All firms must make decisions on how to grow their business and optimize profitability. This course introduces concepts and tools to make such decisions in practice using data, statistical analysis, and artificial intelligence (AI). We develop a framework that starts with a business objective and uses historical or experimental data to develop an actionable plan.
The class is hands-on. In the assignments, you will work with real-world data, statistical tools, and agentic AI. You define the business objective and analysis plan, then delegate technical implementation, especially coding, to an AI agent. You remain responsible for evaluating its work and ensuring its correctness. Using the results, you propose a final plan and present and defend it to senior management or your company's investors.
The course covers pricing and promotion decisions, personalized marketing and customer relationship management (CRM), advertising and digital marketing, and customer loyalty. Applications include product-line price optimization, personalized targeting for customer development, retention, and acquisition, measuring advertising effectiveness and profitability, and designing loyalty programs.
Linear and logistic regression are the primary statistical tools used in the course. We also discuss how to measure causal effects and how to design and implement business experiments. No programming background is required. You will go through a one-hour introduction to data analysis in R to understand and manage the whole workflow.
Business 37000 (or 37100) and 41000 (or 41001 or 41100) recommended. Cannot enroll in 37113 if 37103 or 37105 or 20620 or 37115 taken previously: strict.
This course will have a Canvas site.
Based on group assignments, a final take-home project, in-class quizzes, and class participation. Cannot be taken pass/fail. No auditors. This course has a strict class attendance policy, and attendance will be recorded each week. Students who miss class in both week 1 and week 2 will be unable to succeed in the course and will be dropped from the course. In other words, students must begin attending class no later than week 2.
- Allow Provisional Grades (For joint degree and non-Booth students only)
- Early Final Grades (For joint degree and non-Booth students only)
- No auditors
- No pass/fail grades
Description and/or course criteria last updated: October 05 2026
