Empirical Customer Data Analysis

Winter term 2021/2022
Co-instructor with Martin Schmidberger
Evaluation: 5.6/6


This course focuses on the analysis of large amounts of (customer) data for data-driven marketing. We introduce analytical methods and show how to apply them for better understanding and forecasting customer behavior, and designing marketing activities more effectively and efficiently. Among others, we cover segementing the customer base, understanding the behavior of (web) users, predicting purchase or cancellation probabilities, identifying the determinats of purchase recommendations, or drivers of (dis)satisfaction.

All applications in this course draw upon real-world customer data provided by a leading retail bank.

Topics Covered

  • Data Preparation
  • Linear / Logistic Regression
  • Decision Trees & Random Forests
  • Forecasting
  • Web Analytics
  • Logfile Analysis
  • Customer Base Segmentation
  • Customer Satisfaction and Loyalty
  • Text Mining
  • Legal and Ethical Considerations
  • Scientific Writing

View at Goethe University (in German)

Maximilian Matthe
Maximilian Matthe
Assistant Professor of Marketing