AI for Marketing Mix Modeling: Foundations of Modern Attribution

Marketing Mix Modeling has existed for decades, but the way it is built has changed in the last few years. Modern approaches blend traditional econometrics with machine learning, handle privacy-friendly data, and produce results that update faster than the quarterly reports of the past. This course gives you a calm, structured introduction so you can speak confidently about MMM regardless of which tool your team uses. You will learn what MMM actually measures, where AI strengthens the approach, and how it sits alongside multi-touch attribution and incrementality testing. The course stays grounded in widely used concepts and respects the realities of marketing organizations. What you'll learn: - Understand the goals of Marketing Mix Modeling and how it differs from last-click attribution - Recognize the data inputs MMM requires, including media spend, sales, and external factors - Explore how AI improves classical MMM through nonlinear effects, adstock decay, and Bayesian priors - Compare MMM with multi-touch attribution and incrementality experiments in the same toolkit - Read model outputs including channel contribution, response curves, and saturation effects - Understand the integration points between MMM, planning tools, and broader analytics stacks The course begins with the goals of marketing measurement, moves through how AI extends classical MMM, and closes with practical realities including data quality, governance, and stakeholder communication. Written exercises help you connect each concept to a real product or brand context. This course is designed for absolute beginners with no econometrics or AI background, including marketing analysts, brand managers, and students of marketing science. No prerequisites are needed. The course explains every concept as it appears and respects how real marketing teams actually plan and report.