This session presents a bayesian approach to model cohort-level retention rates and revenue over time. Wolt uses bayesian additive regression trees to model the retention component which they couple with a linear model to model the revenue component. This method is flexible enough to allow adding additional covariates to both model components. This bayesian model allows Wolt to quantify the uncertainty in the estimation, understand the effect of the covariates and forecast the future revenue, and retention rates. The source code is open sourced on GitHub and for the presentation Juan will use synthetic data.