Hi, welcome to Marketing Analytics. I'm so glad you're here. I'm Raj Venkatesan. I'm a faculty at the Darden School, and I've taught marketing analytics for ten years. And I'm as excited about marketing analytics today as I was when I started it. Marketing analytics really is a process where you use data to make better marketing decisions. There are not really any specific definitions of marketing analytics out there, but there are some common understanding that there are three different kinds of marketing analytics. Descriptive, predictive, and prescriptive. Now what are they? Let's look at here. So descriptive analytics is looking at the past, it's ad hoc reports or standard reports, where you're looking at the data and seeing, what happened, how long, and how often? Or even alerts, which says something abnormal is happening and what actions are needed to address this abnormality. So, descriptive analytics, really, is looking at the history and seeing what happened in the past. Now, as you go up the Degree of Intelligence here from Descriptive you get to Predictive and Prescriptive Analytics. Now Predictive Analytics is looking at what will happen, say, if you reduce the price of a product. And randomized testing also includes, in Predictive Analytics, which looks at, how can we look at changing the price or increasing promotion advertising? And what will happen through experiments and AB testing? And optimization is in the Prescriptive Analytics realm, which looks at, what's the best that can happen of all the options out there? So we go from understanding what happened in the past, learning through that, and looking forwarding using that data, using predictive and prescriptive for planning purposes. Now, why is this happening? Why is this interest in analytics? What's driving that? That is driven a lot by this deluge of data that comes from cloud servers, AWS or Azure, all these systems that are allowing us to understand more about consumers. So as a consequence of all of this, there's a large amount of data that is being put out there. So if you look at this report by IDC, they talk about how, in 2013, the amount of data that was put out was about 4.4 trillion gigabytes. Now, the projection is that by 2020, it is going to go up to 44 trillion gigabytes, a tenfold increase. Now, all of this data deluge doesn't really help firms. So there was a report in Harvard Business Review that looked at firms that use analytics and the benefits that they get. Now, they found that in their sample, 34% of the firm that were in the top of the class in using analytics, got about 6% more profitability and were about 5% more productive. And there was another study which looked at people who use analytics, are they really high performers in their industry? Now, let's look at what they found. They found that, in the column, here, you have people who are classified as high performers in the study and companies that were classified as low performers. Now, 65% of the high performers have significant decision support and analytic capabilities, 36% value analytic insights, 77% have above average analytics capability. 73% make their decisions based on data and analysis, 40% use analytics across their entire organization. Now, these numbers in the low performing columns are always lower than the high performers. Which means that the high performing organizations have capabilities that they have developed for using analytics in their decision making and are benefiting from those data driven decisions.