In this lecture we're going to focus on one of the most critical elements of running any business, understanding the lifetime value of the customer. Let's preface this, our discussion today, by noting the following quote from a very famous statistician, George Box. I like the first piece but I also like the second piece. Professor Box said all models are wrong, but some are useful. And that's the spirit in which we're going to study this material today. Customer lifetime value is absolutely central to what we do in almost any business that we run and there are various ways of looking at the customer lifetime value calculation. We're going through some of the fundamentals, and then I'll also give some suggestions about more sophisticated approaches that you might like to follow up on, on your own. The plan for today is to first of all think about customer lifetime value, visually, what does that funnel look like with customers coming in, staying with us and some of them, unfortunately, departing? Then thinking about the individual components of data that we would need to do a customer lifetime value calculation. Finally, what are the implications of this perspective? What kind of customers should we be focusing on? What kind of customers should we ignore? What kind of customers maybe we want to actually get rid of altogether? Then what we'll do, we'll step through a series of calculations with the basic customer lifetime value formula, starting with the most elemental, layering on a few additional things. As I said, I'll also give you some suggestions those of you out there who might like to do some very sophisticated things, there are resources for that also. Now I'd just like you to take a look at this diagram that shows very simply as a schematic, the way that customers come into a firm and the way value is built up over time. Let's imagine that at the beginning of the time period, or when we start our business, let's just call that year one for simplicity, we acquire some customers. Here on the screen we have five different customers that we've acquired. Some of those customers start to buy some product from us and it's the little indicator there of a customer who has purchased from us, we've accumulated some revenue from that customer. The longer we have that customer in the system, maybe we start to sell them other additional services, cross sell, up sell, and so on. The pile of cash that's accumulating on the chart Is getting higher, and then finally, as the customer is with us for an even longer duration, in this case three years, we're able to sell them even more ancillary services. But, of course, some customers may actually leave the system and may end up attritting. We also need to account for the fact that customers don't necessarily stay with us permanently. Now as shown there in the diagram, of course, in addition to the acquisition costs that we talked about in another lecture, we also need to think about the cost to maintain the relationship, the retention cost, and most critically, what is the profit contribution of the customer. Now in the examples that we're going to look at, we're going to think of revenue coming in every period which we'll just define by a year but this might not be the right amount of time for every business that you look at. You might want to look at customer lifetime value on a monthly basis or a daily basis. It really depends on the context of your specific business. But in our examples, we're going to focus on years. Now let's just think of the process of acquiring an individual customer in terms of the decision making funnel that we're all fairly familiar with and I'm drawing a vertical line. We've also discussed these concepts in one of the other related lectures, or related sessions that we do together. We're going to distinguish between the period of which we're acquiring the customer and occurring that cost, the marketing that we have to do to make somebody aware of our business. The marketing that we have to do to nudge them a little bit in their choice process to be favorable towards engaging in a relationship with us. And then we're going to separate that from once the relationship has started, and the revenue that comes in. Hopefully, the revenue that's also recurring over time. In the diagram on the slide, what we're going to do for the next few notes is focus on everything that's there on the right-hand side. On the slides that are to follow, we're going to go through and do some calculations. Now the calculations have a little bit of a mechanical feel. It's always very important to us for us to step back and ask the question, why are we doing this and what might the implications be? Let's imagine as go through and we do the customer value calculations, some customers turn out to have very, very high value, other customers may have a much lower value. Now obviously, the amount of effort we would spend on a customer might be commensurate with the value that they're generating. In addition, some customers may be active, so if I'm United Airlines, I may see a customer who's flying with me every month. I may then see a period of inactivity where the customer has not purchased a ticket for six months. In certain kinds of businesses it's quite difficult to determine whether or not a customer is active or inactive. In fact, there are whole series of models that I'll talk about at the end that we could employ to determine the probability that a particular customer is still with us. Now, of course, if