Enhancing Employee Retention Strategies Through Advanced Predictive Analytics

Cheng Yuxiang; Zhao Xiaomiao

Authors

Cheng Yuxiang Shenyang Institute of Engineering Author
Zhao Xiaomiao Shenyang Institute of Engineering Author

Keywords:

People Analytics, Employee Attrition, Deep Data Analysis, Machine Learning Models, Talent Retention

Abstract

Employee attrition poses significant challenges to organizations, impacting productivity, morale, and operational stability. This research focuses on developing effective methods for early detection and forecasting of employee intentions to leave using advanced analytics techniques.

The study introduces a novel employee attrition model that consolidates essential features into a streamlined framework conducive to accurate prediction. Leveraging machine learning, deep learning, and ensemble learning approaches, the proposed models are rigorously evaluated across diverse datasets, including simulated HR data and real-world responses. Key contributions include the identification of 11 critical features for attrition prediction, validated through a mixed research methodology integrating exploratory and quantitative analyses. Insights gained from model interpretation enable HR managers to understand and address the factors driving attrition, facilitating the implementation of targeted retention strategies. This research underscores the importance of predictive analytics in HR management, offering practical tools to preemptively manage attrition and foster a stable organizational environment.

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