Customer churn prediction for a software-as-a-service inventory management company

Abstract

This thesis proposes customer churn prediction model for a Software-as-a-Service inventory management company in Thailand. Software-as-a-Service is the fast growing and high market values industry as a new emerging online business. Customer churn is a critical measure for this business. Thus, this thesis focuses on seeking a customer churn prediction model specified for a Software-as-a-Service inventory management company in Thailand which is facing a high churn rate issue. This thesis executes the prediction models with four machine learning algorithms: logistic regression, support vector machine, decision tree and random forest. The results show that the optimized decision tree model is capable to outperform other classification models toward recall scorer with validated testing scores of 94.4% of recall and 88.2% of F1-score. Moreover, feature importance scores can highlight useful insights of case-study that business metrics are significantly related to churn behavior. As a result, this paper is beneficial to the case-study company to help indicate real churn customer correctly and enhance the effectiveness in making executive decision and marketing campaign.