Determinants of Employee Attrition: A Business Analytics Approach to Human Resource Management

Abstract

The turnover of employees is a major issue for companies as it impacts workforce stability, productivity and the long-term performance of the organization. To create effective strategies for employee retention and enhance HRM practices, it is essential to understand the factors which cause the employees to leave the organization. The determinants of employee attrition are examined in this study using the IBM HR Analytics dataset that contains 1,470 records and 35 different variables related to the employee, organisations and working environment. A research design involving quantitative method combined descriptive statistical analysis and business analytics approach was used. Data preprocessing was performed to prepare the dataset for analysis, and a Logistic Regression model was developed to examine the influence of employee characteristics on attrition while evaluating predictive performance. The descriptive findings indicated considerable variation in employee income, organizational tenure, job satisfaction, work-life balance, and departmental affiliation. The predictive model achieved satisfactory classification performance, demonstrating the usefulness of business analytics in identifying employees with a higher likelihood of leaving the organization. The results indicated that there are a number of factors in the organization and workplace that affect employee attrition; no one factor can be identified as the determinant. The study reflects the importance of data to support evidence-based human resource management and offers a practical perspective on how to enhance employee retention by making targeted interventions in the organization. These findings add to the body of HR analytics literature in that they show how predictive analytics can help businesses to improve workforce planning and help to make them more effective into the future.