Decision Support Systems 31 Ž2001. 127–137 www.elsevier.comrlocaterdsw
Knowledge management and data mining for marketing
Michael J. Shaw a,b,c,) , Chandrasekar Subramaniam a , Gek Woo Tan a , Michael E. Welge b c
Department of Business Administration, UniÕersity of Illinois at Urbana-Champaign, Urbana, IL, USA National Center for Supercomputing Applications (NCSA), UniÕersity of Illinois at Urbana-Champaign, Urbana, IL, USA Beckman Institute, UniÕersity of Illinois at Urbana-Champaign, Room 2051, 405 N. Mathews AÕenue, Urbana, IL 61801, USA b
Abstract Due to the proliferation of information systems and technology, businesses increasingly have the capability to accumulate huge amounts of customer data in large databases. However, much of the useful marketing insights into customer characteristics and their purchase patterns are largely hidden and untapped. Current emphasis on customer relationship management makes the marketing function an ideal application area to greatly benefit from the use of data mining tools for decision support. A systematic methodology that uses data mining and knowledge management techniques is proposed to manage the marketing knowledge and support marketing decisions. This methodology can be the basis for enhancing customer relationship management. q 2001 Elsevier Science B.V. All rights reserved. Keywords: Data mining; Knowledge management; Marketing decision support; Customer relationship management
1. Introduction In recent years, the advent of information technology has transformed the way marketing is done and how companies manage information about their customers. The availability of large volume of data on customers, made possible by new information technology tools, has created opportunities as well as challenges for businesses to leverage the data and gain competitive advantage. Wal-Mart, the largest retailer in the U.S., for example, has a customer database that contains around 43 tera-bytes of data, ) Corresponding author. Beckman Institute, University of Illinois at Urbana-Champaign, Room 2051, 405 N. Mathews Avenue, Urbana, IL 61801, USA. Tel.: q 1-217-244-1266; fax: q 1-217244-8371. E-mail address: email@example.com ŽM.J. Shaw..
which is larger than the database used by the Internal Revenue Services for collecting income taxes w10x. The Internet and the World Wide Web have made the process of collecting data easier, adding to the volume of data available to businesses. On the one hand, many organizations have realized that the knowledge in these huge databases are key to supporting the various organizational decisions. Particularly, the knowledge about customers from these databases is critical for the marketing function. But, much of this useful knowledge is hidden and untapped. On the other hand, the intense competition and increased choices available for customers have created new pressures on marketing decision-makers and there has emerged a need to manage customers in a long-term relationship. This new phenomenon, called customer relationship management, requires that the organizations tailor their products and ser-
0167-9236r01r$ - see front matter q 2001 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 9 2 3 6 Ž 0 0 . 0 0 1 2 3 - 8
M.J. Shaw et al.r Decision Support Systems 31 (2001) 127–137
vices and interact with their customers based on actual customer preferences, rather than some assumed general characteristics w21,22x. As organizations move towards customer relationship management, the marketing function, as the front-line to interact with customers, is the most impacted due to these changes. There is an increasing realization that effective customer relationship management can be done only based on a true understanding of the needs and preferences of the customers. Under these conditions, data mining tools can help uncover the hidden knowledge and understand customer better, while a systematic knowledge management effort...
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