Employee segmentation and compensation optimization with adaptive clustering

Abstract

Employee satisfaction, retention, and productivity are all directly impacted by employee remuneration, which is a key factor in determining organizational effectiveness. Despite the significance of remuneration, many firms find it difficult to put in place structures that are fair and accommodating of the diverse workforce. This study suggests a thorough, data-driven approach for segmenting data using adaptive clustering in order to maximize employee remuneration. The paradigm allows for accurate and comprehensible employee stratification by including new indicators like the wage-to-Performance Ratio (SPR), wage growth rate, and satisfaction elasticity. A real-world corporate dataset was used, and to guarantee data quality, extensive preparation was done, including feature engineering, normalization, and outlier elimination. Different employee segments were created using adaptive clustering, which was assessed against conventional K-Means and directed by internal validation indices (such as the elbow method and silhouette score). Strategic pay changes are informed by empirical evaluation’s actionable findings, which include identifying strong achievers who are underpaid and long-tenured employees who are stagnant. HR professionals may identify high-potential employee cohorts and adopt data-driven, performance-aligned remuneration plans with the help of the suggested strategy. Additionally, the approach supports evidence-based human capital management in a variety of operational situations by exhibiting scalability and flexibility across different organizational structures.