Sebastian Gabel


“Learning from Big Data” (Autumn 2021, BSc)

Every day, millions of consumers voice their opinions in product-review websites, blogs, and chat rooms. At the same time, retailers collect rich data sets that contain valuable information from their loyalty programs. In this era of big data, the availability of new, larger, and more diversified data sets creates exciting opportunities for marketing practitioners. All that is needed to address these challenges is the right set of tools and the training that helps you to use the tools correctly. This course first teaches how to formalize marketing problems as statistical models. It then shows how to solve marketing problems by complementing classical econometric techniques with modern machine learning (ML) methods. Students learn the needed tools and conceptual frameworks needed to identify and exploit the opportunities that big data sources create.

“Machine Learning in Marketing: Theory and Applications” (WS 2020/21, MSc)

I developed this MSc course for students from quantitative fields such as quantitative marketing, OR, economics, statistics and computer science in mind. The course prepares students in their last year of study for solving real-world marketing problems using modern quantitative methods. The course first reviews theoretical foundations in marketing, statistics, and probability theory and then shows how to formalize marketing decisions as machine learning problems. It also equips students with the necessary tools to implement machine learning pipelines efficiently.

Guest Lectures “Machine Learning in Marketing”

My guest lectures (e.g., MIT Sloan School of Management, WHU School of Management) are based on my research as well as my industry experience that I have gained from founding several machine learning start-ups. I present real-world results derived from field experiments that evaluate the economic benefits of promotion personalization based on modern machine learning stacks.