Employee Attrition Prediction Using Predictive Analytics: A Survey-Based Study

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Employee attrition has become a major concern for organizations across industries due to its impact on productivity, operational continuity, and organizational performance. High employee turnover results in increased recruitment costs, loss of experienced talent, and reduced employee morale. This study investigates the key factors influencing employee attrition and examines the effectiveness of predictive analytics in identifying employees who are at a higher risk of leaving an organization. Primary data were collected from 89 working professionals through a structured questionnaire, complemented by insights from secondary sources such as research articles and industry reports. The study identified factors including career growth opportunities, compensation and benefits, work-life balance, workload, training and development, recognition, supervisor support, and workplace culture as significant determinants of employee retention. The findings demonstrate that predictive analytics can assist organizations in analysing employee behaviour, recognizing attrition patterns, and supporting evidence-based human resource decisions. By leveraging data-driven models, organizations can implement timely retention strategies, improve employee engagement, and optimize workforce planning. The study concludes that integrating predictive analytics into human resource management can enhance employee retention, reduce voluntary turnover, and contribute to long-term organizational sustainability and competitive advantage.