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this paper brings together two topics one very old and well established and one that's new and emerging the old
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topic is Corporate valuation everybody's talking about how you look at a corporation and value it the new topic
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is customer valuation can we look at individual customers or groups of customers and say what they're going to
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be worth in the future what this paper's all about is bringing the two together
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in a really rigorous and and practical Real World Way can we do corporate valuation from the bottom up by looking
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at the value of current and future customers adding all that up and saying that's the value of the corporation that
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basic idea has been around for a while it's been done by a few marketers in the
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past but it's never had the real rigor to win over the the respect of let's say
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Financial people and accountants uh that's what we're trying to do the right
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way so it's it's customer-based corporate valuation but done with with all the rigor with all just the really
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high standards and careful use of data that accountants and and financial professionals would respect of there
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really have been kind of two silos of of work here you the one in the marketing domain and the one in the financial
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domain and the financial domain has really kind of hammered home you know how you value a business by you know
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projecting forward free cash flows discounting those back doing all those kind of nitty-gritty little Financial
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details all in a in a very precise and theoretically correct way so we want to make sure to really draw upon that you
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know know take this problem that really has been solved in finance and apply it in this you know marketing domain you
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know where you perhaps uh some of the financial details have been a little bit looser one of the really interesting
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aspects of This research is that we did this valuation exercise for Dish Networks big publicly traded company
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many people especially in the US would have heard of it the most interesting part about it is that we have had no
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contact with anyone at Dish Network I have never exchanged emails phone calls gotten data from anyone at dish this is
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using purely publicly available data so the data that we use anybody could get access to uh and in fact the methods
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that we're using are fairly common and transparent as well so there's really no
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secret sauce black magic here it's just taking publicly available data reasonably wellestablished methodss but
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just combining them in what one might call a clever but at least thoughtful way in order to come up with this kind
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of valuation and because we've done it in this case for a company again that
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we've had no contact with there's no reason why we couldn't repeat this
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exercise for other companies that make similar kinds of data available yeah the other thing that we're going to do to
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kind of capitalize on you know we think is a very fundamental methodology here is make all this available you know both
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we're going to be collecting data not only on Dish Network but also on many other companies that also disclose uh
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the sort of metrics that we need to perform customer-based corporate valuation so we're going to release the
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data make that available to everyone so they can perform the same exercises themselves and also release the
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methodology so that people can actually Implement these models too you we really
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want to democratize this and make this kind of core concept of valuing the company by valuing the customers of
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widespread yeah so we decided to perform work on Dish Network really kind of by happen stance uh there was no cherry
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picking involved we didn't say let's find all the companies that this works
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for and then Whittle it down to the ones where our model fits no you we basically
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just pick dish just because it was one of the first companies that you know we happened to find a decently long time
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series for we actually didn't even know how long the time series was when we
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first began uh working on the company so we definitely have a very kind of I almost call it like a pipeline type
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procedure for whittling down and identifying companies that you know you could perform this analysis on uh I
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think that process in and of itself has been quite interesting basically we identify Universe of companies there's
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all the compan in the stock market and some percentage of them basically say words in their filings specifically the
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10K and the 10 q that indicate that they may release customer data the sort of customer metrics that that we would need
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so we identify all those companies and we then go down and perform this validation step where we kind of Whittle
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those companies down to the list that actually disclosed the metrics and now we have a team of uh great Wharton
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students here in undergrad as well as a a few colleagues in India who've been
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working with us to actually turn that into raw data sets for each of those each of those companies so dish is just
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one example but uh there's many others we found at least 35 that um also disclose the sort of data that we would
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need to to fit these models for so the real key here would be what kinds of statements companies put out about the
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the number or the nature of their customers it could be how many customers they've acquired or how many customers
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they have at the end of the period some companies will go further and say something about their retention rate or
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their churn rate and and some kind of derived measure that that take some of the raw count data and turns it into
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some kind of more diagnostic metric we would rather work with the raw data we just want to know how many customers
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came in how long they stayed around how many were there at any given point in time uh right now there are absolutely
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no standards for disclosing any of this data or how it would be disclosed one of
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the things that we want to do is to start toate create some of those standards it's not the objective of this
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particular paper but if we can shine light on the value of these customer metrics and how they can be useful to
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marketers to finance people to people throughout organizations then maybe we'll start to have a conversation about
