Qualitative Study on Ensemble Learning-Based-Predictive Model on Classification of Churn for Business Propensity in Nigeria

Abstract The modern business environment is changing rapidly that companies are always looking for new ways to boost sales and profitability. A popular and successful approach is predictive analytics, which uses machine learning algorithms to estimate sales leads and their likelihood of conversion. This instrument has changed the way sales methods are used, making them more successful and efficient at generating revenue. Recognizing customer churn as a critical issue implies understanding that when customers leave a business at a higher rate than desired, it indicates deeper problems or challenges within the organization. The features that are associated with the classification of churn among customers were identified following which relevant data was collected from an online repository provided online by Kaggle. The predictive model for the classification of churn among customers was simulated using the holdout method based on three simulation runs for an ensemble learning model XGBoost. The results of the study showed that the ensemble methods adopted, the XGBoost classifier proved to have the best overall performance having 100% accuracy through the three simulation respectively. However, it was observed that as the proportion of the training datasets increased, the performance of the machine learning classifiers improved. The features that are most important to the classification of churn among customers include: type of customer contract, tenure of using service, satisfaction level of customer support, online security service, device protection service, payment methods, and streaming services. Keywords: customer churn, classification, supervised learning, unsupervised learning, ensemble modeling,