requirements should be when we speak with people like FASB and the SEC. And, you know, we’ll have kind of our ideal case and then we’ll have the realistic case. And then probably the case that they have to deal with for three years in the future. So, as soon as you start having these conversations, you’ll be pragmatic about what actually could be feasible in the relatively near term, I think that’s absolutely critical.
Peter Fader (13:39)
And two more quick points to add to that. You talk about the metrics for show versus dough. And there’s no doubt that we’ll have a somewhat different set of disclosures for internal versus external audiences. But they should still be tightly connected with each other. It’s one thing to say, we’re going to have some metrics. We know they’re really vital. They’re so vital that we’re not going to share them. That’s fine. But we shouldn’t be putting any metrics out there that are junk.
And we’re seeing an awful lot of that. A lot of metrics that are purely for show. And then the problem is we’re still calculating them and devoting resources to them. So, we say, well, it must matter somehow. So, let’s at least agree internally on here’s this core set of metrics and then decide which ones we’re going to keep inside, which ones you might share, how we will communicate about it, instead of having these kind of two separate books. That’s number one. And I kind of related point is there’s such a plethora of data, new data, emerging every day, whether it’s about what people are saying and ratings and views that they’re posting and who they’re connected with and even things that are going on inside their brain or the pupil dilation and all sorts of things. And so, we actually do need to rein it in. We’re big believers in Occam’s Razor. We want a set of metrics explanations that do a good enough job, but then let’s draw the line and say we don’t need anything more than that.
Richard Owen (15:06)
You know, it’s funny you mentioned this issue of CFOs. I’m reminded that a long time ago, probably 10, 15 years ago, been involved in a joint project between ourselves and Bain and Pricewaterhouse around the idea that maybe we could get companies to audit, so it wasn’t technically auditing, you know, NPS scores before they published them. Zero interest in it from companies. And I think in some ways, CFOs were very reluctant to really treat it as anything other than a vanity metric when they published it. There was, and if you go and do a quick search on companies that publish their NPS scores, you’d have to say a lot of the published data looks pretty suspicious. There’s certainly a bias towards upside. Let’s put it that way. And so, it’s, yeah, it really deflects from the point that you should be using this to make a better business. And if you start to get into the issue as a PR story, you’re taking it the wrong direction completely, right.
Daniel, one of the things that you talk about a lot is data analytics here to really identify high value customers. I think if I’m not mistaken, you talk about clustering algorithms being applied to that sort of targeting process. Before I murder it any further, could you elaborate a little bit more on what that involves?
Daniel McCarthy (16:29)
Yeah, so in general, this is kind of more on the marketing use cases that if you were to kind of run a proper model across all the various acquisition cohorts for what the customers will do, then we kind of have this nice apples to apples number for how good different customers are. And we can then use that to understand where the clusters of value are and what may be associated with that value. So, whether you choose to use some sort of non-parametric clustering algorithm, or basically some sort of regression or a random forest, something that’s basically an advanced version of regression. I think they all kind of pushed you in that same direction. It’s just really identifying the best correlates of the high value customers and how they may differ from those of the low value customers. So, that’s again, that’s one of the good things about this framework is we’re using this same model for both the finance use cases and the marketing use cases. We’re not changing the model. It’s literally the same one. And so, there’s kind of a beauty in that. It kind of is this Trojan horse that we kind of get all the finance people hooked and then whoop, income to market in use cases. Yeah, from the very same model.
Richard Owen (17:45)
Do we need to get marketers to think differently about the nature of customers they acquire? So, one of the things that I’ve always observed in the, purely from a CX NPS universe is you don’t create a great NPS by fixing problems. You create it by never recruiting customers who are going to hate you, right? And this notion of sort of ideal customer profile and the distinction in lifetime value created by non-ideal customer profile customers flies against everything that marketers and salespeople are generally targeted to accomplish, which is dollars are dollars, especially transactional sales. So, it’s a huge culture shift, isn’t it, to get these organizations to think in terms of not just the initial sale, but what this implies for the company over the next five or 10 years.
