Data Mining

  • V. Kumar
  • Werner Reinartz
Part of the Springer Texts in Business and Economics book series (STBE)


This chapter describes the importance and benefits of data mining and gives a detailed overview of the underlying process. The data mining procedure breaks down into five subsections: defining the business objectives, getting the raw data, identifying relevant variables, gaining customer insight, and acting. The discussion of these steps will help the reader understand the overall process of data mining. Furthermore, the process steps are illustrated with the case study of Credite Est (name disguised), a French mid-tier bank. Finally, the case study, “Yapi Kredi—Predictive Model–Based cross-sell Campaign,” shows a comprehensive application of data mining.



We thank Frank Block, Ph.D., of FinScore Corporation (Switzerland) for his collaboration on this chapter.


  1. Levine, R. (2015, April 3). Data mining the digital gold rush: 4 companies that get it. Billboard. Accessed May 2, 2017.
  2. Olson, J. (2003). Data quality – The accuracy dimension. Amsterdam, The Netherlands: Kaufmann.Google Scholar
  3. Principe, J. C., Euliano, N. R., & Lefebvre, W. C. (2000). Neural and adaptive systems: Fundamentals through simulations. New York: Wiley.Google Scholar
  4. Turner, D., Schroeck, M., & Shockley, R. (2013). Analytics: The real-world use of big data in financial services. IBM Global Business Services. Accessed May 2, 2017.

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • V. Kumar
    • 1
  • Werner Reinartz
    • 2
  1. 1.J. Mack Robinson College of Business, Center for Excellence in Brand and Customer ManagementGeorgia State UniversityAtlantaUSA
  2. 2.Department of Retailing and Customer ManagementUniversity of CologneCologneGermany

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