Early Warning System of Attrition in the BPO Industry Using Machine Learning Classification Models

Abstract

Employee attrition is one of the factors affecting gross margin erosion in the BPO industry, the fastest-growing industry in the Philippines, due to hiring and training costs. The cost of employee attrition depends on the employee's role and salary/wage level. This study proposed shifting the retention approach from reactive to proactive with the use of an early warning system for employee attrition. The early warning system was powered by Machine Learning Classification Models. The data used in this study are employees hired in 2021 and 2022 from one of the Telco/Communication programs in the BPO Industry. The data attributes considered in this study are composed of Employee status, Employee Performance, Employee Satisfaction, Payroll, Time Off History, Schedule, and employee Observation data. The data is trained and tested in the classification models (Decision Trees, rule-based classification, naïve Bayes, KNN, Logistic Regression, and Random Forest). Models are evaluated using the classification performance metrics (AUC, Accuracy, Precision, Recall, and F1 Score). The model with the highest predictive accuracy is selected and deployed to produce employee classification (Risk of Termination, Neutral, and Positive). This study mainly helps the company reduce turnover and costs and increase gross margin with the help of the early warning system that can predict the status of the employees using significant indicators.