Knowledge Management and Decision Support System
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 a
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,
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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 a
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,
)