# Employee turnover prediction and retention policies design: a case study

**Authors:** Edouard Ribes, Karim Touahri, Benoît Perthame  
**arXiv:** [1707.01377](https://arxiv.org/abs/1707.01377)  
**Date:** 2017-07-05  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1707.01377v1/

## Key points

- The paper explores similarities between customer churn and employee turnover problems
- Authors develop an employee turnover prediction model using classical machine learning techniques
- Model outputs are analyzed to design and test effective employee retention policies
- Leveraging machine learning techniques provides valuable insights into predicting potential turnovers among employees
- Proactive implementation of measures to retain valuable employees is emphasized
- Discussion on retention strategies in the context of employee turnover is innovative
- Practical implications for designing successful retention policies are offered
- Research provides a comprehensive examination of employee turnover prediction
- Useful resources for organizations aiming to improve workforce stability are provided
- Adds value to existing literature on the topic

## Summary

This paper, titled "Employee turnover prediction and retention policies design: a case study," explores the similarities between customer churn and employee turnover problems. The authors, Edouard Ribes, Karim Touahri, and Benoît Perthame, develop an employee turnover prediction model using classical machine learning techniques. They then analyze the model outputs to design and test effective employee retention policies. The study highlights the innovative aspect of discussing retention strategies in the context of employee turnover. By leveraging machine learning techniques, the authors provide valuable insights into predicting potential turnovers among employees. This analysis allows organizations to proactively implement measures to retain valuable employees. The paper emphasizes the significance of this type of retention discussion as it contributes to existing knowledge in the field. The findings presented in this study offer practical implications for designing successful retention policies that can help organizations address employee turnover issues effectively. Overall, this research provides a comprehensive examination of employee turnover prediction and offers useful resources for organizations aiming to improve their workforce stability. The work by Ribes, Touahri, and Perthame adds value to the existing literature on this topic.