Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.
Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
A curated list of awesome marketing science resources including geo incrementality testing, media mix models, multi-touch attribution, causal inference, and more from shakostats.com . Star ⭐ the repo if it helps you, and feel free to contribute your own favorite resources
(ml) - python implementation of bayesian media mix modelling with shape and carryover effect
Media Mix Model with simulated data and stan
Analysing the challenges and opportunities in Media Mix Modelling using sales and media spending time series data.
A production-ready Bayesian MMM framework emphasizing methodological rigor over specification shopping. Full uncertainty quantification, hierarchical modeling, and async fitting via PyMC-Marketing.
Access a curated list of free AI models, APIs, and tools for local inference and production use without hidden costs.
A self-contained Jupyter notebook covering Marketing Mix Modeling from theory to implementation, designed for data scientists.
10-paper marketing science framework with 11 live dashboards. Bayesian MMM, causal inference, probabilistic identity resolution, and real-time streaming attribution. All papers published with Zenodo DOIs.
End-to-end Marketing Mix Modeling & Omnichannel Optimization — PepsiCo
A full-stack Marketing Mix Model (MMM) for a simulated DTC retail e-commerce brand (ShopNova), built from scratch in Python with an interactive React dashboard.
A comprehensive Bayesian Media Mix Modeling system for analyzing marketing channel effectiveness, optimizing budget allocation, and measuring incremental sales impact with MLOps experiment tracking.
Interactive version of Daniel Saunder's blog post
Healthcare lead-generation Marketing Mix Modeling reference implementation with PyMC-Marketing, adstock, saturation, ROI/mROAS, Streamlit dashboard, and budget allocation.
Marketing Mix Modeling with Google Meridian on GA4 data. Bayesian inference, full posterior distributions, PyMC-powered. Applied to real GA4 ecommerce data with step-by-step guide.
🛡️ Hybrid data ingestion pipeline: Elixir concurrency + Rust safety via Rustler NIFs. Built for MarTech AI pipelines with PII sanitization and prompt injection detection.
A production-grade dbt (Data Build Tool) project for advertising analytics with models, tests, snapshots, analyses, macros, and YAML documentation for DSPs (DV360, Amazon, TTD, Yahoo), ad servers (DCM), and Salesforce campaign data.
Lightweight Marketing Mix Model: adstock + saturation + regression. No Meridian, no PyMC — just numpy and scikit-learn. A simpler alternative to Google Meridian for quick MMM analysis.
Production-grade Bayesian Media Mix Model with adstock transformation, saturation curves, and budget optimization
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