A Neural Network Approach to Understanding Employee Retention Dynamics: Insights from Feature Importance Analysis

In today’s business environment, employee retention has become a significant challenge, as employee turnover can lead to decreased productivity, increased costs, and reduced morale. This study aims to leverage neural network technology to predict employees' retention intentions and conduct in-depth analyses based on employee data. A classification model was established considering various factors, including gender, marital status, number of children, education level, years of service, weekly working hours, career development opportunities, salary, and bonuses, to identify the potential risk of employee turnover. The results indicated that the model achieved an accurate classification rate of 95.12% on the test set, demonstrating high effectiveness in identifying employee retention intentions. Feature importance analysis revealed that education level (29.63%), bonuses (27.50%), and the number of children (21.81%) were the primary factors influencing retention decisions. Additionally, working hours, marital status, and career development opportunities also impacted employees’ retention intentions. This research not only provides insights into employee mobility but also offers data support for enterprises to develop effective retention strategies, suggesting that businesses prioritize enhancing education and training opportunities, designing competitive bonus systems, and providing flexible work arrangements and benefits tailored to employees with children.