ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS

D Alao · Adetoye Adeyemo

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AI and HR Technologies Data Mining and Machine Learning Applications Customer churn and segmentation

Abstract

Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, theycarry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order foran organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimizeemployee attrition. This study identifies employee related attributes that contribute to the prediction of employees’ attritionin organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeriawho worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job relatedrecords of the employee were the main data which were used to classify the employee into some predefined attrition classes.Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree modelsand rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictivemodel that was used to predict new cases of employee attrition. A framework for a software tool that can implement therules generated in this study was also proposed.Keywords: Employee Attrition, Decision Tree Analysis, Data Mining

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References

1999Building Data Mining Applications for CRM 1991Career Orientations of MIS Employees: An Empirical Analysis 2008Retaining talent : a guide to analyzing and managing employee turnover 2008Data mining techniques for better decisions in human resource management systems

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