Review
Application of data mining techniques in customer relationship management: A literature review and classification

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Abstract

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.

Introduction

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 and 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.

From the architecture point of view, the CRM framework can be classified into operational and analytical (Berson et al., 2000, He et al., 2004, Teo et al., 2006). Operational CRM refers to the automation of business processes, whereas analytical CRM refers to the analysis of customer characteristics and behaviours so as to support the organization’s customer management strategies. As such, analytical CRM could help an organization to better discriminate and more effectively allocate resources to the most profitable group of customers. Data mining tools are a popular means of analyzing customer data within the analytical CRM framework. Many organizations have collected and stored a wealth of data about their current customers, potential customers, suppliers and business partners. However, the inability to discover valuable information hidden in the data prevents the organizations from transforming these data into valuable and useful knowledge (Berson et al., 2000). Data mining tools could help these organizations to discover the hidden knowledge in the enormous amount of data.

Turban, Aronson, Liang, and Sharda (2007, p.305) defines data mining as “the process that uses statistical, mathematical, artificial intelligence and machine-learning techniques to extract and identify useful information and subsequently gain knowledge from large databases”. Berson et al., 2000, Lejeune, 2001, Ahmed, 2004 and Berry and Linoff (2004) also provide a similar definition regarding data mining as being the process of extracting or detecting hidden patterns or information from large databases. With comprehensive customer data, data mining technology can provide business intelligence to generate new opportunities (Bortiz and Kennedy, 1995, Fletcher and Goss, 1993, Langley and Simon, 1995, Lau et al., 2003, Salchenberger et al., 1992, Su et al., 2002, Tam and Kiang, 1992, Zhang et al., 1999).

The application of data mining tools in CRM is an emerging trend in the global economy. Analyzing and understanding customer behaviours and characteristics is the foundation of the development of a competitive CRM strategy, so as to acquire and retain potential customers and maximize customer value. Appropriate data mining tools, which are good at extracting and identifying useful information and knowledge from enormous customer databases, are one of the best supporting tools for making different CRM decisions (Berson et al., 2000). As such, the application of data mining techniques in CRM is worth pursuing in a customer-centric economy.

This paper presents a comprehensive review of literature related to application of data mining techniques in CRM published in academic journals between 2000 and 2006. A classification of framework is also presented. The paper is organized as follows: first, the research methodology used in the study is described; second, the method for classifying data mining articles in CRM is presented; third, articles about data mining in CRM are analysed and the results of the classification are reported; and finally, the conclusions, limitations and implications of the study are discussed.

Section snippets

Research methodology

As the nature of research in CRM and data mining are difficult to confine to specific disciplines, the relevant materials are scattered across various journals. Business intelligence and knowledge discovery are the most common academic discipline for data mining research in CRM. Consequently, the following online journal databases were searched to provide a comprehensive bibliography of the academic literature on CRM and Data Mining:

  • ABI/INFORM Database;

  • Academic Search Premier;

  • Business Source

Classification method

According to Swift, 2001, Parvatiyar and Sheth, 2001 and Kracklauer, Mills, and Seifert (2004, p. 4), CRM consists of four dimensions:

  • (1)

    Customer Identification;

  • (2)

    Customer Attraction;

  • (3)

    Customer Retention;

  • (4)

    Customer Development.

These four dimensions can be seen as a closed cycle of a customer management system (Au and Chan, 2003, Kracklauer et al., 2004, Ling and Yen, 2001). They share the common goal of creating a deeper understanding of customers to maximize customer value to the organization in the

Classification of the articles

A detailed distribution of the 87 articles classified by the proposed classification framework is shown in Table 1.

Conclusion, research implications and limitations

Application of data mining techniques in CRM is an emerging trend in the industry. It has attracted the attention of practitioners and academics. This paper has identified eighty seven articles related to application of data mining techniques in CRM, and published between 2000 and 2006. It aims to give a research summary on the application of data mining in the CRM domain and techniques which are most often used. Although this review cannot claim to be exhaustive, it does provide reasonable

Acknowledgements

This research was partly supported by The Hong Kong Polytechnic University (Project no.: G-YF20) and National Natural Science Foundation of China (NSFC, Project no.: 70671059).

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