Well, thanks for joining me for our journey through the world of predictive analytics and I know I can't do justice to the whole world of predictive analytics. It's a big world and it's changing over time, but I just wanna give you some of the highlights, some of the words, some of the concepts, some of the patterns. And if you think about it, I've been doing a lot of this during [LAUGH] our time together. Talking about the past and how it's gonna give us some insight about the future. And actually, if you think about it right now, we're exactly half way through our customer analytics course. So, I think it's a good time to look back at everything we've covered so far and give you a little bit of a roadmap of what's to follow. So what have we done? Well, my colleague Rigo Angar has spent a whole lot of time talking about data. All the different kinds of data that's available. The kinds of descriptive analysis that you can do with that data. The kinds of insights that you can draw immediately from the data, basically, as soon as you get it in. And that's great and for a lot of businesses, that's gonna address a lot of the critical questions that they have. And in many cases, you can stop right there. But in many cases, the questions that you're asking, the decisions that you need to make are gonna be about the future and that's where predictive analytics comes in. That's where models come in. So what do we mean by a model? These days, we throw that word around a lot and we don't even think about what it literally means. It's just something we use to kind of do stuff with data and maybe make some predictions, but let's step back and think of what model. Think about a model airplane or a model ship, what are you trying to do when you're building a model? Well, you're looking at something that's very real, very complex. Something that you can't possibly capture every aspect of it, but you just wanna capture the key aspects of it. So that when we look at this thing, it's doing a pretty good job of representing something that is really complex and that's why we're building a model. Again, it's not just a means towards an end, it's often an end unto itself is trying to capture the reality and you know what? Customers making decisions over time, firms interacting with them. Customers interacting with each other is a very, very complex phenomenon. So one of the reasons why we build the models is to try to really do justice to that behavior. We don't necessarily wanna capture every nuance of it, we wanna capture the important stuff. Cuz once we capture the important stuff, it's gonna give us that confidence to make statements about what we think will happen in the next period or beyond and that's been the focus of what this particular session has been. So try not to lay out this dichotomy between models, regression models, data mining, all the things that guru spoke about. That are really, really effective, if we wanna make statements about who's gonna do what in the next period? And again, for many, many decisions, that's all you need. Those kinds of models, that kind of thinking has really dominated marketing analytics for a long time and I think will continue to do so. But today, as we get richer data, greater computational capabilities. And more importantly, an imperative for management that we need to look beyond just the next period. We need to start thinking about different kinds of models. Models that are better suited for the long run. Maybe we're even sacrificing something in the short run as we build these things, but we wanna really capture what's worth capturing. Spent a lot of time talking about it with you. And again, I tried to downplay the models. I'm proud of them, happy to give some metaphor to have you understand what these models are all about, but it's really the insights that arise from them more than the models themselves. Now sometimes when I talk about models and I talk about predictions and I'll show some of the kinds of pictures that I've shown you here and I say, hey, look how good these forecasts are. And a lot of people will look at them and say, yeah, they're nice, but so what? And I hear that question and it's kind of deflating. It's like come on isn't the model good enough for you? And the real answer is in many cases, no it's not. We need to understand what decisions to make on the basis of those predictions and I haven't really told you anything about that. I have just told you about what the patterns are likely to look like, if people keep flipping their coins and so on. What the future might look like? But as managers, it's our job to change the future or at least to leverage what we understand and project about the future. And that's where we're gonna start to move from predictive analytics to prescriptive analytics. Having some idea of what the true underlying process is and what the future might look like. How do we layer on top of that some optimization? So we start to understand, which message do we send to which customer at which time. And lots and lots of other prescriptive questions that manager are asking and academics like myself. Or more importantly, my colleague Ron Berman are spending their lives trying to answer. And after Ron is done talking about prescriptive analytics, then we'll bring in my colleague, Eric Bradlow to tie a ribbon around the whole thing by giving a bunch of nice case studies and examples of descriptive, predictive and prescriptive in action.