Customer Equity: An Interview with Allison Hartsoe, AI Value Creation Consultant and Author

Allison Hartsoe is a leading expert on customer-centricity and the author of “The Age of Customer Equity: Data-driven Strategies to Build a Sustainable Company”.

The story is a familiar one, played out year after year, decade after decade.

Under pressure by investors to drive up earnings, companies keep spending a disproportionate amount of time and resources trying to acquire new customers.

This obsession with short-term growth obliges marketers to focus on the only metric that corporate bosses care about: a bigger slice of market share. Preoccupied by that one measure of success, marketers lose sight of the fact that the surest path to growth is not finding more and more buyers, most of whom are one-and-done purchasers unlikely to buy again, it is getting longtime customers to spend more, more often. Yet repeat customers continue to get treated as an afterthought while the largest share of marketing dollars goes toward buying ads.

As long ago as the 1980s a contrary belief began to take shape amongst marketing academics, calling for a shift in focus from driving demand to managing customer relationships. Customers should be regarded as financial assets, they argued, whose value can be counted on to grow over time. To unlock the unrealized potential within the customer base, marketers needed to invest more proactively in developing the relationship, and that demanded a more equitable balance between acquisition and retention spending. The health of any business, they declared, could be judged by the aggregate lifetime value of all customers which they came to call “customer equity”.1

By the mid-to-late 1990s, as CRM systems were installed to manage sales and service interactions with customers, and as marketing database technology grew more powerful and sophisticated, the individual value of a customer was suddenly visible to anyone who cared to look. Now marketers could say with absolute certainty: the longer a customer remained a buyer, the more valuable they became. In fact, active retained customers were often found to be worth on average three to five times the cost of acquiring them. Yet over the years the concept of customer equity never gained much of a foothold on the corporate performance scorecard. It is still viewed to this day as an intangible metric, undeserving of executive attention.

One problem, of course, is there is no designated line on the corporate balance sheet for a customer equity calculation. Even the generally accepted idea of brand equity is tucked under the nebulous heading of “goodwill”. The other problem is that customer lifetime value is defined in a few different ways. Sure, there is the version you find in most textbooks, dating back to when catalog marketing was in its heyday, but its operational application using actual transactional detail is all over the place. But what really holds customer equity back as a recognized corporate yardstick is the tricky exercise of coming up with a cost attribution formula that everyone can live with. No one enjoys negotiating with finance, least of all marketing. And if finance isn’t onside, customer equity remains just another bit of marketing jargon.

Despite its drawbacks, customer equity can be a vital barometer of company success, according to Allison Hartsoe, whose book “The Age of Customer Equity” spells out how companies can operationalize the concept. Healthy companies have happy customers, she believes, and customer equity is a proxy for the health of the relationship. As a longtime data analytics leader and consultant, she has built a proven AI-powered Customer Equity predictive analytics engine to identify the most promising growth opportunities within the customer base while finding pockets of excess spending. She is a recognized thought leader on customer-centricity and the strategic use of customer data to drive growth.

Stephen Shaw (SS):: What drew you into the rarefied world of customer equity?

Allison Hartsoe (AH): In the early days of my companies, I’d be sitting in the boardroom listening to the conversations of investors and they’d be talking about how many deals they’ve closed and about the metrics they typically care about. I worked with Gary Angel2 at the time, who is just a fantastic leader in the digital analytics world, and he had some groundbreaking ideas about how you analyze and understand the data. At the time we were using large data sets, trying to find pockets of value. And I kept thinking, why don’t we have a stronger connection to the boardroom? We have all this good data, but the minute an analyst comes running in and talks about what they’ve found, they’re speaking another language that the boardroom doesn’t understand. So I went on a bit of a quest, trying to bridge the two.

Now, early on, I could see that the customer was that bridge. And so through the 2000s we got more and more data where we could make that bridge. So then I spent a little time understanding private equity and lo and behold, they’re talking about the customer, but talking about it in a much different way, talking about it as averages, talking about it as trends, like, your industry does 6%, so you should do 6% growth. Very, very large estimations. And that’s when I realized this is the place for me, because I can make that bridge between the strategic and the tactical. And so I think there’s a real value in being able to have a constructive conversation with your investors about why you believe certain actions are the right things to do, but in terms they understand.

SS: So let’s just go back in time a little bit. The language of customer equity first emerged in the mid-nineties. But underlying it is the idea of customer lifetime value, which has been in the toolset of direct marketers, mail order marketers, credit card companies, practically forever. Is the concept of customer equity synonymous with customer lifetime value, or is it broader than that?

AH: I think about customer equity as the total of the future customer lifetime values, up to 15 years within the base. So I use that as the strategic North Star.

