• DocumentCode
    2680787
  • Title

    AI Based Approach of Predicting the Credit Limits of Users to Middle Customer based Mobile Communication Services

  • Author

    Pallegedara, A. ; Amaratunga, V.S. ; Gopura, R.A.R.C. ; Jayathileka, P.D.

  • Author_Institution
    Univ. of Moratuwa
  • fYear
    2006
  • fDate
    8-11 Aug. 2006
  • Firstpage
    588
  • Lastpage
    592
  • Abstract
    Most of the developing countries even, mobile communication has become a matter of course for many people. As markets saturate, the care and retention of existing customers becomes a key element for revenue stabilization for mobile communication network operators. We present a predictive data mining model to reduce the rate of forced churn as a consequence of non-payment: estimations of subscribers´ open amounts if being payers or non-payers allow to prevent subscribers from overspending-and ultimately churning-thus prolonging the customer relationship dwell time and securing future revenues, and hence necessary prediction system would be a great benefit to the mobile communication service providers (SP)
  • Keywords
    customer relationship management; data mining; invoicing; mobile communication; telecommunication services; AI based approach; charge amount estimations; credit limit prediction; customer relationship; data mining model; mobile communication services; revenue stabilization; Artificial intelligence; Artificial neural networks; Communication industry; Consumer electronics; Data mining; Information systems; Mobile communication; Predictive models; Production engineering; Subscriptions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial and Information Systems, First International Conference on
  • Conference_Location
    Peradeniya
  • Print_ISBN
    1-4244-0322-7
  • Type

    conf

  • DOI
    10.1109/ICIIS.2006.365796
  • Filename
    4216657