Quantitative Marketing

The module covers selected topics in quantitative marketing, including but not limited to: - Foundations of quantitative marketing research - Consumer choice models and preference heterogeneity - Introduction to structural demand models - Dynamic consumer behavior and state dependence - Pricing, promotions, and firm decision-making - Digital and platform-based marketing models - Causal inference and experimental methods in marketing - High-dimensional data, text analysis, and machine learning applications - Comparison of structural, causal, and predictive approaches - Interpretation of managerial implications and policy counterfactuals

After successful completion of the module, students will be able to: - Critically read and evaluate quantitative marketing research published in leading academic journals. - Explain core modeling approaches in quantitative marketing, including structural consumer choice models, causal inference methods, and selected digital and data-driven approaches. - Assess identification strategies, assumptions, and limitations of quantitative models used to study consumer and firm behavior. - Interpret quantitative results and counterfactual analyses and translate them into managerial insights. - Communicate complex quantitative research clearly in written and oral form.

Seminar paper (approx. 15–20 pages): - In-depth analysis of one quantitative marketing article, complemented by related literature. - Focus on research question, model structure, identification, results, and critical evaluation. Oral presentation (approx. 30–40 minutes incl. discussion leadership): - Presentation and critical discussion of the selected article in class. The seminar paper and presentation jointly constitute 100% of the final grade, weighted equally. There is no written exam.

Introductory courses in Statistics, Econometrics, and Quantitative Methods / Data Analysis; basic familiarity with R; ability to read academic literature in English.

The module is designed as a highly interactive seminar combining instructor guidance with student-led learning: - Instructor-led input sessions introduce key concepts, modeling approaches, and methodological foundations. - Guided exercises and demonstrations (partly R-based) illustrate core ideas and empirical challenges. - Student presentations of selected seminal and recent journal articles form the core of the seminar discussions. - Moderated group discussions focus on model intuition, identification, and interpretation of results. - Joint replication exercises of selected studies (conducted collectively) provide hands-on insights into empirical implementation and robustness.