Rapid advances in information technology during the last decades have enabled firms to create and analyze large databases of customer interactions and transactions. Data-driven marketing is an approach to implement marketing decisions based on a statistical analysis of big data to improve the profitability of marketing using ROI metrics.
The class is designed to provide a broad overview of data-driven marketing techniques. In the first part of the class we study methods to measure store and market level demand using demand models. Applications include base-price optimization, data-driven price discrimination, and promotions management. We also study the measurement of short-run and long-run effects of advertising. In the second part of the class we cover customer relationship management (CRM) and database marketing. We introduce a general framework to implement customer-level targeting using predictive modeling based on customer lifetime value and return on investment (ROI) predictions. We apply this framework to customer development, retention, and acquisition decisions. The final part of the class focuses on digital marketing and how to predict the effectiveness and profitability of display and search advertising.
Throughout the class we make use of statistical tools, including regression analysis and logistic regression. In particular, all assignments and the take-home final involve practical applications of the concepts covered in class using data and methods implemented in the R statistical computing language.