And it’s entirely understandable why that’s the case because we have this obsession with acquisition costs. And so, thanks to Google, it’s kind of our job as marketers to bring in as many customers as we can as cheaply as possible, and then we’ll educate them to become great customers. And it just doesn’t work that way. And so back to the lifetime value thing, if we can make lifetime value as visible, as visceral, as understandable, as believable as cost per acquisition instead of focusing on, you know, how many can we get as cheaply as possible? It’s what’s it going to take to get those really good ones? And it’s a different kind of conversation. It’s a very different way of going to market. Maybe it starts with those metrics and the credibility or lack thereof in there. But I have this conversation all the time that most of the customers are requiring aren’t so good. So, you know, what are we going to do to turn them to good ones?
It’s not that people are dumb, it’s not that they’re naive, but it’s just a very different way of operating and it’s just hard to get people past that. Once we do, once they embrace lifetime value or see the validity of it, they will start operating differently and start getting better results, but it’s hard to get there.
It’s very fair to say, I mean, I think perfect is the enemy of good enough. And we certainly would rather a company disclose active customer count over time than to not disclose it.
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Richard Owen (19:52)
Right. But Peter, doesn’t it imply a whole way of thinking about what represents a good path to building a company might need to change? Because if we are obsessed with short-term growth in revenue, then we’re saying that we’re sort of hyperbolically discounting the future and we’re going to look for very, very short-term, which means that it does incentivize companies to find any sources of revenue they can up front.
Damn the consequences down the road. And then you create sales teams that are incentivized appropriately to do that. And that becomes self-fulfilling.
Peter Fader (20:31)
We see it all the time and we’re trying to fight that. And Dan said it perfectly, which is we want to use the same models, the same perspectives for marketing and finance. And if in finance we want to create long term growth, then that should carry over to marketing as well. If we can get those two organizations, the leaders to really sync up, it will change the way we do marketing. And I think that would just be in everyone’s best interest. By the way, it’s not that the marketers would mind doing this. It’s not that they don’t want to build long-term growth, they just haven’t been given the kind of the tools, the power, the authority to do so. So, we can make that happen. And again, we do see some signs.
Richard Owen (21:13)
They’re under immense pressure to generate short-term results. And so, it’s completely inconsistent with asking them to think long-term. Let me change gears a little bit because when you think about capabilities companies have, what are the gaps today? Is it a gap in data? Is it a gap in the ability to understand that data? Is it an organizational culture gap? Is it all of the above? You know, could you comment on that?
Peter Fader (21:47)
I think it is all of the above, actually. It’s funny that it used to be data, data, data, data. You’ve got to have the right data. You’ve got to be able to tag and track individual customers. And so, a lot of companies have gotten there, thanks to just the better way that we can see who’s doing what and mobile apps and loyalty programs and so on. And then it became models and the analytics. And that’s what we step in with all lifetime value stuff. And I naively believe that if I can just wave my magic wand over each customer’s head and see their number of future profitability shining, that money will come raining down. Well, it’s not that easy and that takes us to the culture piece. And so, we get people to align and believe and talk and both internally and externally. That’s hard, at least for a guy like me. And I don’t want to speak for Dan, but same, that we’re kind of numbers and analytics guys and we’re just not good at getting that other piece going. But fortunately, we have seen a lot of other companies and a lot of other smart people who have been able to do that and then it just makes it so much easier for those first two things to fall into place.
Daniel McCarthy (22:54)
I mean, we’ll have so many engagements where there is kind of this thought leadership piece where I think it’s primarily to help improve the culture around customer simplicity and by getting the upper management team to be just part of those conversations that don’t get excited about it. And you know, when there’s a will, there’s a way. And so, I think a lot of the other pieces fall into place when you actually have the people up top saying, we want to do this. If they don’t, the flip side is also true.
