Knowledge discovery in databases: an overview
AI Magazine
Communications of the ACM
Exploring the factors associated with Web site success in the context of electronic commerce
Information and Management
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Effect of store design on consumer purchases: an empirical study of on-line bookstores
Information and Management
Customer lifetime value modeling and its use for customer retention planning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
Strategic Database Marketing
Considering application domain ontologies for data mining
WSEAS Transactions on Information Science and Applications
WSEAS Transactions on Information Science and Applications
Review: Data mining techniques and applications - A decade review from 2000 to 2011
Expert Systems with Applications: An International Journal
Group Recommender Model for Boosting and Optimizing Customer Purchases
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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In today's competitive environment, a successful company must provide better customized services, that are not only acceptable to customers but satisfy their needs as well, in order to survive and succeed in gaining an advantage against competition. It has been proven by many studies that it is more costly to acquire new customers than to retain old ones. Consequently, evaluating current customers in order to enhance their lifetime value becomes a critical factor to decide the success or failure of a business. This study applies data from customer and transaction databases of a department store, based on the RFM model, and does clustering analysis to recognize high value customer groups for cross-selling promotions. Study findings show that clustering analysis can locate high value customers, and the company can then apply appropriate target marketing to enhance their lifetime value effectively. The implication for the marketer is that leveraging techniques of data mining can make the most from data of customers and transactions databases and thus create sustainable competitive advantages.