So, so far we've talked about exploratory data collection. We talked about descriptive data collection. Next we want to move on to causal data collection. As you'll notice, this type of data collection is most stringent. Let me give you an example. Many times, when we're thinking about our websites of our companies, we think about the landing pages, think about is landing page A better than landing page B? How do we determine that? That's where we might want to do a field experiment. Some set of customers see landing page A. Another set of customer see landing page B. We look at the click-through rates and then decide which landing page is better. In other words, we're trying to make a causal link between changing the landing page and looking at the click-through rate. That's what causality's all about, and that's what field experiments are all about. So there are many companies that allow you to do this type of AB testing. For example, when you start thinking about AB testing, you need to start distinguishing between correlation and causation. Correlation is the relationship between two variables. So let's take price and sales. Are sales and prices correlated? Probably so. If prices go down, sales might drop as well. Causation is one variable producing an effect in the other. Correlation and causation are not the same thing. Let's take some concrete examples to try to understand the difference between correlation and causation. First, correlation and causation, there are three requirements if you start thinking about causal inference. First of all, is there a correlation at all? That's basically evidence of association between X and Y. Let's say X is price, Y is sales. Is there a relationship between them? But that's not enough for causation. There are two other requirements as well. One is temporal antecedence. X must happen before Y. So if you want to say price changes drive sales, we need to be able to say that price changes occurred before sales changes happened. And finally, there is no third factor driving both of them. In other words, you need to control for other possible factors. So let's take an example to try and understand the relationship between the two, and what's the difference between the two. So there's a popular story that storks, for example, bring babies. It typically started in the colder countries, where they used to see, for example, a bunch of storks going through, and suddenly landing on a house. And a few months later, you see a baby in the house. Well, are stocks really bringing babies? Well, lets try to understand each of the different elements and see whether there is causation going on at all, okay? If you start thinking about correlation, well there might be some kind of correlation. You see houses on which stocks are sitting. You see babies in that house. Are they related? They are related. But is it causation? That's the key question. So on the one hand, there is correlation. Storks and babies in the same house. Is there temporal antecedence? That's true as well. Storks come in, babies come in a little bit later. But is there a third factor driving both? And that's where, for example, causation and correlation comes in differently. What's the key question? Typically, we start thinking about why are storks sitting on a particular house? Typically, the answer might be, and this is in cooler countries, typically, the answer might be because those houses are warmer. Then you start thinking about why are those houses warmer? Typically, those houses might be warmer because they have pregnant women in them. In other words, there is a third factor. Houses being reasonably warm that made the stocks come in and sit on top of that house, and you see a baby coming a few months later in that house. So it's about correlation but definitely not about causation. So that's the key difference when you start thinking about correlation versus causation. Now, when you start thinking about the overall story, you start also thinking about many other companies that can help you distinguish between the two. The key idea to distinguish between these two is to start doing field experiment. In other words, you start systematically manipulating crisis, you start systematically manipulating landing pages to see how they might have a causal impact. So there are many companies out there that will help you do these ab testing. So a simple example might be if you start thinking about two landing pages, landing page A versus landing page B, how do you distinguish which page is better? Well here's what ideally you would like to do. One set of customers coming to your website will see landing page A. Another set of customers, randomly chosen, will see another landing page, landing page B. Then over time, you see the click-through rate, you see the purchases. That can help you determine which landing page is better. This is precisely what many companies help you do. For example, Optimizely is one company that will help you do this AB testing. It will help you understand which landing page is better. In other words, monetize overall landing pages and figure out which page you should be showing your customers. There are many, many other companies as well, which do the same thing. Overall, when you look at this particular space, many companies are starting to help out other companies in terms of landing page design, in terms of overall optimization of the webpage, and so on and so forth. So there are many companies that help you do mobile AB testing. What's the idea here? Basically on your mobile site you also want to answer what landing pages are better, which icons should be shown, and so on. So a company like Optimizely will help you do that. And it also has many other companies that might do the same thing. For example Limplam is another company that does do the same thing, and there are many, many other competitors as well. So overall, when you start thinking about the pricing plan of these different companies, the starter plans typically are free. So it can do a lot of A/B testing on its own. But if you start thinking about your overall landing pages, you start thinking about the overall funnel of purchase, that's when you start paying the big money, which is the enterprise package. What are of the kinds of questions that can be answered using this type of data? First one is web site optimization. What kind of websites, what landing pages, what icons should be shown, and so on? How should they be optimized? Second, you can start thinking about mobile app design. Many companies are coming out with mobile apps. How should they be best designed? And of course, you can take it to the extreme. You can start thinking about how should you design the app in general? But you can also start thinking about what versions of the app should be shown to different customers? The extreme case would be one to one market. Very broadly, all of these companies help you collect data by doing field experiments. In other words, you're systematically manipulating landing pages. You might be manipulating checkout pages, and so on and so forth, to try and understand how the systematic changes cause changes in customer behavior. So what have we done so far in this module? We have looked at three different kinds of data collection, which go hand in hand with three different types of marketing decisions. First we started off with exploratory data collection, where we talked about focus groups, we talked about internet communities. Then we went into descriptive type of data collection where we talked about surveys, we talked about point of sales data, we talked about data related to media planning and so on, so forth. And then we ended up with talking about causality. In other words, we talked about field experiments and other company which help you with field experiments. So when we start thinking about the overall summary of this module, it's about data. But more importantly, it's the synergy between the type of questions that you need to answer and the type of data that's best suited to answer it. It's very, very important to think about the synergy as you start thinking about the type of data you would like to get. If the question you would like to answer is more exploratory in nature, you can do focus groups, you can do Internet communities. If the type of question you want to answer is more descriptive, in other words, you want to get concrete numbers in terms of share wallet, in terms of share. Then the type of data you need to collect is quite different from the exploratory type of data. And finally, if the question that you need to answer is more causal in nature, for example, if I change my landing page, what would happen? Then the type of data you need to collect would be more along the lines of field experiments. So it's very important to keep the managerial goal and the type of data collection in mind because they're highly synergistic. Thank you.