SS: I don’t know if you know Neil Bendel3, but he thinks that the term customer equity is a bit of a misnomer. He says that the strict accounting definition of equity is assets minus liabilities. So he makes the case that customer equity doesn’t really conform to financial reporting standards. And if accountants won’t use it, does that limit it to marketing as a segmentation tool?

AH: Yeah, I think there’s some truth to what he says. And I didn’t understand that until I really got into how you build a financial model to purchase a business. As I was going through that, I was like, wow, there’s a whole lot of flexibility in the numbers when we go to calculate goodwill, which is basically a plug on the balance sheet. I think it’s very important that we lean into how we can take advantage of those numbers without getting wrapped around the axle on where it fits on the balance sheet. I know that’s a contrarian view, because we do need to have a sense of the cost. But I don’t think you need to go all the way down to exactly which dollar goes to which line item in order to get there.

SS: But it does speak to the immense divide between finance and marketing and their inability to speak one another’s language.

AH: I think that’s true, but I think that’s also why I focus on the CEO and the COO, because you have to rise above that. The strategy has to fit the CEO’s mindset, and then the tactics roll down as opposed to trying to define all the tactics and roll up to the strategy.

SS: But is that the orientation of most CEOs today? All they seem to care about is maximizing shareholder value.

AH: Yeah. So I call that financial engineering. Let me give you an example. Friendly’s was a great restaurant that I grew up with and totally enjoyed as a kid. It became a victim of financial engineering. You had two founders who loved their customers with all their heart, and they built this business and grew it into a tremendous asset. And then financiers came in and made them pay rent, drove up the expenses, and drove the customers away. So, Friendly’s went of business. Now, the world that I want is a world where there are more Friendly’s out there. And we want those CEOs to be armed with the right information that helps them become the next Friendly’s. Today CEOs miss a lot of signals in the data that is already at their fingertips. And the private equity firms don’t see it either, because they only care about financial engineering. That’s how we get shrinkflation, and that’s how we get all of these maneuvers that make people unhappy. Now, add a little bit of AI to that, and suddenly we’ve got the kind of world that I’m not sure I would be happy in. I want the kind of world that’s really focused on making my world, my life, better, easier, more convenient, and having the companies and their products be of service to me, not have me be the product.

SS: You’re touching on the crux of what’s going on in society today. On one side is the ethos of customer first thinking, and the other is bottom line thinking.

AH: Yeah, but, you know, it’s a balance. You can’t do one without the other. The company has to have the right economics to stay in business. And so, there’s a place for financial engineering. There can be waste on the balance sheet. But there’s also a place for the customer. And I don’t think we should have one without the other.

SS: Yeah, it’s interesting with your Friendly’s example, because that’s exactly what Red Lobster has gone through, of course.

AH: Spot on. Everybody blames it on the shrimp.

SS: There’s a massive body of literature around managing customers as investments and the concept of customer valuation. What’s your preferred definition of customer lifetime value?

AH: It’s the future values of your customers projected out to 15 years. You want to be able to see around corners for where your business is headed. I tend to not use the language of customer lifetime value a lot because I think LTV, CLV, it’s very muddy. And I cannot tell you how many times I have pulled up, not necessarily an algorithm, but just a mathematical model, and they are essentially doing recency frequency modeling. They’re just looking backward. Now, I don’t know about you, but I wouldn’t want to run my business by looking backward. I have to run it looking forward.

So that’s one thing. The other thing I sometimes see is these compressed aggregates. Whenever somebody tries to model the entire customer base in an Excel sheet – just, no! Unless you have 100 customers, no, you’re not doing it right. You cannot model this in a spreadsheet. You have to look at each individual. So we take into account discount rate, we take into account future projection, we take into account margin. And I take a big breath on that last one because that’s where we start to get into cost allocation. I think it makes sense to put a cost in there, so you get some sense of unit economics. But like I said before, I don’t want to get wrapped around the axle on cost. Why spend six months trying to allocate the costs correctly?

SS: Is it an historical model that just looks at past purchases and then tries to estimate potential value, or is it a predictive value based on behavioural analysis?

AH: It’s always predictive.

SS: So you’ve talked about 15 years being the horizon – explain to me how you get to that number. Some companies plug in a reasonable horizon of, say, five years. Others actually try to work backwards to look at cohorts and the actual average lifetime of active customers. Why 15 years?

AH: It’s the point at which we hit infinity. It’s so far out in the future, one more year doesn’t make a difference. Fifteen years is the customer equity number. So if I want to ask, “Did I make a difference by taking this action?”, there’s two ways to measure it. You can measure it as what happened within the next two years: Did I pull that revenue forward? Or you can look at how much value will I create in the total customer base?

