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we're here today to discuss some new
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Wharton research about customer lifetime
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value and company valuation here to talk
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with us is wharton marketing professor
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peter fader Pete thanks for being here
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always a pleasure Rachel and Wharton
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doctoral student Dan McCarthy Dan
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welcome back yeah thank you very much
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for having me so Pete first of all could
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you give us a quick summary of a
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research now this is a bit of a follow
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up from something you've done previously
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where you looked at company evaluation
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of companies that have customers that
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buy subscriptions and this is a little
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bit different so could you tell us a
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little about that old research and then
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how you're building upon it in this way
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sure well there's no such thing as old
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research here this is all brand new
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there's this idea of customer based
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corporate valuation it's actually a
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concept that's been floating around but
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Dan and I are taking it very very
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seriously and we want to bring just just
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a tremendous amount of rigor and breadth
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to it in the previous work we looked up
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euerle on the contractual or the
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subscription-based side so if a company
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knows when a customer's leaving it's
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kind of easy to put to project what the
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rest of their life is gonna be and and
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the payments tend to be kind of steady
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but most businesses are what we call
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non-contractual which is the you're just
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buying things on occasion and then
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there's a long hiatus between them and
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and if you think about this is the kind
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of behave you have with with a retailer
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or with a travel firm or even
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pharmaceuticals or media consumption
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most businesses are non contractual and
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that makes it much more difficult to
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understand who's doing what and to
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project it into the future so a big part
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of what we're doing here is trying to
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take this this broader concept of
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customer based corporate valuation but
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make it just as palatable in the
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non-contractual setting as it has been
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in the contractual subscription-based
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one and and everything add yeah that's a
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it's exactly right I'd say one of the
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key challenges in this work is really
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the the data that we need to be able to
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perform this estimation and then project
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forward you know how many purchases
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customers are gonna make and how much
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they're gonna spend on each of those
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purchases so again we want these
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methodologies to be as broadly
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applicable as possible the vantage point
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of this work is really someone who's on
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the outside looking in and so maybe it's
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a shareholder a hedge fund private
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equity firm any number of financial
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institutions that are kind of everyday
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looking at the public markets and trying
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to say so I want to buy this company do
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I want to sell this company and those
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those those people they don't have
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access to the internal transaction logs
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to the company and what they have access
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to are usually the sort of information
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that a company would put and its
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quarterly or its annual filings and so
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basically we went at this problem by
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saying imagine that a company was to
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disclose a certain very small set of
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common customer metrics what are the
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ones that would really enable us to be
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able to perform this first estimation
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procedure for the various model
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components and use those to project
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forward
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eventually what total revenues are going
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to be and Dan tell me a little bit now
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once you've how did you figure out what
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those were and then what did you do with
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them yes we figured it out by doing a
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large-scale simulation analysis so kind
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of the step one was saying this is a set
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of metrics that we think could be usable
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and really they needed to kind of
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satisfy two main criteria the first is
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that they're actually used by companies
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so we found many metrics that
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essentially we have found public
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companies that are using them in
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disclosing them regularly and then the
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second criteria is really that they
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actually help inform our model and so
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the way that we answered that was we
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said imagine that I was to generate data
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from all these different possible types
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of worlds
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basically any different type of non
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contractual company and just let these
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metrics kind of Duke it out so we took
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every possible collection of this set
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and said imagine that I only got to see
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those metrics let me try to predict the
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future and then essentially see which
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combinations of metrics kind of
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percolate to the top absolutely I think
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it makes sense to talk about the metrics
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themselves so as Dan said we've done a
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lot of scraping of financial statements
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listening to CEO conference calls seeing
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what third-party firms are saying about
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the size and age of customer bases and
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we came up with six metrics so metric
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number one the most common one that a
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lot of companies will report is what we
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call active users so how many people
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have made
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transaction used our product or service
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sometime within the trailing 12 months
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number two is what we call heavy active
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users so how many people have made a
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repeat purchased have engaged with us at
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least twice over that trailing 12 months
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number three would be this idea of a
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forward repeat rate of all the people
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who made a transaction with us back in
