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PyMC-Marketing

pymc-marketing.io

Free plan LinuxSelf-hosted
Visit pymc-marketing.io
Spec score 7.5 #12 of 24 in Marketing Performance Management Software

What it is

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.

Spec sheet

Purpose
PyMC-Marketing is an open-source Python library for Bayesian marketing analytics, including marketing mix modeling and customer lifetime value models.Read 8 Oct 2026 · pymc-marketing.io
Support
The maker offers a free 30-minute strategy session with its experts and a contact option.Read 8 Oct 2026 · pymc-marketing.io
License
PyMC-Marketing is free and open source under the Apache 2.0 license.Read 8 Oct 2026 · pymc-marketing.io
Budget optimization
Built-in budget optimization allocates marketing spend across channels to maximize ROI.Read 8 Oct 2026 · pymc-marketing.io
Experiment calibration
MMM models can be calibrated using empirical experiments, including lift tests.Read 8 Oct 2026 · pymc-marketing.io
Inference options
The MMM API supports multiple NUTS samplers, including BlackJax, NumPyro, and Nutpie, and PyMC backends allow GPU acceleration.Read 8 Oct 2026 · pymc-marketing.io
Installation
The library can be installed with pip using `pip install pymc-marketing`; the documentation says no conda environment is required.Read 8 Oct 2026 · pymc-marketing.io
Learning resources
The maker provides a guided learning path, example notebooks, and an API reference.Read 8 Oct 2026 · pymc-marketing.io
Maintainer
PyMC-Marketing is built and maintained by PyMC Labs and the core developers of PyMC.Read 8 Oct 2026 · pymc-marketing.io
Maker background
PyMC Labs describes itself as a Bayesian AI consultancy founded by core members of the PyMC community.Read 8 Oct 2026 · pymc-labs.com
MMM features
Its MMM API supports custom priors and likelihoods, adstock and saturation transformations, time-varying effects, diagnostics, and out-of-sample predictions.Read 8 Oct 2026 · pymc-marketing.io
Organization details
The opened PyMC Labs about page does not state a headquarters or the consultancy's founding year.Read 8 Oct 2026 · pymc-labs.com
Other model types
The library also includes customer lifetime value, customer choice, Bass diffusion, and alpha-stage predicted incrementality by experimentation models.Read 8 Oct 2026 · pymc-marketing.io
Requirements
The current documentation requires Python 3.12 or later, PyMC 6.0 or later, and ArviZ 1.2 or later.Read 8 Oct 2026 · pymc-marketing.io
Uncertainty
The library provides Bayesian uncertainty quantification, including posterior distributions and credible intervals for predictions.Read 8 Oct 2026 · pymc-marketing.io

Plans and prices

PyMC-Marketing Free Apache 2.0 open-source Python library Read 8 Oct 2026 · pymc-marketing.io

Every PyMC-Marketing plan and price →

Strong points and limits

Strong points

  • Free and open source under Apache 2.0
  • MMM supports custom priors and likelihoods
  • Calibrate models with experiments such as lift tests
  • Includes budget optimization across channels
  • Provides posterior distributions and credible intervals

Limits

  • Requires Python 3.12 or later
  • Requires PyMC 6.0 or later
  • Requires ArviZ 1.2 or later

Who it suits

It suits teams doing Bayesian marketing measurement, customer lifetime value analysis or budget allocation who can work in Python. The documented Python, PyMC and ArviZ versions are prerequisites to consider.

Verdict

PyMC-Marketing combines flexible MMM workflows with uncertainty estimates, experiment calibration and channel budget optimization. Its requirements and Python-library format make the documented environment an important part of the fit.

Where Specifiction places it

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