• The conceptual framework is clear and logical.
• The literature review is outdated as this journal was written 18 years ago and not identify the gap in the literature.
• The research illustration did not support the purpose in a strong way.
• The use of graphs and figures supported the purpose; however, it should be as and appendix.
• Finding and conclusion needed more elaboration.
• The usage of an illustrative application for data mining on a fictitious bank.
• While the author had fifteen variables listed, he wrote in text as thirteen.
• Lack of elaboration in term of explaining CRM in the illustration and in the journal conclusion.
• The possible usefulness of this research to other industrial areas such and insurance and healthcare.
• Identify CRM in a clear way.
• Explain data mining elaborately.
• Update the literature review and identify the gaps between different sources
• Apply the data mining and CRM as a case study in one of the leading banks also other industries.
• Explain the charn modelling in detail
• Apply the data mining in non-fictitious bank.
• Explain the data mining potentials in details and how it can improve the banking industry.
Application of Data Mining Techniques in Customer Relationship Management: A Literature Review and Classification
E.W.T. Ngai,Li Xiu,D.C.K. Chau
This review of literature for the application of data mining to CRM technologies has been verified review and classification process independently. The results of this paper indicate that the customer retention research area received most of the research interest. Most are linked to individual marketing and loyalty programs respectively. On the other hand, the models of classification and correlation are the two models commonly used in data mining in CRM. Our analysis provides a road map to guide future research and facilitate the accumulation of knowledge and creativity regarding the application of data mining techniques in CRM
Data Mining Application in Customer Relationship Management of Credit Card Business
• Ruey-Chyi Wu
• Ruey-Shun Chen
• C.-C.C.J.Y. Chen
This Journal employs data mining tools and effectively discover the current spending pattern of customers and trends of behavioral change, which allow management to detect in a large database potential changes of customer preference and provide as early as possible products and services desired by the customers to expand the clientele base and prevent customer attrition.