Identifying Employees Likely to Resign After Receiving Bonus

Executive Summary

This analysis identifies employees most likely to resign after receiving bonuses. Using a logistic regression model, we evaluated factors like department, performance rating, and bonus size to predict resignation risks. The model achieved an ROC AUC score of 1.0, showing excellent accuracy. These insights can help the company manage turnover by tailoring retention strategies and adjusting bonus policies.

Problem Statement

Employee turnover is a challenge, especially after bonuses are distributed. This analysis predicts which employees are most likely to resign based on their bonuses, performance ratings, and departments. The goal is to reduce turnover and optimize retention strategies.

Approach

• Data Collection: We analyzed employee data, including department, performance rating, and bonus amount.
• Modeling: A logistic regression model was used to assess resignation probabilities. We evaluated the model's accuracy using the ROC AUC score.
• Evaluation: The model achieved an ROC AUC score of 1.0, meaning it effectively distinguishes between employees likely to stay and those at risk of resigning.

Results

Key Insights

• High-Risk Departments: Quality Assurance, Logistics, and Marketing have the highest predicted resignation rates.

• Performance Rating Impact: Employees with performance ratings of 1 or 5 are most likely to resign, regardless of bonus size, suggesting dissatisfaction or a lack of alignment.

• Bonus Effectiveness: High bonuses do not necessarily retain employees. In Quality Assurance and Logistics, high-risk employees remain likely to resign despite large bonuses.

Visualization

Explore the complete interactive visualization here:

Strategic Recommendations

• Retention Initiatives: For high-risk departments, consider non-monetary rewards, role-specific engagement activities, and career development programs to enhance job satisfaction and retention.
• Bonus Allocation Adjustments: Reduce or reallocate bonuses for employees likely to leave, especially those with lower performance ratings. For valuable high-risk employees, add retention-focused incentives to bonuses.
• Performance & Engagement Monitoring: Monitor employees with performance ratings of 1 or 5. Offer support to address dissatisfaction or consider role realignment to decrease turnover risks.

Conclusion

This predictive model helps identify resignation risks based on department, performance rating, and bonus size. By addressing these risks with targeted strategies, the company can reduce turnover, strengthen employee retention, and better allocate retention resources.