Predicting employee turnover is a major challenge for organisations. While this topic has been studied for over a century, most research relies on specially collected data that may not reflect the data companies can access. As a result, many business applications fail due to data limitations or legal restrictions. This study aims to explore how including employee performance data can improve the accuracy of turnover predictions.
The authors analysed data from 1,518 sales employees in Germany, Switzerland and Austria, including human resource (HR) records, employee satisfaction surveys and performance data. A machine learning model was used to predict employee turnover.
The results show that turnover prediction is most accurate when performance data is included, with an accuracy score of 0.8998. Models without performance data perform significantly worse, which highlights the strong impact of performance data on predicting employee turnover.
This study contributes to turnover research by demonstrating and quantifying how employee performance metrics improve prediction accuracy. Unlike many studies that rely on artificial data sets, the authors use real-world company data and can thus offer insights that are relevant to HR professionals and business leaders.
