Why Your Business Really Is Only As Valuable As Your Customers by [email protected]
Many businesses will say that their customers are their most valuable assets, but few understand how true that is — or how accurately a measurement of customer-based value can price their whole enterprise.
In their new paper, “Valuing Subscription-based Businesses Using Publicly Disclosed Customer Data,” Wharton marketing professor Peter Fader and Wharton doctoral candidate Daniel McCarthy, along with professor Bruce Hardie of the London Business School, successfully built a model that allowed them to do just that — with formulas that concretely link the value of a business’s customers to the overall value of the firm. Fader and McCarthy recently spoke with [email protected] about how their work can help corporate finance folks and retail investors alike, why they are making their models public, and why all customers are most decidedly not created equal.
An edited transcript of the conversation appears below.
The Value of Customers
Peter Fader: This paper brings together two topics — one very old and well established, and one that is new and emerging. The old topic is corporate valuation. Everybody is talking about how you look at a corporation and value it. The new topic is customer valuation. Can we look at individual customers or group of customers and say what they are going to be worth in the future? This paper is all about bringing the two together, in a really rigorous and practical, real-world way. Can we do corporate valuation from the bottom up by looking at the value of current and future customers, adding all that up and saying, “That’s the value of the corporation”?
The basic idea has been around for awhile. It has been done by a few marketers in the past. But it has never had the real rigor to win over the respect of, say, financial people and accountants. That is what we are trying to do, the right way. So it’s customer-based corporate valuation, but done with all of the rigor, with all the really high standards and careful use of data, that accountants and financial professionals would respect.
Daniel McCarthy: There really have been two silos of work here: the one in the marketing domain, and the one in the financial domain. The financial domain has really hammered home how you value a business by projecting forward cash flows, discounting those back. Doing all of those nitty-gritty little financial details, all in a very precise and theoretically correct way. So we want to make sure to really draw upon that — take this problem that really has been solved in finance, and apply it to the marketing domain, where perhaps some of the financial details have been a little bit looser.
Experimenting with Your Satellite Provider
Fader: One of the really interesting aspects of this research is that we did this valuation exercise for Dish Network — a big, publicly traded company. The most interesting part about it is that we have had no contact with anyone at Dish Network. I have never exchanged e-mails, gotten phone calls or gotten data from anyone at Dish. This is using purely publicly available data. So the data we use, anybody could get access to, and, in fact, the methods that we are using are fairly common and transparent as well. There is really no secret sauce black magic here. It’s just taking publicly available data [and] reasonably well-established methods. We’re just combining them in what one might call a clever but at least thoughtful way, in order to come up with this kind of valuation. And because we’ve done it in this case, there’s no reason why we cannot repeat this exercise for other companies that make similar kinds of data available.
McCarthy: The other thing that we’re going to do to capitalize on what we think is a very fundamental methodology here, is make all of this available. We’re going to be collecting data not only on Dish Network, but also on many other companies that disclose the sort of metrics we need to perform customer-based corporate valuation. We’re going to release the data to everyone, so they can perform the same exercises themselves, and also release the methodology so people can actually implement these models, too. We really want to de-marketize this, and make the core concept of valuing the company by valuing the customers widespread.
We decided to perform work on Dish Network really by happenstance. There was no cherry-picking involved. We didn’t say, “Let’s find all of the companies this works for, then whittle it down to the one that a model fits.” No, we basically picked Dish just because it was one of the first companies that we happened to find a decently long time series for. We actually didn’t even know how long the time series was when we first began working on the company.
We definitely have a pipeline procedure for whittling down and identifying companies you could perform this analysis on. I think that process, in and of itself, has been quite interesting. Basically, we identify a universe of companies, all of the companies in the stock market, and some percentage of them use words in their SEC filings — specifically the 10-K and the 10-Q — that indicate they may release the sort of customer metrics that we would need. Then we whittle those companies down to a list of those that actually disclose the metrics. And then we have a team of great Wharton undergrads, as well as a few colleagues in India who have been working with us, to actually turn that into raw data sets for each of those companies. So Dish is just one example, but there are many others. We found at least 35 firms that also disclose the sort of data that we would need to fit these models.
“There is really no secret sauce black magic here. It’s just taking publicly available data, reasonably well-established methods.”–Peter Fader
Key Data – and Whether or Not You Can Get It
Fader: The real key here would be what kind of statements companies put out about the number or the nature of their customers. It could be how many customers they have acquired, or how many customers they have at the end of the period. Some companies would go further and say something about their retention rate or their churn rate, some kind of derived measure that takes some of the raw count data and turns it into some kind of more diagnostic metric. We would rather work with the raw data; we just want to know how many customers came in, how long they stayed around, how many were there at any given point in time.
Right now, there are absolutely no standards for disclosing any of this data or how it would be disclosed. One of the things that we want to do is to start to create some of those standards. That’s not the objective of this particular paper, but if we can shine light on the value of these customer metrics, and how they can be useful to marketers, to finance people, to people throughout organizations, then maybe we will start to have a conversation about which metrics would be reported, how they