3 credits
Spring 2026 Lecture Upper Division
This course develops analytical skills for working specifically with "consumer-level" data, focusing on how individual behaviors inform marketing strategy. Distinct from broader marketing analytics, the course primarily emphasizes understanding the decision-making processes of consumers and how these behaviors translate into firm outcomes and strategies. Main topics include churn analysis, segmentation and targeting, customer lifetime value (CLV), intervention assessment, and conjoint analysis - each taught through the lens of identifying more and less valuable consumers. A key objective is to interpret behavioral patterns that drive strategic decisions in marketing and customer management. The course also includes hands-on training in Python, one of the most widely used tools for data analysis and AI development. Students will gain exposure to modern AI and machine learning techniques such as clustering, random forests, XGBoost, and support vector machines (SVM), and apply them to real-world consumer analytics problems. By the end of the course, students will be able to leverage AI tools to derive actionable insights about consumer behavior and design data-driven strategies that align with firm goals.
Learning Outcomes1Use data analytics to improve customer engagement and elevate the overall customer experience.
2Clarify the role of analytical customer relationship management in achieving strategic marketing goals across various industries.
3Carry out analytical skills through practical experience with customer datasets.
4Recognize advanced modeling techniques commonly employed in the field.
5Summarize the financial advantages of adopting marketing strategies based on the concept of 'Customer Lifetime Value'.
Prerequisites
Restrictions
made by Purdue students.