Richard Owen (23:26)
So, I mean, it’s partly a cultural problem, hearts, and minds issue. I think that you’re right about the data. I mean, most companies I speak to, their first response is, you can’t believe how bad our systems are and our data sets are. And the reality is nobody has some perfect and idealized view of how all our data is organized. I mean, that’s just not, that doesn’t exist in the real world. And everyone’s systems are always a bit of a mess and they’re trying to organize them. But it seems like that becomes an excuse for not moving forward as opposed to a legitimate impediment, right? There’s no reason not to commence and at least start with something simple.
Peter Fader (24:05)
Sure, so a couple of things on that. So, we either make excuses, or we just throw a ton of money at IT and say just build these systems and then we’ll just understand. Yeah, so we need to start somewhere. I think Net Promoter Score was actually a really nice starting point to understand that not all customers are created equal and to just get this sense of how many are these, awesome right tail gonna be with us forever customers versus the eh, so, so ones. So, let’s tag that and track it over time.
And that was a very, very good starting point. Actually, created a lot of interest. Then it’s a matter of filling in that next step. So, let’s augment or maybe even replace that attitudinal measure with the more behavioral stuff that we can calculate off of the transaction logs. And that’s one of the things that I’ve been pushing for, that we’ve been doing it both in our commercial work and I have this new book on the customer base audit. So can we come up with, as Dan said before, these auditable metrics, a small set of them that are going to be closely tied to behavior, closely tied to tactics. It’s going to be that next step in this evolution, just to kind of get our, our internal house in order and enable us to make the right kinds of decisions, have the right kind of accountability, make more money.
Daniel McCarthy (25:24)
The other thing that we often recommend is we’ll kind of analogize what we do to being kind of like a doctor. And the companies are the patients. It’s as if the patients don’t know what their key vital metrics are. And in the same way that you want to know, you know, what’s your cholesterol, companies should know what’s their CLV across the cohorts and over time and different business units and all the rest of it. And they just don’t know that. And so, it’s as if they’re kind of flying blind with their own health. Before we think about how can we make everything better? I think it can be helpful for the patient to know what their numbers are. And so just kind of getting that initial health checkup, it’s a good place to start.
Richard Owen (26:01)
Yeah.
Right, and stretch the analogy there. It’s not just about, you know, I want to have health checkup. We were essentially capable of getting telemetry data on our health near continuously, right? And it’s not perfect, but nevertheless, we’ve gone from an environment where, yes, maybe you went to see the doctor once a year or something and, you know, pulls out the stethoscope to the type of data that’s gathered continuously about your health and the telemetry information that becomes available. And that can be used in predictive models, and it can be used to understand and anticipate risk. And so, you apply that to the business, it makes sense. What we see all the time though, seems to be this huge divergence in capability. You’ve got 10% of the companies that are just ripping ahead. And then this middle ground that seems to be absolutely paralyzed.
And the question is what’s holding back the middle ground here. I mean, is it anxiety? Is it just capability? You know, one of the analogs here, if you look at marketing, what happened in the early 2000s, you know, marketing automation kicked in for the first time. And every marketer woke up and said, I need a team that understands data, right? I need a team that understands how to calculate performance of our entire marketing pipeline. We’re either going to build that team, we’re going to put the systems in, or we will just fall behind.
We need that moment, don’t we, for customer analytics at some level where corporations make the same conclusion. We’ve got to have the systems, we’ve got to have the data, we’ve got to have the internal capabilities, or we’re just not going to be able to keep up in the market. It seems to be happening, but not at quite the pace I would have thought. Is that your perspective?
Peter Fader (27:55)
It’s a cycle. And we’ll see the same narrative you told will happen every 10 to 20 years. So, in the 70s, early 80s, it was a point-of-sale scanner data. Ooh, that was gonna change everything. And companies were starting to try to figure out how to embrace it. And it got them further, but not all the way. Then you mentioned early 2000s. Let’s go back 10 years before that, the whole CRM revolution. That was 90s, that was the early 1990s. Again, we’re going to build all these systems. We’ll have that 360-degree view of the customer. Again, move the need a little further, but not as far as it should. Early 2000s, so we’re going to start talking about, again, kind of richer data systems. Today, we’re going to talk about machine learning and AI and all this kind of corporate telemetry that’s now possible. We’re going to push the need a little bit further, but not far enough. So, we’re getting there bit by bit.
