Chicago Booth Lecture Series Marketing at Booth

Colin Davis: Hello, everyone. As many of you are still entering into the room. I wanna thank all of you for joining us for this third installment of our Chicago Booth MBA Masterclass series. My name is Colin Davis, and by, on behalf of our full-time our evening, weekend, and executive MBA programs, it is my pleasure to welcome you and to serve as your moderator for this session. Now for those of you that might be joining us for the very first time, our Masterclass series features our world-renowned Chicago Booth faculty, and they give you really a glimpse into the classroom experience, as well as our unique approach to business education, right? And that's really what we call the Chicago Approach, right? It's something that's rooted in fundamental discipline, evidence-based frameworks and academic rigor. And what happens is you learn how to better define problems. You learn how to consistently ask the right questions, really to help you get to the best solutions. And that's regardless of the industry you might be in. And that's regardless of how the world around us might change. And at Booth, you know a crucial part of the MBA experience is really the extremely talented and the collaborative community that you join when you become a student here, all right? And that absolutely includes our incredible faculty. Our faculty teach across all of our MBA programs and they do truly become part of your network. And all of you that are joining us from all over the world for the next 90 minutes are in for an exceptional treat. Our featured faculty member, professor Pradeep Chintagunta, he's one of the leading minds in empirical research on marketing. And tonight he's going to share insights and provide really an introduction into how we approach marketing here at Chicago Booth. OK? Now what'll happen is we'll save some time toward the very end of our session for Professor Chintagunta to answer some questions live. So at any point throughout the MBA Masterclass session, please feel free to use the Q&A box as we go along. What'll happen is I'll make sure that we queue up many of those questions and I'll save them toward the end so we can answer as many as possible, OK? Now without further delay, I'd love to turn it over to professor Chintagunta. He is the Joseph T. and Bernice S. Lewis Distinguished Service Professor of Marketing. We are very appreciative of your time. As we know, he is teaching two courses this quarter: Marketing Management as well as Foundations of Advanced Quantitative Marketing. He's been at Booth for, I believe 25, years now and has been recognized numerous times for his teaching excellence, as well as this research. Professor Chintagunta, if I went through all of your accolades I'm sure we'd be here all night. So with that, the floor is yours. I want to thank you very much for joining us.

