n this specific domain of customer base analysis, probabilistic approaches from the ‘‘Buy ’Till You Die” (BTYD) model family represent the gold standard, leveraging easily observable Recency and Frequency (RF, or RFM when including also the monetary value) metrics together with a latent attrition process to deliver accurate predictions (Schmittlein, Morrison, & Colombo, 1987; Fader, Hardie, & Lee, 2005; Fader & Hardie, 2009). The simple behavioral story which sits at the core of BTYD models – while ”alive”, customers make purchases until they drop out – gives these models robust predictive power, especially on the aggregate cohort level, and over a long time horizon. Extended variants of the original models (e.g., Zhang, Bradlow, & Small (2015), Platzer & Reutterer (2016), Reutterer, Platzer, & Schröder (2021)) improve predictive accuracy by incorporating more hand-crafted summary statistics of customer behavior. However, including customer covariates is cumbersome and an approach to account for time-varying covariates has only just recently been introduced by Bachmann, Meierer, and Näf (2021) at the cost of manual labeling and slower performance. Even advanced BTYD models can be too restrictive to adequately capture diverse customer behaviors in different contexts and the derived forecasts present customer future in an oftentimes too simplified way