Predictive Analytics and Workforce Outcomes in Oil Services Firms: Evidence on Turnover Reduction, Employee Engagement, And Productivity in Bayelsa State, Nigeria

This study examines the effect of predictive analytics on turnover reduction, employee engagement, and employee productivity in oil services firms in Bayelsa State, Nigeria. Using a cross-sectional survey of 241 employees and managers drawn from six oil services companies, the study employs simple regression analysis to test three null hypotheses. Findings reveal that predictive analytics exerts a significant positive effect on turnover reduction (β = 0.512, R² = 0.262, p < 0.01), employee engagement (β = 0.467, R² = 0.218, p < 0.01), and employee productivity (β = 0.443, R² = 0.196, p < 0.01). These results establish that the systematic application of data-driven workforce intelligence meaningfully improves critical human resource outcomes in the highly volatile oil services sector of Nigeria's Niger Delta. The study contributes original empirical evidence to the emerging literature on people analytics in developing economy contexts and offers practical recommendations for HR professionals, oil firm executives, and Bayelsa State government regulators.