Pradeep Chintagunta: Thank you -- thanks a lot, Colin. And thank you everyone for joining us this evening. Welcome to Chicago Booth if you're coming here for the first time, and welcome back if you are, you've visited us before. So I wish this had been under better circumstances where I could have welcomed you to one of our beautiful campuses: the two in Chicago, the one in London, or the one in Hong Kong. But I think given how important this decision is, I'm glad that we are able to have this session where hopefully you'll get some idea as to what it is to be in a class at Booth. And you get all the information that you need to make this very important decision. We really want you to be here because you want to be here. And the match between the school and your needs, I think is critical. And our job is to make sure that you get all the information you need to make your decision. I also wish that this had been a little bit more interactive, which it would've been if it were here live -- but in the meantime I'm hoping that you're all staying safe, healthy and your families are safe and healthy. So that's a little bit about what the -- what I was, you know, thinking of beginning this evening with. So what exactly is it that I plan on covering today? So the plan for today is I thought I'd give you a sense as to the marketing faculty -- who we are as a group -- and tell you a little bit both about the teaching and the research. Colin alluded to this in his introduction, where he talked about the importance of these two aspects. So I'll try to blend the two when I go through this. I'd also like to talk a little bit about the Chicago Booth Approach to Marketing. Again, Colin mentioned what the Booth Approach overall is. And I want to sort of hone down a little bit on what we think makes us unique as a marketing group. And then I want to talk about three aspects of the classroom experience -- or rather two and then a third one which is perhaps also relevant. The first is to talk a little bit about cases and frameworks. So what is a case? A case is essentially a business situation or a business context, which the participants in the class and the faculty jointly try to analyze in the course of a class session. And then I want to talk a little bit about how you go from a specific case to sort of evolving to a framework that you can then use to try and study problems that are of a similar nature, or similar types of business problems. So that's the first aspect. The second aspect I want to talk a little bit about is what Chicago is perhaps best known for, is the data and analytics, which underpin a lot of the decisions that we like to make. And finally, I'd like to take a step back and say: Is the only role of marketing to essentially increase profit, increase sales, build branch? Are those the kinds of things that marketers are trained for? Or can we use the same tools and techniques that we use in marketing also for the larger good? At the end of the day, we all live in a social context. And we would, I think, like to make the world a better place, if we could; and to the extent that marketers can do that, I'd like to touch a little bit about some of the things that we are involved with as a faculty. OK? So here is a, here is a slide which gives you the set of the tenured and tenured track faculty, along with the clinical faculty that we have in marketing. It of course excludes the many great adjunct faculty we have -- but there, you know if I did that, then you know, the pictures would get so small that you wouldn't be really able to see anybody. The reason I'm putting this up is not simply to sort of go through each face and tell you a little bit about them. That's not my purpose because at the end of the day, these are all just pictures on a page. Rather, I think my objective is to give you sort of the assorted backgrounds from which the different faculty come from. So some of us have a business background. We might have been engineers before that. And then we became, came into marketing. Others actually have an economics training and came into marketing through economics. That would be people like JP Dube, Günter Hitsch, Giovanni Compiani, Brad Shapiro, et cetera. There are still others who come from a very strong psychology background. So they were trained as psychologists that then moved into marketing; examples of those would be Dan Bartels, Ayelet Fishbach, Abby Sussman, et cetera, right? So this -- they represent, or we represent amongst us, a very diverse group of backgrounds. Now why is that important? At the end of the day, as Colin pointed out, we are a discipline-based school. So in some sense, the fact that all of us essentially harken back to our backgrounds, which might be in some base discipline, allows us then to relate to common themes across courses. So for example: If you think of economics, obviously it is filled with economics. If you look at the behavioral sciences groups, psychology plays a big role there. But now think of other areas like finance, right? So in finance as well, you can think about the underlying issues related to economics. You can also think about behavioral finance. Now, those areas then also depend upon underlying frameworks in economics and psychology, right? So these basic disciplines become the common language or DNA for you, then, as a participant to be able to relate across this entire broad set of courses that you'll be taking in your time at Booth. So there's a common DNA. The point I want to make is that there's a common DNA, which underlies the faculty across various groups -- which then hopefully helps you as a participant integrate across these diverse fields to then come up with a sort of a holistic or integrated view of the firm. And I think that's what sort of separates us from most other schools, most of the business schools that you might also be exploring. So that's essentially some background about our marketing faculty. I'll try to return to some of these as I go along in terms of the specific areas that I'll be covering. So what is the Chicago Booth Approach to Marketing? So we like to think of this as, you know, multiple steps. You always, of course, start with a business problem or a problem definition. That's always your starting point. But then the way we would like to think about this is in these steps. First, we like to come up with sort of a theory or high hypothesis for the problem, right? What exactly underlies this problem that we are facing? Come up with a set of alternative hypotheses, then go look for evidence that might either support or reject these hypotheses we might have. Using that evidence, now come up with a policy or strategy that you want to implement in the marketplace; implement that policy and then evaluate it. OK? Now what is really the benefit of a strategy which does this? The real benefit to our mind is first of all, it's logical; there's a framework underlying almost everything you do. The second thing is that not all theories obviously are going to be "accepted," quote unquote, in the real marketplace. So it'll be good to know what are the hypotheses that get rejected, what are the hypotheses that don't get rejected, et cetera. But to us, the most important reason for having this kind of underlying framework is that it helps in accountability. One of the biggest challenges with marketing has often been that the accountability part might be an issue that a lot of organizations might face when thinking about marketing: Why do I want to invest in advertising? Why do I want to do X? Why do I want to do Y? And these kinds of questions can be more easily addressed if we have an underlying logic and underlying framework, and underlying evaluation method that we are using, or our strategies. So that to me is what the Chicago Booth Approach to Marketing is. If you have questions again, you know, feel free to put them on the chat and I'll try to get to them, you know, depending upon how much time we have, OK? So the first set of topics I want to cover is with respect to cases and frameworks. And as I said before, the case basically is a representation of a business situation with an associated problem that we are then trying to solve. And then from that, we are trying to derive a framework for solving similar types of questions or problems, OK? So there are a bunch of us who teach the basic marketing strategy class, which is typically the introductory class, regardless of which program you're at. So I teach in, I teach this class, Berkeley Dietvorst, Sanjay Dhar -- a bunch of us teach this. And again, you can see the variation in our backgrounds that get represented in this class. So Berkeley and Abby have a psychology background; Brad and Arnold have an economics background; whereas the rest of us, Sanjay, Anita and I, come from more of an engineering background, then followed by a more formal business training, right? So we bring a variety of perspectives, so depending upon the kind of perspective of that you think you want to really build upon, you can actually choose to take a class from the person that you think is the best match for your needs. OK: So let me start with, you know, how you go about thinking about it, about a case. So the first thing that, as I said, the case has is that it has some kind of a business problem, which is set within a particular context. So just to give you an example, and this is sort of a canonical example of a case -- it's a very old case example and I think many of you might have encountered it already -- which is the context of cannibalism among chickens. OK, so you're sitting there wondering, you know, yeah: I'm spending all this time, you know, listening to this guy and he's talking about chicken and cannibalism among chickens. But it turns out that it is a real business problem because if you think about the chickens on farms, chicken have a very strong sociological ordering. So there are chicken that are senior in the high hierarchy, the chicken that are junior in the hierarchy, et cetera. And one of the things that then happens is that the senior chicken peck the junior chicken -- which means that you have some kind of -- as the famous phrase "pecking order" in organizations comes from -- this idea from chicken where the senior chicken then peck the junior chicken. Now what's the consequence of this happening? The consequence of this happening is if you are lower in that sociological ordering, then you tend to die, right? Because you get pecked all the time. So to -- in order to address this problem, the way in which typically farms handle the issue is through what is called "de-beaking." Not a very pleasant, pleasant experience. So basically what they do is that they line up the chicken and chop off their beaks. It's a process called de-beaking. You won't consider that particularly humane, but that so happens to be the reality in many farms. So now what is the consequence of this happening? The consequence of this happening is multitude. First of all, the chicken can die out of the trauma caused by the de-beaking process, so you're back to this situation of mortality. The other thing is that you may not die because of the trauma but it will affect your productivity: that the chicken now starts laying fewer eggs than it did before. And finally is the issue that you now have to put more feed in the feeders for the chicken because the chicken don't have long enough beaks, so they can't really reach down that far -- so you need to raise the level of the feed. As a consequence lot of the feed gets wasted. OK. So what do I do now? I need to think of a way of solving the problem. And along came a company, which said, "OK: The way we solve this problem is by putting contact lenses inside these chicken. OK, so in their eyes. So what is the purpose of the contact lens? Unlike the contact lens that we typically use to improve our vision, the contact lenses in this case actually distort the vision, right? And as a consequence of distorting the vision, I really cannot recognize whether the chicken standing next to me is senior to me or junior to me in the hierarchy. And as a consequence, I'm more reluctant to peck. That's sort of the thesis behind this product. OK? And so the question, the business problem then becomes: How do I come up with a price for this lens? You know, try to come up with a solution. Of course whether the solution works or not is a different issue altogether. But the question is: How do I come up with a price for this lens that I'm gonna stick into the chicken? OK? So the hypothesis behind the pricing strategy -- that one hypothesis, and they can be several -- one hypothesis behind the pricing strategy that you might come up with is that farmers pay depending upon the value they get out of them, out the lens, right? And it is the, not just the value but the value they get relative to the current way in which they are fulfilling the need, which is by de-beaking, OK? And this is a concept which is referred to as the economic value to the customer. So what is the economic value to the customer? The economic value to the customer basically says, I start off with something I call the reference value. The reference value in this case would be simply the price that the farmer is paying for the de-beaking process, which most likely is going to be the cost associated with the labor involved with the de-beaking process. OK? So that's going to be your reference. On top of that, you're going to add something which is better than the current way of doing business. And those are aspects we refer to as the positive differentiation value, OK? So what might be positive differentiation value, go back to the consequences of de-beaking; one was an increased mortality. Perhaps now the mortality rate would come down. There's not going to be as much trauma, so the productivity of eggs goes up And now because the chicken has a full beak, it can actually dig down to the bottom of the feed and you don't have to put as much feed and waste as much feed anymore, OK? So that would be a positive differentiation part. That's not the only thing that might be going on. There could be negative differentiation as well. You don't know, because this is a new product. You don't know whether it's going to last very long. You don't know whether it's going to be successful. And if you switch to this and the company goes bust, then you're stuck with chicken with lenses. But now when new chicken come along, you'll have to figure out something else to do. So that gives you an idea as to what the economic value to the customer is. But at the end of the day, to come up with a price, I now need to quantify each component of economic value. And I think that's one of the things that becomes important to do if you want to come up, come up with a price. So the way you would then go about thinking further about this problem is to say the economic value is in some sense the maximum value that the farmer might be willing to pay, right? Because it includes all the benefits and takes out all the costs. So this gives you in some kind, in some sense, an upper bound on the price that the farmer might be willing to pay. But a lot of farmers might simply be constrained by the amount of money they have, and so they might just be limited to what they're paying for the de-beaking; they can't afford to pay more. In which case, you now have other factors that will play a role in determining what the price is going to be. And I'm not going to list them out here; if this had been sort of an interactive class, or they've gotten you to tell me some of the other reasons that could play a role. For example, I'm sure some of you are thinking, oh, what about competition, right? What would happen if perhaps the folks who do the de-beaking lower their price or something else is happening, or some other technology comes along which can solve this problem in a less painful way, right? So all these are now gonna be factors, which will help the farmer come up with an actual price. These factors now collectively will help you to go from just thinking about economic value to thinking about the framework for pricing, right? So what you can now do is you can list out; I start off with thinking about economic value. That seems to be a logical place to start because ultimately, a farmer is not going to buy if the price is above the economic value that they're ever going to get from this product. So that gives me an upper bound at a starting point. Now, I think about all the other factors that might play into the framework. These could be how sensitive the customer is. In this case, as we said, some farmers may not simply be able to pay more than the price that they're paying now -- in which case they're very price sensitive and you can't really charge a high price for them. There could be still others, still other factors that, like competitive reactions: How might your competitor respond? And there are things like a customer's emotional response. This may not be true for business-to-business products like the contact lens, but it could very well be true for cases like orange juice. I don't know how many of you, at least in the U.S. -- I know many of you may not be from the U.S. -- how many of you remember how much the most popular carton of orange juice contained, right? So if you go back a few years, a typical carton of orange juice contained about 64 ounces, right? So that was the size of the carton. And then at some point, the cost of orange juice went up. Firms wanted to raise prices but consumers had this sticking point, like $2.99 or some such price. And it was very difficult to raise the price above $2.99. So there was, there would've been an emotional negative emotion response to charging a price higher than the $2.99. So what did the firms end up doing? They started reducing the amount of orange juice that goes into the carton. So we went from 64 ounces to 59 ounces; many firms are still at 59 ounces. But then some other firms thought, "Hey, you know, I want to go even lower. How do I do that? I don't use a plastic carton -- sorry, I don't use a cardboard carton anymore. I now use a plastic bottle." And that plastic bottle, what you find is they're only 52 ounces, right? And so as a consequence, I think, you know, taking to account the emotional response of consumers I think is critical. Richard Thaler, who won the Nobel prize -- and I'm sure you know, you've heard of him or even heard him -- does a lot of work in this area, trying to understand what are these sort of emotional responses that consumers might have to things like price. And ultimately after you take into account all these, all these issues, you have to ask yourself the question: Am I making enough money so my corporate objectives of who I want to be are being met, right? So you go from the point where you start -- starting with a specific hypothesis about pricing -- to then gathering data about different factors that might affect pricing, to then coming up with a framework that you can now use in context, way beyond just thinking about contact lenses for chickens, right? So I hope that gives you some idea. And now of course, you come to the evaluation part, right? So we had a hypothesis. We gathered some data. Now we are going to essentially test the hypothesis with the data we have gathered. In the case of a, in the context of the case, one of the challenges that we face is that we only observe the action of the firm, right? The firm can take one action. The firm might end up pricing the lenses at 10 cents. That's the only thing we observe. And the other thing we observe is whether the firm was successful or not, right? Now using that information, it's generally very hard for us to make, draw conclusion about whether or not this was quote unquote "the right price," right? That's very, very hard to evaluate because our calculations might have resulted in a very different number from the calculations that the firm went through. And so in essence, unless you have whoever the protagonist of the case is coming and talking to you, it's very hard for you to stress test whether or not that was quote unquote "the right price." OK? So one way of figuring this out is what is sampled as a market test. A market test is basically actually launching the product in the market and seeing whether it succeeds or fails. And if it fails, obviously, you know the price is wrong -- you don't know what the right price might be but, you know, you might know that the price is wrong. The other way is through a test market where you have many different geographic markets, for example, and you try out a different price in each geographic market and then make a decision as to which price, price to try. Now all these are very difficult to do in the context of the case unless the case itself describes the company going through those steps, right? And so these are some of the limitations of simply using a case method. And so going beyond the case, a particular case, you can come up with other examples. I don't have the time today, but, you know, you can look at examples as diverse as, you know, how do you price an elevator to how do you price an alternative to in vitro fertilization, which has, you know, all kinds of sort of interesting aspects to it, right? So you can think about doing many, many cases about pricing and then sort of think about whether or not your framework works. But then it's very hard, as I said, necessarily to figure out what sort of the right price to charge would be. OK? And this is where I want to go to the next set of, set of topics that might be relevant, which is: How do you actually use data to make some of these decisions, OK? So here is another business problem. This is a business problem where, you know, it involves a company called ZipRecruiter. Some of you who are in the U.S. might be familiar with ZipRecruiter. ZipRecruiter is basically a website where firms that are interested in hiring people with various skills post their requirements for those potential employees. And then potential employees can, you know, also have their material in there -- and essentially there could be a match between the, an employer and the employee, OK? So that's basically the way in which -- so I noticed a couple of folks have raised hands, are we taking questions now? Or are we just gathering questions at this point?

