Data mining
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
The CRM handbook: a business guide to customer relationship management
The CRM handbook: a business guide to customer relationship management
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Accelerating customer relationships: using crm and relationship technologies™
Accelerating customer relationships: using crm and relationship technologies™
International Journal of Information and Communication Technology
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First, we classify the selected customers into clusters using RFM model to identify high-profit, gold customers. Subsequently, we carry out data mining using association rules algorithm. We measure the similarity, difference and modified difference of mined association rules based on three rules, i.e. Emerging Patten Rule, Unexpected Change Rule, and Added/Perished Rule. In the meantime, we use rule matching threshold to derive all types of rules and explore the rules with significant change based on the degree of change measured. In this paper, we employ data mining tools and effectively discover the current spending pattern of customers and trends of behavioral change, which will 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.