Title: Managing employee turnover: machine learning to the rescue

Authors: Owen P. Hall

Addresses: Graziadio School of Business, Pepperdine University, Malibu, CA 90263, USA

Abstract: Organisations continue to face ongoing employee retention and recruiting challenges, which have become even more acute due to the COVID-19 pandemic. In today's unstable economy, employee retention is still one of the hot button issues facing many HR managers. Employee turnover has cost organisations billions of dollars each year. The empirical results from the current study, which included employee demographic, preference, and performance data, suggests that machine learning-based predictive models can provide automatic and timely employee assessments, which allow for both the identification of employees that may be planning to leave and the implementation of appropriate amelioration initiatives. Job engagement, work satisfaction, experience, and compensation are but four of the factors found to be closely aligned with an employee's decision to leave. The primary purpose of this paper is to highlight how machine learning can reduce employee turnover through early detection and intervention.

Keywords: machine learning; human resource management; employee turnover; actionable knowledge discovery; intervention strategies; cost optimisation; market churn; decision trees.

DOI: 10.1504/IJDS.2021.117472

International Journal of Data Science, 2021 Vol.6 No.1, pp.57 - 82

Received: 04 Dec 2020
Accepted: 18 Mar 2021
Published online: 07 Sep 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article