Colin: So Professor Chintagunta a lot of the questions actually could be held toward the end.

Pradeep: OK.

Colin: Let me just do a quick check to see if there's any that are relevant to what you covered. So there was one question referencing the framework that you introduced initially. Obviously the example was more focused on agriculture...

Pradeep: Yeah.

Colin: ...and I know you kind of referenced the IVF afterwards, but is: How do you kind of apply this framework to other types of industries?

Pradeep: Absolutely, yeah. So yeah, maybe I spend a minute there. So, you know, the economic value concept is, you know, fairly standard. So if you think about, like, elevators: So there's an example here which I have of, from MonoSpace Elevator. I don't know how many you are familiar with the elevator space but it's kind of fascinating space, right? For the longest time, at least for medium-rise and low-rise buildings, you use what are called hydraulic elevators, right? So basically what you had was some machine room on top of the building; you had a box, had to fully attach to it. There was a steel cord attached to it and basically, you know, the motors in the machine room would haul the elevator up and down. That's the way it was. So that required having a machine room on top. And then of course, hydraulics -- and hydraulics have all kinds of issues like fire hazards, et cetera. So along comes KONE, a company which then invented something called a MonoSpace product. That's basically this green thing that you see here -- it is a small structure, which is embedded within the shaft itself, right? And that's what drives the elevator going up and down. Now this is an amazing breakthrough because, you know, it's just going to obviate entirely the need for a machine room on top or anywhere else. Other elevators have machine rooms below; some have been machine rooms on the side, right? So you just get rid of the need for a machine room entirely. Now you can start thinking, "How do I apply the economic value of that to the customer concept in this situation?" Immediately after that, you can think about the fact that you now don't have to construct an entire machine room. So all the savings associated with using that space for some other reason than a machine room and saving on the cost of building that machine room, that's immediately going to be a positive differentiation value. What else might be a positive differentiation value? It's going to be the fact that you don't have a fire hazard as much as you had before, 'cause as soon as you have hydraulics then you're gonna have a fire hazard, right? And so you'll have to deal with that. You'll have to deal with that issue as well. So as soon as you start thinking in terms of the framework and what are the steps I need to follow in the framework, it then becomes easier for you to think about what is it I specifically need to quantify. Now interestingly in this case, what happened was that since the firm was a much smaller firm in the marketplace, it felt that it was very hard for it to raise prices on all, on the customers, even though it offered a lot of additional value. So it shaded the price a little bit and priced closer to the hydraulics -- although it had, you know, the margins were much higher than the margins for the hydraulic elevator. So even though they didn't price much higher than the hydraulic elevators, they were able to get a huge market share without really having to price significantly higher while at the same time making some money, right? So again, hopefully that gives you some sense as to how you would go about applying this framework. Now having said that, it's not as if, you know, you can do this in all contexts. I think the first thing you think about is what about a completely new-to-the-world product? And absolutely I understand that but then again, the way, the place you want to start is with the need. What is the need that the customer is facing? How is that need currently being met? What is it that it costs the customer to meet that need? And now I can think about: What is it that I can offer to this customer? Is it better or worse than the way in which the need is being fulfilled? Now if the need is not being fulfilled at all, then you have an even a more difficult problem, but there are ways in which you can elicit data. And part of what you will do at Booth is learn about methods for all these different kinds of contexts, right? So there's something called conjoint analysis which some of you might be familiar with. That's something that we would then use to try and figure out, you know, how you would price this entirely new product. So I completely realize I can't do justice to an entire -- there's a whole course on pricing by the way -- and I can't do justice to the entire course in 20 minutes, but I hope that gives you some idea that once you have a framework, and you start thinking about the framework, it's easier for you now to figure out how you can quantify and then hopefully try to evaluate, yeah. I hope that answers, at least tries to answer your question.

Colin: Yeah.

