Predicting employee attrition using tree-based models
International Journal of Organizational Analysis
ISSN: 1934-8835
Article publication date: 3 March 2020
Issue publication date: 19 October 2020
Abstract
Purpose
The purpose of this study is to develop tree-based binary classification models to predict the likelihood of employee attrition based on firm cultural and management attributes.
Design/methodology/approach
A data set of resumes anonymously submitted through Glassdoor’s online portal is used in tandem with public company review information to fit decision tree, random forest and gradient boosted tree models to predict the probability of an employee leaving a firm during a job transition.
Findings
Random forest and decision tree methods are found to be the strongest attrition prediction models. In addition, compensation, company culture and senior management performance play a primary role in an employee’s decision to leave a firm.
Practical implications
This study may be used by human resources staff to better understand factors which influence employee attrition. In addition, techniques developed in this study may be applied to company-specific data sets to construct customized attrition models.
Originality/value
This study contains several novel contributions which include exploratory studies such as industry job transition percentages, distributional comparisons between factors strongly contributing to employee attrition between those who left or stayed with the firm and the first comprehensive search over binary classification models to identify which provides the strongest predictive performance of employee attrition.
Keywords
Acknowledgements
The authors would like to acknowledge Andrew Chamberlain, Chief Economist at Glassdoor, for sharing the job transition data set that served as the data source for this study. The authors would also like to thank the anonymous referees and the editor Prof Peter Stokes for their comments which improved the exposition of this article.
Funding: S. Taylor would like to acknowledge partial support by the Grant Agency of the Czech Republic DyMoDiF – Dynamic Models for Digital Finance grant 19-28231X.
Competing interests: The authors have no competing interests.
Citation
El-Rayes, N., Fang, M., Smith, M. and Taylor, S.M. (2020), "Predicting employee attrition using tree-based models", International Journal of Organizational Analysis, Vol. 28 No. 6, pp. 1273-1291. https://doi.org/10.1108/IJOA-10-2019-1903
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited