Data Mining Techniques for Customer Relationship Management

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Abstract Advancements in technology have contributed to the new business culture, where the Customer Relationship Management (CRM) is in the centre of a business concern. CRM is a widely implemented strategy for managing and fostering long term, profitable relationships with specific customers (Ling and Yen, 2001). The automated data mining tools made it possible to move beyond the analyses of the past events and data mining tools can be used to address problems that were seen as too time-consuming in the past, providing new opportunities for businesses within relationship management. This report identified that CHAID and neural networks are two of the most commonly used data mining techniques within CRM domain. However, each of these techniques has advantages and drawbacks, which should be taken into consideration when deciding on its appropriateness. 1. Introduction Nowadays, organisations are more concerned with increasing customer value, and realise that customers are more predictable than ever thought before. As argued by Written and Frank (1999), consumers engage, negotiate and purchase according to certain patterns engraved into transactional and behavioural records. This report will discuss data mining techniques for CRM. Real life case studies will be analysed and two types of data mining techniques will be discussed, focusing on their appropriateness to CRM. 2. Customer Relationship Management According to Swift (2001) and Ngai (2005), CRM consists of four dimensions, which can be seen as a closed cycle of customer management system. They share a common goal of creating a deeper understanding of customer behaviours to maximise its value to the business in the long term. Data mining techniques can be ... ... middle of paper ... ...telligence, Planning, 23, 582–605. Petrissans, A. (1999). “Customer relationship management: the changing economics of customer relationships”. White Paper prepared by Cap Gemini and International Data Corporation. Written, I.H. and Frank, E. (1999) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco: Morgan Kaufmann Publishers. Swift, R. S. (2001). Accelarating customer relationships: Using CRM and relationship technologies. Upper saddle river. N.J.: Prentice Hall PTR. Thomas, L.C., Oliver, R. W. and Hand, D.J. 92005) “A Survey of the Issues in Consumer Credit Modelling Research”. Journal of the Operational Research Society, 56, 1006-1015 (September 2005). Turban, E., Aronson, J. E., Liang, T. P., & Sharda, R. (2007). Decision support and business intelligence systems (Eighth ed.). Pearson Education.

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