Pradeep: OK: So back to this, back to the issue of ZipRecruiter. Here is a company that, again, is interested in pricing. So, you know, this would give you a very, very different perspective, but with a very sort of heady data and analytics perspective to exactly the same problem. OK? So ZipRecruiter is a platform or website where you have potential employers and potential employees come together. The way in which ZipRecruiter makes money is by charging a subscription fee to the employers or to the employee with the employers. So employers pay a monthly subscription fee, they can either pay monthly, quarterly or annually. And so they've, since they started, they basically used a flat price. It was either $99 or $199 a month. And the question to them at some point was, are we really doing sort of the right thing in terms of our pricing? It seems to work, people are signing up; there, you know, we could be happy with what we are doing but is it really the right price that we are charging? OK and so the objective here, the business problem, is: Can we think about what they refer to as an optimal pricing mechanism? And what do I mean by optimal? It's not in the way you would think that, you know, you're price discriminating just because someone can pay more or someone can pay less. It is again based on the type of needs, right? So what are the types of jobs that you need filled? And how do I change my pricing depending upon how responsive or how sensitive to your, to price given the kind of job that you're trying to fill? So just to give you an example: Suppose you really need your hospital -- you're in the middle of COVID -- you really need to hire a nurse for a particular task. Then maybe your willingness to pay is going to be higher than a different context where there is not such urgency for filling the need -- in which case, those characteristics can end up influencing how much you are willing to pay to ZipRecruiter in order to hire someone who meets your needs. OK? I hope, I hope that that distinction is good. All right, so what they then try to do is precisely that, is that. Can we then try to understand the differences across potential subscribers where these differences are related to, you know, how much they're willing to pay depending upon differences in their characteristics? And what I mean by characteristics -- as I said before, in terms of a nurse and being of urgent need -- these are going to be characteristics both of the jobs as well as the recruiters, right? So for example, if you are the type of company that is very successful, making a lot of money, maybe you are more willing to pay more because you want sort of the perfect match for your needs or something like that. OK? So what you want to do is to try and understand how different subscribers are in terms of their willingness to pay for the ZipRecruiter service; relate those differences to differences in the characteristics of the jobs and the firms; and then apply this sort of algorithm that you derive to new subscribers or new firms that want to hire who come onto the platform. OK? So the way they did this is by actually running an experiment. So the hypothesis was, people have different willingness to pay. So let us try and figure out what this willingness to pay is. So what they did was they said, whenever a new subscriber hits the paywall at ZipRecruiter -- provides all the information about themselves about the job they want -- then we are going to randomly assign them to one of these 10 prize bins, right? So we either went as low as $19 a month to as high as $399 a month, right? And we would then observe whether or not the person who hit the paywall actually subscribed or did not subscribe. So what do I know now? I know the decision to subscribe. I know the characteristics, right? And now I can figure out using -- and I'll get to this, there's various machine learning tools that underlie this -- I can now figure out what is sort of the optimal price that I need to charge for someone else coming onto the platform with that set of characteristics, right? The same set of characteristics as those that we have just figured out the price for it, OK? So that's basically the objective. So when they actually ran the experiment -- and this is very reassuring for those of you who are familiar with demand curves in economics -- basically what you find of course is that the lower the price, the more people sign up. So on the Y axis here, you have the proportion of people who signed up who face a particular price. And so as the price, with the lower price, obviously this is a much higher proportion of people who are willing to subscribe. With a much higher price there are fewer willing to subscribe. And so that's a very nicely, nice downward sloping demand curve. So now, given this demand curve and given the responses of the individual firms, I can come up with an optimal price for each subscriber. And I can obtain that optimal price, given the set of characteristics for the job and the employer of the firm that you're given me at the start of this process, right? And then I can implement this new pricing rule for those who arrive. And I can check, I can also check the validity of this, right? So I've come up with an optimal price. I can basically see now by implementing the optimal price, do I get the same number of people subscribed as I expected based on my algorithm? Or is it very different from the number that I got based on my algorithm. So that's essentially what the firm ended up doing. It also figured out what their, the best uniform prize would be -- because, I mean, part of the challenge could be in actually charging different firms, different prices. So the question was whether they would -- the current price that they're charging, the $99 or $199, is that sort of the optimal price? And what they found was, that is not the optimal uniform price and you can actually make $36 more if we charge this new optimal uniform price. And we can make even more money than that if we tailor made our price based on the characteristics of the jobs and the firms that that wanted to hire, right? And so what this tells you now is I get sort of the right price; I get the right price, I get the profits. I can now go to an entirely independent group of new subscribers who come in, implement this price and see in fact, if my profits are higher or lower, right? And so what I'm able to do is I can measure my ROI. There's clear accountability, right? And so if I know that the experiment failed, then it should be the case that in this new set of people who are not part of my experiment, where I try to implement my pricing policy, if I get very different levels of profits and revenues, then it's telling me something about potentially, things I did wrong in terms of my experiment. Right? Of course, things could have changed as well. And there are many other reasons why you may not get the same numbers. But I think at least it gives you a clear path to diagnose where things might have gone wrong in this process of setting the price. So by laying out specific steps, and within a particular framework, I think you're able to sort of diagnose these problems much better. Now, what makes this sort of very different from the other example I gave you is the following. First of all, there's an implementation challenge because the number of characteristics I took, all the features that firms might have, and all the features of the jobs that they might be looking for. But then in some sense, the dimensionality of the characteristics of the jobs and the firms is going to be very, very large, right? And then they're going to be millions of variations of these, right? And for each variation, have to figure out what the optimal price is going to be. Right -- and so it's going to be very hard for you to do this through any sort of simple method that we discussed in the case of the chicken. You now need some formal machine learning statistical procedures to try and figure out how do I relate the information from my experiment to try and figure out what is going to be the optimal price for any given combination of these characteristics -- thousands of which have gathered data from, I've now gotten data for, right? And so that essentially are these types of skills that you will learn at Booth, right? You learn not only how to do a case discussion and how work through a given context, but it'll also figure out what are sort of the types of algorithms I need to be able to scale up my pricing to a point where I can, you know, at a very detailed level, come up with what should be the right decision for that particular firm. There is also another problem: That problem has to do with the timeliness. Remember, as soon as the person inputs the characteristics of the job they want and they hit enter, ZipRecruiter has to be able to pull up a price for them immediately. They can't, they won't just like sit around twiddling their thumbs when at the background, some, you know, machine is trying to crank out what the right price is going to be. So this has to be done with very, very small latency levels, right? And so within 25 to 40 milliseconds, you'll have to be able to figure out what the optimal price and kick it back. So that again requires a level of sophistication in terms of the algorithms -- which, you know, the types of skills that you will learn at Booth will give you at least a sort of a preliminary view into how exactly you should be doing those kinds of things. Right? So in terms of the pricing strategies there are two of our faculty who teach exclusively pricing strategies: JP Dube and Sarah Moshary both of them teach pricing. We have a different set of faculty who teach, who teach data and analytics. I had another illustration for data analytics but I think in the interest of time, I'm going to skip that. So I'm going to jump ahead. This is, the business problem here is about how do you price a reading app, which basically was a free app, and trying to monetize that. I mean, how do you come up with sort of the optimal prize for an app that doesn't want do any advertising because it feels that it affects the user experience -- but at the same time needs revenues, otherwise they're going to go bust. I'm not going to get into that particular example so if you pardon me, I'm gonna sort of jump ahead. But again, the idea is that you can come up with a specific pricing strategy based on a theory, based on some evidence, and then implementing it in the marketplace to see whether it's successful or not. So in this particular case, what happened was that the firm implemented the strategy, the pricing strategy that was picked, and went from having zero revenues to making about $1.8 million in an 11-month period -- which is not a small amount considering many of the apps that you see that basically, you know, are not able to generate any revenues at all. So I'm going to skip that. The only point I want to make here is that there are three of our faculty who deal with data and analytics in terms of the kinds of courses they offer. Guenter Hitsch essentially teaches two classes: One is called Data-Driven Marketing, the other is called Data Science. Giovanni Compiani, who is the most recent addition to our faculty, also teaches these classes. And then there's Sanjog Misra, who teaches Algorithmic Marketing. So between the three of them, they offer, you know, several classes on marketing analytics. Now the school as a whole offers a whole host of other classes as well, which deal with a broader category of analytics, but focused on marketing. We have, we have three of our faculty who, who exclusively teach classes in that domain, OK? And the last few minutes I have, what I thought I would do is to sort of step back. I mean, in all this time I've been talking about how you help firms make decisions. These could be small firms, like ZipRecruiter was a small firm. The contact lenses for chicken -- again, much smaller firm. But also these are techniques that can be used for much larger firms as well. So it's -- but it's been focused mainly on trying to improve the performance of larger firms. The question then is, you know, all these tools are great. You're going to get equipped with these tools in your time at Booth. The question is at some point you might want to go beyond that and say, "You know, I learned these skills. Can I, can these skills actually translate to actually, to doing good -- to doing good in the world?" And the motivation for this is to keep in mind that for every business that can hire an MBA, there are thousands of small businesses in the world that can't hire, can't afford to hire an MBA. And so they simply do not have the kinds of skills that many of you are likely to be trained in. Yeah: I have these three participants raising their hands. So if these are, again, questions are relevant to what I just said, I'm happy to take those questions, Colin.

