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 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
Bayesian media mix modeling (MMX) framework with latent state decomposition and SKAN bias corrections for historical & causal attribution
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