I don’t think, keep in mind, I’m a marketing professor, we’re not going to count on the marketers to get us all the way there. If we can build that bridge to finance though, if we can get the finance people to embrace this idea of customer-based corporate valuation and say, hey marketers, come along for the ride, I think that’s going to give us much more progress, much more dramatically than anything that we’ve seen before from the marketing side.
Richard Owen (29:18)
Well, maybe not come along for the ride, but here’s where we’re gonna stick our money. Now you can either get in the right end of this or the wrong end of this.
Daniel McCarthy (29:26)
All right, they were willing to give you some budget, figure this out. What’s going on here? At least now they’re kind of looking at the right measures and saying, this is where we need improvement or things are going well in this other place. So put the pressure on the pain.
Richard Owen (29:40)
I do think there is an acceleration and opportunity around this though. I think the amount of data companies are capturing has changed dramatically. Some of that’s a function of the internet where 20 years are so in and so the amount of businesses conducted in full sort of recorded high definition via systems has changed dramatically in 20 years. Almost every industry has eventually gone to that place. So, the data quantities have improved but not necessarily the organization of the data. The cost of being able to apply computation to it is dropped dramatically. The tools are available today that are far, far better than they were. It seems like we’re back to the issue of culture and approaches actually being far more of a barrier to this than anything technical. And perhaps you guys could comment on this applicability in business to business. Because as I think, we focus almost entirely on B2B, and B2B seems in some ways behind the curve relative to business consumer businesses. So, I see you’re shaking your head, Peter. So, what’s your perspective on B2B?
Peter Fader (30:51)
B2B, first of all, it is more about relationships. We understand that. We don’t have to convince us. We talk to so many B2C companies, we say we’re in the process of transforming from being a transaction business to a relationship business, which is great. But in B2B, the idea of cultivating and just managing relationships comes more naturally because there’s fewer customers and each one matters more. So, the ideas that not all customers are created equal, and we have this disposition to lean into some more than others and occasionally just let some go. That’s going to, that just happens more naturally in B2B. A lot of the work that we’ve done is trying to show B2C companies how we can take some of these understandable best practices from B2B and just scale them so we can do them with millions of customers instead of tens of customers. So, in a weird way, we often look to B2B as the best practice.
What’s interesting though, is that for B2C companies to do it, that’s going to require a lot of data analytics and the technology, which the B2Bs didn’t necessarily need. But then they look at it and say, hey, we don’t have that. We’re behind the curve. We’re actually ahead of the curve. So, it’s kind of this weird learning from each other kind of thing. And obviously, the two are different in their needs. But the basic ideas that we’re talking about, lifetime value, customer centricity, applies equally well to both.
Daniel McCarthy (32:20)
I think one of the barometers of how in sync with customer centricity in particular industry is the volume of disclosures that they put in their filings already. And I put B2B SaaS kind of up there with telecom as being an industry where you often see this is how many paying clients we have. This is how many clients we have above a certain threshold, annual recurring revenue, net revenue and other measures like that. Most of the times that we’ll see cohort level revenue data, I’d say maybe half the time it’s with B2B SaaS firms. And so that’s wonderful. But I think it also is a testament to buy-in somewhere that’s causing people to say, we wanna put these things into our public filings, even though it means that we’re gonna get a whole bunch of scrutiny about it potentially. And maybe it’s, we would say it’s not competitively sensitive, but often times, when we talk about putting new disclosures in filings, that’s the immediate first reservation that people will have that, oh, it’s gonna give fuel to our competition, they’re gonna take us down because we’re putting this in there. Yeah, I think that the fact that that fight has been won by the disclosers within the B2B firms, that I think is saying something in its own right.
