Stochastic Churn Modeling with Dynamic Attribution and Bayesian Estimation

Parametric probability models, such as Beta-Geometric, are workhorse models for contractual customer churn prediction. Due to their simplicity, robustness to missing and censored data, and managerially relevant statistics, those models are applied to different business sectors such as healthcare and finance. Nonetheless, the existent models tend to assume a stationary churn process or an identical distribution of latent churn rate. The Beta-Logistic model by Hubbard et al. (Survival prediction-algorithms, challenges and applications. PMLR, pp 22–39 [17]) allows for time-invariant covariates and captures non-identically distributed individual churn. To further accommodate time-varying determinants of churn rate, we apply a Grassia(II)-Geometric (G2G) model by Fader and Hardie (Incorporating time-varying covariates in a simple mixture model for discrete-time duration data [9]). Grounded on the flexible model structure, we propose Bayesian estimation and inference of G2G and empirically assess its prediction performance. Using a workforce dataset from an electronic manufacturing service company, we show that G2G with greater flexibilities outperform extant models in terms of model fitness and employee churn prediction. Additionally, we identify major determinants of churn processes in manufacturing plants and generate cohort-wised survival curves. With built-in interpretability and posterior inference, our Bayesian G2G modeling approach can be useful for churn prediction in marketing and operations management.