So the last part of the lecture I wanna talk about today are some really cool applications of Advanced Management Marketing Science by Leading Firms. And these are firms that are bringing all five aspects together. They're using better data, better exploratory methods, better predictive methods. They're using better optimization to actually make business decisions that influence the products and services they sell. So let me start with the first one. Many of you may know Kohl's Department Store. It's a very large national chain department store, and kind of the low to medium end department store. And by the way, remember from my earlier slide this isn't Google, Amazon or Facebook. This is a brick and mortar retailer, Kohl's doing the following. They're doing what's called Smartphone Targeting. So for example, they have data on your geospatial location when you walk in the store. And you might say, well how do they have that? Well, if you have your Wi-Fi turned in on your cell phone. Turned on on your cell phone. The minute you walk into the store and you pick up the Wi-Fi network in that store. It knows your geospatial location. So they're using their Wi-Fi network to know your geospatial location. Everyone's cell phone has what's called a fixed IP address, so they can know it's you. They can now link that to what you've done if you've gone on to their website kohls.com. Let's say they know that Eric Bradlow was standing in front of the shoe aisle. They can now actually send me a real time discount for shoes, whether through text or it could even be a phone call. They can actually send me a real time discount cuz I'm standing in front of the shoe aisle. So let's now reveal this in terms of my five forces, if you like. Why can Kohl's do this? They have data that it's me through my phone and the Wi-Fi network. They may have linked this hopefully, to my behavior online and possibly through their data network and online and offline in the store. They know physically where I'm standing. And now, the action they are taking is to send me a targeted or contextual discount given my physical location. This is extremely valuable data. There's the old adage in marketing, it's not just selling the person the right product, it's the right product at the right place at the right time. Kohl's is taking advantage, that's what I'm saying, what's the better time to send Eric Bradlow a men's shoe discount than when he's standing right in front of the men's shoe aisle. It's not valuable 30 minutes before I get to the store, it's not as valuable 30 minutes after, it's valuable right when I'm standing there. So this is an example of a company that's recognizing they can collect better data. They can use that data for decision making, and they're gonna operationalize against it. Here's another one, Netflix. I think so many of us have become huge fans of the content that Netflix is creating. As a matter of fact, for those of you who don't know by 2020, there will be more video content consumed on Netflix than any other provider that include YouTube, that includes any broadcast at nbc.com. Think about it Netflix the US population will be watching more content on Netflix than any other place in the world, that's amazing. Now, most people think well Netflix is getting lucky in creating content. Not so fast. That's not true. So let me tell you what Netflix is doing. Netflix is doing what's called meta tagging data meaning of course, when you log onto Netflix, they know what you watch. This is the ultimate in customer analytics. They can measure customer by customer what it is you're watching. But here's what they're also doing. Every show you watch gets what's called meta tags or if you like attributes or descriptors. So they know if Eric Bradlow watched a police show that takes place in the 1970s in a warm weather city. So imagine having that corpus of data from millions and millions of customers. Well now, rather than saying, what show could we create? Now, imagine the director sitting there saying, I see what the data's telling me. People really like police shows that take place in warm weather cities in the 1970s. Hey, let's create a police show from warm weather in the 1970s. And so what companies like Netflix are doing is they're using data mining and customer analytic methods to create content. And actually this reminds me of a project I worked on ten years ago was how to optimally design ads of using attributes of which ads were successful. And I remember getting a rude awakening. And this is maybe an idea before its time. I remember going to all the different ad agencies and saying, you know what, I know you use art to create ads. I got a scientific way for you to do it. I know how much music you should have in an ad. I know whether you should have dogs in an ad. I know whether you should have kids in an ad. I thought they would embrace me like, Eric Bradlow you're the messiah. Well, they weren't ready for Science and Art. They were only ready for Art. I'm hoping things that what Netflix are doing can bring more Science to the problem of creation of content. Another example, American Express. Obviously one of the big problems American Express faces today is churn modeling. They wanna know who's gonna give up their American Express card, why? Well, one of the drivers that you heard about in the other lectures of this marketing content was the idea of customer lifetime value. I don't think I need to repeat, but I'll repeat briefly Churn is a big part of customer lifetime value. If someone churned through American Express, American Express makes no revenue from them after they've churned. So you say well, what does American Express need analytics for? You apply for an American Express card, you fill out a lot of data. What's the problem? Well, there's no problem except when American Express has found is that your social network data is a very strong predictor of whether or not you're going to Churn. So what American Express is doing which is what many firms are doing today. They're scraping, this is all legal. They're legally scraping data from the World Wide Web of let's say, Eric Bradlow. Like what did I say on Facebook and how many friends do I have? And what photos of my posting on Flickr, and all this other kind of stuff. And they're taking that data