Understanding employee retention using statistical and machine learning models

Abstract

Employee turnover can be very expensive for businesses. The company's overall performance is impacted by employee retention problems. In the 21st century, having a strong mechanism for retaining talented workers is emerging as a strategic advantage. Companies are beginning to understand that their employees are their most valuable resource because it takes so much time and money to bring on a new hire. According to one study, the cost of replacing a departing employee equals about 150% of their annual salary. Nowadays, organizations are trying to learn more about employees' intentions and the factors that could lead to them leaving the company through a variety of HR analytics techniques. But nothing is happening on a larger scale; everything is occurring in silos. The goal of this project was to provide more clarity on the use case of machine learning in employee retention and to identify the critical themes that cause employees to leave their current organization. As part of the project, we conducted in-depth interviews, analyzed surveys, and conducted secondary research. Our findings revealed that pay scale, location, career growth, and work quality are the most important factors for employees. The career growth factor came into force when employees perceived a lack of challenge in their current role and, as a result, looked out for new switch opportunities. Furthermore, we discovered that employees' attachment to their work, team, and manager, as well as the company as a whole, is critical for sustaining employee satisfaction. Finally, in terms of the use case of machine learning in HRM, it has enormous potential and would enable HRM functions to be more proactive rather than reactive. In comparison to other ML algorithms and logistic regression, our report shows that the artificial neural network algorithm has the highest accuracy for predicting employee turnover. Our recommendation encourages businesses all over the world to perform the basic exercise of keeping current employees happy, and machine learning algorithms will greatly assist in this endeavor. The algorithm can predict which high-performing employees are more likely to leave. The HR department can then take the necessary steps to reduce turnover. These measures would help to retain highly productive employees while also ensuring their well-being.