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2015 how many came back and did it again
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sometime in 2016 the the fourth and
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fifth would be kind of the flip side of
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that so of all the purchases that we had
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today what percent of them are from
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customers who did something with us in
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the previous year and that one we can
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either do on a customer by customer
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basis of all the customers who bought
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with us what percent were with us
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previously or all the orders that were
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placed with us this year what percent of
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them are by customers who bought
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previously and then last and least
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reported is the idea of frequency of all
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the customers who have done anything
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with us in the past year how many things
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did they do
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how many purchases did they make or so
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on so that's the big six and as Dan said
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we went into it without a really strong
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sense of which one or ones would win out
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but it was really interesting to do this
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grand bake-off across lots of different
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worlds as you said and to see there
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actually were some very strong
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consistent results so when you did when
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you did this bake-off what was the
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winning what was the winning metric here
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what was what are some of the
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conclusions that you were able to make
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from this sure of course it's always
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easy in hindsight and I'm kind of
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stalling right now because I want our
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listeners and viewers to think that
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among the six metrics that I just listed
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if you had to pick say two of them which
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ones would bubble up to the top and
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would give you the ability as Dan said
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to to kind of uncover the repeat buying
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pattern as if you had the granular data
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at your fingertips
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well it turns out that far and away the
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number one metric is the least commonly
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reported one that I mentioned which is
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frequency so if you can tell us among
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the people who did anything how many
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things did they do on average that now
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by itself any one metric isn't going to
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get you far but if you take that
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frequency metric and you come
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with any of the other five it doesn't
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even matter frequency plus anything
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gives you really good predictive
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capability just winning by a nose among
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the five was the first one I mentioned
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just active users so if you can say
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here's the number of people who have
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done anything with with us and here's
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the average number of things that
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they've done you take that one-two punch
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and it's remarkable how well you can
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uncover future purchasing and then
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benefit from all the the the other good
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things that you can do with those
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insights yeah I think that really sums
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it up I'd say one thing that was a bit
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surprising was the fact that when we
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went from two metrics to three there was
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virtually no improvement in our ability
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to predict future purchases that really
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the bulk of the the benefit that we got
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was by moving from kind of the best
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single metric to the best pair and what
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was almost equally interesting was you
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can't have too much of a good thing so
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as we kind of kept that process going
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you know what's the best quartet what's
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the best combination of five that we
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went when we went from the best
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combination of five to the best
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combination of six performance actually
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got slightly worse and it got a bit more
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variable from scenario to scenario and
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so actually you know sometimes less is
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better yes I think it was kind of an
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interesting example of Occam's razor
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with the data so Dan tell me if now I'm
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a company or I'm a customer I'm anyone
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that might be interested in this sort of
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data what's the best way to take your
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findings and apply them in the real
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world really I would just demand that
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companies disclose these metrics I think
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emphasizing both to the company itself
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and perhaps even to regulators whose
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responsibility it is to ensure that
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investors have the information that they
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need to you kind of make informed
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investment decisions we're basically
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saying that these metrics are really
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informative so I think it would be
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wonderful to just kind of speak up and
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get people to disclose the metrics on
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the back end we have this you know
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fairly complex estimation technique
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that's needed to really kind of take
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that scattered bunch of customer metrics
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and be able to kind of map it down to
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the underlying parameters of these
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models that we have for how
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customers are acquired over time how
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many purchases they make and the spend
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associated with each of the purchases so
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there is some you know some math
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involved and some have heavier duty
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computation than we saw you say in the
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subscription-based paper but it is
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certainly quite doable and I think as
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these method methodologies kind of take
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hold we'll see them you know be kind of
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made more readily available do you have
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anything to add it also I guess I'm
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curious like what do companies have
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reasons not to release this data I mean
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is there a reason why they wouldn't want
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to and how do you get over that so first
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let's talk about some of the other
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benefits then we could talk about some
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of some of those strategic aspects as
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well so obviously the motivation here is
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customer based corporate valuation so
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can the investor make more informed
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decisions about the current and future
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health of a company's customer base and
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therefore the value of the enterprise
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but it can be useful in other ways as
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well so this could be a great source of
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competitive intelligence so given that
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these metrics are not that hard to get
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say from a third-party firm or from a
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company's first-party disclosures you
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can start to see of rival companies
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playing this game about other
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competitors in the same sector to
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understand where they stand not just in
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terms of overall sales but in terms of
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the nature of the customer base so so we
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think that there'll be quite an
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imperative to be reporting these things
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not only out of some kind of fiduciary
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responsibility but for some of these
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kind of competitive activities as well
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which goes directly to the other part of
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your question this idea of should there
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be some sense of when you disclose which
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kinds of metrics right now it seems kind
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of haphazard there are some companies
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that disclose some metrics all the time
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others that do it never there seems to
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be no logic as to why certain companies
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do or don't and which ones they disclose
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there's some companies out there that
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just just boast about disclosing metrics
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may because it makes them look a little
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bit more rigorous or sciency but no one
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ever looks at these things and can
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really make heads or tails out of them
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or map them back to actual or projected
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future revenues so we want to see much
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more
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Smart's about who discloses what when
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and and maybe companies would then start
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to say so
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are they revealing these metrics now
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because that they're trying to signal
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something or or maybe they're gonna stop
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revealing them because they're trying to
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hide something so there's gonna be this
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whole chess match about who discloses
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what when that'll be kind of interesting
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when we get to that point right now it's
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just so early on that if companies are
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doing this is all it really seems that
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gets more kind of the ceo's ego as much
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as any kind of real information value we
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want to see just again much more a rigor
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and discipline about who's disclosing
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what when and why do you feel like this
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is gonna become people are gonna become
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more interested in doing this and more
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interesting talking about it as their
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becomes more of this realization that
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maybe you're not saying everything there
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is to say about a company by just
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disclosing its financials that there is
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this other element there that may have
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nothing to do with earnings or revenue
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at least not for that quarter that
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that's right so I so for many companies
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that they they go to Wall Street on a
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quarterly basis and Wall Street's kind
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of look at them saying your earnings
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aren't up to snuff but they say you know
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what we're investing in the customers
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well so far it's just been a trust me
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but now we can provide definitive proof
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so now investors can say is that if
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that's really true
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then show us these two metrics so that
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we can make that assessment for
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ourselves so I want that the companies
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to be more forthright about it I want
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investors to demand it I want
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competitors to be kind of curious
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regulators as Dan said I just want just
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a more active conversation about this
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this concept of customer based corporate
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valuation I think it's in everyone's
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best interest and of course I'm a
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marketing professor I think that if we
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can establish that there's something
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here then it will also trickle down
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through the rest of the organization as
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well and start to impact other kinds of
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decisions that the company is going to
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be making I say the one other thing I
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would add to that it's a very legitimate
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concern that some companies would have
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that as soon as they start disclosing
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these metrics that you know there's some
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sort of negative you know repercussion
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from that and I think there's been some
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very interesting recent work that was
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just put out and one of the top
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marketing journals by a colleague of
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ours Bern Ciara that basically came to
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the conclusion that when companies
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disclose forward-looking metric
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it really helps investors make more
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accurate projections of revenues thus
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lowering their kind of perception of the
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uncertainty about what's what's to come
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in the future and that actually can help
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increase the valuation of firms and so
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all else being equal disclosing
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forward-looking metrics can actually be
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beneficial to the stock price so it can
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help just kind of overcome some of those
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concerns that companies might have so
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dan do you feel like that there are
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other misperceptions that might this
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research might dispel I've I'd say yeah
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for one just the competitive concerns
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you know we're showing that these
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metrics can be actually very helpful for
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projecting what will happen in the
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future I'd mentioned the one about you
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kind of too much of a good thing but
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it's a another kind of spin on that
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would be just the overwhelming kind of
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influence of quality over quantity that
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it's an extremely important it's
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extremely important to have the right
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combination of metrics and oftentimes
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having a very small set of very good
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metrics can be much more beneficial than
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having kind of the wrong set of very
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numerous metrics so in that large-scale
