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ad_response_tutorial

This tutorial shows users how to evaluate advertising response using last click attribution, experiments, marketing mix models and attribution models. By applying these methods to the same (synthetic) data set, users will learn how the methods compare. We also illustrate the data manipulation that is required to prepare typical raw advertising data for analysis. Examples are worked in R and slides are provided in LaTeX.

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data-science-machine-learning-ai-resources

A curated set of resources for data science, machine learning, artificial intelligence (AI), data and text analytics, data visualization, big data, and more.

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mmm_stan

Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

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Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.

pyprobml

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Jupyter Notebooks for the Python Data Science Handbook

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