Colin: Yes, we had -- so we do have one that is pretty relevant. Some of these, I think we can still probably wait until the end. One of these questions was: "Will there be sampling dependencies based on the number of characteristics and combinations? So in your prior example, will this necessarily kind of equate to an apples-to-apples comparison?"

Pradeep: I mean, that's a good question, right? So obviously you can't cover the entire range of -- right? -- you can't cover the entire range within the experiment, right? You can't obviously keep running this experiment forever. This experiment ran, I think, for a limited period of time and all the data you gather is going to be data that you get during that period, right? After that you'll have to, you know, you have to come up with a price because people, you know, just can't come and get these random prices, right? And so you have to be very careful about that. And so you're absolutely right. So if someone new comes in whose characteristics are going to be different, you need some way of extrapolating from what you collect in your sample to actually what you are to -- to what you'll have to come up with in terms of a price for people with different characteristics. Now in this particular experiment, there was no sort of, excuse me, stratification of the sample to try and accomplish this in some sort of a clever way. But in principle you can think of things like that, right? So you can think of saying, basically, "I want to gather information from these sets of companies with these characteristics." And you can do that as well -- I don't think it was done in this particular context -- but certainly, right? And so we have a whole course, which will cover things like experimental design. So Oleg Urminsky, haven't talked, talked about him. OK, so Oleg is this gentleman over here. Oleg teaches a class, which is entirely dedicated to, how do you actually create these experimental conditions that are appropriate for you to solve your business problem? And the reason why I'm talking so much about experiment conditions is because I think increasingly as we are in an online world, the need to experiment and the appetite for experimentation has gone up a lot. And as a consequence, I think when you graduate, it's useful for you to know what's the principles of experimentation are going to be. And so we have a whole course, which is dedicated to that. Again, can't do justice through all this in a, excuse me, short period of time. But I hope, I hope I give you some idea about this. So the last part that I wanted to spend the remaining 10 minutes or so that I have is in thinking about some of these bigger questions, right? Can marketers actually do good -- good for the world -- by using their skills in some way? And who are the folks who probably don't have these skills? These are folks who run small business are small entrepreneurs, especially if you think about a lot of the emerging markets. I'm originally from India. India, those of you have traveled to, you know, there are thousands and thousands of small businesses that line up the streets. The country where I do a lot of my work though, is in Africa -- sorry, the continent, where do most of my work there is in Africa. And the countries I worked on are Uganda and Rwanda. And that's sort of the example that I'm gonna to give you now -- which is to get a sense, is to try and see whether marketing tools really can help improve a lot of these small businesses that may not have the skills, you know, the skills to actually improve their businesses with the types of skills that many of you are going to be equipped with once you graduate from an MBA program. So again, the context is Uganda; the types of businesses that at least I was interested in businesses like these. So in fact, these are some of the types of businesses that we looked at. The reason why we focused on Uganda is that, of course, it has a very high rate of entrepreneurship and even more important than that, many of these folks actually speak English. And I'll explain in a minute why English is going to be, going to be important. So the target population, again, just to give you some more sense was small firms in these markets. Here are a couple of examples: a lady who runs a hair salon and Ricky over here, who runs a small electrical store. So we basically started off looking at about 20,000 of these businesses -- basically going from door to door, knocking on, knocking on doors -- asking whether people were interested in developing skills that might help their business. Of the 20,000, about 4,000 said that they were interested. And even of the 4,000 that were interested, only about 1500 are what we call growth-oriented in the sense that they wanted to actually grow their businesses. OK? So some of the criteria we used just to make sure that these businesses are not sub, subsistence businesses that might go out of operation very quickly: We focused on the owners of these businesses who have been at least trading for three months, who have a physical structure for their store, and who speak English. OK? And what really we wanted to bring to these businesses was coaching in terms of business, right? So these are folks who don't have any formal business skills or training. And we said, if we can associate these business people with someone -- a professional, trained professional somewhere in the rest of the world who's willing to volunteer his or her time for an hour a week, over a period of 12 weeks -- are we going to see any substantial improvement in the business itself, in terms of things like sales, profits, et cetera, right? We also wanted to make sure that we didn't have these coaches have to actually go to the location of the firm. And so this was done entirely remotely, via methods such as Skype, right? These days, of course, maybe you use Zoom but at the time we did this, we used Skype. So it was voluntary. So these coaches were volunteering their time. It was remote. You didn't have to do it on location. And it was not prescriptive in the sense that the coach and the business would figure out between themselves, what were sort of the kinds of questions that the business wanted answered, OK? And so we recruited coaches from all over the world. There were actually some from Russia -- I mean, this map doesn't show it, but there were folks in Russia as well. And so essentially we had this situation where we had these coaches who were dealing with in our case only Uganda -- although the platform, which is called Grow Movement, also looked at Rwanda and Malawi. OK? And so what we did was we took these firms that we had identified -- as I said, 1,500 growth firms. We had capacity only to meet the needs of about 600 or so firms. And so we ended up with about 530 firms who got this intervention. So basically they would talk to these coaches over a 12-week period, an hour a week, and try to gain some skills from the coach about how they could address their business problems. And then we had 400 other companies, which did not get the coaching right away but they were promised that they would get coaching after we were done with the first batch, because we were having a capacity constraint. So as I said before, no a priori matching between the business owner and the coach -- was completely random who was matched with whom -- and it was not prescriptive. The coach could do, was not told in any way that they had to focus on certain topics or certain aspects; it's completely left open. OK: So a little bit about these businesses, right? So, you know a fairly huge proportion, a large proportion of them, were women owners; they work very hard, six-and-a-half days a week, they had monthly sales. You know, it depends. I think, you know, in some months it was $1,300; in the months that they're gonna look at it's gonna be closer to about $4,000. But they basically were not very large businesses at all, right? And the coaches on the other hand were, as I said before, folks like you, who either have a lot of business experience already, or after you graduate from a program, which gets you an MBA, and you've been working for some time; you want to volunteer your time, you can sign up. In fact, any of you can sign up now, if you want. You can go to the Grow Movement website and then sign up to be a volunteer coach. And the keys is you're willing to volunteer time. So what is it that we actually end up seeing? So prior to the intervention, if you compare the sales levels, the treatment and the control group are exactly the same. The 530 who got the coaching and the 400 who did not, before they got the coaching, they were exactly the same in terms of sales. But if you look at the change in sales as a consequence of this just simple 12-week intervention in terms of business skills -- this is 24 months. It is a full two years after the 12-week intervention. The sales go up quite a bit, right? As I said, on the base of about $4,000 you are getting an increase of about $1,500 or so. So this is a fairly substantial increase that you see in the sales. So a lot of the kinds of skills that we talk about in business schools are applicable not simply for the large corporations that you might end up in, but they also matter for the smaller businesses. We then looked at, because we did this random matching between the coach and the business, we are now able to go back and look at the background of the coaches to see whether differences in, whether different types of coaches had an impact. And it turns out yes. In a lot of cases they, the coaches -- whether they were from a marketing background, a consulting background, or from some other background -- did have an effect relative to the control group. Some of you who are familiar with statistics are probably looking at these very large ranges here and saying, "Well, you know, a lot of these are not different from zero." You're absolutely right. That's because these are just the raw data. But if you take out a lot of the noise via a regression, what you do find at the end is relative to the control group, the marketer coaches, they helped increase sales, profits and employment. We didn't do anything with the other types of coaches because we were interested in market, being from marketing. And so in effect, what this is telling us is that marketing as an intervention does have an impact -- not just on large businesses, which I've already shown you about how profits can increase, et cetera -- but also on small businesses. And you don't really need a whole lot. You need some kind of a willingness to volunteer your time to help out these businesses, right? And so the way we would like to think about -- that we would like to think about marketing at Booth -- is that we have systematic frameworks that, you know, we would like to derive either through looking at specific cases and growing from there, or by systematically looking at data, using analytics and coming up with these implications for the firm. Having said that, we are also very cognizant about the larger role that we play as marketers in this, in society. And so a lot of the work we do is aimed at also trying to make sure that, you know, we, you know -- that the kinds of things that we teach actually has positive implications on the larger society. I did not mention this, but we then did another project in Rwanda where we basically provided these businesses with analytics, simple analytics. Basically these are businesses that don't keep track of their data at all. They have no idea how much they sold. So we basically gave them an app on a smartphone to keep track of their sales. And at the end of each month, we fed them back their own sales in the form of a graph or a trend -- how they were doing last month relative to this month -- and then saw whether that changed their decision making and low and behold it does, right? Just knowing the fact that you sold less last month of a given product and more this month of that same product because you've made some changes, then helps them to go off and make decisions that improve the performance of the business. So not just in terms of the broad, general business skills that I talked about in Uganda, but very specific marketing analytics also seems to have an impact on these firms. So our hope is that this gives you a sense as to, you know, we span a very broad range. I told you about the kinds of classes you would do, a little bit about the kind of research we do. I sort of mixed it all up just because as Colin pointed out earlier this evening, that you know, both teaching and research have to play a role in the context of the classroom. So you walk away, not just knowing what perhaps you would get at many of the other programs, but also what is sort of unique to us, what is unique to our DNA, and how you might potentially benefit from them. So with that, I think I'm going to stop. I wish you all the very best of luck in whatever you choose to do, wherever you plan to go. I wish you the very best. I hope you have, you know, find the program that is of interest to you. Obviously we hope, you know, you would find Booth a good home for you. But, you know, we understand that there are lots of great choices out there. And so, yeah. So if you have any questions, feel free to ask. I think we are still around for almost 30 minutes or so, so happy to take questions.