SS: I think about the recurring revenue that you can reliably expect year over year from your customers. There is the unrealized potential – the share of wallet going to the competition. And then there’s revenue that can come from selling new products and services that might not even have been imagined yet. How do you model for potential growth?

AH: I only look at the goodness of the base. If you cannot create a sustainable company on the backs of your existing customers, you don’t really have a company. The durability of the business is what I care about. And that’s why the subtitle of my book is “Data Driven Strategies for A Sustainable Business”. It doesn’t mean sustainable like ESG. It means sustainable as in you can control when you take financing and when you don’t.

SS: The other approach floating around, certainly as far as CLV goes, is you have a separate model for acquisition versus existing customers. In other words, factoring into your model future customers that you don’t even have yet.

AH: So I do pick out new customers in the base as a separate group for special treatment, but I don’t look at it as future acquisition of phantom customers. I look at it as, how do I feed a look-alike model? So if these are the pockets where solid revenue exists, what are the attributes that correlate with good revenue? And so I I’m a little less speculative. I stick to the durability of the customer base and then I go fishing for more people like that.

SS: So, I want to dive into another area that’s always been a bit of a mystery to me and that is the whole concept of retention, which is obviously a key part of the calculation. But here’s the thing: Retention rates can vary, right? Does the calculation adjust based on the varying longevity of a customer?

AH: When I run the models, I run them again, and again, and again, and again. So the model doesn’t just run once a year or once a quarter, it’s running all the time. So, that’s already picked up in the model as they change over time. So, you bought two years ago, you haven’t bought again: You’ve got a little red light on that says there’s a problem here. That’s hard to pick up in the data when you’re just looking at product sales.

SS: Sometimes the revenue loss is masked because the company has raised prices. Meanwhile, their core customers are fleeing to the competition.

AH: And that’s so important. What you’ve alluded to is the quality of the customer base, and that quality is what we look for, what we seek. That’s what I mean by durability.

SS: CLV is usually based on a net margin calculation. Going back to the question of costs, do you take into account net margin at the individual level?

AH: So, technically, if I’m from the Wharton School, the answer is yes, this is how we calculate CLV, we’ve got to put in the costs. So let’s say you’re operating on a net margin basis and you’re projecting forward as best you can. Will you see the love of your customers as easily as you might if you gave more weight to the top line? Because I find that for most businesses, an excess of revenue solves a lot of problems.

Our job is to get more creative about ways that we drive value creation. So, yes, you can put in all kinds of operational costs. But why? Yes, you could be very, very precise. But I’m not talking to the CFO. I’m talking to the CEO, the COO. I’m talking to people who want to drive value creation. If I let them get wrapped around the axle about all those different costs, at the end of the day, it might not even matter when they go to sell their company.

SS: Marketers continue to be challenged on why they deserve 8% or 12% of the budget. CLV might be the answer if the marketer can say they’ve increased the average value of a customer by a certain amount over a specific period of time instead of trying to prove ROI campaign by campaign.

AH: Yeah, exactly. So I’m lifting the quality of the customer base every year by a certain amount, and that is a much better way to have a marketing conversation. And then the CFO can worry about cost attribution. If I’m the marketer I’d be looking with a critical eye at loyalty programs, coupons, discounting, anything that can officially erode a customer base: You know, I need more sales, I’ll go push out a coupon offer, versus really getting to know the customer base and finding what they need and what they want from you. That’s a slightly different calculation.

SS: Well, it’s short term versus long term thinking. You build relationships over time.

AH: That’s the word: Relationships.

SS: So I want to ask you about Fred Reichheld’s idea of earned growth rate4 where he talks about building in the value of a customer’s referral value into the CLV calculation. Do you actually factor in word-of-mouth in building your models as well?

AH: I think referral value makes sense because you’re always looking for word of mouth as the most cost effective acquisition technique. We know that companies which have great referral value tend to do incredibly well. Yet when it comes to a data centric point of view, you want to know, were they already planning to buy, or were they not planning to buy?

SS: Should CLV be thought of as an absolute measure or should it be thought of as a relative measure? Is it a way to rank and stratify customers? Too simplistic?

AH: No, I don’t think so at all. I think that is exactly how we want to be thinking about it. If we’re projecting outward into the future, I don’t know what’s going to happen tomorrow. Neither do you. It’s a probability. So, it’s a guess and it should be treated as a guess. Is it going up? Is it going down? What’s causing it to go up or down? That’s a great way to unpack your business.

SS: You’ve actually built a predictive analytics model using AI to do exactly what you’ve been describing today. Without giving away any of the secret sauce, you’ve obviously got some magic going on. How does it work exactly?

