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Application Of Data Mining Techniques In Customer Relationship Management: A Literature Review And Classification

机译:数据挖掘技术在客户关系管理中的应用:文献综述与分类

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Despite the importance of data mining techniques to customer relationship management (CRM),there is a lack of a comprehensive literature review and a classification scheme for it.This is the first identifiable academic literature review of the application of data mining techniques to CRM.It provides an academic database of literature between the period of 2000-2006 covering 24 journals and proposes a classification scheme to classify the articles.Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM.Eighty-seven articles were subsequently selected,reviewed and classified.Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification,Customer Attraction,Customer Retention and Customer Development) and seven data mining functions (Association,Classification,Clustering,Forecasting,Regression,Sequence Discovery and Visualization).Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper.The review and classification process was independently verified.Findings of this paper indicate that the research area of customer retention received most research attention.Of these,most are related to one-to-one marketing and loyalty programs respectively.On the other hand,classification and association models are the two commonly used models for data mining in CRM.Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.
机译:尽管数据挖掘技术对客户关系管理(CRM)十分重要,但仍缺乏全面的文献综述和分类方案。这是首次将数据挖掘技术应用于CRM的可识别的学术文献综述。提供了2000-2006年间涵盖24种期刊的文献学术数据库,并提出了对文章进行分类的方案。确定并审查了900篇文章,它们与将数据挖掘技术应用于CRM的直接相关性;其中有87篇文章随后对87篇论文进行了选择,审查和分类。每篇论文都按四个CRM维度(客户识别,客户吸引力,客户保留和客户发展)和七个数据挖掘功能(关联,分类,聚类,预测,回归,序列发现)进行了分类。和论文的可视化)。论文进一步分为CRM e的9个子类别根据每篇论文的重点研究了不同数据挖掘技术下的元素。对审查和分类过程进行了独立验证。本文的研究结果表明,客户保留的研究领域受到了最多的研究关注。其中,大多数与以下方面相关:另一方面,分类模型和关联模型是CRM中数据挖掘的两个常用模型。我们的分析提供了指导未来研究并促进与数据应用有关的知识积累和创造的路线图。 CRM中的挖掘技术。

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