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
(ml) - python implementation of bayesian media mix modelling with shape and carryover effect
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
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.
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.
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
Production-grade Bayesian Media Mix Model with adstock transformation, saturation curves, and budget optimization
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.
Bayesian media mix modeling (MMX) framework with latent state decomposition and SKAN bias corrections for historical & causal attribution
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