IDEAS home Printed from https://ideas.repec.org/a/taf/jmkthe/v29y2019i1p45-66.html
   My bibliography  Save this article

Data mining approach to professional education market segmentation: a case study

Author

Listed:
  • Mehraneh Davari
  • Payam Noursalehi
  • Abbas Keramati

Abstract

In this research, a combination of both quantitative and qualitative approaches is used to identify different market segments in the education industry. To solve the research problem, an exploratory approach to data mining is used and, using a series of interviews with experts, the factors affecting segmentation are identified. Then, using the clustering method (in the form of specific two-step and K-means algorithms), customers are clustered and features of each cluster are identified. This research is based on data provided by a large Iranian research and education company. After examining the clusters identified in both methods, it is determined that the clusters provided by the two-step algorithm are more in line with the organizational and market reality of the business. Finally, the marketing mix model is used to formulate strategic approaches and actions.

Suggested Citation

  • Mehraneh Davari & Payam Noursalehi & Abbas Keramati, 2019. "Data mining approach to professional education market segmentation: a case study," Journal of Marketing for Higher Education, Taylor & Francis Journals, vol. 29(1), pages 45-66, January.
    • Handle: RePEc:taf:jmkthe:v:29:y:2019:i:1:p:45-66
    DOI: 10.1080/08841241.2018.1545724
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/08841241.2018.1545724
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/08841241.2018.1545724?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:taf:jmkthe:v:29:y:2019:i:1:p:45-66. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/WMHE20 .

    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.