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which metrics would be reported how they would be defined how often they would be
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reported uh and standards around the the derived metrics that come off of them as
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well so it's very very early and none of that has really happened yet in any kind
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of formal way but we're hoping that this is the first step in that direction
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uh I'd say the big one is the procedure really does work you know you can perform this you can project out the
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cash flows but now we're augmenting that you know kind of core financial model
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that you would use if you were just working at an investment Bank incorporate this customer metric data
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and use that to get an even more precise estimate of how much these companies are
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worth we arrived at a price that was within something on the order of 5% of what the company had been trading at at
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the time they disclosed uh the filing that we would have used to train our model on so we were very happy with the
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fact that you know without really any sort of jimming around with the numbers uh we were able to come up with a very
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sensible valuation for the firm uh there were a number of other interesting findings you know for example you really
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do need to allow for the fact that different customers are are different from each
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other you have some who are valuable some who are are not so valuable you some who want to leave immediately and
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some who are going to stay around for for a very very long period of time so even though these companies don't
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disclose this sort of data you know we were able to infer that dish for example they had a kind of a hardcore loyal
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segment of about you know 15% of the customers that have been around for over 10 years just very very long term so by
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allowing for different customers to have different values uh we arrived at a much
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higher valuation than we would have had we done kind of the standard thing within marketing which is to kind of
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assume that all customers are kind of like the average customer customer that would kind of leave you with a price
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that's much much lower than you know what the company had been selling for at
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the time anyone who knows my work knows that I celebrate heterogenity I love to find and exploit and explain and talk
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about the importance of differences across customers this project is quite different because we have no direct
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indications of heterogeneity we have no ability to track individual customers to
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say oh this one left after a month this one left after 10 years we have no data like that we just have aggregated counts
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of how many customers came in at a given time and how many were present at at the
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end of a given period we need to infer the heterogeneity we need to fit models to this very poor very sparse overly
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aggregated data and infer our best guess about how the customers differ from each
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other not only is that essential for us to come up with accurate valuations but it leads to all kinds of very very
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useful Diagnostics that managers whether it's of Dish Network or another firm
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could really make great decisions from so for instance we think about the 8020 rule that a small fraction of our
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customers are going to capture a disproportionate value from the corporation it's it's a really valid
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concept I spend a lot of my time trying to take a company's internal data to try
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to uncover the 8020 rule is it really as concentrated as 8020 or maybe it's it's
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less concentrated than that in this case we don't have the data to do that but we
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do it anyway because we're building really good models on the sparse data we
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can still make inferences about how long different kinds of customers have stayed
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around we find out in this particular case our best guess is that it's it's
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far less concentrated than 8020 but still there's a good deal of het gen8 and if we ignore it our valuations and
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our ability to make kind of action oriented statements about the company would be way
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off one of the most interesting conclusions from our work actually doesn't appear in the paper in a lot of
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the work that I do we like to make hold out forecast let's look at the models
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that we've built and project what sales or whatever is going to be in the future
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and see how well we do and we tend to do pretty well but in this case besides just coming up with with forecasts on
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our own there already exists a universe of forecasts which is to say Wall Street
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analysts so we have thousands of of Wall Street analysts or at least dozens of people who on a very regular basis are
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making statements about what the earnings of Dish Network or any publicly traded company is going to be one
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quarter two quarter sometimes two years from from now well we can compare our models against their own models or
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judgment or forecast and keep in mind these are people whose jobs depend on coming up with reasonably accurate
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forecasts so one of the things that we did and again because the paper is so rich there's so much going on we
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actually didn't include it in there but we're always happy to talk about it is
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we compared our own forecast to those of the Wall Street analysts and on average
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they were actually considerably more accurate uh as showing that not to say that the Wall Street analysts do a bad
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job but some of these very simple marketing models applied to relatively simple sparse data can do at least a
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good a job as folks who have access to the CEO and and and and uh and data that that we couldn't even imagine having
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yeah the Crux of this work is really coming up with customer lifetime values so we're going to take our crystal ball
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out and we're going to project forward what every customer is going to do into
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the infinite future and you know really that's kind of what's driving this model
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for you know what revenues are going to be and thus what the stock price is going to be and what's most surprising
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is that even when we're making such short-term forecast we're able to do so
