Analysis of customer churn in the banking industry using data mining

Abstract

Today, banks have a very important place in the great economic environments of countries. As in every sector, there are many competitors and a great competitive environment in the banking field. Especially individual customers prefer digital channels to make their banking transactions faster and easier. Banks need to take fast and industry-leading steps to meet these expectations of their customers. They need to differentiate themselves from the competition with innovative features by giving importance to digital. The main goals of the banks in the competitive environment are gaining new customers, increasing customer loyalty, reducing customer churn rates, and providing superior customer satisfaction. In this study, customer data belonging to a bank were analyzed with data analysis algorithms. Customer churn analysis was performed using different machine algorithms. The model was created on the Knime platform. This study performs a customer loss analysis using data mining algorithms. The aim is to reveal the reasons for losing customers, the elements of customer loyalty and to help develop customer relations activities accordingly.