Measured vs PyMC-Marketing

Measured is an AI-powered marketing effectiveness platform for enterprise brands. It combines causal experiments, media mix modeling, and advertising-platform data to assess media impact. Geo-matched and first-party split tests measure incremental sales, while automated tools cover machine-learning market selection, audience and geographic splitting, test design, and calibration. Media mix models use incrementality tests as causal priors and can be updated weekly, monthly, or quarterly. The Media Plan Optimizer runs budget scenarios and allocates spending toward marginal ROI, growth, or profitability goals. Measured lists more than 300 fully managed integrations across analytics, business intelligence, CRM and email, ecommerce, social, search, and TV and OTT, plus a REST API for cross-channel performance data. The platform runs on web and API. Pricing is available on request, and each customer is paired with an analytics expert.

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.