The Role of Demographic and Behavioural Data in Predictive Analytics for Employee Retention

Abstract

Organisations continue to face a significant difficulty in employee retention, which calls for creative solutions to anticipate and reduce turnover. The purpose of this study is to better understand how behavioural and demographic data might improve predictive analytics for employee retention. A specific machine learning algorithm was deployed, namely Gradient Boosting Technique to develop a predictive model which was then assessed by utilising an extensive dataset from a mid-sized technology company, which included behavioural indicators (performance ratings, attendance records, engagement survey scores, and training participation) and demographic variables (age, gender, education level, marital status, and tenure). The results show that the accuracy of turnover estimates is greatly increased by integrating behavioural and demographic data. The most important indicators of attrition were found to be behavioural elements, specifically performance and engagement levels, however demographic factors like age and tenure also had a big impact. This study emphasises the value of a comprehensive approach to predictive analytics in HR, which helps businesses to develop focused retention plans that increase employee stability and productivity. In order to improve retention results and further develop prediction models, future research should investigate other data sources.