Predicting Employee Turnover: Scoping and Benchmarking the State-of-the-Art

Employee turnover presents a significant challenge to organizations. High turnover rates impose substantial costs on organizations, e.g., direct costs resulting from rehiring efforts and training new employees, and indirect costs resulting from the loss of expertise and declining organizational productivity. Hence, predicting employee turnover is an important task for human resource departments and organizations as a whole, as it can help to proactively approach employees at risk of churning to improve retention and workforce stability. With ever more data at hand and increasing competition in the labor market, analytical tools are essential to improve workforce management and aid human resource managers in their decision-making. Yet, the existing literature on predictive analytics for employee turnover is scattered and fails to present a coherent and holistic view. To find common ground in the established literature, the paper provides a scoping and benchmarking of the state-of-the-art. The scoping concludes that established research results are difficult to compare due to inconsistent methodologies and experimental setups. To address these issues, an extensive benchmarking experiment is conducted involving 14 classification methods and 9 datasets. The results provide a unique focal point for research on employee turnover prediction and aim to benefit academic research and industry practitioners. The code and public datasets are available on Github to facilitate further extension of the research.