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IJML 2012 Vol.2(6): 874-877
doi: 10.7763/IJMLC.2012.V2.256

Improving Customer Relationship Management Using Data Mining

Gaurav Gupta1 , Himanshu Aggarwal2

  • 1RIMT-Institute of Engineering & Technology, Punjab, India.
  • 2University College of Engineering, Punjabi University, Patiala, India.

Abstract

Customer Relationship Management (CRM) refers to the methodologies and tools that help businesses manage customer relationships in an organized way. Data Mining is the process that uses a variety of data analysis and modeling techniques to discover patterns and relationships in data that may be used to make accurate predictions. It can help you to select the right prospects on whom to focus. The essence of the information technology revolution and, in particular, the World Wide Web is the opportunity to build better relationships with customers than has been previously possible in the offline world. For instance, as part of their CRM strategy, a business might use a database of customer information to help construct a customer satisfaction survey, or decide which new product their customers might be interested in.

Keywords

  • CRM
  • LCV
  • Marketing
  • Relationship
  • Data Mining
256-L40070

How to Cite

Copied

Gaurav Gupta and Himanshu Aggarwal, "Improving Customer Relationship Management Using Data Mining," International Journal of Machine Learning, vol. 2, no. 6, pp. 874-877, 2012. https://doi.org/10.7763/IJMLC.2012.V2.256

Copyright & License

Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.
  • E-mail: editor@ijml.org
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