Data-Driven Employee Retention Strategies in India: A Search for the Best-Fit Model - Settur, Sreekanth (Digital Repository of Theses - SSBM Geneva, 2025)
This research focuses on the field of Human Resource Management (HRM), particularly on crafting effective, data-driven strategies to tackle employee attrition. It moves beyond traditional theories and subjective perspectives by leveraging interpretable machine learning models to gain actionable insights. The study aims to build a strong framework for understanding and mitigating the factors contributing to employee turnover by predicting their likelihood of leaving the organization. By employing data analytics, it seeks to refine HR decision-making processes, improve retention rates, and cultivate a more stable and engaged workforce. Additionally, the research underscores the significance of data-driven methodologies in developing retention strategies that not only address attrition but also strengthen employer branding. By using interpretable machine learning models, the study ensures that predictive outcomes are both clear and practical, enabling HR professionals to apply targeted interventions effectively. This approach combines methodological rigor with customization, ensuring strategies are evidence-based and tailored to organizational needs. The study introduces a range of machine learning techniques, including linear, tree-based, and ensemble methods, to predict employee turnover. Ultimately, this research contributes to HRM by demonstrating how advanced analytics can be leveraged to establish a more resilient and appealing workplace, thereby enhancing the organization’s competitive position in the talent marketplace. Key words: Data-Driven HR, Employee Engagement, Employee Retention Strategies, Human Resource Management (HRM), Interpretable Machine Learning Models, Organizational Behavior