Colin: Yes, Professor Chintagunta. Thank you so much for that overview. We did have quite a few questions come in. So I wanna make sure that we have maximum time to be able to talk through those. So I'll go ahead and get started with some of the introducing ones. So one we have has to kind of do with some of the pricing theory that you talked about, right? So this individual mentioned the fact that some of the kind of theoretical studies of pricing can sometimes differ from reality, right? So oftentimes people might think that they're willing to pay more or less but when the time comes, you know, maybe they're not comfortable buying a certain product or service at that specific price. How can studies kind of account for the inevitable human biases that might exist?

Pradeep: Great, that's a great question. So this is the classic gap between what we think of as stated preference -- so when you ask someone in a survey, they might tell you something -- and reveal preference, which is what they actually end up doing. And these two could be quite different, quite different from each other. There are several ways; obviously, you know, the ideal situation is if you could get sort of reveal preference data, either on a small scale, right? Where an experiment like the one I told you with ZipRecruiter, alternatively, you'll have to derive, you know, hypothesis based on your cumulative experience over time. So there there are techniques, which basically help you. And this doesn't, is not a very, a complicated technique but if you can essentially keep track over time of how your decisions have worked out in the past -- based on survey data, correlating it with, you know, the actual outcomes in the marketplace -- then when you're faced with a new situation, you can try to apply those types of relationships to any new situation you face as well. So for example, if you, your assumption is if I priced a product at $100 I'm going to get a 30 percent market share -- in the past, when you did that, you got only a 20 percent market share. You want to keep track of that information because that information is now going to inform you the next time you want to make a prediction that you have to appropriately adjust for that, right? This is a major challenge. I mean there is no, there is no silver bullet for it. So largely it comes either through experience on the one hand or through, you know, the experiments that I talked about. There is also a whole host of techniques referred to as you know, Bayesian methods, right? Where people start off, it's some prior belief that they might have on what's going to happen and then they use data to formally update those beliefs. And if you're interested in those kinds of questions, then I'm sure if you're familiar with statistics, you will know that Chicago is actually the birthplace of Bayesian econometrics, right? So how do you actually use to do these kinds of updating as new data become available in the marketplace? So I think, you know, not just in marketing, but Sanjog Misra teaches a course on Bayesian methods, but also the econometrics and statistics group offer these kinds of methods as well.

Colin: Absolutely. Thank you. And on a somewhat related note, we got a number of questions kind of on a similar topic. So the EVC framework that you mentioned kind of, you know, suggests that the person might have a reference value for a different product, right? So we got a lot of questions about what happens in situations where either you're trying to introduce a new product or there's some type of service that may not necessarily have a comparison -- you know, for example, this person, the individual specifically asked about a COVID vaccine...

Pradeep: Yeah.

Colin: ...you know, that doesn't necessarily have to have a standardized price to refer to. How do you kind of modify that in these cases where there's maybe not necessarily an initial market demand to begin with?

Pradeep: Absolutely. So actually the case that I wanted to talk about, the IBM case, was a case like that, right? Because I think, you know, you're all familiar with in vitro fertilization. One of the big challenges with IVF of course is that the person who wants to bear the child has to be injected with hormones, you know. And that whole process of, you know, going through these several weeks of hormonal injections and the, what it does to the body and to the mind -- these things can be extremely, you know, extremely difficult to manage. So IVF was basically a method which came along, which eliminated the need for those hormonal injections. Right? And so you would think, look, it should be, you know, easy to, you know, easy to price this, because why don't you basically charge what, something based on your cost, right? So that your cost of producing this, you add some margin on top of it, and that should be the price. Now of course, it turns out that the cost of making this is very, very small, right? It's only about $50 or so, $100. Whereas the cost of an IVF procedure was much higher. But more than that, the question was, how do I figure out measure of the physical and the psychological cost associated with having to go through this hormonal therapy, right? That is not an easy thing to price. And it's not just a struggle that you or I might have. It's also a constant struggle for companies, right? And so there what you try to do is, you try to, you know, get as -- try to triangulate the answer in a variety of different ways. So one of the ways in which you can get information from is people who have expertise, right? So people who have come up, for example, with a vaccine for SARS, right? Or a vaccine for some other kind of a kind of an illness previously. Ask them, "Given these potential differences between that situation and ours, what do you think would be a price for this?" And this is, you know, very sort of, you would call an arbitrary way of doing it. But it gives you sort of a good benchmark or baseline to start from, right? Then you go off and start to try to look at other ways in which you can quantify the things that might happen, right? If you actually fall ill, how many days of work do you end up losing? Right? So how many other people do you and end up infecting and how much of work or income do they end up losing? You can eliminate all that. Then what is the value that, is it going to provide to the individual? Again, no hard science or a silver bullet for things like this. But you have to think systematically, right? It might be difficult to come up with a value right away but you can think, "OK, so if the person is losing work, if the person, you know, can't go in for so many days, if the person infects some other people as well, you know, what are the resulting costs of that?" So you can try and construct at least a ballpark figure. At the end of the day, you know, you have to still make a choice. But hopefully these things will give you at least a broad framework that you can apply when you face situations like this.

Colin: It's interesting, the example you bring up because we also got a number of questions related to kind of that psychological focus.

