Customer Relationship Management and Big Data Mining

  • Yi Hui LiangEmail author
Part of the Studies in Big Data book series (SBD, volume 8)


Successful customer relationship management (CRM) requires enterprises to interact flexibly with their customers. Enterprises must quickly and effectively find complex customer data from large quantities of data by big data mining to help understand and interact with them by suitable marketing tactics, increase the value to the customer, and improve their competitive advantages of enterprises. In this chapter, discuss big data mining, customer relationship management, customer value, and propose a case study of big data mining for customer relationship management with data of the Automotive Maintenance Industry.


Big data mining Customer relationship management Customer value 


  1. 1.
    Berson, A., Smith, S., Smith, M., Thearling, K.: Building Data Mining Applications for CRM. McGraw-Hill, New York (2000)Google Scholar
  2. 2.
    Bloom, J.Z.: Tourist market segmentation with linear and non-linear techniques. Tour. Manag. 25(6), 723–733 (2004)CrossRefGoogle Scholar
  3. 3.
    Cerny, P.A.: Data mining and neural networks from a commercial perspective. In: the 36th annual ORSNZ conference. Christchurch, NZ (2001)Google Scholar
  4. 4.
    Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996)CrossRefGoogle Scholar
  5. 5.
    Chen, Y.L., Hsu, C.L., Chou, D.C.: Constructing a multi-valued and multi-labeled decision tree. Expert Syst. Appl. 25(2), 199–209 (2003)CrossRefGoogle Scholar
  6. 6.
    Cheng, B.W., Chang, C.L., Liu, I.S.: Enhancing care services quality of nursing homes using data mining. Total Qual. Manag. 16(5), 575–596 (2005)CrossRefGoogle Scholar
  7. 7.
    Diebold, F.X.: `Big Data’ dynamic factor models for macroeconomic measurement and forecasting. In: Dewatripont, M., Hansen, L.P., Turnovsky, S. (eds.) Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress of the Econometric Society. Cambridge University Press, Cambridge, pp. 115–122 (2003)Google Scholar
  8. 8.
    Edelstein, H.: Building Profitable Customer Relationships with Data Mining, Executive Briefing. SPSS inc., Chicago (2000)Google Scholar
  9. 9.
    Fan, W., Bifet, A.: Mining big data: current status, and forecast to the future. ACM SIGKDD Explor. Newslett. 14(2), 1–5 (2013)CrossRefzbMATHGoogle Scholar
  10. 10.
    Fausett, L.: Fundamentals of Neural Networks: an Architectures and Applications. Prentice Hall, New York (1994)Google Scholar
  11. 11.
    Hruschka, H., Natter, M.: Comparing performance of feed forward neural nets and k-means of cluster-based market segmentation. Eur. J. Oper. Res. 114(3), 346–353 (1999)CrossRefzbMATHGoogle Scholar
  12. 12.
    Hung, C., Tsai, C.F.: Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Syst. Appl. 34(1), 780–787 (2008)CrossRefGoogle Scholar
  13. 13.
    Jang, S.C., Morrison, A.M.T., O’Leary, J.T.: Benefit segmentation of Japanese pleasure travelers to the USA and Canada: selecting target markets based on the profitability and the risk of individual market segment. Tour. Manag. 23(4), 367–378 (2002)CrossRefGoogle Scholar
  14. 14.
    Kahan, R.: Using database marketing techniques to enhance your one-to-one marketing initiatives. J. Consum. Mark. 15(5), 491–493 (1998)CrossRefGoogle Scholar
  15. 15.
    Kim, S.Y., Jung, T.S., Suh, E.H., Hwang, H.S.: Customer segmentation and strategy development based on customer lifetime value: a case study. Expert Syst. Appl. 31(1), 101–107 (2006)CrossRefGoogle Scholar
  16. 16.
    Kohonen, T.: Self-Organization and Associate Memory. Springer, Berlin (1984)Google Scholar
  17. 17.
    Kotler, P.: Marketing Management. Prentice-Hall, New York (2000)Google Scholar
  18. 18.
    Lee, J.H., Park, S.C.: Intelligent profitable customers segmentation system based on business intelligence tools. Expert Syst. Appl. 29(1), 145–152 (2005)CrossRefGoogle Scholar
  19. 19.
    Liang, Y.H.: Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Syst. Appl. 37(12), 7489–7496 (2010)CrossRefGoogle Scholar
  20. 20.
    McCarty, J.A., Hastak, M.: Segmentation approaches in data-mining: a comparison of RFM, CHAID, and logistic regression. J. Bus. Res. 60(6), 656–662 (2007)CrossRefGoogle Scholar
  21. 21.
    Pandys, A.S., Macy, R.B.: Pattern Recognition with Neural Networks in C++. CRC Press, Boca Raton (1996)Google Scholar
  22. 22.
    Pedrycz, W.: Granular Computing: Analysis and Design of Intelligent Systems, CRC Press, Boca Raton (2013)Google Scholar
  23. 23.
    Vellido, A.P., Lisboa, J.G., Meehan, K.: Segmentation of the on-line shopping market using neural networks. Expert Syst. Appl. 17(4), 303–314 (1999)CrossRefGoogle Scholar
  24. 24.
    Shin, H.W., Sohn, S.Y.: Product differentiation and market segmentation as alternative marketing strategies. Expert Syst. Appl. 27(1), 27–33 (2004)CrossRefGoogle Scholar
  25. 25.
    Smith, W.R.: Product differentiation and market segmentation as alternative marketing strategies. J. Mark. 12, 3–8 (1956)CrossRefGoogle Scholar
  26. 26.
    Tokunaga, H., Atlam, E.S., Fuketa, M., Morita, K., Tsuda, K., Aoe, J.I.: Estimating sentence types in computer related new product bulletins using a decision tree. Inf. Sci. 168(1), 185–200 (2004)Google Scholar
  27. 27.
    Weiss, S.M.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann, Burlington (1998)Google Scholar
  28. 28.
    Wu, M.L.: Application Practices of SPSS Statistics. Song-Gun Bookstore (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Information Management DepartmentI-SHOU UniversityKaohsiungTaiwan, ROC

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