Segmentation of Internet Banking Users Based on Expectations: A Data Mining Approach

Abstract

In recent years,Internet-based banking services have become the focus of competition in Iran's banking system. In this respect, Internet banking users' identification and segmentation leads to better understanding of users' needs and expectations and planning to meet them. This in turn will result in improving the image of the bank and obtaining competitive advantage. In this research, seven banks of Pasargad, Mellat, Parsian, Saman, Eghtesad-e-novin, Tejarat and Melli are selected as rival brands. According to scrutiny, the expectations of internet banking users were identified in the form of 17 indicators. Using closed questionnaires, necessary data was collected from 274 users of Internet banking services in selected banks. At the first stage, based on exploratory factor analysis, five factors were identified which include: ease of use, variety of e-banking services, security, speed of providing services and reliability. In the second stage, by applying k-means procedure, optimum number of clusters was detected equal to 6. Then the expectations of each cluster were evaluated. The result showed that the average of expectations and frequency of demographic variables between clusters are different. So the extracted clusters have good quality.