Using Machine Learning Algorithms to Analyze Customer Churn in the Software as a Service (SaaS) Industry

Authors : Levent ÇALLI, Sena KASIM

Pages : 115-123

Doi:10.21541/apjess.1139862

View : 24 | Download : 12

Publication Date : 2022-09-30

Article Type : Research Paper

Abstract :Companies must retain their customers and maintain long-term relationships in industries with intense competition. Customer churn analysis is defined in the literature as identifying customers who may leave a company to take appropriate marketing precautions. While customer churn research is prevalent in B2C insert ignore into journalissuearticles values(Business to Customer); business models such as the telecoms and retail sectors, customer churn analysis in B2B insert ignore into journalissuearticles values(business to business); models is a relatively emerging topic. In this regard, the study carried out a customer churn analysis by considering an ERP insert ignore into journalissuearticles values(enterprise resource planning); company with a software as a service insert ignore into journalissuearticles values(SaaS); business model. Different machine learning algorithms analyzed ten features determined by selection methods and expert opinions. According to the analysis results, the random forest algorithm gave the best result. Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.
Keywords : Customer Churn, SaaS, Machine Learning, Random Forest, Data Mining