we have a business that's more of a subscription service, then it's much more obvious whether the customer is active versus inactive. If I discontinue my cell phone service with AT&T, they'll certainly know that. If I cancel my subscription to The Economist, The Economist will also know that but there are many situations in which the status of the customer is not always clear. In addition to calculating customer lifetime value, we also need a method or a model or a way of thinking about the chance that a customer's, in fact, active. If we combine those two pieces of information, the value of the customer with the status, we yield some fairly interesting and intuitive implications. Customers who are very high value and are currently active, we want to do everything we can to foster and encourage loyalty from among those customers. Customers who are relatively low value but actually fairly active, still part of our system, transacting with us, clearly we want to increase their average transaction size or the number of products and services that they buy from us. Customers who are high value who are either inactive or maybe customers belonging to competitors, we may want to focus on those individuals with very specific and targeted promotions to switch them into our business. Of course, in the final quarter, customers who are relatively low value and who appear to have declined in the usage, or be somewhat inactive, we may really just minimize the effort that we focus on those particular groups. One thing I can't emphasize strongly enough is that customer lifetime value is not only a calculation but it's a very important conceptual way of thinking about what customers do for a business and why, in fact, we should cultivate customers. When we think about customers as assets rather than just someone with whom we have a transaction, it really changes the way we do the entire business. I'll give a shout out to a friend of mine, Sir Neal Gupta at the Harvard Business School, wrote a very interesting book about ten years ago called Customers as Assets. Really emphasizing the fact that it's not just the calculation but it's the mindset that we have that becomes very critical if we want to have a successful entrepreneurial venture. What are some of the practical things we might do with a customer lifetime value calculation? Most fundamentally, we're able to answer the question, is it worth the effort and at what price should we attempt to acquire a particular customer? We can also ask and answer the question, what is it costing us to retain a particular customer? We can also, if we have the CLV data, we see the distribution, from very low value customers to high value customers, or even customers for whom the CLV might start to look negative. We might want to get rid of those customers. Finally, if we took the value of all of the individual customers put together, this would give us an estimate of the value of the enterprise, and a way of potentially valuing companies. And mergers and acquisitions and so on. So, these are just some of the many practical decisions that you as an entrepreneur might want to take as a result of doing the calculation. So with that in mind, let's now start to step through the most basic calculation, and then I'll give you some other references for those of you who like to do some homework on some more sophisticated approaches but again the most important thing here is the concept. And so, in order to embrace the customer lifetime value philosophy and start to do the calculations, you're going to need some data. And the bare minimum data has the following four components. So first of all, you will need to know what kind of a margin you're getting from a particular individual customer. So this is just really the revenue that you're getting and minus any variable costs you have of servicing that customer. You also need to have some estimate of the chance that the customer will be retained. What's the probability that given the option, the customer renews a contract with you, or the customer remains a customer in a certain time period if it's a non-contractual business? I'll talk a little bit about where these numbers can come from also. The third thing that you'll need to know is of course, the discount rate, or the interest rate that you might like to apply. Obviously, money that I get from a customer in the future is not as value as the money that I'm getting today, and you may need to apply, or you will need to apply, an appropriate discount rate. We'll take the easy way out in our calculations and we'll just use 10%. And then finally, you need to think about what's the right period over which customer lifetime value should be calculated. So, a recent consulting project I was doing for a friend of mine who graduated here at the Warden School was focused on a business where I calculated the CLV every quarter. So these were sellers in a home shopping network and a holding trunk shows on their home, and these sellers would have a trunk show every season to sell apparel. So, I basically hit four observations per year, the time period was the quarter. For your business, the time period might be the week, or it might be the year, but it's a very important decision to think about choosing the right time period over which we're going to do the calculation. So, these are the four things that we need to know. Some of them are more difficult to come up with than others. Probably, the research would say the most challenging thing to figure