Richard Owen (33:42)
In the B2B SaaS industry, a lot of the companies have grown up with this environment, right? So, the venture capitalists were the first to say, we want to look at this kind of cohort analysis. And then the private equity companies who are, obviously at the other end, you’re either IPO or you’re gonna end up in a private equity company or both, were also quick to bring these analytics to, so I think these companies grew up in this environment. The decision to disclose is an interesting one, but they’re quite used to the idea of communicating to investors these metrics. So, this ecosystem built up that way. I think that B2C companies, to your point, may not have been familiar with that, but there are some industries. I mean, I think of insurance, for example, where lifetime value is intrinsic. You can’t think of it as transactional, surely. I mean, at the end of the day, insurance companies make money because their customers come back and they re-up their policy year after year and ideally don’t make any claims.
And so that’s an industry that I’ve already seen a lot of analysis being turned to the issue of why do people switch at contract renewal, and they have huge amounts of data because their customers interact with them almost entirely digitally today Whereas in B2B you have this problem of heterogeneity in the customer base, right? You know essentially every customer is somewhat unique which is a solvable data problem. It’s just different methodologies. But I would have thought a lot of consumer industries where they have this fundamental recurring nature of revenue would understand the importance of building these new data sets.
Daniel McCarthy (35:24)
Especially the young ones. I think that they’re not kind of encumbered by their history. I think that a lot of the older ones, they hadn’t done it for a while. And so, there’s kind of this inertia that, well, we didn’t do it before. So why do we need to start now? Then also they oftentimes had a lot of their business coming through channels that weren’t as trackable. And so, if you did a whole bunch of business in stores and a lot of the transactions were in cash, and it could be such that it just was less feasible for them to have the sort of capturing behind enough level that they would want to really invest the time. But a lot of the young companies, they don’t have all that, they haven’t been around. They’re looking at kind of everything with fresh eyes. So, for one, this just kind of makes sense. And for two, you oftentimes they start digitally and their trackability is, it’s kind of been very good out of the starting gate. And so, some of those natural pushback you get with the bigger companies just don’t happen with smaller companies.
But in B2B, the idea of cultivating and just managing relationships comes more naturally because there’s fewer customers and each one matters more. So, the ideas that not all customers are created equal, and we have this disposition to lean into some more than others and occasionally just let some go.
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Richard Owen (36:23)
Younger companies as well, let’s not forget, they’re David to somebody else’s Goliath. And so, they’re always looking for an edge. I mean, if they don’t have an edge, they don’t exist. I mean, all the advantages are with the large incumbents. So, if you’re a small startup and speaking from experience, you’re constantly thinking, how on earth can I get an edge up on these companies that are bigger, more established, they have stronger financial bases. So, if you can see a way of gaining competitive advantage, in insurance it might turn out that a fundamentally better understanding of the type of customers that yield long-term renewals is a big advantage. If you can do that, you can pick out of the market a segment of customers that actually turns out to be the most profitable group and literally starve the incumbents of the profitability oxygen, if you like.
Peter Fader (36:54)
What’s interesting about some of the younger upstart companies is that on one hand, they understand these data needs, they have better analytical capabilities. They’re actually, in many cases, more willing to disclose the kinds of metrics to show what’s going on. On the other hand, sometimes they’re the first ones to fall into the trap of growth at all costs. Let’s just establish this huge customer base and then your profits will show up later on.
So, there’s a real contradiction between some of the data analytics practices that they have and disclosure practices and then some of the day-to-day tactics that they’re following. We’re starting to see a lot of people questioning a lot of the direct-to-consumer sorts of things and saying this whole sector is collapsing under its own weight and we’re saying wait a minute, wait a minute, it’s not all bad. There are some really good things that are going on, we just need to fix the not so good things, we can’t tie all of it together.
Richard Owen (38:02)
Direct consumers had a bad run, as you say. I think that it’s intrinsic to the way they are financed. At the end of the day, if at the core of this, you have a venture capitalist that believes he needs to get 100x return on his investment and has a five-year horizon, and we’ll see how the well-lots survives as a model with the benefit of history.
