MARK 3243: Customer Analytics (Undergraduate)

Undergraduate elective | Georgetown McDonough | Spring 2027

Organizations today make decisions in an environment rich with customer data—from online transactions and app usage to loyalty programs and customer reviews. Customer analytics turns this data into evidence for marketing decisions: which customers to target, who is likely to leave, what to recommend, and whether a campaign actually worked.

This hands-on course teaches the tools and reasoning used to understand, predict, and influence customer behavior. No prior coding experience is needed: students work in Google Colab with AI-assisted coding tools, focusing on the logic of the analysis rather than memorizing syntax.

The first part covers descriptive and predictive analytics—visualizing customer data, segmenting customers with cluster analysis, estimating their lifetime value, predicting their choices with logistic regression and decision trees, building product recommendations with association rules, analyzing customer sentiment on social media, and personalizing prices.

The second part turns to causal inference: how to tell whether a marketing action actually caused a change in customer behavior. Students learn to design and analyze A/B tests, use quasi-experiments when randomized tests are not possible, and identify which customers respond differently to the same intervention. The final part examines analytics in practice: how companies run experiments at scale and weigh their ethical implications. Through in-class workshops, analysis of recent articles in the news, and group presentations, students learn to translate analytical results into clear, actionable marketing recommendations.