out is what is the right retention rate? Now, one way we can get at the retention rate is the following. Imagine we begin our business. Let's make it easy, January 1st, we have 100 customers that start with our business. We then wait a particular of time let's say, in this example, one year and we see from those initial 100 customers how many customers are still remaining with the business. If 80 customers are still in the business, one simple estimate of the retention rate is 0.8 or 80%. So looking at a core order of customers that entered our business at the same time, waiting a certain amount of time to then see how many of those are left. That's one way that we can estimate the retention rate. So with this is mind, let's now go forward and do some calculations. So in this diagram, we're going to visualize the process of revenue coming in from a particular customer and I encourage you perhaps even to draw a diagram of this sort for your own customers in your own business. So let's assume a fairly simply example, that the business that we're running is something like a mobile phone service and that every year, the customer is spending $250 with us in terms of the net margin. So the revenue may be a little bit higher minus some costs that we have of variable costs maintaining that customer, we're getting $250 at margin. Now, in addition, that cost is $400 in the beginning to acquire the customer. Perhaps we had to give them a free cellular phone or something like that. So, with this information in hand, over five periods, so that's the lifetime that we're assuming that the customer is going to live for. We can start to execute on the calculation of customer lifetime value. So, let's go through this and do it together. And let's do it in a very, very simple sense, so we'll assume that there's no customer attrition. Wow, wouldn't that be a great business. The customer never leaves us, stays with us with probability one every period for five years. Secondly, let's assume that we're not going to do any discounting in the calculation, that the money that we receive from the customer in year five is just as valuable as the money that we receive in year one. Of course our finance colleagues would not like us to make such an assumption, so we'll modify that shortly. But le's go ahead and do the calculation. So, in this case, the customer lifetime value is just the five increments of $250 at margin, $1250 minus the acquisition cost of $400 which leaves a net customer lifetime value of $850. So the reason I'm showing you this example is as we start to layer in other things like attrition and discount rate, what you'll notice is that the customer's lifetime value the number starts to decline. And it starts to decline quite dramatically so for those of you who are students of business history which I hope many of you are, if you think about things like the Internet boom and bust ,there are a lot of companies during the 1.0, maybe still even today, that were grossly over valued or grossly optimistic about what the value of the enterprise was because they made some rather heroic assumptions about the values of their customers. Either the margin that they were getting every period or the chance that the customers would actually stay with the business and be retained, so. That's one important point that I want you to look for, even in this very, very simple example. Just to see how dramatically the value is going to change as we start to relax those assumptions. So, let's go ahead and do that. And so, now let's turn again to our familiar diagram where we have the flow of margin coming in from the customer every period, but this time around we're going to add one additional assumption, which is that customers, unfortunately, might decide to leave us. There's going to be some probability of attrition that's going to cause the customer lifetime value number that we ultimately calculate to go down. Now, this is something that actually is quite subtle and quite important. And for those of you who are students of business history, which I hope many of you are, if you think back to the boom and bust of things like Web 1.0, part of the reason that happened is that people made gross overstatements or grossly misstated assumptions about what the value of different businesses were. Based on the underlying customer transactions and the underlying customer asset in particular, they either assume that the margin that was going to come in from the customer was much higher than what it turned out to be because the customers couldn't be monetized in the way people thought. Or the entrepreneurs though that the chance of retaining customers was much higher than it actually was, because they discounted the fact that a competitor could steal a customer, or a customer might just leave of natural causes, doesn't really find the value in the service that we, as the entrepreneurs would like to believe that they have. And what you'll notice here, this is a very important element of the customer lifetime value calculation is that even a fairly small degradation in the retention rate can have a dramatic effect on the number that ultimately get's calculated. So, lets go through and do this, and then the example, we're going to assume a retention rate of 80%. Now that's actually a pretty good number. So, I think we'd be fairly pleased as your colleagues and instructors in this course if there was a 80%chance from week to week that you kept engaged with the class and applying the materials. What