---
title: "Resources"
source: PyMC-Marketing (Bayesian MMM)
source_url: https://github.com/pymc-labs/pymc-marketing
licence: Apache-2.0
domain: economics-and-strategy
subdomain: pymc-marketing
date_added: 2026-04-25
---

# Resources

## Videos

### Bayesian Marketing Science - Solving Marketing's 3 Biggest Problems

In this talk I will present two new open-source packages that make up a powerful and state-of-the-art marketing analytics toolbox. Specifically, PyMC-Marketing is a new library built on top of the popular Bayesian modeling library PyMC. PyMC-Marketing allows robust estimation of customer acquisition costs (via media mix modeling) as well as customer lifetime value.
In addition, I will show how we can estimate the effectiveness of marketing campaigns using a new Bayesian causal inference package called CausalPy. The talk will be applied with a real-world case-study and many code examples. Special emphasis will be placed on the interplay between these tools and how they can be combined together to make optimal marketing budget decisions in complex scenarios.

Click through the video thumbnail below to [watch the video](https://www.youtube.com/watch?v=RY-M0tvN77s).


### Bayesian Marketing Mix models: State of the Art and their Future
The video below is a discussion, recorded in 2022, about the state of the art of Bayesian Marketing Mix Models (MMM) and their future. Click through the video thumbnail below to [watch the video](https://www.youtube.com/watch?v=xVx91prC81g).


### PyMC-Marketing: Bayesian Approach to Marketing Data Science

In this webinar, you'll learn about the history and development of PyMC-Marketing from Ben Vincent, and why using an open source package like this can be advantageous for your marketing analytics needs.

The two main components of PyMC-Marketing - media mix modelling and customer lifetime value - will be presented by Ben Vincent, with input from Niall Oulton, and Christian Luhmann with input from Colt Allen, respectively. You will learn how PyMC-Marketing can be used to accurately model marketing campaigns and determine their effectiveness, as well as how it can help businesses understand and optimize their customer lifetime value. Click through the video thumbnail below to [watch the video](https://youtu.be/7a_HL5BRB-s?si=fitQK_GrQcoSNWJq)


### Bolt's Evolution towards MMM with PyMC with Carlos Agostini
The video below is a discussion highlighting Bolt's adoption of PyMC for building Marketing Mix Models (MMM) and more. This webinar will take you through the various approaches Bolt initially considered for its MMM before ultimately deciding on PyMC. Click through the video thumbnail below to [watch the video](https://youtu.be/djXoPq60bRM?si=fitQK_GrQcoSNWJq)


### PyMC-Marketing Yearly Catch-up & New Use Cases with Niall Oulton and Carlos Agostini

We're excited to present a live demo, meticulously designed to showcase a selection of compelling use cases. These cases are not just theoretical constructs; they are real-world applications demonstrated by actual users of PyMC-Marketing. Witness firsthand how our platform has been instrumental in their success.

To add more substance to our session and ensure it's not just informative but also engaging, we've planned a unique segment featuring the new GPT model developed by Niall. The new GPT will be the most powerful assistant to create MMM models. Click through the video thumbnail below to [watch the video](https://youtu.be/ikCK76gq65Q?si=fitQK_GrQcoSNWJq)


## Business Cases

* PyMC Labs: [Bayesian Media Mix Models: Modelling changes in marketing effectiveness over time](https://www.pymc-labs.com/blog-posts/modelling-changes-marketing-effectiveness-over-time/)
* Bolt: [Better budgeting with Bayesian models: Bolt’s story with PyMC-Marketing](https://bolt.eu/en/blog/budgeting-with-bayesian-models-pymc-marketing/)
* Shell & Databricks: [Revolutionizing Tech Marketing: The Synergy of PyMC and Databricks](https://www.databricks.com/blog/revolutionizing-tech-marketing)

## Blogposts

* PyMC Labs: [Improving the Speed and Accuracy of Bayesian Media Mix Models](https://www.pymc-labs.com/blog-posts/reducing-customer-acquisition-costs-how-we-helped-optimizing-hellofreshs-marketing-budget/)
* 1749: [Measuring Marketing Effectiveness Over the Long-Term](https://1749.io/resource-center/f/measuring-marketing-effectiveness-over-the-long-term)
* 1749: [Marketing Mix Modeling in a Modern Era: Time-Varying Parameters](https://1749.io/resource-center/f/marketing-mix-modeling-in-a-modern-era-time-varying-parameters)
* 1749: [A Comprehensive Guide to Marketing-Mix Modeling](https://1749.io/resource-center/f/a-comprehensive-guide-to-bayesian-marketing-mix-modeling)
* Juan Orduz: [Media Effect Estimation with Orbit's KTR Model](https://juanitorduz.github.io/orbit_mmm/)
* Juan Orduz: [Media Effect Estimation with PyMC: Adstock, Saturation & Diminishing Returns](https://juanitorduz.github.io/pymc_mmm/)
* Juan Orduz: [Media Mix Model and Experimental Calibration: A Simulation Study](https://juanitorduz.github.io/mmm_roas/)
* Dr. Robert Kübler: [Convenient Bayesian Marketing Mix Modeling with PyMC-Marketing](https://towardsdatascience.com/convenient-bayesian-marketing-mix-modeling-with-pymc-marketing-8b02a9a9c4aa)
* Ryan O'Sullivan, [Is Multi-Collinearity Destroying Your Causal Inferences In Marketing Mix Modelling?](https://towardsdatascience.com/is-multi-collinearity-destroying-your-causal-inferences-in-marketing-mix-modelling-78cb56017c73)

## Tutorials

- [Example Problem: JellyPop](https://github.com/PhilClarkPhD/mmm)

## MMM Explainer App

- [MMM Explainer App](https://pymc-marketing-app.streamlit.app/). Dynamic and interactive visualization of key Marketing Mix Modeling (MMM) concepts, including adstock, saturation, and the use of Bayesian priors.
