Google Meridian vs PyMC-Marketing

Google Meridian is a free, open-source Bayesian marketing mix modeling library for estimating channel contribution and ROI, modeling response curves and planning budgets. It is designed to estimate causal marketing impact and incremental outcomes for treatment variables. Teams can use geo-level data for media effect estimates, incorporate business knowledge through priors, and include controls such as Google Query Volume. Meridian supports reach and frequency modeling for video and search query volume data to account for organic demand. Its budget optimization and scenario planning features help teams assess channel allocation. Meridian GeoX supports geo experiments whose results can be used to calibrate models; automated calibration and expanded model health diagnostics are also supported. The Scenario Planner notebook requires access to at least one trained model stored in Google Drive or Google Cloud Storage. Meridian Studio is a Google Cloud-powered enterprise platform for managing models, with capacity to run up to hundreds simultaneously. Google describes Meridian as intended for cross-functional measurement teams, including analysts, business users, data scientists and technical practitioners. Support includes community discussions and expert support through Discord, plus technical troubleshooting through GitHub.

PyMC-Marketing is an open-source Python library for Bayesian marketing analytics, with tools for marketing mix modeling (MMM) and customer lifetime value. Its MMM API supports custom priors and likelihoods, adstock and saturation transformations, time-varying effects, diagnostics and out-of-sample predictions. Models can be calibrated against empirical experiments such as lift tests, and built-in budget optimization allocates spend across channels to maximize ROI. Bayesian outputs include posterior distributions and credible intervals for predictions. The library also includes customer choice, Bass diffusion and alpha-stage predicted incrementality by experimentation models, alongside predictive CLV, cohort analysis and customer segmentation. It runs on Linux and can be self-hosted. Current documentation requires Python 3.12 or later, PyMC 6.0 or later and ArviZ 1.2 or later; installation is available through pip with `pip install pymc-marketing`, without requiring a conda environment. The library is free under the Apache 2.0 license. PyMC Labs and PyMC core developers maintain it, and learning resources include a guided path, example notebooks and an API reference.