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

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

2025

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 and historical measures, can enhance the identification of at-risk individuals. By adopting behaviorally informed analytics, organizations may achieve more targeted and effective retention strategies, ultimately supporting more stable workforce management.

Paper Citation

in Harvard Style

Mazo R., Nogueira Júnior M., Soares de Quadros A., Vieira A. and Brandão W. (2025). Predicting Employee Turnover Using Personality Assessment: A Data-Driven Approach. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 582-592. DOI: 10.5220/0013436400003929

in Bibtex Style

@conference{iceis25,
author={Reynold Mazo and Maurício Nogueira Júnior and Arthur Soares de Quadros and Alessandro Vieira and Wladmir Brandão},
title={Predicting Employee Turnover Using Personality Assessment: A Data-Driven Approach},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={582-592},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013436400003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Predicting Employee Turnover Using Personality Assessment: A Data-Driven Approach
SN - 978-989-758-749-8
AU - Mazo R.
AU - Nogueira Júnior M.
AU - Soares de Quadros A.
AU - Vieira A.
AU - Brandão W.
PY - 2025
SP - 582
EP - 592
DO - 10.5220/0013436400003929
PB - SciTePress