Mastering Marketing Mix Modelling In Python

This post is the first part of a series on mastering Marketing Mix Modelling (MMM) using the pymc-marketing package in Python. It offers a detailed guide on Bayesian MMM, exploring key concepts like model training, validation, calibration, and budget optimization. The post covers simulating data, training the model, validating the results, and understanding default and custom Bayesian priors. Additionally, a practical demonstration using Python code provides hands-on experience with MMM, focusing on adstock effects, saturation, and parameter recovery.