Application of data mining techniques in customer relationship management-literature review
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
Customer relationship management (CRM) comprises a set of processes and enabling systems supporting a business strategy to build long term, proﬁtable relationships with speciﬁc customers (Ling & Yen, 2001). Customer data and information technology (IT) tools form the foundation upon which any successful CRM strategy is built. In addition, the rapid growth of the Internet and its associated technologies has greatly increased the opportunities for marketing and has transformed the way relationships between companies and their customers are managed (Ngai, 2005). Although CRM has become widely recognized as an important business approach, there is no universally accepted deﬁnition of CRM (Ling & Yen, 2001; Ngai, 2005). Swift (2001, p. 12) deﬁned CRM as an ‘‘enterprise approach to understanding and inﬂuencing customer behaviour through meaningful communications in order to improve customer acquisition, customer retention, customer loyalty, and customer proﬁtability”. Kincaid (2003, p. 41) viewed CRM as ‘‘the strategic use of information, processes, technology, and people to manage the customer’s relationship with your company (Marketing, Sales, Services, and Support) across the whole customer life cycle”. Parvatiyar and Sheth (2001, p. 5) deﬁned CRM as ‘‘a comprehensive strategy and process of acquiring, retaining, and partnering with selective customers to create superior value for the company and the customer. It involves the integration of marketing, sales, customer service, and the supply chain functions of the organization to achieve greater efﬁciencies and effectiveness in delivering customer value”. These deﬁnitions emphasize the importance of viewing CRM as a comprehensive process of acquiring and retaining customers, with the help of business intelligence, to maximize the customer value to the organization.
Machine Learning Workbench for Data Mining
Data Mining Using High Performance Data Clouds-Experimental Studies Using Sector and Sphere
FREE IEEE PAPER