Hey, it's Peter Fader again. It's been a while since we put together the content for my predictive analytics component of the customer analytics program, and I'm glad to see how many people have found it interesting. That you've enjoyed the buy till you die models, the BG BB in particular, little case study involving donations to a public radio station and so on. And I've been just delighted by all the questions. The feedback. The requests for more that we've gotten since then. So I'm happy to comply. So I have a bunch of new an old content for you to help take some of those models and put them into a proper context. So first I'd like to give you the historical story that led to that model, as well as some of the amazing things that have happened, not only since creating the model, but even since putting out the earlier content that you've seen in this program. So I've been developing these kinds of models for a long, long time, 30 plus years as a professor at the Wharton School at the University of Pennsylvania. And I really believe in this stuff, our ability to predict how many customers are going to require? How long are they going to stay with us? How many transactions they're going to make over that horizon? How much stuff they're going to buy, and how much they're going to spend when they make those transactions? Our models for each one of those behaviors works really, really well. And so for years and years I have been going to companies and saying here, please use my models doing things like this program and coming up with other cases and spreadsheets and technical notes, other kinds of videos and so on. You please go use my models, they work really well and they have really important implications for you. Now, all of you have had some appreciation for the models. That's why you're still watching, but there's a lot of folks out there who have different kinds of skepticism, they're, that's very academic. Where in the real world over here? Or they say that might be fine for other kinds of companies, but our company is different. Although they come up with a litany of excuses not to pay attention to the models. So in 2014, along with some of my students, I took a lot of these models and created a company around them. The company was called Zodiac. And it was basically taking exactly the same model that you've seen me talk about already. We added just a few bells and whistles to it, but even the basic model you've seen gets you pretty far. And of course I'll be happy to talk about some of those bells and whistles as well. And we started the company, Zodiac. We got some funding from some big VCs, we hired a CEO. We did all the real corporate stuff on it, and it was amazing. It was incredible how many companies were out there that either knew that they wanted and needed lifetime value. Or they've been reading some of those books and decided that it was the natural next step. Or they didn't even know that they needed lifetime value or that they wanted to be customer-centric. But they had specific marketing tactical questions that couldn't be answered in a better way than the kinds of models that we've discussed. So Zodiac was a very nice success. We worked with a bunch of retailers. We worked with a bunch of pharmaceutical companies, travel and hospitality. I worked with a bunch of telecommunications firms, a wide variety. Gaming companies, just a wonderful variety of different firms. In many cases it was all about acquisition. Which kinds of customers should we acquire? How much should we be willing to pay in order to get them? Some of it was about retention. So who are the kinds of customers that we should be willing to give that discount to, in order to keep them around a little bit longer? So it was around customer development. So what's the best offer that we should make to certain kinds of customers to get them to buy more to get them to be a little bit more valuable with us? So wide variety of different use cases, most of which had a marketing orientation to them. Let me just tell you a little bit more about it. So you can see over here, this is just basically a screenshot from Zodiac's homepage. And I'm watching your eyes right now. I'm looking to see how you're looking at this slide. And yep, I can see you're doing it. Yeah, there's that big circle in the middle there, and you're looking at that distribution of lifetime value and you're starting to ask some questions about it. You're saying, well, that's kind of a weird looking distribution, isn't it? You expect everything to be normally distributed, but as we discussed before, there's nothing normal about customer behavior. At least, there's nothing normal about the distribution of customer behavior in particular when we're talking about lifetime value. And you'll notice that there's this big pile of customers to the left. Most of our customers aren't that valuable. They are even not going to stay with us a long time. They're not going to buy that often, when they do, they're not going to spend a lot. So most of our customers are hey, so, so. Doesn't mean we necessarily want to fire them because it is, after all, most of our customers. But we do want to be careful about treating them a little bit differently than check it out. All these customers over there on the right side of the picture. See that spike on the right. And again, I see how your eyes go right to it. And you start to ask questions like, what's up with those customers? How are they different than the customers over to the left? Are there different demographics? Do they use our products differently? Did we acquire them in some different way? What's different about them? And therefore what is it that we as a company can do in order to create more value with them and extract it from them? The idea customer-centricity is to say that not all customers are created equal, and we need to be smart about which customers we are going to be centered