we'll see however, is even with an 80% retention rate, there's going to be quite a dramatic reduction in the value that we got from the model previously, the value previously, was a total of $850. Where we would assume customers would always stay with us. For five years, and we also assume no time value of money, we'll get to that one in a second. So, what we're also going to do here, is you notice in the calculation is that at the end of every period, the customer can either leave with probability 0.2, or stay with probability 0.8, and this process gets repeated until the end of the 5th period. Now, those of you who're sort of looking at the slide, there are those of you have studied a little bit of statistics in mathematics, might say, hang on a minute, there's quite a simplifying assumption here. You've assumed that the probability that the customer is there by the end of year 2 is 0.8, at the end of year 3 is just 0.8 squared, at the end of year 4 is 0.8 cubed. So implicitly I've assumed that the retention rates are independent from one year to the next. Which seems like a pretty unrealistic, shall we say, assumption. But, let's go back to where we started our session today, with the notion that all models are wrong. Thank you. But some are useful. So clearly it's a bit of an unrealistic assumption but it's made not only just for mathematical convenience but also because it may not in fact be such a bad assumption after all. So imagine that my colleague Stephanie is a customer of AT&T. The longer she stays with AT&T we might argue that her retention rates going up, she likes the service, she's getting used to it, she really doesn't want to go anywhere else. At the same time, there could be other factors causing her retention rate to be potentially going down. Competitors like Verizon chasing after her. She's just getting a little bit tired of the service, and so on. So, as my colleague and friend, Sunil Gupta, at Harvard Business School might argue, there are forces pushing retention rates up, there are forces pushing them down. So assuming that they're roughly constant, is not such a bad thing to do. Now of course, getting the right number in the first place by doing as I said before, looking at a cohort that entered at the same time period and asking how many remained after a certain point through time to calculate the retention rate that's probably the most critical thing of all. So let's go through now and do the numbers. I'm now just computing the expected contribution which is the margin modified by the retention rate. So I've done that there in the slide. We add all those numbers up. We get a net value now of $840, not $1250, and of course we have to subtract out the initial acquisition cost, which I just assumed to be $400. So now we have a customer lifetime value of $440. Wow, that's a big drop from $850. So you can imagine why this retention rate is just so critical. In fact, the academic research suggests of all the four elements in the mathematical formula, the one that produces the most leverage or impact over the final number that you calculate is in fact the retention rate so always be wary of somebody who's making an assumption about retention. That's just too heroic or too unrealistic. Because if they're doing that they're going to be grossly over estimating the value of individual customers so as entrepenuers we always want to err on the side of caution and off course to sensitivity. Let's see what the result looks like if we assume 90%, or 80%, or 70%, and so on. So we now have completed the second calculation, let's continue on, and do a third one. And so now on the screen in front of us we have the familiar flow diagram of, margin coming in every period from the customer for five periods. We also have on top of that the retention factor, which we assumed in the beginning that they are with probability one. And then in every period they have a chance of 80% of staying with us 20% of leaving, and on top of that I've computed the expected contribution, $250 dollars, $200 dollars, $160 and so on down. Now in this case we are just going to have one final component, which is something that our finance colleagues, or your CFO at your start up, or your venture would be really concerned about, which is of course the time value of money. So we're retaining the assumption that customers will be with us, with probability 80%. So they'll churn or we'll lose them with probability 0.2. In addition let's just assume that the value of money that comes in is not as valuable in the future as it is in the present. That's a pretty good assumption. And let's have a discount rate of 10% just to make the math easy and now let's go through and do that calculation so the discount factor that's applied to the first piece of money that comes in at the end of year one is just one divided one plus the discount factor which is 10%. At the end of two years that's just again, one, divided by one plus the discount rate 10%, and then we square the whole thing, and then we cube and so on. What we see is we get now a value of $664. We subtract out the $400 to acquire the customer. Wow, almost nothing left. We're down to $264. So just think about this for a moment. We've done a very, very simple and very stripped down example. I really hope that you're going through and thinking about your own customers in applying the same kind of logic and we started with a customer lifetime value of $850. We're now down to a number that's roughly about a third of that