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
Skills
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