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PyMC-Marketing MMM

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Master Bayesian MMM modeling with PyMC-Marketing.

Data & Analytics#saturation#bayesian#gam#marketing-mix#adstock#hierarchical-priors
Authorbenmaier
Version1.0.0
Installs0

System Documentation

What problem does it solve?

PyMC-Marketing MMM enables users to design and configure Bayesian Marketing Mix Models with adstock, saturation, and hierarchical priors, helping deliver principled inferences for marketing effectiveness.

Core Features & Use Cases

  • Flexible MMM architectures from simple to multidimensional hierarchies with Bayesian priors and GAM components
  • Built-in adstock and saturation transformations for carryover and diminishing returns
  • Priors configuration, model building, and posterior inference workflows with diagnostics
  • Guidance for using patterns (single-series and multi-market) and evaluating model fit

Quick Start

Provide a minimal example by creating an MMM with GeometricAdstock and LogisticSaturation, building the model with your data, and running a first fit.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: PyMC-Marketing MMM
Download link: https://github.com/benmaier/decision-agent-placeholder/archive/main.zip#pymc-marketing-mmm

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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