Skip to main content

Customer Relationship Management and Big Data Mining

  • Chapter
  • First Online:
Information Granularity, Big Data, and Computational Intelligence

Part of the book series: Studies in Big Data ((SBD,volume 8))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Berson, A., Smith, S., Smith, M., Thearling, K.: Building Data Mining Applications for CRM. McGraw-Hill, New York (2000)

    Google Scholar 

  2. Bloom, J.Z.: Tourist market segmentation with linear and non-linear techniques. Tour. Manag. 25(6), 723–733 (2004)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Edelstein, H.: Building Profitable Customer Relationships with Data Mining, Executive Briefing. SPSS inc., Chicago (2000)

    Google Scholar 

  9. Fan, W., Bifet, A.: Mining big data: current status, and forecast to the future. ACM SIGKDD Explor. Newslett. 14(2), 1–5 (2013)

    Article  MATH  Google Scholar 

  10. Fausett, L.: Fundamentals of Neural Networks: an Architectures and Applications. Prentice Hall, New York (1994)

    Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  14. Kahan, R.: Using database marketing techniques to enhance your one-to-one marketing initiatives. J. Consum. Mark. 15(5), 491–493 (1998)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  16. Kohonen, T.: Self-Organization and Associate Memory. Springer, Berlin (1984)

    Google Scholar 

  17. Kotler, P.: Marketing Management. Prentice-Hall, New York (2000)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  21. Pandys, A.S., Macy, R.B.: Pattern Recognition with Neural Networks in C++. CRC Press, Boca Raton (1996)

    Google Scholar 

  22. Pedrycz, W.: Granular Computing: Analysis and Design of Intelligent Systems, CRC Press, Boca Raton (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  24. Shin, H.W., Sohn, S.Y.: Product differentiation and market segmentation as alternative marketing strategies. Expert Syst. Appl. 27(1), 27–33 (2004)

    Article  Google Scholar 

  25. Smith, W.R.: Product differentiation and market segmentation as alternative marketing strategies. J. Mark. 12, 3–8 (1956)

    Article  Google Scholar 

  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. Weiss, S.M.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann, Burlington (1998)

    Google Scholar 

  28. Wu, M.L.: Application Practices of SPSS Statistics. Song-Gun Bookstore (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yi Hui Liang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Liang, Y.H. (2015). Customer Relationship Management and Big Data Mining. In: Pedrycz, W., Chen, SM. (eds) Information Granularity, Big Data, and Computational Intelligence. Studies in Big Data, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-08254-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08254-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08253-0

  • Online ISBN: 978-3-319-08254-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics