4:43

I may then see a period of inactivity where the customer has not purchased

Â a ticket for six months.

Â In certain kinds of businesses it's quite difficult to determine whether or

Â not a customer is active or inactive.

Â In fact,

Â there are whole series of models that I'll talk about at the end that we could employ

Â to determine the probability that a particular customer is still with us.

Â Now, of course, if we have a business that's more of a subscription service,

Â then it's much more obvious whether the customer is active versus inactive.

Â If I discontinue my cell phone service with AT&T, they'll certainly know that.

Â If I cancel my subscription to The Economist,

Â The Economist will also know that but there are many situations

Â in which the status of the customer is not always clear.

Â In addition to calculating customer lifetime value, we also need a method or

Â a model or a way of thinking about the chance that a customer's, in fact, active.

Â If we combine those two pieces of information, the value of the customer

Â with the status, we yield some fairly interesting and intuitive implications.

Â Customers who are very high value and are currently active, we want to do everything

Â we can to foster and encourage loyalty from among those customers.

Â Customers who are relatively low value but actually fairly active, still

Â part of our system, transacting with us, clearly we want to increase their average

Â transaction size or the number of products and services that they buy from us.

Â Customers who are high value who are either inactive or

Â maybe customers belonging to competitors, we may want to focus on those individuals

Â with very specific and targeted promotions to switch them into our business.

Â Of course, in the final quarter, customers who are relatively low value and

Â who appear to have declined in the usage, or be somewhat inactive,

Â we may really just minimize the effort that we focus on those particular groups.

Â One thing I can't emphasize strongly enough is that customer lifetime value

Â is not only a calculation but it's a very important conceptual way of thinking about

Â what customers do for a business and why, in fact, we should cultivate customers.

Â When we think about customers as assets rather than just someone with whom we have

Â a transaction, it really changes the way we do the entire business.

Â I'll give a shout out to a friend of mine,

Â Sir Neal Gupta at the Harvard Business School,

Â wrote a very interesting book about ten years ago called Customers as Assets.

Â Really emphasizing the fact that it's not just the calculation but

Â it's the mindset that we have that becomes very critical if we want to

Â have a successful entrepreneurial venture.

Â What are some of the practical things we might do with a customer

Â lifetime value calculation?

Â 7:20

Most fundamentally, we're able to answer the question, is it worth the effort and

Â at what price should we attempt to acquire a particular customer?

Â We can also ask and

Â answer the question, what is it costing us to retain a particular customer?

Â We can also, if we have the CLV data, we see the distribution,

Â from very low value customers to high value customers, or

Â even customers for whom the CLV might start to look negative.

Â We might want to get rid of those customers.

Â Finally, if we took the value of all of the individual customers put together,

Â this would give us an estimate of the value of the enterprise, and

Â a way of potentially valuing companies.

Â And mergers and acquisitions and so on.

Â So, these are just some of the many practical decisions

Â that you as an entrepreneur might want to take as a result of doing the calculation.

Â So with that in mind, let's now start to step through the most basic calculation,

Â and then I'll give you some other references for

Â those of you who like to do some homework on some more sophisticated approaches but

Â again the most important thing here is the concept.

Â And so, in order to embrace the customer lifetime value philosophy and

Â start to do the calculations, you're going to need some data.

Â And the bare minimum data has the following four components.

Â So first of all, you will need to know what kind of a margin

Â you're getting from a particular individual customer.

Â So this is just really the revenue that you're getting and

Â minus any variable costs you have of servicing that customer.

Â You also need to have some estimate of the chance that the customer will be retained.

Â What's the probability that given the option,

Â the customer renews a contract with you, or the customer remains a customer

Â in a certain time period if it's a non-contractual business?

Â I'll talk a little bit about where these numbers can come from also.

Â The third thing that you'll need to know is of course, the discount rate, or

Â the interest rate that you might like to apply.

Â Obviously, money that I get from a customer in the future

Â is not as value as the money that I'm getting today, and you may need to apply,

Â or you will need to apply, an appropriate discount rate.

Â We'll take the easy way out in our calculations and we'll just use 10%.

Â And then finally, you need to think about what's the right period over which

Â customer lifetime value should be calculated.

Â So, a recent consulting project I was doing for

Â a friend of mine who graduated here at the Warden School

Â was focused on a business where I calculated the CLV every quarter.

Â So these were sellers in a home shopping network and a holding trunk shows on

Â their home, and these sellers would have a trunk show every season to sell apparel.

Â So, I basically hit four observations per year, the time period was the quarter.

Â For your business, the time period might be the week, or it might be the year, but

Â it's a very important decision to think about choosing the right

Â time period over which we're going to do the calculation.

Â So, these are the four things that we need to know.

Â Some of them are more difficult to come up with than others.

Â Probably, the research would say the most challenging thing to figure out is

Â what is the right retention rate?

Â Now, one way we can get at the retention rate is the following.

Â Imagine we begin our business.

Â Let's make it easy, January 1st,

Â we have 100 customers that start with our business.

Â We then wait a particular of time let's say, in this example, one year and

Â we see from those initial 100 customers how many customers are still

Â remaining with the business.

Â If 80 customers are still in the business,

Â one simple estimate of the retention rate is 0.8 or 80%.

Â So looking at a core order of customers that entered our business at the same

Â time, waiting a certain amount of time to then see how many of those are left.

Â That's one way that we can estimate the retention rate.

Â So with this is mind, let's now go forward and do some calculations.

Â 11:05

So in this diagram, we're going to visualize the process of revenue coming in

Â from a particular customer and I encourage you perhaps even to draw a diagram of this

Â sort for your own customers in your own business.

Â So let's assume a fairly simply example,

Â that the business that we're running is something like a mobile phone service and

Â that every year, the customer is spending $250 with us in terms of the net margin.

Â So the revenue may be a little bit higher minus some costs that we have of

Â variable costs maintaining that customer, we're getting $250 at margin.

Â Now, in addition, that cost is $400 in the beginning to acquire the customer.

Â Perhaps we had to give them a free cellular phone or something like that.

Â So, with this information in hand, over five periods, so

Â that's the lifetime that we're assuming that the customer is going to live for.

Â We can start to execute on the calculation of customer lifetime value.

Â So, let's go through this and do it together.

Â And let's do it in a very, very simple sense, so

Â we'll assume that there's no customer attrition.

Â Wow, wouldn't that be a great business.

Â The customer never leaves us,

Â stays with us with probability one every period for five years.

Â Secondly, let's assume that we're not going to do any discounting in

Â the calculation, that the money that we receive from the customer in year five

Â is just as valuable as the money that we receive in year one.

Â Of course our finance colleagues would not like us to make such an assumption, so

Â we'll modify that shortly.

Â But le's go ahead and do the calculation.

Â So, in this case, the customer lifetime value is just the five increments of $250

Â at margin, $1250 minus the acquisition cost

Â of $400 which leaves a net customer lifetime value of $850.

Â So the reason I'm showing you this example is as we start to layer in other things

Â like attrition and discount rate, what you'll notice is

Â that the customer's lifetime value the number starts to decline.

Â And it starts to decline quite dramatically so for

Â those of you who are students of business history which I hope many of you are,

Â if you think about things like the Internet boom and bust ,there are a lot of

Â companies during the 1.0, maybe still even today, that were grossly over valued or

Â grossly optimistic about what the value of the enterprise was because they made some

Â rather heroic assumptions about the values of their customers.

Â Either the margin that they were getting every period or the chance that

Â the customers would actually stay with the business and be retained, so.

Â That's one important point that I want you to look for, even in this very,

Â very simple example.

Â Just to see how dramatically the value is going to change

Â as we start to relax those assumptions.

Â So, let's go ahead and do that.

Â And so, now let's turn again to our familiar diagram where we have the flow

Â of margin coming in from the customer every period, but

Â this time around we're going to add one additional assumption,

Â which is that customers, unfortunately, might decide to leave us.

Â There's going to be some probability of attrition that's going to

Â cause the customer lifetime value number that we ultimately calculate to go down.

Â Now, this is something that actually is quite subtle and quite important.

Â And for those of you who are students of business history, which I hope many of you

Â are, if you think back to the boom and bust of things like Web 1.0,

Â part of the reason that happened is that people made gross overstatements or

Â grossly misstated assumptions about what the value of different businesses were.

Â Based on the underlying customer transactions and the underlying customer

Â asset in particular, they either assume that the margin that was going to

Â come in from the customer was much higher than what it turned out to

Â be because the customers couldn't be monetized in the way people thought.

Â Or the entrepreneurs though that the chance of retaining

Â customers was much higher than it actually was,

Â because they discounted the fact that a competitor could steal a customer, or

Â a customer might just leave of natural causes, doesn't really find the value in

Â the service that we, as the entrepreneurs would like to believe that they have.

Â And what you'll notice here, this is a very important element of the customer

Â lifetime value calculation is that even a fairly small degradation in the retention

Â rate can have a dramatic effect on the number that ultimately get's calculated.

Â So, lets go through and do this, and

Â then the example, we're going to assume a retention rate of 80%.

Â Now that's actually a pretty good number.

Â So, I think we'd be fairly pleased as your colleagues and

Â instructors in this course if there was a 80%chance from week to week that you

Â kept engaged with the class and applying the materials.

Â What we'll see however, is even with an 80% retention rate,

Â there's going to be quite a dramatic reduction in the value that we got

Â from the model previously, the value previously, was a total of $850.

Â Where we would assume customers would always stay with us.

Â For five years, and we also assume no time value of money,

Â we'll get to that one in a second.

Â So, what we're also going to do here, is you notice in the calculation is that at

Â the end of every period, the customer can either leave with probability 0.2,

Â or stay with probability 0.8, and

Â this process gets repeated until the end of the 5th period.

Â Now, those of you who're sort of looking at the slide,

Â there are those of you have studied a little bit of statistics in mathematics,

Â might say, hang on a minute, there's quite a simplifying assumption here.

Â You've assumed that the probability that the customer is there by the end of

Â year 2 is 0.8, at the end of year 3 is just 0.8 squared,

Â at the end of year 4 is 0.8 cubed.

Â So implicitly I've assumed

Â that the retention rates are independent from one year to the next.

Â Which seems like a pretty unrealistic, shall we say, assumption.

Â But, let's go back to where we started our session today,

Â with the notion that all models are wrong.

Â Thank you.

Â But some are useful.

Â So clearly it's a bit of an unrealistic assumption but it's made not only just for

Â mathematical convenience but

Â also because it may not in fact be such a bad assumption after all.

Â So imagine that my colleague Stephanie is a customer of AT&T.

Â The longer she stays with AT&T we might argue that her retention rates going up,

Â she likes the service, she's getting used to it,

Â she really doesn't want to go anywhere else.

Â At the same time, there could be other factors

Â causing her retention rate to be potentially going down.

Â Competitors like Verizon chasing after her.

Â She's just getting a little bit tired of the service, and so on.

Â So, as my colleague and friend, Sunil Gupta,

Â at Harvard Business School might argue,

Â there are forces pushing retention rates up, there are forces pushing them down.

Â So assuming that they're roughly constant, is not such a bad thing to do.

Â Now of course, getting the right number in the first place by doing as I said before,

Â looking at a cohort that entered at the same time period and

Â asking how many remained after a certain point through time

Â to calculate the retention rate that's probably the most critical thing of all.

Â So let's go through now and do the numbers.

Â I'm now just computing the expected contribution which is

Â the margin modified by the retention rate.

Â So I've done that there in the slide.

Â We add all those numbers up.

Â We get a net value now of $840, not $1250, and of course we have to

Â subtract out the initial acquisition cost, which I just assumed to be $400.

Â So now we have a customer lifetime value of $440.

Â Wow, that's a big drop from $850.

Â So you can imagine why this retention rate is just so critical.

Â In fact, the academic research suggests of all the four elements in

Â the mathematical formula, the one that produces the most leverage or

Â impact over the final number that you calculate is in fact the retention rate so

Â always be wary of somebody who's making an assumption about retention.

Â That's just too heroic or too unrealistic.

Â Because if they're doing that they're going to be grossly over estimating

Â the value of individual customers so as entrepenuers we always want to err

Â on the side of caution and off course to sensitivity.

Â Let's see what the result looks like if we assume 90%, or 80%, or 70%, and so on.

Â So we now have completed the second calculation, let's continue on, and

Â do a third one.

Â And so now on the screen in front of us we have the familiar flow diagram of,

Â margin coming in every period from the customer for five periods.

Â We also have on top of that the retention factor,

Â which we assumed in the beginning that they are with probability one.

Â And then in every period they have a chance of 80% of staying with us

Â 20% of leaving, and on top of that I've computed the expected contribution,

Â $250 dollars, $200 dollars, $160 and so on down.

Â Now in this case we are just going to have one final component, which is something

Â that our finance colleagues, or your CFO at your start up, or your venture would

Â be really concerned about, which is of course the time value of money.

Â So we're retaining the assumption that customers will be with us,

Â with probability 80%.

Â So they'll churn or we'll lose them with probability 0.2.

Â In addition let's just assume that the value of money that comes in

Â is not as valuable in the future as it is in the present.

Â That's a pretty good assumption.

Â And let's have a discount rate of 10% just to make the math easy and

Â now let's go through and do that calculation so

Â the discount factor that's applied to the first piece of money that comes in at

Â the end of year one is just one divided one plus the discount factor which is 10%.

Â At the end of two years that's just again,

Â one, divided by one

Â plus the discount rate 10%, and

Â then we square the whole thing, and then we cube and so on.

Â What we see is we get now a value of $664.

Â We subtract out the $400 to acquire the customer.

Â Wow, almost nothing left.

Â We're down to $264.

Â So just think about this for a moment.

Â We've done a very, very simple and very stripped down example.

Â I really hope that you're going through and

Â thinking about your own customers in applying the same kind of logic and

Â we started with a customer lifetime value of $850.

Â We're now down to a number that's roughly about a third of that even with having

Â what seems to be a pretty good retention rate.

Â And also applying a fairly modest discount rate of

Â about 10% to the time value of money.

Â So, we can see that when one starts to really build in some more realistic

Â assumptions about whether or not customers will be retained and

Â the value of revenue that we might be getting in the future.

Â When you do those two things, you end up with a dramatically lower number.

Â That's really the bottom line here.

Â So, with this in mind, I'm now going to give you a couple of quick and

Â dirty simple ways to do this calculation without going through and

Â doing the adding up from every single period.

Â And also talk about some extensions that I would really love for some of you

Â to do for your homework, if you really need to dig a little bit deeper, and

Â go into a more sophisticated approach.

Â So now let me just give you one other very simple way,

Â just in a very rough sense, to calculate customer lifetime value.

Â In the examples that we just went through, remember that the customer

Â was sticking around for five periods, five years in our example.

Â We only added up the data for a period of five.

Â Let's imagine now however that the customer keeps on going, period six,

Â period seven, period eight, but

Â of course in every period, the chance that they stick around is declining,

Â declining, so it would be 0.8 to the five, 0.8 to the six and so on.

Â So, if we do then, we make that assumption that the customer is, in some sense,

Â almost going to be around forever, but with the declining chance every period.

Â So, in this case the customer lifetime value is simply the return, or the margin

Â divided by the churn rate, the churn rate is 0.2 or 1 minus the retention rate.

Â That gives us 1,250.

Â Subtract off the $400 acquisition cost.

Â If we wanted to then modify the formula again, assuming that the customer

Â is not there for five periods but six, seven, eight, keeps on going.

Â It's just going to be the margin $250 divided by the churn rate,

Â 0.2 + the discount rate of 0.1.

Â So these are some really simple heuristics that you can use to apply to

Â to different customers to see which ones are more valuable, which ones are less so.

Â And of course, in all of these cases, whether you do the summing up.

Â Over a certain number of periods, or whether you use the direct formula

Â assuming that the customer is always going to be around.

Â Always be critical as entrepreneurs about the assumption around retention.

Â And always try to do some experimentation or some sensitivity.

Â 24:12

And now as we conclude this lecture,

Â there is three things I would like you to think about.

Â And in fact to do as a homework exercise as we always do

Â at the end of our sessions together or the end of our time together.

Â So, let's try and take the customer lifetime value concept and

Â put it into action.

Â So first and foremost what I'd like you to do is to look at your own business as

Â an entrepreneur, and try and make those four decisions.

Â First of all, try to figure out what you think

Â the margin is that you're getting from every customer, number one.

Â Number two related to that, think about what is the right time period

Â over which you should be doing the calculation.

Â Number three, and probably most challenging, try to

Â develop an estimate of the probability that the customer is being retained.

Â And then number four, talk to your finance people, your CFO, and

Â think about what's a reasonable way to discount

Â the revenue stream that you're getting from the customers.

Â So try and put those four components together, and then start to apply those

Â calculations to the individual customers that you have at your particular business.

Â Number two, if you'd like to do it for a business that's already out there,

Â that I think some of you, at least in the United States might be familiar with,

Â a business that's challenging Gillette.

Â It's called Dollar Shave Club.

Â I'd like you to think what the customer lifetime value might be for

Â a customer who entered Dollar Shave Club.

Â Now I can assure you that a company like Dollar Shave Club

Â is most certainly thinking about customer lifetime value as a calculation.

Â So I'd like you to go through the exercise of trying to apply it to this

Â particular business.

Â As you do that, also think about which of the three razors in front of you

Â might be the one that's most likely to be purchased by most customers.

Â I'll give you a clue.

Â There's something called the compromise effect that suggests that customers might

Â gravitate towards buying the $6 option as opposed to the 1 or the 9.

Â And then finally, for some of you out there who have a strong background in

Â mathematics, or have a penchant to do things that are a little bit more

Â sophisticated, you've got the basic framework in mind, that's good.

Â All models are wrong, but some are useful.

Â You've got the basic elements and the philosophy, which is critical.

Â I would really encourage you to look up my friend and

Â colleague here at the Wharton School, Peter Fader.

Â F-A-D-E-R, Pete Fader.

Â He's probably one of the world's leading experts in the study

Â of customer lifetime value with more sophisticated mathematical modeling, and

Â Pete has kindly made most of the software available.

Â