pymc-marketing-mmm-clv
PyMC-Marketing provides a Bayesian analytics toolbox for Media Mix Modeling, Customer Lifetime Value, and Buy-Till-You-Die models. It supports probabilistic inference, adstock transformations, saturation effects, and budget optimization.
Is pymc-marketing-mmm-clv safe to install?
Review the source first: our audit of pymc-marketing-mmm-clv's source files found 6 shell commands, 0 external URLs, file reads and writes (high risk). Every command and URL listed appears verbatim in the skill's source. The skill provides instructions for executing shell commands and Docker containers, and it performs local file I/O for data loading and model persistence.
How we audit skills: our security review methodology.
Who is this skill for?
Data scientists and marketing analysts performing quantitative marketing research and budget allocation.
What can you do with it?
- Quantify marketing channel impact on business outcomes
- Optimize marketing budget allocation across channels
- Forecast customer lifetime value
- Model customer dropout and transaction frequency
- Calibrate models with lift test results
How good is this skill?
Quality score: 5/10. The documentation is comprehensive, providing clear code examples for all core features, including model setup, diagnostics, and optimization.
What does the skill file contain?
# PyMC-Marketing: Bayesian Marketing Analytics > Skill by [ara.so](https://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 ```bash # Using conda (recommended) conda create -c conda-forge -n marketing_env pymc-marketing conda activate marketing_env # Using pip pip install pymc-marketing ``` ### Docker ...
Frequently asked questions
What types of adstock transformations are supported?
The library supports Geometric, Delayed, and Weibull adstock transformations.
Can I use different samplers for model fitting?
Yes, the library supports PyMC, NumPyro, BlackJax, and Nutpie samplers.
How does the library handle budget optimization?
The library provides an allocate_budget method that accepts total budget constraints and channel-specific bounds to maximize ROI.
Does the library support saving and loading models?
Yes, models can be saved to and loaded from .nc files using the save and load methods.
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