TAILIEUCHUNG - Data mining techniques for customer relationship management

Adding a further layer of complexity, Judica and Perkins (1992) found that because the environment of consumption is most frequently a social gathering, consumers are less likely to take a chance and risk a poor quality product. This has a marked influence on the wine selection process because the product becomes a means to social recognition and approval for the buyer rather than simply a beverage. As a result, in many consumption situations the consumer does not have to decide whether or not they will consume a particular product or brand, but what product or brand to select from a. | Pergamon In ocietv Technology in Society 24 2002 483-502 locate techsoc Data mining techniques for customer relationship management Chris Rygielskia Jyun-Cheng Wang b David C. Yen a a Department of DSC MIS Miami University Oxford OH USA b Department of Information Management National Chung-Cheng University Taiwan ROC Abstract Advancements in technology have made relationship marketing a reality in recent years. Technologies such as data warehousing data mining and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining the extraction of hidden predictive information from large databases organizations can identify valuable customers predict future behaviors and enable firms to make proactive knowledge-driven decisions. The automated future-oriented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection CHAID . While differing approaches abound in the realm of data mining the use of some type of data mining is necessary to accomplish the goals of today s customer relationship management philosophy. 2002 Elsevier Science Ltd. All rights reserved. Keywords Customer relationship management CRM Relationship marketing Data mining Neural networks Chi-square automated interaction detection CHAID Privacy rights Corresponding author. Tel. 1-513-529-4826 fax 1-513-529-9689. E-mail address yendc@ . Yen . 0160-791X 02 - see front .

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