IDEAS home Printed from https://ideas.repec.org/a/igg/jban00/v3y2016i2p33-49.html
   My bibliography  Save this article

Text Mining to Identify Customers Likely to Respond to Cross-Selling Campaigns: Reading Notes from Your Customers

Author

Listed:
  • Gregory Ramsey

    (Department of Information Science and Systems, Morgan State University, Baltimore, MD, USA)

  • Sanjay Bapna

    (Department of Information Science and Systems, Morgan State University, Baltimore, MD, USA)

Abstract

This paper reports on the results of extracting useful information from text notes captured within a Customer Relationship Management (CRM) system to segment and thus target groups of customers likely to respond to cross-selling campaigns. These notes often contain text that is indicative of customer intentions. The results indicate that the notes are meaningful in classifying customers who are likely to respond to purchase multiple communication devices. A Naïve Bayes classifier outperformed a Support Vector Machine classifier for this task. When combined with structured information, the classifier performed only marginally better. Thus, customer service notes can be an important source of predictive data in CRM systems.

Suggested Citation

  • Gregory Ramsey & Sanjay Bapna, 2016. "Text Mining to Identify Customers Likely to Respond to Cross-Selling Campaigns: Reading Notes from Your Customers," International Journal of Business Analytics (IJBAN), IGI Global, vol. 3(2), pages 33-49, April.
    • Handle: RePEc:igg:jban00:v:3:y:2016:i:2:p:33-49
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.2016040102
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jban00:v:3:y:2016:i:2:p:33-49. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.