Employee Retention in Tech Startups: A Predictive Analytics Approach

Abstract

Introduction- Given the dynamic and competitive landscape of technology startups, understanding the factors influencing employee turnover is crucial.

Objectives-This study aims to identify these key drivers and develop predictive models that accurately forecast retention rates, enhancing strategies for employee retention.

Research Gap- Over the past decade, significant advancements have been made in predictive analytics, which can offer actionable insights for managing workforce stability more effectively.

Methodology-This research utilizes machine learning techniques—specifically Logistic Regression, Random Forest, and Gradient Boosting Machine—to analyze a dataset of 1,470 samples reflecting demographic, professional, and satisfaction-related features.

Result- Findings reveal that compensation, work environment, job satisfaction, and career development opportunities play pivotal roles in employee retention. Each model's performance was evaluated, with Gradient Boosting Machine showing the most effective results across multiple metrics, particularly in handling class imbalances inherent in employee attrition datasets.

Conclusion-The practical implications of this study suggest that tech startups can significantly benefit from implementing targeted retention strategies that focus on enhancing compensation packages and work-life balance. Future research should explore the integration of broader organizational factors and external economic influences to refine the predictive accuracy of turnover models further. This study contributes to the existing literature by providing a detailed analysis of the applicability of machine learning in predicting employee turnover, offering a foundation for future innovations in retention strategies within the startup ecosystem.