Paramark vs PyMC-Marketing

Paramark has no write-up on Specifiction yet; its figures are above.

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.