Predictive HR Analytics and Employee Attrition Modelling: A Strategic Approach to Workforce Retention in the Indian Context
DOI:
https://doi.org/10.31305/rrijm.2025.v10.n6.022Keywords:
Predictive HR Analytics, Employee Attrition, Workforce Retention, Logistic Regression, Decision TreesAbstract
This research explores the application of predictive HR analytics in modelling employee attrition risks, highlighting how organizations can shift from reactive to proactive workforce strategies. By examining a range of predictive modelling techniques—including logistic regression, decision trees, random forest, neural networks, and survival analysis—the study demonstrates how data-driven insights can enhance retention planning and decision-making. Using a case study of an Indian mid-sized IT firm (TechNova), the paper illustrates the real-world impact of predictive models, emphasizing model performance, interpretability, and ethical deployment. The findings reveal that while complex models offer high accuracy, models like random forests and decision trees strike the best balance between precision and practical usability. The study also discusses ethical implications, integration challenges, and recommendations for HR teams to implement predictive analytics effectively. The paper concludes that predictive HR analytics is not just a technological advancement but a strategic asset for fostering employee engagement, retention, and organizational resilience in the Indian business context.
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