Pradeep: Mmm hmm.

Colin: Someone asked about, you know, how do you really think about and consider the issue of ethics in marketing? You know, for example, with Purdue, right? So how do you recognize some of the potential consequences of some of the marketing strategies and the potential implication that they might change social behavior?

Pradeep: Yeah, absolutely and I think, you know, you can -- I think this is perhaps something that you can fault many businesses for, business schools for. I don't think we spend enough time discussing this. They do come up and I think that's the beauty of the case method, right? So when you're discussing the case -- so I teach, I used to teach at least, this case on in vitro maturation, and one of the big questions would come up is, you know, for some people, having a baby alive, a healthy baby is worth everything, right? So I mean, you know, how do you quantify the value of having a baby? But as a company you have to come up with a price, right? Because at the end of the day, you have invested a lot of time and effort into this. So absolutely, I think you do need to take into account ethics. And I, you know, I'd be remiss if I said, that's not important. What is, I think the good news is that we have now started -- and I think Nick Epley, who's not in the marketing group, is teaching a very valuable course on business ethics. And you'll definitely be exposed to that class if you came here to Booth. And one of the things that we are trying to also develop through the Kilts Center in marketing, is how do we try and incorporate some of those ideas into the curriculum that each of us is supposed to handle or deal with, right? So this is very much a topic that is top of mind for us. And I hope, you know, by the time you, you show up here, you would've done a lot more on this. Some of you might have noticed, we do have in the Kilts Center of Marketing, a new series called "Marketing for Good." This is a new series, which would include things like ethics. For those of you interested, the videos actually get posted on YouTube. I did one of these videos -- one of these programs on the Black Lives Matter movement -- and, you know, what are sort of the business responses to the Black Lives Matter movement. And these are all topics that I think we are very interested in, and I think we are working towards.

Colin: Excellent. Thank you. And as I said, there was a follow-up that was a little bit related and kind of in the ballpark of that same topic. How do you kind of introduce and consider regulation, but then also differentiate between how you navigate -- and kind of legal versus some of the illegal pricing discrimination that might exist, or even anticipate, if you know your product or your service might, you know, benefit certain demographics or certain demographics might derive greater economic value from a product or service?

Pradeep: Absolutely, right? And there's all these, these recent discussions about algorithmic biases. So how -- and these are things that are definitely part of our curriculum. In fact, I was, you know, there's a very interesting exercise on the pitfalls of algorithmic pricing which I plan to introduce in my class in the winter. So again I think, you know, these are issues that are important. The one thing I would say on that is the benefit of coming to a place like Chicago is that we have, of course, an excellent law school. And many of our students take classes not just in the business school, at least I think the full-time, evening and weekend folks, but you can also take classes in the law school. So there, we have several experts in these areas in the law school and the ability to actually cross-register for those classes, I think will help you sort of fulfill those needs. I mean, we can't -- I mean, I have no expertise in the law. I can't speak to law by myself, obviously. There are going to be regulatory issues. So for example, when I talk about a pharmaceuticals case, I definitely talk about regulation; I talk about ethics and those kinds of issues. But there are going to be legal aspects, which I can't really opine on. And I thought, I think the benefit of having such of a rich university, like, rich in terms of the intellectual knowledge, university like Chicago, is that you can avail yourself of those kinds of resources as well.

Colin: Excellent. Thank you. Excuse me. We have a question, excuse me, referencing the ZipRecruiter example. This individual wanted to understand if additional data points like the length of subscription channel, retention rate, how those might play a role in defining the pricing strategy and based off of some of those added elements, would a reduced price potentially look bad for that company if you were able to find additional information or findings that came up later on in the process.

Pradeep: Absolutely. I think, you know ... so right now, I think the way I described it was, it was almost like a transactional situation, right? But as soon as you think about subscription, subscriptions are not, you know, do not presuppose that it's purely transactional because you're paying monthly over a period of time. So sometimes you gotta think about the relationship value as well. So there's a, in many of our classes we talk about the concept of customer lifetime value, and the customer lifetime value as being an important determinant in terms of the pricing strategies that we might adopt. Again, I'm not the one who teaches pricing. So I can't speak specifically to what, you know, what ZipRecruiter might have done or not. But I can tell you that in general, customer lifetime value is a very important, important aspect that we always keep in mind -- especially in terms of, you know, subscription services, right? I mean, some of you might have encountered this very old example of, you know, the lifetime value of a loyal Taco Bell customer. I don't know how many of you have sort of read that. I recall reading an article somewhere, which said that, you know, the lifetime value of a very loyal Taco Bell customer was $11,000, right? Now if you think about what a transaction at Taco Bell might be, it's just about five bucks or, you know $10, or whatever. But as soon as you start recognizing that a customer who walks through the door may not be worth just $5, but worth $11,000, now your behavior towards that person is gonna be quite different, right? And so the kinds of things that you take into account when you focus not just on acquisition of the customer but also on the retention, right? Most of what I talked about in my example was just the acquisition of the customer. But I think what the person asking the question was also the referring to is retention and what is the role that retention pays, plays? And absolutely when you're thinking about subscription pricing you definitely have to worry about retention as well.

Colin: Gotcha. Excellent, thank you. And one question, this individual also talked about, you know some, in specifically with international markets, right? So how do you kind of account for pricing across different markets where there's different conversion rates and different levels of affordability as well?

Pradeep: Yeah, so clearly there are many, many strategies that firms have followed. There are some firms that follow, like, global pricing strategy, which is essentially to say, you know, if I sold an iPhone, I'm going to sell it at the same price anywhere. But even in such cases you have not, we have noticed now, that the firms are trying to tailor their, their product lines depending upon the countries, right? And so the idea somehow is that you can either create a broad enough product line where you focus specific elements of the product line in different countries, depending upon things like affordability, et cetera, right? While at the same time, allowing those in those countries who can afford it to actually access the product, perhaps through some other channel. So that would be one strategy. The other strategy would be basically to say, "I'm going to play the global pricing game." Then there's the other way in which you can deal with this is by launching sub-branch, right? So you can basically say, you know, "I have my core brand, which I'm going to use in a bunch of countries, and then I'm going to launch sort of a sub-brand, which is specific to a given country." So that touches not just upon the pricing aspect, but there, they would also talk about your product aspect, the product line aspect, as well as the branding aspect, right? Which is, you know, do you position your brand the same way across the different countries? And you can see that not all companies position their brands the same way. So example, one of the first cases that I typically teach is a case on Citibank cards in India, and there the positioning of Citibank in a country like India is very different from the positioning of Citibank in the U.S. right? So in the U.S. it's your standard one of many, many banks that seem roughly equivalent to you in quality, whereas in a country like India, Citibank is viewed as a very premium brand. So at least it used to be -- maybe that has also changed a little bit over time -- but in general, it is viewed as a very different brand than it's viewed, viewed in the U.S. So absolutely, one of the things that we do talk about international cases is discuss these differences across countries in terms and how company strategies vary across countries based on, you know, your positioning that you choose to take or your branding, your pricing, et cetera.

Colin: That's excellent. Thank you. We have a couple kind of more forward-looking questions as well that came in that are pretty interesting. So one of them was kind of asking you in the next five to 10 years, do you think that some of the increased regulations around consumer data and, you know, sharing of data, how will that impact market research industry at a larger scale?