around. Again, now that we're going to ignore the other customers, but we're going to focus disproportionately on those customers on the right to figure out what makes them different. To figure out what other kinds of products and services we should develop, deliver, partner with other firms in order to enhance their value and make them want to stay with us longer and spend more, and to use it to drive acquisition decisions. So again, what makes them different? How do we find more customers like them? And so this is not just conceptual. We're doing this at full commercial scale. Working with a variety of firms, some b to c, some b to b, domestic international, big and small, to help them find those customers. To help them leverage those customer assets. Doing things like you can see in this slide over here. And I won't say what company this is from and in some sense it doesn't matter. So all the time, companies are doing different kinds of acquisition experiments. In this case it was involving Facebook look alikes. So the way it works is you go to Facebook and say hey Facebook, find me customers like. And then you're given some kind of profile. And for so many companies, let's say we're working with some kind of, I don't know, digitally native women's accessory company. We say hey Facebook, find us millennials. We need to have millennials. We know that our focal customers are millennials. Except for the fact that that's often wrong. So for instance, we actually did work with the digitally native women's accessory company, who knew that their focal customers were millennials. Then we would wave our magic CLV wand. We would find the value of each and every customer out there, we'd find these surprising insight. So, for instance, that big bar on the right over there, there might be very few millennials there at all. That might be baby boomers, and other kinds of customers. In fact, there would be some heterogeneity, and not just among the customer base as a whole, we kind of understand that, but even with that green bar, ok, even with those focal customers there's going to be some differences there. But the differences among those focal customers are going to be different kinds of differences and others are a little bit narrower, a little bit different than the customer base as a whole. So we go to Facebook and say, hey, Facebook find us a blend of customers who match the mix of characteristics. Whether it is demographics. Whether it is media habits. Whether it is the kinds of things that they post, the kinds of people they are connected with on Facebook. Find us a group of people who are similar to these folks in the green bar over there. And if you look carefully at the slide, you'll see how amazing it is. I've highlighted just the Zodiac line here. In other words, that's the green bar, and saying help us find customers like that. And basically to make it real brief and looking for those just right customers, number one, we find a group of customers who have higher conversion rates and there's more of them are willing to make those purchases. Number two, they spend more money but number three and this is really important. Well, everyone else is chasing after the millenials, we're going after baby boomers and some kind of mix of customers who weren't immediately obvious, is actually pretty cheap to acquire them. because while all the other companies are running in one direction, because it's what their ad agencies tell them to do, we're running in the direction that the data is telling us to go. And so basically we're finding better customers cheaper. So if you look at the ROAS, return on advertise spend, it's like printing money. So we work with a lot of different companies. Again, this was just a case where we're doing Facebook look alikes for acquisition purposes. But you can look at this slide over here, I know it's a bit of an eye chart, but that tells you something like that. It tells you about the wide variety of different kinds of use cases and applications that we see for lifetime value. And will talk about some more of them as we go on. But basically the last thing on the Zodiac story. If you notice I've been talking in the Tense about it. What happened to Zodiac? This was my baby. This is my dream come true to take all of this research and make it as accessible as possible to a wide variety of firms able to do it really fast, really efficiently in commercial scale. So what happened? Well, in early 2018, one of our clients came to us and said, we really like this stuff. We want it all. And we said, well, this is great, terrific. We're glad that you're so happy with it. We'll hire some more data scientists and some more customer success managers to make sure that we can meet all of your needs. And this company said no, no no, you don't understand. We want it all and we said, well, you can have it all, all of these. And they said, no, no, we want it all and we're going to make it worth your while. Long story short, in March of 2018, we sold the company to Nike and it was a glorious outcome. And not only because well, first of all it was kind of a dream come true to found a company around my research and see it get that kind of acceptance. But the validation that it provided, remember the skepticism that I referred to before, all of these companies saying, we're different, it doesn't apply to us. All of a sudden, when a company like Nike buys Zodiac, all these other companies are saying, hey, wait a minute, we want some of that too. And of course, the answer was, too late. Nike owns it now, but it was a great outcome in so many ways. An for me professionally and personally, just one of the greatest moments in my career to see that kind of validation, that kind of acceptance of the same kinds of models that you've been learning about here. So it was a very nice chapter, but there's so much more to talk about, so let's take a little break and then will do just that.
