In Part 1, we covered the conceptual framework of MMM optimization. Now let's get practical: we'll walk through real optimization scenarios using Mixed Integer Programming (MIP) with discretized response curves.
This guide provides actual numbers, formulations, and step-by-step solutions for common optimization problems you'll encounter in production.
New to MIP for MMM? Check out Mixed Integer Programming for MMM optimization for the mathematical foundations, solver selection, and why MIP guarantees global optimality.
Let's start with a realistic e-commerce company running six marketing channels with a $10M annual budget.
À propos de l'auteur
Cyril Noirot
Lead Data Scientist
Data scientist freelance. Je conçois et déploie des systèmes de décision — prévision, pricing, marketing measurement, optimisation.