Before coming to Booth, I had little idea what marketing was about. I worked as a management consultant and never had marketing-related projects. To be honest, I used to think of marketing as a soft area: people who are good at making advertisements and publicity campaigns.
It only took one marketing class at Booth to completely change my mind.
Marketing Strategy with professor Bradley Shapiro changed the way I make decisions and even altered my MBA path mid-program. At first, I wondered what a PhD in Economics was doing teaching a marketing class. But I quickly learned my preconception of marketing was badly mistaken. I saw that marketing goes well beyond “P for Promotion.” Professor Shapiro’s course focused on pricing strategies and data-driven approaches that consider consumer heterogeneity. By the end of the class, it was my belief that marketers should be the ones making the most important decisions in companies. In fact, I liked it so much that I ended up doing my summer internship in a marketing strategy role for a pharmaceutical company.
I’m currently in another course that has made me even more of a marketing supporter—Data Science for Marketing Decision Making with professor Günter Hitsch. It has been by far the most data intensive, analytic, and fully practical class I’ve ever taken. Marketing is intrinsically behavioral, so what better to predict consumer behavior than past behavior?
These days, there is more data available than ever before about consumer behavior as well as the tools and computing power required to analyze it. Chicago Booth makes sure we take advantage of that by providing us with the data and resources we need to process all this information.
The databases we use in class consist of millions of recent observations about consumer behavior, which are rarely available. For example, we use years of Nielsen data on purchases made by thousands of households across all retail outlets in the US. We then complement that information with Zillow data about the households’ sociodemographic characteristics. There are also databases about advertising campaigns, promotions, competitors’ prices, etc. from which we can get great insights. In order to process all this info, we study state-of-the-art statistical and machine learning tools and use R to rapidly process the data. And when databases are too large to process in our computers, we can access Booth’s servers and perform analyses much faster.
What I like most about Professor Hitsch’s class is its experimental nature: how to use tools to perform interesting analyses that answer relevant questions, as opposed to just learning some key concepts. This approach is ideal because in the future we might not remember the technical details of a particular regression method, but we’ll remember what it can do.
Now I know how to model demand elasticities to determine prices and discounts, how to manage customer relations to decide where to target marketing campaigns, and how to analyze household-level data to decide which products to launch, retire, promote, or advertise. Knowing how to do these things is important because in real life these decisions are often made based on people’s “hunches,” as opposed to following a data-driven approach to make informed determinations.
Needless to say, the marketing skills and techniques I’ve learned at Booth are sure to be part of how I’ll make decisions going forward and in my future career.