Predicting Employee Turnover Using Personality Assessment: A Data-Driven Approach

Reynold Navarro Mazo, Maurício Pereira Nogueira Júnior, Arthur Rodrigues Soares de Quadros, Alessandro Garcia Vieira, Wladmir Cardoso Brandão

Abstract: Employee turnover represents a persistent challenge for organizations seeking to maintain stability, retain institutional knowledge, and control costs. Traditional predictive models often rely on static employee records and demographic variables, providing limited insight into the nuanced behavioral patterns that precede workforce attrition. This study leverages the PACE Behavioral Profile Mapping (BPM) framework to integrate behavioral features into a machine learning–based turnover prediction pipeline. Clustering techniques were employed to ensure model generalization for specific company clusters, and hyperparameter optimization was performed using Optuna. The resultant CatBoost models demonstrated notable improvements in predicting turnover risk, particularly for employees at higher risk of departure, when PACE-based behavioral indicators were incorporated. These findings suggest that a more comprehensive characterization of employee tendencies, beyond conventional demographic an