Leveraging Predictive Analytics for Enhancing Employee Engagement and Optimizing Workforce Planning: A Data-Driven HR Management Approach

Purpose: This study examines the use of predictive analytics in Human Resource Management (HRM) to enhance employee engagement and optimize workforce planning. It explores how data-driven insights enable proactive HR strategies, improve retention, and align workforce plans with organizational objectives. Challenges such as data quality, ethical considerations, and cost implications are also addressed.Methodology: The research is grounded in the Resource-Based View (RBV), emphasizing human capital as a strategic asset. A systematic review of literature, industry reports, and case studies highlights the applications of predictive analytics in engagement, recruitment, retention, and workforce planning.Findings: Predictive analytics enables organizations to identify at-risk employees, personalize engagement strategies, and forecast future workforce needs. It improves recruitment processes by identifying candidates likely to succeed and reduces turnover by analyzing risk factors such as job satisfaction and performance metrics. Workforce planning benefits include accurate skill gap identification, and staffing need predictions. Challenges include ensuring data quality, navigating ethical concerns like privacy, and managing implementation costs.Originality/Value: This study links predictive analytics with strategic HRM, emphasizing its potential to enhance decision-making and organizational competitiveness. Recommendations include investing in data quality ethical practices and training HR teams to foster a data-driven culture and sustainable workforce management.Purpose: This study examines the use of predictive analytics in Human Resource Management (HRM) to enhance employee engagement and optimize workforce planning. It explores how data-driven insights enable proactive HR strategies, improve retention, and align workforce plans with organizational objectives. Challenges such as data quality, ethical considerations, and cost implications are also addressed. Methodology: The research is grounded in the Resource-Based View (RBV), emphasizing human capital as a strategic asset. A systematic review of literature, industry reports, and case studies highlights the applications of predictive analytics in engagement, recruitment, retention, and workforce planning. Findings: Predictive analytics enables organizations to identify at-risk employees, personalize engagement strategies, and forecast future workforce needs. It improves recruitment processes by identifying candidates likely to succeed and reduces turnover by analyzing risk factors such as job satisfaction and performance metrics. Workforce planning benefits include accurate skill gap identification, and staffing need predictions. Challenges include ensuring data quality, navigating ethical concerns like privacy, and managing implementation costs. Originality/Value: This study links predictive analytics with strategic HRM, emphasizing its potential to enhance decision-making and organizational competitiveness. Recommendations include investing in data quality ethical practices and training HR teams to foster a data-driven culture and sustainable workforce management.

Keywords:

Predictive analytics , workforce planning , employee engagement , HR analytics , data-driven HR , human resource management , machine learning , organizational behavior