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Relation between Data Mining and Business Fields in the Four Dimensional CRM Model

Author

Listed:
  • Iva Salov

    (University of Zadar, Department of Economics, Croatia)

  • Aleksandra Krajnovic

    (University of Zadar, Department of Economics, Croatia)

  • Ante Panjkota

    (University of Zadar, Department of Economics and Maritime Department, Croatia)

Abstract

Various academic sources show how data mining techniques (DM) can be successfully applied in 4-dimensional CRM model. However, there is no known systematic synthesis related to the correlation between DM, CRM, and business fields (e.g., banking, e-commerce, telecommunication, etc.). This paper deals with that problem throughout the review of primary and secondary sources of the application of DM techniques in the four CRM dimensions related to different business fields in the period from 2006 to 2016. The proposed research model is multidimensional where, for the sake of simplified representation, its base consists of three dimensions – DM techniques, CRM dimensions and business fields. Each mentioned dimension is multi-dimensional by itself. This preliminary study indicates the high impact of DM on CRM dimensions in all business fields with high interaction with customers. Besides that, implicitly or explicitly customer’s privacy and security issues arose as a problem in most recent studies on the application of DM techniques in the frame of the contemporary CRM systems. In that sense, we proposed extended CRM model with customers’ security and privacy as root dimension.

Suggested Citation

  • Iva Salov & Aleksandra Krajnovic & Ante Panjkota, 2017. "Relation between Data Mining and Business Fields in the Four Dimensional CRM Model," MIC 2017: Managing the Global Economy; Proceedings of the Joint International Conference, Monastier di Treviso, Italy, 24–27 May 2017,, University of Primorska Press.
    • Handle: RePEc:prp:micp17:439-455
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    References listed on IDEAS

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    2. A. Prinzie & D. Van Den Poel, 2006. "Exploiting Randomness for Feature Selection in Multinomial Logit: a CRM Cross-Sell Application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/390, Ghent University, Faculty of Economics and Business Administration.
    3. K. Coussement & D. van den Poel, 2008. "Integrating the voice of customers through call center emails into a decision support system for churn prediction," Post-Print hal-00788086, HAL.
    4. Vishal Bhatnagar & Jayanthi Ranjan, 2011. "Time to implement data mining in insurance firms for effective CRM and CRM analytics," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 9(1), pages 1-24.
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