Welcome to Customer Analytics. I'm Professor Raghu Iyengar. I'm a professor of Marketing in the Wharton Marketing Department. I've been here for about ten years or so. And during this time, I've taught marketing research and other customer analytics related courses. So today we'll talk about descriptive analytics. I hope you're as excited as I am to talk about descriptive analytics. So what's descriptive analytics? Descriptive analytics can be defined in a variety of ways. So one is that descriptive analytics is a way of linking the market to the firm through decisions. Another way of thinking about descriptive analytics, it's the information that's needed to make actionable decisions. And yet another way is it's principle for systematically collecting and interpreting data. What's the common thread here? The common thread is getting good data. But what I also want to talk about is that it's the synergy between data and decisions that managers have to make. That makes for good analytics. So what are the different kinds of decisions that managers might have to make? One set of decisions might be purely exploratory in nature. So think about a brand manager, they're looking at their brand sales and suddenly they start dropping. Question is, why are they dropping? Is it because customers preferences have changed? Is it because customers like competitors? There could be a variety of things going on. So in that sense, at this stage it's purely exploratory in nature. You're trying to understand why things are not working out the way we expected. Another set of questions can be purely descriptive. For example, again going back to the brand manager, I want to know, what's my customer's share of wallet? How much are they spending with me? How much are they spending with my competitors? Who are our customers? What's our segmentation like? So these kind of questions require hard data, in terms of understanding how much customers are, for example, buying our products or other competitors' products. Yet another set of questions can be purely causal. The idea here is for example, if I'm changing the landing page on my website, how would it change consumer behavior? Would it change, would it increase it in terms of click through rate? Would it bring it down, and so on. So these questions, the one on the right, most extreme, the causal questions, require systematic data collection and careful thought in terms of how to collect data. So what we see here is going from left to the right, the type of data that needs to be collected, the type of conditions that the data needs to be collected under, also keep changing. So as we go on in this module today, we'll talk about different kinds of questions that managers need to answer and what type of data is best suited to answer those questions. So let’s begin with the exploratory type of data collection. So exploratory type of data collection is typically done to develop initial hunches or insights. Again, recall the example that we started out with, the brand manager thinking about why the sales are dropping. There could be a variety of different reasons. And usually this type of data collection is a first step and a very important step to get a broad understanding of what the underlying problems could be. And it provides broad guidelines on what you should look for more rigorously. What's a typical technique that comes to mind when you start thinking about exploratory data collection? It's focus groups. Focus groups have been there for a long time. What's a focus group? Basically, you have about eight to ten customers in a room. Usually you have a moderator who designs the overall flow of the focus group. And you want these people to come and talk about the brand, their sentiments about the brand, perhaps feed off each other, in other words, you can observe dynamics. It's reasonably unstructured. It's free flow conversation. And what are you trying to do as a brand manager? Get insights into what might be some pain points for consumers. Now in this day and age of data analytics and big data, focus groups have morphed in some sense through many different ways. Market research online communities or Internet communities are basically focus groups on steroids. You can think about variety of different companies offering this service. For instance, Vocalpoint. What Vocalpoint does is basically rather than looking at 10 to 20 people, you start thinking about 100 to 200 or sometimes 500 people in a group. And you're monitoring them not for one time but over a period of six months to a year. What's the idea here? The idea here is to build relationship with your consumers. Over time, these 100 to 200 people start building relationships with each other. They become more and more comfortable talking about the real feelings and real insights. Vocalpoint, of course, is not the only company doing it. There are many, many other companies. For instance, C Space is one of them, and many other competitors as well, okay? There are many advantages of Internet communities. One is that it enhances engagement with customers. So these customers are together talking to each other, talking to the brand, for about six months to a year. So clearly this close concentration in terms of talking to each other, communication with the brand, it really enhances that engagement. Second, shorter deadlines are possible. Typically focus groups, there are logistical issues in terms of trying to get these people in a room, get a moderator and so on. Because you are looking at these customers for about six months to a year, you can actually have much shorter deadlines. There are aha moments that come out. The most famous example is Kraft's 100 calorie pack. What did they do? They basically had a community, they had worked with C Space, they had a community that started looking at, what do people want in snacks? What was the insight? It's not that people wanted to stop eating snacks, what they really wanted were snacks with lower calories. Nabisco's 100 calorie pack has been an amazing success. But there are caveats as well. What's the big caveat? ROI can be very hard to determine. Why? Because as you start engaging with an Internet community, early on it might be quite difficult to forecast what kind of insights will come out. For Kraft, this was great. But it will not be the case for every possible example. So thinking carefully about when you might want Internet communities, thinking carefully about is it worth the investment of six months to a year. Very important to determine as you start going into the idea of collecting data from customers over an extended period of time.
