Beyond Demographics: Using HRMS Data and Employee Sentiment Analysis to Predict Turnover and Improve Retention

Retention of employees is one of the significant challenges in today's competitive job market world, with the traditional method of turnover prediction relying on a large amount of data and limiting its effectiveness. This research, “Beyond Demography: Using HRMS data and employee sentiment analysis to predict turnover and improve retention,” introduces a novel approach that combined the human resource management system (HRMS) with sentiment analysis of employees to enhance retention strategies. By integrating metrics such as performance, tenure, and compensation from HRMS and sensitive data from surveys, emails, and internal communication, the research aims to build a more comprehensive model. An advanced Natural Language Processing (NLP) technique is employed to extract the key features from employee feedback, revealing hidden dissatisfaction patterns, and machine learning algorithms (ML), including random forest, Gradient Boosting and SVM will analyze this combined dataset and provide more accurate detection of the turnover risk factors. This research work mainly focuses on developing advanced predictive monitoring and evaluation of the interventions through data analysis to ensure the dynamic retention approach. Ultimately, this research offers a holistic approach for predicting and preventing turnover and promoting a more productive workforce.