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simulation analysis we actually found
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that if you gave us the right set of two
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metrics it could do much better than
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another set of five metrics so that kind
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of surprised me in terms of
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misconceptions I'd say the final one
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that comes to mind is up until this
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point most people have been using
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customer metrics as kind of a dashboard
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that every month or every week the
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company will have a spreadsheet that
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sent out to the organization and it's
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got this long list of all these
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different customer metrics and how each
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of them has gotten better or worse over
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time and usually people are looking at
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each of these different metrics is kind
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of you know in its own world yeah that
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essentially if it went up it must be
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good if it went down it must be bad and
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let's figure out why here we're kind of
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saying we don't just need to look at
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these metrics kind of on a standalone
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basis they're not ends unto themselves
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essentially we can be able to kind of
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tie them up and summarize the you're
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kind of effective you know what happened
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over the past week or month on the
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overall valuation of the firm by kind of
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putting them all together and weaving
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them into this integrated model
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now Pete I guess anything from you but
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also do you feel like cus the companies
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that don't operate on a subscription
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model do you feel like there was the
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percentage out there that maybe they
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couldn't do something like this that
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that was just for the subscription-based
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businesses of the world it's not for us
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do you think this dispels that if it
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well it does dispel that but actually
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was a worse problem that there were a
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lot of these kinds of non-contractual
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non-subscription firms out there and
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they would just kind of make metrics up
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to make it seem like they could use some
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of these subscription oriented
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approaches so for instance in the
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subscription world you have a formal
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retention rate you know of all the
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people who had a subscription with this
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last period how many of them renewed you
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know whether they did it or not there is
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no equivalent metric in the
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non-contractual world there's no way
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that you that you kind of actively raise
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your hand or a worse yet if you're not
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present if you don't make a purchase if
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I didn't buy anything from Amazon in the
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past twelve months doesn't mean that I'm
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gone as a customer it just means that
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I'm a light buyer so what a lot of
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non-contractual firms were doing is they
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would take some of these metrics like
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the one I mentioned before the for word
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retention from the for word repeat rate
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among all people bought in 2015 how many
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also bought in 2016 this you know what
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that's kind of like a retention rate
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let's just treat it that way and then
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pretend that we're a subscription
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company well that's just wrong and and
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not only is it conceptually incorrect
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but if you were to follow it all the way
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through and some of the earlier
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published research showed this that your
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ability to assess the overall value of
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the customer base is going to be way way
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way off and the Diagnostics that you get
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around it would be off as well which
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takes me back to the other part of your
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question one of the other benefits of
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doing all this is to create credibility
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for the marketing organization if we can
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say here are the really meaningful
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metrics and here's the way that we can
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tie them together to make statements
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about valuation instead of having these
00:17:25
things being little trophies on a shelf
00:17:28
where the rest of the organization say
00:17:30
the people in the CFO's office would say
00:17:31
that's nice let the marketers play with
00:17:34
their toys if we can show the CFO and
00:17:37
again other folks to the organization
00:17:39
that these metrics can actually help
00:17:40
with day-to-day operational decisions as
00:17:43
well as bonafide long
00:17:45
strategy it's going to cast the entire
00:17:47
marketing organization under a very
00:17:49
different light and to me personally
00:17:51
getting that kind of respect getting the
00:17:54
kind of common language is more
00:17:56
important than taking some of these
00:17:58
valuation models and trying to find a
00:18:00
little bargain here or there as a hedge
00:18:03
fund might do they're both interesting
00:18:05
they're both important but for me it's
00:18:07
that the common language and respect for
00:18:08
marketing that that's number one
00:18:10
now P can you tell me what's next for
00:18:12
this research where you go where are you
00:18:13
gonna go from here well right now we're
00:18:16
just establishing these the basic
00:18:18
methods like here are the metrics you'd
00:18:19
want to use here's the way you tie them
00:18:21
together to get the overall valuation
00:18:23
but it starts to lead to all kinds of
00:18:24
other questions including some that
00:18:26
we've already touched on this idea of so
00:18:28
strategically what kinds of metrics
00:18:30
should I disclose when but I really
00:18:32
should defer to Dan McCarthy because
00:18:34
this is his dissertation work and he's
00:18:36
been thinking of a lot of other
00:18:38
different directions to take it that
00:18:40
will be part of what he does when he
00:18:41
graduates from here and becomes a
00:18:43
professor down at Emory so so Dan you
00:18:46
tell us what's next yes in terms of the
00:18:49
boxes that have already been checked we
00:18:51
kind of think of it along a few
00:18:52
different dimensions you know what's the
00:18:54
sort of data that we have you who's
00:18:56
performing the exercise what is the goal
00:18:59
and what's the type of firm and right
00:19:01
now we've primarily focused on external
00:19:04
stakeholders you know like the
00:19:05
shareholders or the private equity firms
00:19:07
or the hedge funds who are their goal is
00:19:10
really to make an accurate estimate how
00:19:12
much the value the firm should be and
00:19:14
we've done that for both contractual and
00:19:16
for non contractual firms so in terms of
00:19:18
where we go from here I'd say one of the
00:19:20
big questions is can we move beyond
00:19:23
measurement imagine that I'm inside the
00:19:25
company is there anything else that I
00:19:26
might want to do so maybe we can be able
00:19:29
to kind of manage value not just measure
00:19:31
it and kind of part and parcel with that
00:19:34
we'll be using internal data as opposed
00:19:37
to external data instead be one example
00:19:39
another is in both the subscription
00:19:41
based valuation paper and a non
00:19:44
subscription valuation paper we were
00:19:46
kind of only looking at one company at a
00:19:48
time yeah so whether it's Dish Network
00:19:50
Sirius XM or this apparel retailer and
00:19:53
in the current paper we didn't really
00:19:56
focus on any any come
00:19:58
Pettit --iv effects or anything like
00:19:59
that so I think an interesting area of
00:20:01
future work would be imagine that we had
00:20:03
multiple companies in the same industry
00:20:05
could we learn something you know that
00:20:08
we wouldn't have learned about if we
00:20:10
were looking at each of the companies
00:20:11
individually make better predictions or
00:20:13
just learn about competitive effects so
00:20:15
I think the sky's the limit
00:20:17
you know we're just really scratching
00:20:19
the surface and I'm very excited to see
00:20:21
where we can go from here dan thanks so
00:20:23
much for being here with us again always
00:20:25
great to talk with you Rachel thanks
00:20:27
yeah thank you very much for having us
00:20:40
[Music]
