Revup Marketing vs Robyn

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

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.