Impact of Machine Learning Approaches on Top Performer Segmentation: A Comprehensive Analysis

Abstract

In the dynamic landscape of Human Resource Management, the quest for identifying and nurturing top performers has gained prominence, with Machine Learning emerging as a transformative tool. This comprehensive review delves into the state-of-the-art machine learning approaches for top performer segmentation, aiming to bridge the gap between traditional HRM practices and data driven methodologies.

The paper begins by contextualizing the significance of top performer segmentation within HRM and delineates the limitations of conventional performance evaluation methods. Leveraging insights from a thorough literature review, the research explores the evolution of performance analytics, emphasizing the paradigm shift from traditional methods to ML driven approaches.

The methodology section details the data collection, preprocessing steps, and an extensive array of ML models employed, including supervised learning algorithms, ensemble methods, deep learning architectures, and clustering techniques. Evaluation metrics are carefully chosen to ensure robust model performance assessment, and interpretability techniques are applied to unravel the black box nature of certain ML models.

Results stemming from the analysis present a nuanced understanding of the effectiveness of different ML models in top performer segmentation. Feature importance analysis sheds light on the key factors influencing top performance, offering actionable insights for HR practitioners. Clustering results, where applicable, uncover natural groupings within the workforce, revealing patterns that contribute to performance differentials.

The discussion section interprets the results, drawing implications for HRM practices and comparing the efficacy of ML models against traditional approaches. Ethical considerations and potential biases in the segmentation process are addressed, emphasizing the need for responsible AI practices in talent management.

The paper concludes with a synthesis of key findings, practical implications for HR professionals, impact of machine learning approaches and a forward-looking perspective on the future integration of ML in top performer segmentation. This comprehensive review serves as a valuable resource for academics, HR practitioners, and organizational leaders navigating the intersection of machine learning and talent management.