Let me spend the next five or ten minutes or so telling you about some radically new data sets in marketing. Cuz what I've talked to you about for the first you know 20, 30 minutes or so here, are kind of traditional TV data, online data, etc. Let me talk to you about some radically new data sets in marketing, and let me encourage everyone listening to this to think about how you might be able to use these kinds of data sets for better decision-making. So, one of the favorite studies that I've talked about,and I'll talk about again in the future is, imagine you could track the following data. Imagine you could take data. Let's take a supermarket and a store. Imagine you could collect data on people's intentions. So what did they intend to buy prior to going into the store? Now why would you wanna know this? Well, then you could compare what they intended to buy with what they actually bought and then you could see how much unplanned purchasing happens. Imagine you could collect shopper path data and a radio-frequence identification data. In other words you could track where the customer is in the store, and that's really valuable. Cuz again, let me go back to the example I gave before. Let's imagine you're a manufacturer of children's cereal, and the reason why your sales are low isn't because your product isn't liked, but because no one goes to the part of the store where your product is located. Well, you can change that problem by buying different shelf space and moving your product throughout the store. Field of vision, imagine you can actually have eye-tracking data; where you can actually measure what products people are looking at. Cuz imagine for example, you're a soda, you make soda and your soda's sitting there on the shelf but nobody looks at it. Well if they don't look at it they can't buy it. And last is purchase data. So the exciting part about analytics today is again, imagine having all of these data sets at the individual customer level and linked between them. So let me give you an example of a project that I worked on about five years ago, along with my colleague Pete Fader and a former doctoral student of ours, Sam Wi. This was a data set called Path Tracker, where we actually tracked people moving around stores. And you can see a little supermarket cart sitting there. And you can see these red concentric circles moving out from there, that's meant to represent a silent ping. You can't hear it. But it's a silent ping that's being sent out to the different scanners around the store, that allows you to triangulate, within a foot, where the person is located throughout the store. And so, this is the kind of data sets that are available today in application. Now five years ago when we worked on this project, this was done by attaching little devices to the bottom of supermarket carts. That was the best technology. That was the golden age of marketing in 2009, 2010. Now five years later, most of us, if not all of us have cell phones in their pocket, and you should know, your cell phone company knows your geospatial location at any point in time and they can monetize that value of the geospatial location. So let's imagine you have a data set where you now not only know what people buy at the check out counter, remember going back a few slides that was 1980s data, the scanner data, but now imagine I could know where you were physically in the store. And by the way, this isn't just for physical in store data. This could be applied to website data. I not only know what websites you went to, but the time you went to those websites, how long you spent on those websites. Just think about this as being spacial data. So, imagine a world where customers could be tracked inside the store. So, this solid line you're seeing now in front of you, that represents a customer's path, one customer's path throughout the store. The little black squares you see represent their physical location every five seconds. And the red squares represent the products they purchased at various locations in the store. And notice like most stores, you pick up your cart on the right. You go around counter-clockwise, and then you check out, which is typically at the end, at the bottom, center of the store. So this would be a typical path of someone throughout the store. How do shoppers move throughout the store?, might be one question you're interested in answering. I remember as a child, this was well before I was born, but there was a famous show on, Leave it to Beaver, which talked about a show that was taking place in the 1950s was meant to be an all American family, and they always would show the person going up and down the aisles of the supermarket. Up-down, up-down, up-down, up-down. It actually turns out, and actually it's through analytics do we know this today, it turns out that's actually not how people move throughout the store. People do not go up and down the aisles, as a matter of fact if you go in one side of an aisle and go out the other side that's called a traverse. If you go in one side of an aisle, out the other side and back around the end cap that's called a zig zag, where you go down let's say aisle five around and then up aisle six, that's called a zig zag. It turns out most customers make only one traverse, meaning they only go down an aisle once. It turns out most people do what are called excursions, they go inside an aisle and then out the same side they came in on. Most people do not go up and down the aisles. And we now know this because of analytics at the customer level. So this is not how people move. What I'm now showing you a picture of is a store plan-o-gram where you can see where most people move. And notice in the center of the aisles, you can see the center here, nobody ever goes to the center of the aisles. Most of the time people only go to about a third of the way and then they come back out the same side. Now, why is that valuable? Well let me go back to my emergency room doctor example. I'm a manufacturer of a product, I'm selling it inside a supermarket, my sales aren't what I would like them to be. And now I start to wonder, huh, I wonder why? I wonder if it's because people don't like my product, or I wonder if it's cuz when people enter the store, they don't ever visit my product. Well customer analytics now allows you to do this. What you can see from this store level layout is that people just don't go to the middle aisles that often. They pretty much only do on what's called the racetrack, or the outer ring of the store. The almost never do in the interior aisles, and the basic idea is that's bad shelf space, so you should not be paying for shelf space where customers do not go. And as a matter of fact you can show that, and we've analyzed data from thousands and thousands of different stores. This may come as a surprise to you. In physical stores, super markets, clothing stores, sporting goods stores, etc., most customers, on any given visit, only cover about 25% of the store. That's remarkable, so 75% of the store on any visit they do not go to. And that's an opportunity, it would be valuable to know that. What I'm now showing you here is what's called a heatmap of the store. Red is hot, green is not as hot, but still pretty much hot, yellow is less hot, and then blue is cold. Notice where's really really cold, the middle of the aisles, cold. The outer ring of the store, hot, the race track, which is why you see firms paying lots of money for the outer ring of the store, they want to be on the end cap, they want to be on the end aisles, and by the way the only way you know this. No. Could you actually have humans sitting in the store watching where people are going and recording? You could. Very expensive. Can't be scaled. Measured with a lot of error. Could you put cameras in the store, which has been done for years. You could, but it's very obtrusive. Now all the sudden people know they're being watched and maybe they behave differently. Plus you have to take that video data and you have to make it quantifiable. What customer level analytics has done is, I can now track customer by customer where they go inside my physical store, and I can actually tie that to their purchasing data, so now I can target them even better with products and services.