A Comparative Study of Employee Churn Prediction Model

Abstract

Churn phenomenon commonly occurs in customer loyalty towards brand product or services. Customer churn becomes critical issue that any industry would make best effort to avoid. Churn problem may arise within the organisation, called employee churn. Employee churn creates myriad and adverse effects to the organization as it correlates with unfairly workload distribution, great deal of money lost and also extra time needed to find a replace, which may result in the rise of customer dissatisfaction rate. The purpose of this study is to find the best model to predict employee churn. A successful prediction model for employee churn is significantly needed in order to avert various negative impacts for the organization. There are three popular classification models for prediction, namely naÏve bayes, decision tree, and random forest. This study will compare performance of the aforementioned models by using Human Resource Information System (HRIS) from one of Indonesia's renowned telecommunication company. The data collected for the study spans for 2 years period, started from 2015 until 2017. The findings from the study suggest that the best classification model is random forest due to its immense accuracy of 97.5%. The second-best method is naïve bayes with 96.6%, and the lowest accuracy of classification model is decision tree with 88.7%. The study concludes that the most reliable and accurate classification model to predict employee churn is random forest.