You write that teaching the core marketing course to Columbia MBAs was one of your main inspirations for the book.
During the dot-com bubble, I noticed there were a lot of companies with a lot of valuation that I knew was just not sustainable. Basically, the students were less than enthusiastic when greeted with that news, so I decided to prove to them why. The basic proof was this: a business runs on revenue, revenue comes from customers, so if I can project customer revenue, I can project the value of a business. And in point of fact, several of the dot-com companies were grotesquely overvalued, based on any reasonable projection of revenues that would come from customers.
We had a model that basically married together one of the fundamental approaches in marketing, which is to predict life cycles of product diffusion curves — which we use to predict the number of customers — and then straight old extrapolation to project the margin per customer. We put the two together. So our theory, if you want to call it a theory, was that customer growth would not continue forever, that at some point it would level off.
How does your approach differ from previous methods of valuing customers?
Most people have not valued customers in the past. Those that have are people in direct marketing and direct mail, in particular, who have a model called RFM: recency, frequency and monetary value. So, you measure how recently a customer has been with you, how frequently they buy stuff from you and how much money they spend on it, and you use that to get some sense of how valuable a customer is.
We took the discounted cash flow paradigm and used that to estimate the value of an individual customer. So we simply project into the future the revenues and costs at a customer level and then figure out what that customer is worth. To me, it was very logical and quite obvious, and while there are a number of other customer books out there that also talk about that notion, nobody had pushed it as far as we did. In particular, we show that if you sum the projected values of the customers, that number would give you some indication of what the firm ought to be worth. That was the unique link.
The closest to this has been what the direct marketing people have done, because they have terrific data sets on individual customers. We were looking at aggregate data and trying to get a sense of what an average customer is worth. We used basically public information. To say, “We have a great method to measure the value of a company if you can get access to internal company data” is not overly useful.
You used many examples and case studies in the book. Are any especially representative or illustrative in supporting the methods you advocate?
What got us interested in the telecom cases was that the price per customer was jumping dramatically in a series of mergers and acquisitions, which suggests the market wasn’t efficient. So we wanted to project how many customers would a Verizon or a similar company end up with and how much could they expect to make per month, and therefore per year, off them. Project it out, discount it back in present-value terms, add it up across all the customers and see what that company would be worth; or, at the individual customer level, look at what we projected a customer would be worth and compare it to the implicit price from the merger and acquisition. They overpaid, in my opinion.
We also had, for some of the dot-coms, an interesting difference in that some of them were pretty close to our estimates and some of them were grossly overvalued according to our method. One of them is eBay. And you can argue eBay’s got a different business model, because they’ve got both buyers and sellers going through there.
Can you talk more about your method and how firms can use it?
You can use it at the individual customer level to figure out what an individual customer or segment of customers is worth on a per head basis and then relate that to how much effort you spend to acquire and retain them. If a customer is going to generate discounted cash flow profits of $400 over their lifetime, you don’t want to spend $500 acquiring them. And yet, I think an awful lot of companies, because they have not done that relatively simple calculation, have overpaid for customers and either over- or underpaid on retention. If a customer’s worth $20,000 to you and you’re spending $10 a year to retain them, you’re probably not spending as much as you should.
Does your method offer a formula to determine the right amount to spend in acquiring and retaining a customer?
The method gives an upper bound. You would not spend more for a customer than they are worth. If a customer is worth $400 to you and you spend $399 on them, it’s a good deal — you made a dollar. Is a dollar on $399 a good rate of return? That depends on the firm’s cost of capital, risk preference, etc.
Is your method equally relevant to all industries?
Yes. A friend of mine at Rite Aid got data on their stores. I think this method perfectly allows you to value a retail chain. All you need to know is how many stores they’re going to have and how much they’re going to make per store, and that basically is what the value of the chain is. In essence, what people in finance do is project out revenues and discount them back at the aggregate level of the chain. All we’ve done is bring it down to the individual store level and then aggregate it back up.
So for a going business that’s been around for a while, where it’s pretty obvious how many units there are going to be and how much they’re going to make per store, the methods are the same. But if you’re in a situation where none of the stores are making money and you discount that back, you get a negative number, implying that the chain is worthless. The advantage of our method is that we look forward, so we would get a number that makes more sense in that scenario.
How do companies account for investments in customers on their balance sheets? How would you account for this investment?
The accounting rules are such that you do not account for the value of customers or the value of brands, which together in lots of organizations make up well over 50 percent of the value of the firm. Most companies, consistent with the accounting regulations, expense all marketing. Sometimes they capitalize R&D, but they expense marketing. We would view it as an investment. If I spend $400 to get a customer or if I spend $400 to get a piece of milling equipment or a photocopy machine, they’re all investments. You get some payback over some period of time. There is some risk that the thing will break down, which is the same as the customer defecting. There’s an annual maintenance cost for a photocopy machine or a piece of milling equipment or to retain a customer.
Have you noticed any change in firms’ behaviors as a result of this book? What would you like to see change as a result?
Well, I think firms should be much more rigorous in evaluating strategy through some kind of metric. I think customer lifetime value is a very useful metric. It’s not the only one I would use, but it’s an important one for evaluating decisions, certainly merger and acquisition decisions. Let’s assume Harrah’s has decided they want to get more customers to their casinos. You know how much it costs for the program they plan, whether it’s a TV ad blitz during the Super Bowl or direct mail or whatever. How do you figure out if that makes any sense?
One thing you can do is say, “I know what new customers are worth to me. I have to get so many new customers to break even to justify this expenditure — is that reasonable or not?” Often, when you look at that question, the answer falls into one of two categories: “Of course it’s reasonable, that’s going to be easy,” which means you spend the money in a hurry; or, “There’s no way we can generate that many customers from this campaign,” in which case you can save that money.
Donald Lehmann is the George E. Warren Professor of Business in the Marketing Division at Columbia Business School.
Read the Research
"Managing Customers as Investments: The Strategic Value of Customers in the Long Run"