Title: Machine learning models for predicting customer churn: a case study in a software-as-a-service inventory management company

Authors: Naragain Phumchusri; Phongsatorn Amornvetchayakul

Addresses: Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand ' Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

Abstract: Software-as-a-service (SaaS) is a software-licensing model, which allows access to software on a subscription basis using external servers. This article proposes customer churn prediction models for a SaaS inventory management company in Thailand. The main focus of this work is seeking the most suitable customer churn prediction model for this case-study SaaS inventory management company which is currently having a high churn rate issue. This paper explores four machine learning algorithms, which are logistic regression, support vector machine, decision tree (DT) and random forest. The results show that the optimised DT model is capable of outperforming 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 are investigated for practical insights to identify features that are significantly related to churn behaviour. Therefore, the findings can help the case-study company indicate customers who are going to churn more precisely and enhance the effectiveness of managerial decisions and effective marketing movement.

Keywords: churn prediction; machine learning; software-as-a-service; SaaS.

DOI: 10.1504/IJBIDM.2024.135146

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.1, pp.74 - 106

Received: 27 Apr 2021
Accepted: 30 Aug 2022
Published online: 01 Dec 2023 *