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well you know so essentially to come up with a stock price that's more of a long-term game but we're actually able
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to do very well uh kind of doing short-term predictions as well so that was definitely very striking to
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me I've been spending a lot of time lately talking about customer centricity
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trying to get companies to understand there's gold in them their Hills when it
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comes to the customer data and it can not only improve their marketing practices but pretty much every part of
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the organization we can make better decisions about how our sales people are doing about what kind of products we
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develop how well our production people are doing and on the finance side as well so for me this idea of
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customer-based corporate valuation has been kind of a a Holy Grail I've been
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going after that if I can win over the CFO and other people who are running the finance accounting and and control parts
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of the business then I can get these strategic ideas of customer centricity to spread much more broadly much more
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impactfully throughout the organization so for me it it really is reaching out and saying hey Finance guys a lot of the
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stuff that I've been talking about to the marketing people it's relevant for
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you too and it will help you make better decisions about your company and maybe your competitors and so there I've been
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really happy to see the results but of course it's much more than just that yeah one of the other kind of practical
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implications is you kind of just speaking from a financial standpoint I've been a hedge fund for a number of
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years before coming back seeing the light and going to to Academia so you can take the warten kit out of Finance
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but you can't take the finance out of the Wharton kid uh but you could definitely construct some sort of a
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trading strategy to be able to exploit this data systematically across all the companies that disclose it so again
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there's on the order of you know say 00 companies let's say uh in the entire
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universe who disclose enough data to be able to construct some sort of customer-based valuation model uh the
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idea would be you could find some companies that seem overvalued and some that seem undervalued uh just by the
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results that the models entail for each of those individual companies so buy the
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ones that are cheap sell the ones that are expensive you know you're not taking
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on any any real risk U I think such a strategy could be potentially very interesting and in turn that makes it
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very interesting for the executives of those firms knowing that you know people are buying and selling their stocks
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based on this information so the whole idea of standardizing these metrics making sure that you're improving the
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the health of your business kind of from a a Bottoms Up way you know making sure
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your customer metrics are strong I think it just reinforces something that they really probably know they should be
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doing in any case as Dan mentioned there's all kinds of interesting hedge fund possibilities and a lot of folks
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who are managing hedge funds or working with corporate valuation other ways have
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been looking very carefully at this work I want to say right now that I'm a marketing guy I have no intention of
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developing a hedge fund if other folks want to take this research and do it that's great I'll find that really
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rewarding even if I don't have a piece of it for me it's it's more about
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corporate strategy more generally it's more about making better use of the data
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assets that we have for me the big payoff would be if if folks with hedge funds and other investors go pounding on
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the doors of other public corporation saying You must reveal some of these other uh customer oriented metrics that
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we can't make the right decisions unless you do so and maybe for accounting standards boards to start to to Make
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Some Noise about that as well so if we can change the kind of data that's being
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put out there and the kind of conversations that are having Beyond just the making money thing I want to
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change business practices and and the kinds of data that we look at and the way that we use it say one other very
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practical implication is there's a whole stream budding stream of research about
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basically if I'm going to spend uh marketing dollars to acquire customers should I take that hit on my income
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statement immediately or should I capitalize that as an asset because I'm acquiring these customers who are worth
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a lot of money uh you know part of our research is showing just how valuable these customers are and one of the
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implications is they're going to stay around for a very long period of time so
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we come up with an estimate that the average customer at dish is going to remain a customer for about 5.5 years
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that's that's very very long and you have some customers who are going to be
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around for over 10 years so if I'm going to spend subscriber acquisition expense
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dollars yeah I really I'm investing in an asset you know these customers are
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assets but they're not acknowledged as such so I think over 80% of Dish's
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subscriber acquisition costs are immediately expensed um and as a result you know they kind of take the hit and
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of profits don't look as good in the short term so you know there's been work
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by people like Shuba shason at Boston University uh who are kind of saying that doesn't really jive with the
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underlying economics of the business if I'm going to spend money and I'm going
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to basically acquire an asset uh I should be able to recognize the asset and kind of have the expense associated
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with the asset bleed away as the as the assets value bleeds away so you know we think that this kind of dovetails very
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nicely with that stream of research we see these ginormous Acquisitions taking place in many cases
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where where one company is buying another largely because of their customer base so you can think about
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when Facebook bought what's app or just endless rumors about Yahoo and AOL we're
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just buying the customer base but what are those customers worth well that's
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what this research is all about and it's interesting to see sometimes in the