even with having what seems to be a pretty good retention rate. And also applying a fairly modest discount rate of about 10% to the time value of money. So, we can see that when one starts to really build in some more realistic assumptions about whether or not customers will be retained and the value of revenue that we might be getting in the future. When you do those two things, you end up with a dramatically lower number. That's really the bottom line here. So, with this in mind, I'm now going to give you a couple of quick and dirty simple ways to do this calculation without going through and doing the adding up from every single period. And also talk about some extensions that I would really love for some of you to do for your homework, if you really need to dig a little bit deeper, and go into a more sophisticated approach. So now let me just give you one other very simple way, just in a very rough sense, to calculate customer lifetime value. In the examples that we just went through, remember that the customer was sticking around for five periods, five years in our example. We only added up the data for a period of five. Let's imagine now however that the customer keeps on going, period six, period seven, period eight, but of course in every period, the chance that they stick around is declining, declining, so it would be 0.8 to the five, 0.8 to the six and so on. So, if we do then, we make that assumption that the customer is, in some sense, almost going to be around forever, but with the declining chance every period. So, in this case the customer lifetime value is simply the return, or the margin divided by the churn rate, the churn rate is 0.2 or 1 minus the retention rate. That gives us 1,250. Subtract off the $400 acquisition cost. If we wanted to then modify the formula again, assuming that the customer is not there for five periods but six, seven, eight, keeps on going. It's just going to be the margin $250 divided by the churn rate, 0.2 + the discount rate of 0.1. So these are some really simple heuristics that you can use to apply to to different customers to see which ones are more valuable, which ones are less so. And of course, in all of these cases, whether you do the summing up. Over a certain number of periods, or whether you use the direct formula assuming that the customer is always going to be around. Always be critical as entrepreneurs about the assumption around retention. And always try to do some experimentation or some sensitivity. And now as we conclude this lecture, there is three things I would like you to think about. And in fact to do as a homework exercise as we always do at the end of our sessions together or the end of our time together. So, let's try and take the customer lifetime value concept and put it into action. So first and foremost what I'd like you to do is to look at your own business as an entrepreneur, and try and make those four decisions. First of all, try to figure out what you think the margin is that you're getting from every customer, number one. Number two related to that, think about what is the right time period over which you should be doing the calculation. Number three, and probably most challenging, try to develop an estimate of the probability that the customer is being retained. And then number four, talk to your finance people, your CFO, and think about what's a reasonable way to discount the revenue stream that you're getting from the customers. So try and put those four components together, and then start to apply those calculations to the individual customers that you have at your particular business. Number two, if you'd like to do it for a business that's already out there, that I think some of you, at least in the United States might be familiar with, a business that's challenging Gillette. It's called Dollar Shave Club. I'd like you to think what the customer lifetime value might be for a customer who entered Dollar Shave Club. Now I can assure you that a company like Dollar Shave Club is most certainly thinking about customer lifetime value as a calculation. So I'd like you to go through the exercise of trying to apply it to this particular business. As you do that, also think about which of the three razors in front of you might be the one that's most likely to be purchased by most customers. I'll give you a clue. There's something called the compromise effect that suggests that customers might gravitate towards buying the $6 option as opposed to the 1 or the 9. And then finally, for some of you out there who have a strong background in mathematics, or have a penchant to do things that are a little bit more sophisticated, you've got the basic framework in mind, that's good. All models are wrong, but some are useful. You've got the basic elements and the philosophy, which is critical. I would really encourage you to look up my friend and colleague here at the Wharton School, Peter Fader. F-A-D-E-R, Pete Fader. He's probably one of the world's leading experts in the study of customer lifetime value with more sophisticated mathematical modeling, and Pete has kindly made most of the software available. Many of his algorithms can also be implemented in standard software tools, such as even Microsoft Excel. So those are the three pieces of homework. Until next time and our next lecture together, I really hope that you go out and that you apply these principles, and I wish you every success in isolating, developing, and cultivating successful relationships with your customers.