If that’s the thesis, which is extraordinarily challenging for any company, then this notion that you might sacrifice that growth momentum even for a longer term or viable business, when some of your investors may not be worried about a long-term viable business, right? They might be worried about how they get the next round done or how do they get out of this investment. So, there’s a lot of counterincentives to thinking long-term. And you could always go right back to the LPs who say, the reason I’m investing in this is because I want to triple my money in five years. That doesn’t really sit comfortably with the practicalities of building a built to last business. Rather than dig down that, one of the things I know that the team was interested in hearing you talk about, Peter, was storytelling as a tool for complex ideas. So, changing gears on this a little bit here. And I remember early on, I think when we first met, one of the things you guys have both talked about what how communication, on how CLTV in some ways was a hard concept to communicate. And do you think storytelling is a technique to demystify this, or how do you think about that as a tool?
Peter Fader (40:48)
Absolutely. You know, for years and years, we’ve been pushing the models and saying, look. They work. Try them out. Here’s some spreadsheets. Here’s some r code. Here’s some videos and technical go use them. And companies would say, oh, you know, you could talk to the folks in analytics, but I’m the c m I’m not gonna deal with all of that. And that’s why I started writing all these books on customer centricity.
It really is just using, you know, storytelling narrative as, Dan said it, a Trojan horse around the models. Just to get people to say, oh, this is compelling. This is interesting. Never thought about it that way. How do I do this?
Well, step into my office.
So, it it’s been it’s been very, very effective, to kind of, you know, capture people’s attention. But the problem is sometimes it does have kind of a cheap talk quality to it. Sometimes people will hear all the stories and absorb the stories, and they love the stories. When we ask them to start doing the heavy lifting, okay. Now it’s time to roll up your sleeves. I said, well, no. Just can you stay with the stories for one more day? So, we just have to be careful that that the stories are a means to an end and not an end unto themselves.
Richard Owen (41:02)
Well put. Well, gentlemen, perhaps one thing we could sort of close on here is your views, looking forward a little bit here. I mean, obviously you’ve been involved in this for a while. You’ve seen the arc of this progress. And I would imagine that you must feel pretty optimistic at this point. I mean, there’s a lot of things moving in the right direction. We can quibble about the pace, but we know that technology’s getting there, analytics are getting there. You know, when we look to, first of all, would you share that optimism and secondly if you were to sort of point to one or two things that you think are going to be the breakthroughs that are going to get more widespread adoption, what would you say those would be as well?
Peter Fader (41:46)
You start then.
Daniel McCarthy (41:47)
Tough question. Yeah, I mean, certainly we would 100% agree that things are moving ahead very nicely. You know, and I think we would also agree that we’d hope that the pace would be a bit quicker, but at the same time, I think, you know, we’ve got the perspective of history that these things do just take time. It’s not something that happens overnight. So, yeah, I think all the signs are in place that, you know, hopefully, I’ve given the joke that I want this stuff to be boring. And what I mean by that is it’s just so common that people talk about CBCV as if they’re talking about a DCF valuation model. It’s just what you do. Like you wouldn’t think of doing it another way. And I think to get there, you need awareness. And I think Richard, to your earlier point, you need this stuff to not be too expensive. The data needs to be readily available. The tools are all there. And so, all the natural reasons why you wouldn’t do it, it’s just kind of gone away. I think now it’s still we have more awareness to build.
It’s still somewhat, how do I bring all these models together? And so, there’s still a lot of that that needs to happen. But I think it’s very clear that things are moving in the right direction. So yes, I think to your question of what the catalyst will be, that kind of, this is it. I’m not sure that I can think of one. I feel like it is just kind of a gradual process. I would say that with the same store sales, I don’t know if you’ve read the history behind how that kind of grew in terms of its adoption as a measure. But actually, there it was kind of this discontinuous shift that there was this one calamitous failure of this one company. And some guy had been tracking the same-store sales growth. And that was it. That showed that this company was going to go down. And after that happened, all these other retailers started disclosing it. Maybe it could be something like that that, by looking at it this way, everyone said, you know, oh shoot, you know, we need to be keeping track of this stuff. So maybe that would be the analogy here, but honestly, it is kind of hard for me to say for sure.
