Predicting voluntary turnover through human resources database analysis

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.

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.

It was concluded that data in the personnel system indeed lead to valuable predictions of turnover.

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.

The study of a data-driven approach for turnover investigation has not been done so far.