Pymc Marketing Mmm Clv

This is PyMC's full-featured Bayesian marketing analytics suite, and it's legitimately production-ready. You get proper MMM with adstock transforms (geometric, delayed, Weibull), saturation curves, and time-varying effects. The budget optimizer uses your fitted model to reallocate spend across channels, and you can calibrate against actual lift tests. CLV models cover the standard BTYD approaches (BetaGeo, Pareto/NBD, Gamma-Gamma). It supports NumPyro, BlackJax, and Nutpie samplers if PyMC's default NUTS is too slow. The Docker setup and extensive plotting methods suggest this was built by people who've actually shipped marketing models. If you're doing attribution or LTV work and want proper uncertainty quantification instead of point estimates, this beats rolling your own Stan code.

skills.sh installs: 794
GitHub stars: 4
last updated: Jul 14, 2026

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Install

$ npx -y skills add aradotso/marketing-skills --skill pymc-marketing-mmm-clv --agent claude-code

Installs into .claude/skills of the current project.

Files

PyMC-Marketing: Bayesian Marketing Analytics

Skill by ara.so — Marketing Skills collection.

PyMC-Marketing is a Bayesian marketing analytics toolbox built on PyMC. It provides production-ready implementations for Media Mix Modeling (MMM), Customer Lifetime Value (CLV), and Buy-Till-You-Die (BTYD) models with full probabilistic inference capabilities.

Installation

Basic Installation

# Using conda (recommended)
conda create -c conda-forge -n marketing_env pymc-marketing
conda activate marketing_env

# Using pip
pip install pymc-marketing

Docker Installation

The project provides Docker support for Jupyter-based workflows:

cd scripts/docker
docker build -t pymc-marketing .
docker run -p 8888:8888 pymc-marketing

Core Capabilities

1. Media Mix Modeling (MMM)

MMM helps quantify the impact of marketing channels on business outcomes with:

SKILL.md on GitHub

Details

Repository: github.com/aradotso/marketing-skills
Category: Marketing & SEO
First seen: Jul 14, 2026

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