AMMM Documentation (Version 2.5.1)
AMMM Documentation¶
Build evidence-led marketing attribution
AMMM helps you build, analyse, and optimise Marketing Mix Models using Bayesian methods with PyMC.
Whether you are running your first MMM or iterating on a mature measurement framework, AMMM focuses on reproducible workflows and clear outputs.
Includes: Bayesian inference · Adstock · Saturation · Budget optimisation · Prophet seasonality · Diagnostics (R-hat, ESS) · Model comparison (ELPD)
Documentation Sections¶
New to AMMM? Start here for installation, data preparation, and running your first model.
Perfect for: First-time users, quick setup
Practical step-by-step guides for common MMM tasks like configuring models, interpreting results, and optimising budgets.
Perfect for: Completing specific tasks
Deep dives into MMM methodology, model components, statistical concepts, and the theory behind the framework.
Perfect for: Understanding concepts
Technical details including configuration parameters, output file descriptions, and complete API documentation. API pages are generated at build time by AutoAPI.
Perfect for: Detailed specifications
Data validation tools, model diagnostic checks, and guidance for ensuring robust model performance.
Perfect for: Quality assurance
Solutions for common errors, performance tips, debugging strategies, and frequently asked questions.
Perfect for: Solving problems
MMM Workflow¶
graph LR
A[Prepare Data] --> B[Configure Model]
B --> C[Fit Model]
C --> D[Validate Results]
D --> E[Optimise Budget]
D --> F{Satisfied?}
F -->|No| B
F -->|Yes| G[Deploy Insights]
style A fill:#FFF4EC,stroke:#D9815E,stroke-width:2px
style C fill:#FBF7F2,stroke:#1D3D59,stroke-width:2px
style D fill:#F2F2F2,stroke:#1D3D59,stroke-width:2px
style E fill:#FFF4EC,stroke:#CF7744,stroke-width:2px
style G fill:#FBF7F2,stroke:#1D3D59,stroke-width:2px
Documentation Structure¶
Getting Started: Installation and first model tutorials
How-to Guides: Task-oriented guides for specific objectives
Explanation: Conceptual background and methodology
Reference: Technical specifications and API docs
Diagnostics: Validation and quality assurance
Troubleshooting: Problem-solving and debugging
About this documentation¶
Practitioners: Start with Getting Started → Quickstart, then run the pipeline via
runme.py.Engineers: See Guides and the API Reference. API pages are produced during the build by AutoAPI.
Analysts: Refer to the Output Schema and Guides on interpreting results to work with CSV outputs.