Cassandra vs PyMC-Marketing

Cassandra is a web-based marketing measurement platform for teams deciding how to invest in growth. It combines marketing mix modeling with incrementality testing, including instant incrementality and performance incrementality reporting. Its models examine how paid media, organic activity, and offline or brand campaigns contribute to business outcomes. Teams can use budget scenario planning and forecasting before committing spend, as well as ROI reporting. Listed data sources include advertising platforms such as Google Ads, Meta Ads, and TikTok Ads, alongside commerce, email, and CRM services including Shopify, Klaviyo, and Salesforce. Scheduled files can bring in data from sources without an API, and custom connectors can be built for a model. Essentials starts at €2,700/mo and includes up to three MMM models and five incrementality experiments per year; it has a six-month minimum commitment. Complete and Scale also require six months, while Enterprise pricing is custom. A 14-day trial is available without a credit card. Cassandra is based in Milan and says it achieved SOC 2 compliance in 2024.

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.