Cassandra vs Robyn

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.

Robyn is a free, open-source Marketing Mix Modeling package from Meta Marketing Science. It estimates media channel efficiency and effectiveness, adstock rates and saturation curves using machine-learning techniques. The package is intended for granular datasets with many independent variables, particularly those used by digital and direct-response advertisers with rich data sources. Robyn automates hyperparameter optimization with evolutionary algorithms and uses ridge regression to address multicollinearity and overfitting. Its time-series modeling accounts for trend, seasonality and holidays, and models can be calibrated against ground-truth methods such as geo-based tests, Facebook Lift and MTA. The budget allocator uses a constrained nonlinear solver to seek outcome gains through budget reallocation; model one-pagers support comparisons. Robyn does not require personally identifiable or individual-level data and does not rely on cookies or pixel data. A stable R version is available on CRAN, alongside a development version on GitHub. A Python version is documented as beta and may have translation issues. The package is MIT licensed; support options include a public user group and GitHub issues.