Lessons learned: A case study using data mining in the newspaper industry
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Candace L. Gunnarsson is owner-operator of S2 Statistical Solutions, Inc. founded in 1992, and performs statistics consulting and training seminars. Dr Gunnarsson has taught statistics courses for more than ten years in the fields of education, psychology, and business at Xavier University, The Union Institute, and University of Cincinnati.
Mary M. Walker is Professor of Marketing at Xavier University in Cincinnati, Ohio, and in this role was the recipient of a grant sponsored by the General Electric Company to develop a course on data mining. She has served as Xavier’s Acting Vice President for Information Resources, overseeing all information technology during 2004–2005.
Vern Walatka is a chemical engineer and chemist with experience as a SAS programmer and data analyst. He currently consults on topics of survey analysis, GIS, and mapping software.
Kenneth Swann is Senior Data Analytical Manager with a background in software management and market research. He has organised three successful annual SAS one-day conferences for statisticians and researchers.
Keywords data mining, data-driven decision making, customer relationship management (CRM), decision tree application Abstract Many organisations across a variety of industries are engaging in the process of data mining as part of an overall strategy for business intelligence, customer relationship management (CRM), including churn prevention. This paper provides an overview of the data mining process and illustrates a case study in which data mining is utilised as a churn prevention tool for a major Midwest USA newspaper. For this case study, a decision tree, a common modelling technique, was the analytical tool of choice. Lessons learned throughout the data mining process are provided to offer insight and to promote the sharing of information. Strategies for getting started in the data mining process are presented to encourage organisations to embrace a data-driven strategy for business intelligence, CRM and churn prevention. Journal of Database Marketing & Customer Strategy Management (2007) 14, 271–280. doi:10.1057/palgrave.dbm.3250058
Mary M. Walker Williams College of Business, Xavier University, 3800 Victory Parkway, Cincinnati, OH 45207, US Tel: + 1 513 745 2980; e-mail: [email protected]
INTRODUCTION Many organisations across a variety of industries are engaging in the process of data mining as part of an overall strategy to improve business intelligence, customer relationship management (CRM) and churn
prevention. Data mining is the computerassisted process of analysing data from different perspectives and distilling it into actionable information. More than ever, companies are using this technique as they recognise their raw transactional data as a
© 2007 Palgrave Macmillan Ltd 1741-2439 $30.00 Vol. 14, 4, 271–280 Database Marketing & Customer Strategy Management www.palgrave-journals.com/dbm
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valuable source of unique information, which, once analysed via data mining, can create knowledge that advances organisational o
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