IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Predicting Customers Churn in a Relational Database

Listed author(s):
  • Catalin CIMPOERU


  • Anca Ioana ANDREESCU


Registered author(s):

    This paper explores how two main classical classification models work and generate predictions through a commercial solution of relational database management system (Microsoft SQL Server 2012). The aim of the paper is to accurately predict churn among a set of customers defined by various discrete and continuous variables, derived from three main data sources: the commercial transactions history; the users’ behavior or events happening on their computers; the specific identity information provided by the customers themselves. On a theoretical side, the paper presents the main concepts and ideas underlying the Decision Tree and Naïve Bayes classifiers and exemplifies some of them with actual hand-made calculations of the data being modeled by the software. On an analytical and practical side, the paper analyzes the graphs and tables generated by the classifying models and also reveal the main data insights. In the end, the classifiers’ accuracy is evaluated based on the test data method. The most accurate one is chosen for generating predictions on the customers’ data where the values of the response variable are not known.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:,%20Andreescu.pdf
    Download Restriction: no

    Article provided by Academy of Economic Studies - Bucharest, Romania in its journal Informatica Economica.

    Volume (Year): 18 (2014)
    Issue (Month): 3 ()
    Pages: 5-16

    in new window

    Handle: RePEc:aes:infoec:v:18:y:2014:i:3:p:5-16
    Contact details of provider: Postal:

    Phone: 0040-01-2112650
    Fax: 0040-01-3129549
    Web page:

    More information through EDIRC

    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:aes:infoec:v:18:y:2014:i:3:p:5-16. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Paul Pocatilu)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.