Predictive Workforce Analytics for Employee Burnout, Engagement, and Retention Using Machine Learning

Authors: Dr. V. Sumathy, Dr. K. Vimala

Abstract: Employee burnout, disengagement, and voluntary turnover have become serious issues for organizations, causing reduced productivity, a degenerating culture, and declining financial performance. The current state of HR analytics tools relies on outdated backward-looking surveys that cannot measure all facets of the process. This paper presents a predictive workforce analytics framework using multiple sources of data, including HRIS data (demographic information, length of service, performance evaluation), digital exhaust data (email meta-data, Slack data, calendar), and passive sensor data (badge data, mobile phone usage). In this study, 15,000 employees over 24 months were analyzed, resulting in three predictive models for employee burnout, engagement, and turnover: the Temporal Fusion Transformer model predicting burnout (AUC = 0.92; burnout prediction horizon is eight weeks), the Gradient Boosting Machine predicting employee engagement (accuracy = 86%), and the Ensemble Survival Model predicting employee retention (C-index = 0.84). Our predictive framework identifies significant behavior patterns: after hours' digital communication (strongest predictor of employee burnout), network entropy (strongest predictor of employee engagement), and declining performance trend (strongest predictor of turnover). In a 12-week randomized controlled experiment involving 2,000 employees, we show that AI-powered interventions decrease burnout rates by 34% and voluntary turnover rates by 28%.

DOI: http://doi.org/10.5281/zenodo.20671790