Designing a Marketing Mix Model with Modern Bayesian and ML Techniques

Building a Marketing Mix Model that earns trust requires more than running an algorithm on a few quarters of spend. The data you assemble, the priors you choose, the way you validate against holdouts, and the visualizations you offer marketers all decide whether the model is used or shelved. This course walks through those choices in the order they typically arise. You will work through written design exercises that mirror how a marketing analytics team would plan a modern MMM. The emphasis is on the practical tradeoffs that matter when data is messy, channels overlap, and stakeholders ask hard questions. What you'll learn: - Assemble and clean marketing spend, sales, and external data including economic and competitive signals - Engineer features for adstock, saturation, and seasonality with clear domain assumptions - Compare Bayesian MMM approaches and machine learning extensions including gradient boosting and neural networks - Validate models with holdouts, geo experiments, and consistency checks across periods - Translate model outputs into budget recommendations and scenario plans - Build visualization and reporting tools that help marketers explore tradeoffs interactively The course progresses from data preparation through feature engineering, modeling, validation, and reporting. A capstone written exercise asks you to draft a one-page design for an MMM project targeted at a specific brand and channel mix. This course is designed for data scientists, marketing analysts, and decision scientists with some software background. No prior MMM experience is required. The course treats the model as a design problem and stays close to how real marketing teams actually plan and evaluate.