AH: I always start with the core data. So you need a date, you need a time, you need what was purchased, how much was spent, and you need a customer identifier. So you need four pieces. Well, three pieces of data technically. But if you’re going to do anything at all with it, then you need to add some other fundamentals, like what was the address? Or what was the product that was purchased?

Now, where it gets more complicated is, and this is the question every company should be thinking about, what are you going to do with that data? Are you going to go after reducing costs? Are you going to route people differently in your call centre? Are you going to think about how you make a better recommendation?

So there’s a lot of different facets to execute and that controls how many other pieces go alongside the core data. But there’s nothing that says that you can’t start with one or two and build, and build, and build. Because again, my approach is iterative. The more data you give it, the better it gets to why.

SS: But you need a pretty good historical footprint, I would imagine.

AH: I need about three years worth of data. So it does depend a little bit on the kind of company and how customer centric they are.

SS: Well, let’s take a bank as an example. They have multiple lines of business, each of which has different behavioural patterns which suggest different lifespans across different products, whether it’s credit cards or mortgages.

AH: Yeah, but that’s just a dimension. And here you’re touching on product centric versus customer centric thinking. So, a company will often come in thinking about those lines of business, right? But as a customer I am not a checking account. I am a person, and I have a lot of different needs. So you have to shift that thinking. You have to make product a subset of the customer calculation.

SS: But is it conceivable that built into the model would be projections based on those individual lines of business?

AH: It’s a dimension. So when you run the calculation, you look at the overall customer, and then you describe the customer by things within that value calculation. So they might have product A, B, and C, or some set of products. That product is a secondary model.

SS: From a marketing planning perspective, how should CLV be operationalized?

AH: So if I’m a marketer, I want to say I’m going after a customer group that is worth $X million according to my CLV calculation and set out as a goal to increase that value. My baseline measurement is my current customer equity projection. Then after a period of time I’m going to measure it again to determine if I achieved that goal. That’s the number I take back to the CFO. But that is not a 15 year number. That’s more of a two year, three year number, depending on the purchase cycle.

SS: That number should presumably guide how many dollars get assigned to acquisition versus retention. And companies today, I would argue, still underfund their customer strategy in favor of acquisition because the emphasis is on growth and more growth.

AH: Yes, I would say that’s true. Most companies don’t believe that there might be a limit as to how many customers they can acquire. In an agentic AI5 world, you might not be able to reach me as easily as you can reach me today. You might want to spend a lot more time thinking about your current customer base.

SS: Let’s go back to tying customer equity number calculations back to market capitalization or company valuation. What’s your perspective on that?

AH: If I’m the CEO and I want to have a good conversation about the value of my business, I want to be armed with a number that gives me the strongest ability to say my customer base is worth X. And then the conversation that happens with the private equity firms or the venture capitalists is “Here’s what I believe: Tell me why you think that’s wrong”, as opposed to what happens today, which is the firms come in and they do a bottom up financial calculation. And in that calculation you get aggregates and they’re using average percentages. So even though the customer equity number might not be an approved accounting measure, I think the ability to negotiate from a position of strength is valuable.

SS: You state in your book that customer equity is a way to measure the “goodness” of a company. What you mean by that?

AH: If you are constantly satisfying your customers and they’re happy with you and you’re happy with them, you will see this ongoing durability that’s reflected in the customer equity number. So I like to think about customer equity as a “U” shaped curve where, when we don’t pay enough attention to it, we’re overspending, we’re not optimizing our business. The efficiency is way too low. And then as we come through the bottom of the “U”, we start getting resonance and efficiencies that are really beautiful. And then as you get to the other side of the “U”, that’s the time where innovation has to take over and you need to start thinking out of the box.

1 – The term was first coined in a 1996 Harvard Business article called “Manage Marketing by the Customer Equity Test” by Robert C. Blattberg and John Deighton.

2 – Gary Angel is currently CEO and Founder at Digital Mortar which provides advanced measurement and analytics tools for optimizing physical spaces. Previously, he managed EY’s Digital Analytics Center of Excellence.

3 – Neil Bendle is Associate Professor of Marketing at the Terry College of Business, University of Georgia. In addition he is Director of the Marketing Accountability Standards Board (MASB).

4 – Earned growth rate measures the revenue growth generated by returning customers and their referrals.

5 – Agentic AI are AI systems designed to autonomously pursue complex goals and workflows with limited direct supervision.

Stephen Shaw is the Chief Strategy Officer of Kenna, a marketing solutions provider specializing in delivering a more unified customer experience. He is also the host of the Customer First Thinking podcast. Stephen can be reached via e-mail at sshaw@kenna.