Improving Customer Relationship Management in Electronic Transaction Expansion in Banking Sector by using Data mining techniques

Vol.6 No.3

Year: 2012

Issue: January-March

Title: Improving Customer Relationship Management in Electronic Transaction Expansion in Banking Sector by using Data mining techniques   

Author Name: Bhaskar Reddy Muvva Vijay, Luel Brahen   

Synopsis:   

Today, many businesses such as banks, insurance companies, and other service providers realize the importance of Customer Relationship Management (CRM) and its potential to help them acquire new customers retain existing ones and maximize their lifetime value. Data mining gives an opportunity, uses a variety of data analysis and modeling methods to specific trends and relationships in data detection. This helps to understand what a customer wants and anticipate what they will do. In this paper we examines, the application of k-means clustering and classification decision tree J48 algorithm of data mining on CRM in the case of EFT of POS service of the Dashen Bank S.C. These have been discovered within the framework of CRISP-DM model. The results demonstrate the final dataset consists of 110000 records in which different clustering models at k values of 6, 5, and 4 with different seed values have been traced and evaluated against their performances. Thus, the cluster model at k value of 6 with default seed value has shown a better performance by using Weka-3-7-2 tool.