Assistant Professor of Quantitative Marketing

HEC Paris

Research Statement

My research interests meet at the interface of empirical quantitative marketing, management economics, and information systems. Specifically, my research is concerned with pricing and advertising issues in two-sided markets, foremost the digital economy. Methodically, my research is based on quantitative empirical modeling, applied econometrics, distributed statistical computing, causal machine learning, as well as large-scale field- and lab-experiments. My research has been published in top-tier academic (e.g., the Journal of Marketing Research, the Journal of Product Innovation Management, or the International Journal of Research in Marketing) as well as management-oriented journals (e.g., Marketing Review Sankt Gallen, GFK Marketing Intelligence Review). In my research projects, I often collaborate with the industry to answer research questions at scale.

Research Topics

  • Quantitative and Data-driven Methods in Marketing

  • Customer Lifetime Value-Based Customer Acquisition

  • Demand Estimation

  • Pricing and Willingness-to-Pay

  • Advertising Effectiveness

  • Data Privacy and Regulation

Research Methods

  • Empirical Modelling

  • Applied Econometrics

  • Distributed Statistical Computing

  • Causal Machine Learning

  • Large-scale Field- and Lab-Experiments


  • Statistical Computing: R, Python, Stata

  • Cloud Computing: Amazon Web Services (AWS), Amazon Simple Storage (S3), Amazone Elastic Map Reduce (EMR), Amazon Elastic Cloud (EC2), Amazon Redshift

  • Big Data Analysis: Apache Hadoop, Apache Spark

  • Web Analysis: Google Analytics, Google Tag Manager, Adobe Analytics, Adobe Tag Manager

  • Online Experimentation: Adobe Test & Target, Optimzely

  • Languages: English | German | French