Predicting voluntary turnover through human resources database analysis

Purpose: This paper aims to question whether the available data in the human resources (HR) system could result in reliable turnover predictions without supplementary survey information. Design/methodology/approach: A decision tree approach and a logistic regression model for analysing turnover were introduced. The methodology is illustrated on a real-life data set of a Belgian branch of a private company. The model performance is evaluated by the area under the ROC curve (AUC) measure. Findings: It was concluded that data in the personnel system indeed lead to valuable predictions of turnover. Practical implications: The presented approach brings determinants of voluntary turnover to the surface. The results yield useful information for HR departments. Where the logistic regression results in a turnover probability at the individual level, the decision tree makes it possible to ascertain employee groups that are at risk for turnover. With the data set-based approach, each company can, immediately, ascertain their own turnover risk. Originality/value: The study of a data-driven approach for turnover investigation has not been done so far.