We present a Bayesian approach to model cohort-level retention rates and
revenue over time. We use Bayesian additive regression trees (BART) to model
the retention component which we couple with a linear model for the revenue
component. This method is flexible enough to allow adding additional covariates
to both model components. This Bayesian framework allows us to quantify
uncertainty in the estimation, understand the effect of covariates on retention
through partial dependence plots (PDP) and individual conditional expectation
(ICE) plots, and most importantly, forecast future revenue and retention rates
with well-calibrated uncertainty through highest density intervals. We also
provide alternative approaches to model the retention component using neural
networks and inference through stochastic variational inference.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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