and they're adding that as extra variables in predicting whether I'm going to Churn. Like for example, if I posted on Facebook today. Oh man, I'm broke and I just lost my job. That's probably pretty predictive of whether I'm going to Churn or maybe not pay back my American Express card. Probably pretty valuable for American Express to know that, so this brings to bear a lot of issues. Better data, the company can manage and collect that data. They can quantify that data, meaning they're using what are called natural language processing techniques to take the textual data from the stuff I've posted on the web. And they're turning that into numerical data that they can fit into a numerical Churn model. That's remarkable to me how far this has come. And again, not only are companies doing this but you should know this as a customer. When you post stuff on the web, you should know that companies are scraping this. As a matter of fact, it's not just American Express that's doing this. The next person who that's thinking about hiring you is scraping information from you about your social media usage, and they're deciding whether you meet their standards or not based on this. So this is very valuable data that is now part of the analytics arena. The next example is in health care. While I'm not an expert in health care, I am an expert in analytics. And the number one problem in health care today, the two big problems in health care today is number one, what it says on the right? Patient adherence. So how do you get patients to take the medications that are prescribed to them? That's one big challenge. The second is what's called predictive analytics and that's what I'm showing you on the bottom before. If you think about the way medicine works today, and why it costs so much to the government and to the planet is because we wait till you get sick and then we treat you in the optimal way. But imagine I could predict what from your medical records, which is the picture I'm showing you on the left. Imagine I could predict the illnesses you're going to get 20 years before you get them. Now, instead of trying to solve the problem by treating you optimally, I try to prevent you from getting this disease optimally. So imagine I knew that Eric Bradlow, I was gonna have high blood pressure 20 years from now. Well why don't I start taking the drug right now that prevents me from getting high blood pressure? And now, I've turned a reactive problem into a pro active problem. And this is the future of analytics in health care today. It's trying to predict peoples diseases far in the future but if you think about what we've been talked about when you watched me and my colleagues talk about, this is analytics. This is marketing analytics. I'm gonna target individual customers based on their patient records, based on what drugs they've taken, based on their maybe family history, based on their consumption of goods and services. I'm gonna target them optimally for the right way to make them adhere to drugs, and with the right protocol to prevent the future illnesses. This isn't backward looking, it's forward looking. This is maybe one of my favorite ones which is Google Free Taxi. If you haven't heard, Google has been working for the last five years on driverless cars. Now you might say wow, that's kind of interesting, that would be kind of cool. Yeah, but here's an opportunity that they are thinking about to monetize it. So what you see on the left is just an example of purchase history data. What you see on the right is a formula for computing the customer lifetime value. Imagine Google doing the following, I'm just picking Bloomingdale's as an example. Imagine I go onto bloomingdales.com and I start saying, I like this and I like this, and I like this and I start thinking about all kinds of stuff I wanna buy. Now, Bloomingdale's can measure this and now I'd say, hey wait a second it's Bradlow, Bradlow's back. He's a very valuable customer. Imagine Google partners with Bloomingdale's and says wait a second, I understand Bradlow buying online's valuable, but imagine we sent a driverless car over to Eric Bradlow's house to pick him up and drive him to Bloomingdale's for free. So image Google partnering with retailers to actually offer a free ride to a store based on your customer lifetime value. This may be if you like the Mount Rushmore of analytics. It's Google, who kinda knows what you're doing, partnering with a brick and mortar or an online retailer to send an actual car to your home based on customer lifetime value. That has all the aspects of data capture, kind of analysis prediction, and action which is physically sending the car to your house. And if you think by the way that this is fantasy and this is never coming, it's coming. In the next ten years, I guarantee you that not only will there be driverless cars on the road, but there will be driverless cars on the road that are tied to analytics. Starbucks. Starbucks is actually a much more brilliant firm around analytics than people give them credit for. And if you see here, I've given you these two pictures which seem like an oxymoron, like they don't go together, like customer loyalty equals no deal. What do you mean customer loyalty equals no deal? I give the best deals to my customers, to my better customers, right? No, that's not right. The people you should give the best deals to are the people that giving them the deal changes their behavior. So let's imagine Eric Bradlow stops at 8 o'clock in the morning at Starbucks every single day. Do you think if they give me a deal I'm gonna stop at Starbucks? How can I stop there more than every single day? Maybe they can give me a deal to get larger wallet share. Maybe instead of getting my muffin at work and my coffee at Starbucks, I get both at Starbucks. But you don't give deals to your best customers, you give deals to your customers for which the ROI is the highest. And Starbucks recognizes this through their loyalty program. They want to give deals to the people right on the brink of being loyal. They wanna turn disloyal or infrequent customers into frequent ones. They're not trying to turn frequent customers into frequent customers cuz frequent customers are already frequent customers. So they've taken analytics to the next level. Where they