Data mining techniques for customer relationship management

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文件名称：Data mining techniques for customer relationship management

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更新时间：2013-12-16 03:56:35

Data mining customer relationship management

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.: [phone]; fax: [phone]. E-mail address: [e-mail] (D.C. Yen). 0160-791X/02/$ - see front matter  2002 Elsevier Science Ltd. All rights reserved. PII: S01 60 -791X(02)00038-6