Customer Analytics

3 Credits · Level: Undergraduate

Organizations today make decisions in an environment rich with consumer data—from online transactions and app usage to customer feedback and social interactions. The field of customer analytics enables marketers to transform this data into insights that inform strategic and operational decisions. This course introduces the analytical tools and reasoning used to understand, predict, and influence customer behavior. It begins with an introduction to R and generative AI tools for analytics, emphasizing the use of coding and automation for data exploration and visualization. The first part focuses on descriptive and predictive analytics, where students learn to summarize customer data, identify behavioral patterns, and apply statistical models such as logistic regression, decision trees, and clustering. The second part turns to causal inference, focusing on how to design and interpret experiments that determine whether specific managerial actions cause changes in customer outcomes. Students explore quasi-experimental methods, A/B testing, and moderation analysis, developing the ability to assess evidence and quantify uncertainty. The final part emphasizes analytics in practice, addressing how experiments and data models are implemented at scale and evaluated for ethical implications. Through coding assignments, business cases, and short group analyses, students will develop the ability to translate complex analytical findings into actionable marketing decisions and communicate them effectively.