recognize, you don't just treat your highest revenue customers the best, you treat your customers the best for which the ROI of that expenditure is the highest. And by the way, that's typically not customers who buy the most from you. People get this wrong all the time. High value customers are already loyal, they already buy a lot and you probably get a lot of their dollar wallet share already. It's that interior customer, it's that middling customer that you wanna tip towards a heavy buyer and not towards a light buyer. Those are the ones you should target and Starbucks knows this. The next one, Call Centers. A lot of you may not want to admit it, unfortunately you can't see me and I can't see you. But a lot of you may not want to admit this, but how many of you have ever called a company on the phone cuz you had a bad experience with a product and started screaming into the phone or started screaming at the person on the other line? I think everyone of us has to be honest and say, we've done this. Now you might say, what does this have to do with analytics? Well, it has everything to do with analytics, and how marketing and sales are done today. Imagine Eric Bradlow calls on the phone, let's imagine I call Comcast, my local provider. And I'm not saying anything. I like Comcast, they're a good cable provider but lots of people call up that they're upset with Comcast. And I'm not picking on Comcast. They know they have if you'd like, a customer satisfaction issue with Comcast. Let's imagine I call up Comcast and I start screaming into the phone, my cable service is out again, the picture is blurry blah blah blah, I start screaming at him. Well, two things. The first one is gonna be not surprising to you. First of all, Comcast knows it's me that's calling cuz I'm calling from my home. They can see I have ten TVs in my house and by the way, I'm not exaggerating. I'm a TV family. I like TV. I have a lot of Comcast boxes which means I'm a very valuable customer to Comcast, so they can see that. Notice already, look at the data. My phone is now linked to my account. They can see it's me. They don't ask me it's me. They know it's me when I call. And the person that's answering the phone knows my lifetime value. They can see how much I've spent over the last, my average monthly purchase. And they can see that I've been a customer for 15 years. Second thing is, they can listen to the intonation, software now can listen to the anger in my voice and decide and put a script up there for the person it went Bradlow, angry. Churn, not good. Gotta do something about this. And so here's an example where a firm has taken really, natural language and intonation software and merged it with CRM systems and database management and they're merging these together they could do one of two things. One is, the script could be for the person. Wait, calm down Mr. Bradley. We understand you're angry, etc. Or they could patch me through to a different person who's better at handling angry people. And that's even a greater possibility of using analytics. So here's an example of a firm that's doing better customer relationship management. Call center routing, if you like through the use of analytics. Amazon, ship before you buy. This is kind of again, one of my favorite ones. You see a picture here again of someone's purchase history and you can see a purchase for a picture of a drone. No, I don't think Amazon's really gonna have drones flying above my house, ready to drop a package off. But what Amazon can do. And I mentioned this briefly in the introduction and a little bit in the summary is what Amazon can do is they can predict what Eric Bradlow's going to buy in advance of me buying it. They can ship it to a local retailer near my house. And so if I order it, I'm gonna get it that day. Imagine the possibility and the customer lifetime value you can build by saying if you order by noon, the product will be at your home today by 5 o'clock. Think about what you'd be willing to pay for that. Now again, what allows Amazon to do this? Number one, they've got really good data cuz they're tracking data at the individual customer level. Two, they've got a recommendation engine and they build it to do predictive modeling. So they can predict what Eric Bradlow's gonna buy in the future. Not what Eric Bradlow bought in the past. What Eric Bradlow's going to buy in the future. Number three, they have extensive distribution. But to local retailers near my home. Number four, they have an ability to act and charge for this. So are they gonna do it for Eroc Bradlow for free? No, that'll be Amazon Prime Prime. Amazon Prime Prime won't be free shipping, it might be free shipping but here's an extra price you pay if you want it that day. And so imagine the power that analytics has brought to these kinds of companies. So just to wrap up and to talk to you about kind of the ultimate takeaways here. What I've tried to talk to you about is technology meets management and marketing science. So what you should be focusing on, if you're sitting here listening to this lecture. The first thing you should be thinking of is technology's wonderful it's cool, but this isn't the thing about your personal home use and devices. How can I use technology, whether it's eye tracking data, GPS data, web data, purchase data, survey data, how can I use this data to better understand my customer? The second, it's never the golden age of data. So if you're still making decisions today with store level data or aggregate data, you're probably giving up a lot of money cuz there's data at a more granular level that's gonna allow you to make the same decisions, but at the level of the individual customer. Third, if you think this is nice to know stuff, I'm not in the nice to know business. If I had a dollar for every study that was done that's nice to know, that's great. But I'm into problems and the way I think of analytics is towards action. And this is real monetization. Do you think Kohl's, Google, Amazon are doing this cuz this is nice to know. They're doing it cuz they see a real opportunity to make money. So I want to thank you for your attention. Go on to my website, go on to the customer analytics website. Don't ever stop learning about analytics and how it can be applied to your particular business?
