• DocumentCode
    1967478
  • Title

    Customer Churn Prediction for Telecom Services

  • Author

    Yabas, Utku ; Cankaya, Hakki Candan ; Ince, Turker

  • Author_Institution
    Sch. of Eng. & Comput. Sci., Izmir Univ. of Econ., Izmir, Turkey
  • fYear
    2012
  • fDate
    16-20 July 2012
  • Firstpage
    358
  • Lastpage
    359
  • Abstract
    Customer churn is a big concern for telecom service providers due to its associated costs. This short paper briefly explains our ongoing work on customer churn prediction for telecom services. We are working on data mining methods to accurately predict customers who will change and turn to another provider for the same or similar service. Sample dataset we use for our experiments has been compiled by Orange Telecom from real data. They posted the sample dataset for 2009 Knowledge Discovery and Data Mining Competition. IBM has scored the highest on this dataset requiring significant amount of computational resources. We are aiming to find alternative methods that can match or improve the recorded highest score with more efficient use of resources. Dataset has very large number of features, examples and incomplete values. As the first step, we employ some methods to preprocess the dataset for its imperfections. Then, we compare and contrast various ensemble and single classifiers. We conclude the paper with future directions for the study.
  • Keywords
    data mining; signal classification; telecommunication services; IBM; Orange Telecom; associated costs; computational resources; customer churn prediction; data mining competition; knowledge discovery; single classifiers; telecom service providers; Data mining; Decision trees; Educational institutions; Prediction algorithms; Telecommunication services; Vegetation; churn prediction; data mining; machine learning; pattern recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Software and Applications Conference (COMPSAC), 2012 IEEE 36th Annual
  • Conference_Location
    Izmir
  • ISSN
    0730-3157
  • Print_ISBN
    978-1-4673-1990-4
  • Electronic_ISBN
    0730-3157
  • Type

    conf

  • DOI
    10.1109/COMPSAC.2012.54
  • Filename
    6340176