Peter Fader (43:56)
Yeah, it’s a great point, Dan. I think it is going to be something calamitous. It’s going to be maybe just a series of class action lawsuits that are driven by you know, the disclosure or nondisclosure of different kinds of customer metrics. We’re seeing more of that happening. The whole Twitter Musk lawsuit really was around what should be disclosed and how should it be measured.
So, as we see more of those things happen. We’re going to see some of these regulatory bodies step in and say, listen, we are going to clean this up. Okay. And CFOs, we know you don’t want to disclose some of this stuff, but you have to. So, it’s something like that is going to happen. And everyone’s going to kind of snap in line and these metrics, these approaches will then be well accepted, will be boring. They won’t necessarily replace the old ways of doing things. It would be great to see companies doing valuations both from this bottom-up approach using these marketing models as well as the traditional top-down ones and let’s see how they’re different when do they or don’t they line up well that should be much more rule than exception and we’re getting there.
Richard Owen (45:15)
I think it’s not necessarily an optimistic note to imagine that somebody might explode over this, and that would teach everyone a lesson. But I think there’s a lot. I’d like to think that companies will quietly behind the scenes, apply this kind of thinking to getting significant business advantage, become successful, create great case studies of success, rather than, as you said, and maybe more pragmatically what will happen is there’ll be some colossal face plants and people will react, keeping with human nature but maybe we could keep our fingers crossed as a slightly gentler path.
Daniel McCarthy (45:50)
Maybe the more benign one, we’ve had these conversations about disclosure. And one of them was with someone who has actually pretty decent influence within the software as a service industry. And I think we’ll often say, yeah, we should start creating standards around these measures, but as soon as you start talking along those lines, you need to get specific. And they’re all right, so what exactly do you mean? You say, tell me active customers. Well, what is a customer? And if I’ve got multiple business units in the operating region, are they multiple customers or one customer? And as soon as you start having to kind of peel back the onion a little bit, it gets awfully complicated. And so, I think that whole process, we need to think through all of that.
If we really want to get serious about disclosure, we need to like think very seriously about exactly all these little nitty gritty details. And if we don’t do it, are they going to do it? Some CFO has no incentive to go through this exercise for him or herself, so maybe the formation of industry specific norms, yeah, I think that that could be one that might not involve companies just crashing and burning and then other companies feeling, uh-oh.
Richard Owen (47:02)
Well, that’s a great note to end on then, gentlemen. So, Peter, Dan, thank you very much, absolute pleasure. Lots more we could talk about, but thanks again for your time.
ABOUT THE CX ICONOCLASTS
Daniel McCarthy is an Assistant Professor of Marketing at Emory University’s Goizueta School of Business. His research specialty lies in applying cutting-edge statistical methodology to contemporary empirical marketing problems. Notably, he popularized the concept of customer-based corporate valuation and focuses on areas that include customer lifetime value. His work has been published in top-tier academic journals and recognized with prestigious research awards. McCarthy’s insights have also been featured in major media outlets, including the Harvard Business Review and the Wall Street Journal1.
Richard Owen is celebrated as a leading figure in the Customer Experience industry, primarily known for his contribution as CEO at Satmetrix, where he and his team, along with Fred Reichheld, developed the Net Promoter Score methodology, now the globally dominant approach to customer experience measurement. His efforts further extended to co-authoring “Answering the ultimate question” with Dr. Laura Brooks, establishing netpromoter.com, and initiating both the NPS Certification program and a successful conference series. Owen’s diverse 30-year career has seen him drive technology-led business transformations at Dell, lead software companies like AvantGo to a Nasdaq listing, and Satmetrix to acquisition by NICE Systems, while also engaging in venture investment and board roles. Today, he spearheads OCX Cognition, leveraging machine learning for real-time NPS and customer health analytics.
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