Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
C4.5: programs for machine learning
C4.5: programs for machine learning
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Ganging up on Information Overload
Computer
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
Applying knowledge engineering techniques to customer analysis in the service industry
Advanced Engineering Informatics
Searching customer patterns of mobile service using clustering and quantitative association rule
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Domain driven data mining to improve promotional campaign ROI and select marketing channels
Proceedings of the 18th ACM conference on Information and knowledge management
Mining Customer Change Model Based on Swarm Intelligence
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences: an International Journal
Computers and Industrial Engineering
Keeping track of customer life cycle to build customer relationship
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods
International Journal of Intelligent Information Technologies
Customer Orientation Based Multi-Agent Negotiation for B2C e-Commerce
International Journal of Agent Technologies and Systems
Multi-Agent Negotiation Paradigm for Agent Selection in B2C E-Commerce
International Journal of Agent Technologies and Systems
Computers and Industrial Engineering
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The traditional customer relationship management (CRM) studies are mainly focused on CRM in a specific point of time. The static CRM and derived knowledge of customer behavior could help marketers to redirect marketing resources for profit gain at the given point in time. However, as time goes on, the static knowledge becomes obsolete. Therefore, application of CRM to an online retailer should be done dynamically in time. Though the concept of buying-behavior-based CRM was advanced several decades ago, virtually little application of the dynamic CRM has been reported to date.In this paper, we propose a dynamic CRM model utilizing data mining and a monitoring agent system to extract longitudinal knowledge from the customer data and to analyze customer behavior patterns over time for the retailer. Furthermore, we show that longitudinal CRM could be usefully applied to solve several managerial problems, which any retailer may face.