Rotterdam, South Holland, Netherlands
Passionate serial entrepreneur with 10+ years experience in designing Machine Learning solutions for automated one-to-one marketing, including recommender systems, ad personalization solutions, and price differentiation systems. Seasoned product manager for AI-based software products, able to lead cross-functional teams of tech experts. Strong expertise in FMCG and retailing. Published research on the intersection between Marketing and Computer Science on Machine Learning, Deep Learning, Discrete Choice Modeling, and Applied (Bayesian) Econometrics in leading marketing journals (e.g., Journal of Marketing Research). Ad-hoc reviewer for leading marketing journals and university lecturer in Marketing.
“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.