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Data mining research for customer relationship management systems: a framework and analysis

Author

Listed:
  • Sarika Sharma
  • D.P. Goyal
  • R.K. Mittal

Abstract

Data mining is a new technology that helps businesses to predict future trends and behaviours, allowing them to make proactive, knowledge-driven decisions. When data mining tools and techniques are applied on the data warehouse based on customer records, they search for the hidden patterns and trends. These can be further used to improve customer understanding and acquisition. Customer Relationship Management (CRM) systems are adopted by the organisations in order to achieve success in the business and also to formulate business strategies, which can be formulated based on the predictions given by the data mining tools. Basically three major areas of data mining research are identified: implementation of CRM systems, evaluation criteria for data mining software and CRM systems and methods to improve data quality for data mining. The paper is concluded with a proposed integrated model for the CRM systems evaluation and implementation. This paper focuses on these areas, where there is need for more explorations, and will provide a framework for analysis of the data mining research for CRM systems.

Suggested Citation

  • Sarika Sharma & D.P. Goyal & R.K. Mittal, 2008. "Data mining research for customer relationship management systems: a framework and analysis," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 3(5), pages 549-565.
    • Handle: RePEc:ids:ijbisy:v:3:y:2008:i:5:p:549-565
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    Citations

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    Cited by:

    1. Moric Ilija, 2013. "Clusters as a Factor of Rural Tourism Competitiveness: Montenegro Experiences," Business Systems Research, Sciendo, vol. 4(2), pages 94-107, December.
    2. Huang, Tony Cheng-Kui & Liu, Chuang-Chun & Chang, Dong-Cheng, 2012. "An empirical investigation of factors influencing the adoption of data mining tools," International Journal of Information Management, Elsevier, vol. 32(3), pages 257-270.
    3. HaeOk Choi, 2020. "Geospatial Data Approach for Demand-Oriented Policies of Land Administration," Land, MDPI, vol. 9(1), pages 1-12, January.
    4. Huang, Tony Cheng-Kui & Wu, Ing-Long & Chou, Chih-Chung, 2013. "Investigating use continuance of data mining tools," International Journal of Information Management, Elsevier, vol. 33(5), pages 791-801.

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