IDEAS home Printed from https://ideas.repec.org/a/aes/infoec/v18y2014i3p5-16.html
   My bibliography  Save this article

Predicting Customers Churn in a Relational Database

Author

Listed:
  • Catalin CIMPOERU

    ()

  • Anca Ioana ANDREESCU

    ()

Abstract

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.

Suggested Citation

  • Catalin CIMPOERU & Anca Ioana ANDREESCU, 2014. "Predicting Customers Churn in a Relational Database," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 18(3), pages 5-16.
  • Handle: RePEc:aes:infoec:v:18:y:2014:i:3:p:5-16
    as

    Download full text from publisher

    File URL: http://revistaie.ase.ro/content/71/01%20-%20Cimpoeru,%20Andreescu.pdf
    Download Restriction: no

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. 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). General contact details of provider: http://edirc.repec.org/data/aseeero.html .

    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.

    We have no references for this item. You can help adding them by using 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.