Hi, I'm David Schweidel, I'm an Associate Professor of Marketing at Emory University's Goizueta Business School. I've been down at Emory now for four years. I live in Morningside with my wife and daughter. What brought me down to Goizueta? Well, I grew up in New York, in Connecticut, and in high school, if you're good at math in Connecticut, the only career track that they recommend to you is to go and be an actuary. And so, went to college with that plan. Quickly realized that that wasn't what I wanted to be doing, but was fortunate enough to meet faculty members from the marketing department. Who had shown me that I could use the same tools that I was using as an actual or intern, but apply them to marketing problems. So instead of looking at when people are going to die and when a life insurance policy is going to kick in. Could look at when is a customer going to lapse, and what marketing efforts might we be able to take to prevent that customer from churning? Most of the work that I've done on the research front is in the area of customer relationship management. So looking at customer acquisition, customer retention, cross sale how do we value customers? I've started to take those statistical models and apply those to consumer social media activity. So what content are people posting online? How frequently are they posting online? What's the sentiment or content of what they're saying online? And I've begun working on understanding how can we use available social data to inform what a company or what an organization knows about their customers. The course that we're offering, marketing analytics in Excel, is really driven by the rise of available data for marketers. Marketing is becoming a much more data driven field than it has been in the past. And so we want to be able to use the available data to support the decisions that we're making. So we'll look at a broad range of marketing problems such as pricing decisions, customer targeting decisions. Marketing, mixed modeling, how do I evaluate the relative efficacy of different marketing actions? And we'll conduct our analysis using Microsoft Excel. Not because Excel is the most powerful statistical program that's out there, but it's' the one that we can virtually guarantee you're going to have access to on the job. There's a lot of power that's built into Microsoft Excel that typically isn't exploited. So rather than just looking at it as a means of organizing data, how can we can restructure the data? Conduct our statistical analysis within Excel and then take those findings and then convey them to an audience? So if you're making a decision, what are the visuals that we should be looking at? What are the statistics that we should be looking at? And ultimately, how do I communicate that to other stakeholders?