So I want to talk to you now about a particular metric called customer lifetime value. Maybe a metric that you've never heard of before, but it is a staple metric. When I mean staple, I mean very important metric in the world of direct marketing. And when that is measured it often interacts with pricing decisions. For a direct marketing company certainly they are very interested in prices. Think about Amazon, obviously they're interested in what prices are shown and what products people are buying. But even beyond the price for individual products, many people buy from Amazon repeatedly. Maybe you're an Amazon Prime member, right? And you buy from them a lot of times. They're interested on how their prices translate into the value of the relationship they have with you over time, right? Because that relationship is very valuable to them and the prices that they set for their products, particularly the ones that you're interested in, may very well impact the length of your relationship with them, how much you're willing to spend with them, etc. In many ways, that is the life blood of their business. So that's why we're going to go into customer lifetime value. A lot, I would say the vast majority of direct marketing companies do calculate this as part of the analysis of their business. So what it is, in short, is it computes the dollar value of an individual customer relationship. So if you are acquired as a customer, if Ron Wilcox comes and orders from Amazon, how valuable over time is Ron Wilcox likely to be? And they want to know that. So it's kind of backward looking. They can take individual customer data and say how valuable is that person been in the past? And then use that to project forward. How valuable are they likely to be in the future? And as I said here, it really is a staple metric of direct marketing. Which has gotten a lot more important in the age of electronic commerce. So many of us purchase things online. And so these kinds of relationships can be valued. So CLV, or Customer Lifetime Value, is used for a lot of different things. It's used to understand the financial implications of various prices. And of course that's what I'm going to focus in on in the video but it has a lot of other uses too to determine how much to spend to acquire a customer. If they know that their average customer lifetime value is, let's say, $100, they're probably not going to want to spend more than $100 trying to attract a new customer. So they kind of do a cost benefit of different methods that they might use to acquire customers, what they cost, and whether that's worth it in light of their customer lifetime value metrics. So to determine how aggressively to spend to retain a particular customer, so they look at your particular customer lifetime value projections and decide how much money, are they going to offer you reward programs, etc.? Are they are going to spend money making you feel good about your relationship? And that, it depends on how valuable they think you are. And finally, this is less of a marketing thing than it is finance idea. But for these direct marketing companies it's used to value a company. So if a company is going and trying to get something like venture capital. And don't worry if you don't know what venture capital is. But if you're trying to raise money in markets. One of the ways to value a direct marketing company is to look at all of their customers, look at what their customer lifetime values are, kind of add that up and say, well that's a really big asset for this company. The value of all those relationships they have with customers. So it is very similar mathematically to what you just looked at which is net present value. Typically net present value is used to evaluate financial assets, investments and companies. And customer life time value is really looking at those individual relationships that companies have. So in a broad sense a customer life time value is the expected NPV. It's the expected net present value of the cash flows from an individual customer. That is really the value of that customer relationship. So I want to do a very simple example to kind of set your intuition for this. So imagine a situation, and I'm going to define some terminology, your contribution per period from active customers is called GP. That's just the sales price minus the variable cost. It's basically a margin. And you know that in any given period, let's say Ron Wilcox, Amazon knows that in any given month, this is the average margin that I tend to make on Ron Wilcox or the Wilcox household. So that's GP. Then I've got retention spending per period. That's money that a company might spend to make sure I remain a customer with them. Then I have something called a retention rate. What does that mean? It means if I'm a customer today, let's say with Amazon, how likely is it that the month after that I'm still going to be a customer with Amazon? And how likely it is the month after that and the month after that, etc.? So we're trying to predict the probability that any individual customer continues to order from a given service provider or a retailer like Amazon. And then d is the discount rate per period. And that's exactly what you saw on the MPV calculation, right? That's specific to an individual company and that's the same thing that we're going to use in this particular calculation. So let's do this simple example. When I say t = 0 here, I mean at the time that you acquire the individual customer. So t = 0. We have t = 1, t = 2, t = 3. These are just time periods. They could be months. They could be years. It really depends on your particular business situation. We will talk about how to select time periods in the retail relay case. But for right now just think of those as time periods. Then we have GP, remember that's the amount of money that you're making on an individual customer in any different period. Then you subtract from that that retention spend. So any money that you use to try to keep a customer being a customer has to be taken out of the gross margin that you make on the products that they buy, right? So you have to subtract that out. And this becomes the totality including marketing spend that you make on a customer in any given period. Okay, so understand that. Now what's right below it? It's the same thing but it's multiplied by r, now what is that r? That is the retention rate and what this is trying to get at is the following, I make, let's say I make a certain amount of money when I acquire a customer. Now I'm going to make some more money if they buy from me again. However there's some probability that they're never going to buy from me again. Sometimes that's called attrition, right? And then the opposite of that attrition is what we're measuring here. Which is that retention rate. So let's say if someone buys from Amazon for the first time, what's the probability that they're going to buy the next month? And let's just say that's 90%. Then what you have to do, if you're Amazon, is say, how valuable are they to me? Okay, they bought from me today, I get to count that money, all of that money. But I only get to count 90% of the money that they might spend next month. Because there's some chance that next month they just won't be there. And then the month after that what do we have to do? Well we have to square that retention rate. Because maybe only 90% of the people that ordered the second time will also order the third time. So this will drop that to 81. And then three months later, we'd have to take that 81 and do what? We'll multiply that by 0.9 again. And that's saying, three months down the road are they likely to still be my customer? What's that probability? That's about 0.73. And Amazon has to take those probabilities into account, just like really you think about a net present value calculation. In order to discount those future cash flows because they realize they simply just might not be there.
