Predicting Attrition - a Driver for Creating Value, Realizing Strategy, and Refining Key HR Processes

Authors

Kevin Mendonsa, Southern Methodist UniversityFollow
Maureen Stolberg, Southern Methodist UniversityFollow
Vivek Viswanathan, Southern Methodist UniversityFollow
Scott Crum, MSCI Inc.Follow

Abstract

Talent is the most important asset for every organization's success. While attrition (or churn) and turnover can refer to both employees and customers, this paper will focus on employee attrition only. Many organizations accept attrition as an inevitable cost of doing business and do nothing to adopt or implement mitigating strategies to combat it. World class companies on the other hand take deliberate measures to understand, control and mitigate attrition (turnover) at every stage. Unmitigated attrition can have a devastating effect on an organization's bottom line and market value. In addition, the “invisible" costs of low employee morale, reduced employee engagement, stagnant innovation are more harmful to the well-being of any organization. Predicting employee attrition allows organizations reasonable time to have discussions with employees predicted to leave, in order to retain them if aligned with strategy. It also enables the organization to develop alternatives to proactively address attrition by building appropriate talent pipelines and conducting loss impact analyses especially for key roles and strategic projects. The aim of this paper is to highlight the importance of talent to the success of the organization, its impact to profits and overall market value. We intend to provide a framework for data collection methodologies and the prediction of employee attrition by analyzing multiple factors and attributes using defined machine learning classification techniques and models.

Recommended Citation

Mendonsa, Kevin; Stolberg, Maureen; Viswanathan, Vivek; and Crum, Scott (2020) "Predicting Attrition - a Driver for Creating Value, Realizing Strategy, and Refining Key HR Processes," SMU Data Science Review: Vol. 3: No. 2, Article 2.
Available at: https://scholar.smu.edu/datasciencereview/vol3/iss2/2

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License