Salesperson Churn Prediction with Machine Learning Approaches in the Retail Industry

Due to its high cost, loss of productivity, and most importantly, loss of time in training a new employee, employee retention has become a strategy that has made even more attractive for many researchers and professionals in the field. The purpose of this study is to present a case study that addresses the problem of employee churn and develop a model which predicts employee retention best. In the present study, the most well-known machine learning techniques such as Logistic Regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Support Vector Machine (SVM), XGBoost, Artificial Neural Network (ANN) and Random Forest were used. Finally, the performance of the proposed approaches was evaluated. The numerical results showed that the proposed Naïve Bayes clearly outperformed all other classifiers according to all evaluation criteria except Accuracy. However, Random Forest gave the best results according to the accuracy criterion.