46-894 Analytical Marketing
6 units, Carnegie Mellon University
Marketing has become much more quantitative and data intensive in recent years. Strategies like interactive marketing, customer relationship management, and database marketing push companies to utilize the information they collect about their customers in order to make better marketing decisions. Marketing transaction data—which is a common type of Big Data—often forms the core set of information used for making marketing decisions. This course focuses on how analytic techniques from data mining, machine learning and statistical modeling can be applied to solve marketing problems. The approach in this course is to complete a series of data intensive case studies which gives us a hands-on approach to learning marketing analytics. Each case exposes students to the data, the marketing problem, and a compatible analytical technique. Specifically, the case studies considered include customer segmentation, pricing decision support systems using retail transaction data, understanding customer churn in the cell-phone market, and upgrading freemium customers to paying customers. On the analytical side we use cluster analysis, topic models, regression, mixed models, logistic regression, decision trees, and potentially random forests and gradient boosted trees.
Prerequisites: none listed
Offered: Spring 2027, Spring 2026
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