Survival Analysis for Employee Retention Prediction in Retail and Trade Sector Organizations

Abstract

This paper proposes to study employee retention prediction in a multi-sector organization running retail operations, sales and service centers, and corporate support functions. We examine the uniqueness of the Retail and Trade Sector (RTS) industry and propose to use survival analysis to predict employee tenure and identify retention risks. Using a data set of 4,953 employees in multiple business sectors, we evaluated regression models vs. survival analysis models, and show that our survival analysis approach successfully distinguishes between employees who leave within two years versus those who stay 6+ years. Our methodology employs rate-based temporal feature engineering to capture time-dependent patterns while preventing data leakage, and demonstrates that regression-and classification-based approaches have performance limitations when making accurate predictions for active employees. Survival models enable proactive identification of high-risk employees, providing actionable insights for talent retention strategies, making them particularly useful for dealing with incomplete observations inherent in human resources data.