GRIFFIN / AMMM3 3.1.2

Marketing mix modelling

Build, assess and interpret Bayesian marketing mix models in Python.

Get startedInstall the library and build your first model. Choose an estimatorUnderstand the time-series, FE and CRE contracts. API referenceFind Python entry points and configuration details.

AMMM3 is a Bayesian MMM library built on PyMC and PyTensor.

The public PanelMMM API includes released named presets for one aggregate time series, one-unit fixed effects (FE), and one-unit correlated random effects (CRE). Start with Choose an Estimator before preparing a panel model. The random-effects (re) preset remains release-gated.

Documentation Sections

Getting Started — Installation, quickstarts, first model
Data Preparation — Input data requirements and layout
Model Specification — Estimator choice, PanelMMM equation, transforms, priors, and calibration
Model Fitting — Fitting, prior predictive checks, save/load
Post-Modeling — Diagnostics, contributions, response curves, export
Optimization — Budget allocation and interpretation
Scenario Planning — Planner specifications, library service and comparison outputs
Pipeline Runner — Structured runner, YAML config, staged outputs
FAQ — Econometrics explainers for practitioners
Contributing — Architecture, development setup, testing
API Reference — Module and class reference