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Press you'll see kind of back of the envelope calculations about what One customer base is worth and they're
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appalling the the the analyses that they're doing are terrible the numbers that they're coming up with are way way
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off so we like the idea that people are are having that kind of conversation but
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we want to make it a little bit more educated we want the answers to those questions to be as rigorous as the kinds
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of things that we would expect to see in a conversation about Finance and Accounting not kind of lightweight
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approximations that might be Associated more with marketing yeah so one example that's been in the news lately is kind
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of on the topic Dish Network uh they're in the news about potentially being acquired or merging with other company
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so you know they've been in the news themselves of you trying to find a partner in some way to move forward so I
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think this is a perfect example of how customer-based Corporate valuation could be useful actually for the company that
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that you know we specifically are looking at now our research is all about valuing the customers so all these other
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assets that the company may have that you know really aren't coming from the
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value of the customers we'll leave that to the to the finance guys but specifically for the value of the
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customers we think there's real gold in basically looking at them kind of the
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way that that we are and all the better if we had internal company data we could
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just make those you know uh forecasts even more precise but you know we think even with this Network itself there's
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definitely real potential to think about it in this way yeah I'd say perhaps one of the
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biggest misperceptions that we've seen is not acknowledging the Paramount importance of the value of the customer
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to the value of the firm really establishing that link and then just focusing on the value of the customers
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I'd say perhaps one of the most striking examples of this is just in the accounting rules themselves so if a
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company was to buy a chair and put it right down over here like we're sitting
00:18:30
in now they would be able to capitalize that as an asset on their balance sheet and they don't recognize an expense
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immediately but if I was to spend money to acquire a customer that customer doesn't show up anywhere they're called
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an intangible asset and one of the interesting byproducts of our research is that for Dish example for example
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these customers are expected to basically stay with the firm for about 5.5 years so they have very very long
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useful lives and in fact dish in particular their example of the chair is is the actual dish itself so the money
00:19:04
that they spend on these dishes that you see uh they're able to capitalize and
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they have a useful life of only four years so actually you have this kind of striking example It's Like A Tale of Two
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Worlds that you have this kind of inert object that has a short useful life I'm
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able to capitalize that but you know here are these valuable customers that are generating cash flow over very long
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periods of time and I need to expense that immediately so we think that's you know really something that should be
00:19:30
looked into more carefully another important point that the paper addresses would be the the gap or I'd rather say
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the Synergy between marketing and finance that that marketers will often talk about this idea of customers as
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assets we often say almost just in a purely conceptual way if we add up the value of the customers boom we get the
00:19:48
value of the corporation and and while it's it's a nice idea we haven't seen
00:19:53
work so far that has really won over the Finance and Accounting Community say yeah you know what there there really is
00:19:59
something there now I'm happy to say that there have been a few Mavericks in
00:20:02
Finance and Accounting who have been saying some of these words but it hasn't
00:20:06
really become mainstream yet we're hoping that this paper not because it's
00:20:10
coming out of marketing but just because of the of of the rigor involved on the data side on the analysis on the
00:20:16
substantive implications is going to win over broad interest from folks in all different business disciplines and say
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you know what the marketing folks really do have something to add to the Finance
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and Accounting conversation and of course Finance and Accounting have a lot to add to marketing if we can break down
00:20:30
stereotypes if we can break down silos and make everybody across the organization not only smarter but but
00:20:36
looking at the same kinds of metrics then that's got to be a great thing for
00:20:43
shareholders so all the work thus far has basically been to value a single company so we lay out a framework and
00:20:49
then we apply it to a company we want to show in the next paper that essentially
00:20:53
you can apply this across many many many companies along two Dimensions you know
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one it's not just rehashing the same analysis that we did for one company you
00:21:01
know a 100 times over it's really that there's a whole broad spectrum of data
00:21:06
availability these different companies some that have disclosed a lot some that have disclosed very little and there's
00:21:12
actually a whole heck of a lot of information that you can learn about those companies that may not have
00:21:16
disclosed very much when you kind of look at the patterns across companies so we want to bring all those companies
00:21:21
together in what they call a basian hierarchical model and be able to fill in a lot more of the blanks for
00:21:26
companies where the data is not so good and apply this in a systematic way across all of them
00:21:32
simultaneously another big question is what metrics should be disclosed right now we're limited by what companies such
00:21:39
as Dish Network choose to disclose but are those the right kinds of metrics that investors should be demanding and
00:21:44
would it be the same kinds of metrics for every kind of business so an important caveat of this work is that
00:21:49
we've only looked at companies that operate in a contractual setting where we sign customers up they pay on some
00:21:55
regular basis and then they go and the company knows that they left it's observable churn so this model applies
00:22:01
to pretty much any contractual setting and that's great but there's many
00:22:05
companies out there such as Amazon or a grocery store where there's no formal
00:22:09
contract where people just kind of buy things on an occasional basis and then there's this long Gap and the company is
00:22:15
left wondering is that customer gone or are they just taking a snooze between purchases if you're in a non-contractual
00:22:23
setting what would be the right kind of data that should be disclosed again if you're an investor in a non-contractual
00:22:29
company and you're pounding on the door to that company uh what data are you
00:22:32
going to be demanding for them to disclose it's going to be different kinds of metrics so we want to have a
00:22:37
broader understanding about what are the right kinds of metrics that should be disclosed in different kinds of business
00:22:41
settings and then of course what kind of analysis would we lay on top of it in order to do the corporate valuation
00:22:58
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