Akademik Veri Yönetim Sistemi
Journal of Applied Microeconometrics, cilt.1, sa.2, ss.85-99, 2021 (Hakemli Dergi)
Although large companies try to gain new customers, they also want to retain their old customers. Therefore, customer churn analysis is important for identifying old customers without loss and developing new products and making new strategic decisions for retaining customers. This study focuses on the customer churn analysis, that is a significant topic in banks customer relationship management. Identifying customer churn in banks will helps the management to classification who are likely to churn early and target customers using promotioAlthough large companies try to gain new customers, they also want to retain their old customers. Therefore, customer churn analysis is important for identifying old customers without loss and developing new products and making new strategic decisions for retaining customers. This study focuses on the customer churn analysis, that is a significant topic in banks customer relationship management. Identifying customer churn in banks will helps the management to classification who are likely to churn early and target customers using promotions, as well as provide insight into which factors should be considered when retaining customers. Although different models are used for customer churn analysis in the literature, this study focuses on especially explainable Machine Learning models and uses SHapely Additive explanations (SHAP) values to support the machine learning model evaluation and interpretability for customer churn analysis. The goal of the research is to estimate the explainable machine learning model using real data from banking and to evaluate many machine learning models using test data. According to the results, the XgBoost model outperformed other machine learning methods in classifying churn customers.
likely to churn early and target customers using promotions, as well as provide insight into which factors should be considered when retaining customers. Although different models are used for customer churn analysis in the literature, this study focuses on especially explainable Machine Learning models and uses SHapely Additive exPlanations (SHAP) values to support the machine learning model evaluation and interpretability for customer churn analysis. The goal of the research is to estimate the explainable machine learning model using real data from banking and to evaluate many machine learning models using test data. According to the results, the XgBoost model outperformed other machine learning methods in classifying churn customersns, as well as provide insight into which factors should be considered when retaining customers. Although different models are used for customer churn analysis in the literature, this study focuses on especially explainable Machine Learning models and uses SHapely Additive exPlanations (SHAP) values to support the machine learning model evaluation and interpretability for customer churn analysis. The goal of the research is to estimate the explainable machine learning model using real data from banking and to evaluate many machine learning models using test data. According to the results, the XgBoost model outperformed other machine learning methods in classifying churn customers