Pradeep: Yeah, I think one of the big unknowns here is, you know, how regulations around privacy are going to evolve. And so Europe has already taken, you know, taken big steps in this direction. Many of you might be familiar with GDPR. So GDPR has, you know, placed a lot of restrictions on the kinds of data that you can gather, the kind of data that you can use. And so I think those types of issues are also presumably going to become relevant for us. And I think what is particularly challenging is your company that works across borders, but with very different types of regulations that might exist -- whereas your customers again, could span all these different countries. And then you'll have to figure out what's sort of right way of doing this. So my colleague Sanjog Misra does a lot of work in this, in the privacy domain. I know Dick Taylor in fact, many, many years ago, had also opined upon, upon privacy in the New York Times, I believe. But I think, you know, this is an issue that again, as soon as you talk about data and, you know, you think about the courses, the many courses we offer about data, I think the challenges associated with, you know, getting the data, distorting the data and using the data, I think are going to be an important question. So for example, I can tell you as a researcher, the university is very, very strict in what it requires of us, if we gather any data, right? So anytime we save data on the university service, that's subject to a data use agreement that we have to sign with the administration. And I think increasingly we are getting, I think, a lot more sensitive to data but I think the implications for business is something that you will definitely, you'll definitely encounter and discuss in your classes.

Colin: Excellent. Thank you. And I know you referenced earlier, the talk that you did specifically focused around Black Lives Matter. We had a question come in and basically trying to kind of understand you, what is some of your thoughts and opinions on brands responding to different social causes or issues, right? And I think also, how do you kind of approach that decision whether to or not, and when does it make sense for a company to do so?

Pradeep: Yeah I mean, you know, of course the short answer to that is, you know, there there's a video it's online, you should feel free to look, go through the video. I also have an article in the Chicago Booth Review if you want to look at that. But let me tell you a little bit of what I talk about there. So one of the things is, you know, a lot depends upon who you are as a firm. I think it's an opportunity for you to basically say, what is sort of my mission? You know, what is it that I want to accomplish as a firm? And then make sure that whatever actions you take are consistent with that mission, right? That mission should be clear to everybody. So if you're a Ben and Jerry's, you might have a very sort of specific, you might have a very specific mission, which has this sort of social aspect to it. On the other hand, some of you might, you know, be familiar with Subaru advertising. Subaru is very interesting, right? So one of their taglines is all about love, right? "Love -- that's what makes a Subaru a Subaru." I don't know how many you sort of come across that, but, you know, that's sort of a basic feature of their advertising. Now you can imagine that love as a concept transcends so many different aspects, right? It can include social causes; it could include, you know, things like cruelty to animals, whatever, right? But I think as long as you are able to associate yourself as a brand with some concept like love, then I think it becomes easier for you to justify being involved in all these different sort of social movements or social aspects and be convincing at the same time, right? Because I think the big challenge is, if you're doing this purely out of expediency then it's not necessarily going to work out well for you, right? Because it'll come through to people that you're just doing this for the sake of doing it and not doing it because it is what your core belief is. So one of the things that I point out is, we went and looked at a bunch of firms and looked at how Twitter responded to some of the statements that the firms made. So we looked at Amazon, Apple, I forget the entire list. But we looked at a bunch of these firms. And one of the things we noticed was when these companies just put out statements saying that they support, you know, the Black Lives Matter movement, it didn't necessarily elicit a positive response from people on Twitter, right. Basically because anyone can say whatever they want, it doesn't really signal any commitment. So it's very interesting to see, for example, companies like Netflix -- which, you know, released a series of statements and really did not generate that much of a positive response to their statements and in fact, in some cases it generated a strong negative response as well. It's only when they actually showed commitment by saying, I think they mentioned something like $400 or $500 million -- that they're going to actually invest this money in sort of helping or in directing this money towards this cause -- that you actually see people react very positively to something like this, right? And so that comes from the fact that people need to see a real commitment to that task. And without that commitment, I think people basically will see through things that you do for the sake of, sake of expediency. Now having said that, there's this -- a lot of companies are really, you know, most of their stakeholders may not be the end consumer. For those companies they have to think about their internal stakeholders like their employees, right? What kind of message am I sending to my employees by the kinds of actions that I take? And I think so it, while you might think that, you know, a lot of companies are insulated from something like this, because they don't deal with end consumers, I think that would be sort of a shortsighted look. Because I think at the end of the day, any organization lives in a context, right? And so you have to account for that context in the actions you do. But my suggestion always is to go back to your mission statement. Who are you as a company? What is it that you're trying to achieve? And if you're transparent about that, and you're transparent about, you know, your motives and objectives there, then I think it becomes easier for you to deal with these issues as they come along.

Colin: Absolutely, I would argue that's great advice for any potential MBA applicants as well, authenticity being genuine as well. So thank you very much and probably a time for just one last quick question. And this one is certainly looking ahead, but, you know, as we, you know, kind of enter 2021, are there any particular research or innovative topics that you're kind of excited to pursue as we enter 2021?

Pradeep: Yeah, so I think there are two. If you look at what our faculty are doing, I think there are two sort of broad areas where I think you're gonna see a lot of future research on. One is obviously in this whole data dimension, right? So data analytics, AI, coming up with sort of the, you know, newer and better methods to deal with data, to handle data. I think that's a whole host of topics, which I think are interesting. Personally, a lot of my research has moved in the direction of development, right? So using business skills for a better world. So I work in basically three areas. I work in agriculture and, you know, I've done some work, for example, with farmers in China and the objective there is to get them to switch from toxic pesticides. I don't know how many you're familiar with this, but pesticides are a major, major problem in the food chain everywhere, right? And so the idea is, you know, how do you reduce the toxicity in the food that we consume by getting the farmers -- because they're the ones who can actually affect the change -- How do you get them to switch from, you know, the toxic to the non-toxic pesticides? I'm interested in a whole host of areas around that concept. The second area I'm interested is in education and skill building. And my work in, you know, Uganda and Rwanda basically are in that direction. And the third area, which I'm very interested in is in the healthcare space. So I do a lot of work with tuberculosis, mainly in India -- and also in India, we are now running a very large program, which involves about 240 villages in trying to get them COVID prepared. So basically trying to help them develop a protocol where they can prepare, protect and prevent, you know, their, the villages from actually getting COVID. So we are actually going through that process right now, like training people in the village to actually get better at dealing with these issues. And then we, again, using a lot of the skills that, you know, we learn in marketing, which is like the use of social media, you know, the use of kind of experimentation, the use of technology tools like Skype and in China we used WeChat, right? And so a lot of these tools, how can we use them -- the same tools that we've used for, you know, purely commercial purposes to build big brands and companies -- how can we use them in context, like, you know, health, agriculture and education? So those are my personal interests. I get very excited about them. And so if you gimme a place to talk, and the time, I'd be happy to keep going. So, you know, many of you either it's very late in the night or very early in the morning, so I just don't wanna keep you for much longer.

Colin: No, absolutely. Well, thank you so much, Professor Chintagunta. Certainly been a thrilling and very exciting 90 minutes, appreciate your overview of Marketing at Booth. I appreciate a lot of the insights into specifically the approach to marketing at Chicago Booth as well. We greatly appreciate your time. I wanna thank all of you for joining us as well. No matter where you are in the world, I do encourage you to check out our Kilts Center for Marketing. Again, you can visit them and their website. I encourage you to sign up to receive additional information and insights, and expertise, and faculty research about marketing at Booth. A lot of great and exciting information comes out of that center. And our next MBA Masterclass, which we'll be hosting, will actually be on November 30th. It will be with Professor Devin Pope. It'll be specifically focused on behavioral economics. So if that's of interest and if that fits your schedule again, we would love to join you for -- to have you join us again as well. So thank you again Professor Chintagunta, greatly appreciate your time. Thank you to everyone for your questions and for your focus and attention as well. Thank you for joining Booth for this event. We wish you all well, take care.

Pradeep: Thank you, everyone and I can't recommend Devin enough. So if you have the time and the inclination, please do attend that talk.

Colin: Absolutely. Thank you, take care.

Pradeep: Thanks, bye.