IDEAS home Printed from https://ideas.repec.org/a/eas/buseco/v32y2023i32p47-56.html
   My bibliography  Save this article

Customer Segmentation Using Rfm Analysis And Clustering Algorithms In Express Delivery Industry

Author

Listed:
  • Tuncay ÖZCAN

    (Istanbul Technical University)

Abstract

As in every sector, one of the most important issues in the express delivery industry is to get to know the customers. Understanding customer preferences and identifying loyal and churn customers provides significant competitive advantages for companies. At this point, customer segmentation based on RFM analysis is widely used. RFM analysis segments customers using recency (R), frequency (F) and monetary (M) attributes. In this study, customer segmentation is presented with a case study with the actual data taken from an express delivery company.  For this purpose, firstly, k-means and fuzzy c-means algorithms are used for customer segmentation based on RFM analysis. Then, Silhoutte and Dunn indices are calculated to evaluate the clustering quality and to determine the best number of clusters. Finally, on the basis of the obtained cluster centers, strategic analysis is carried out and churn and loyal customers are determined.

Suggested Citation

  • Tuncay ÖZCAN, 2023. "Customer Segmentation Using Rfm Analysis And Clustering Algorithms In Express Delivery Industry," Eurasian Business & Economics Journal, Eurasian Academy Of Sciences, vol. 32(32), pages 47-56, February.
    • Handle: RePEc:eas:buseco:v:32:y:2023:i:32:p:47-56
    DOI: 10.17740/eas.econ.2023-V32-04
    as

    Download full text from publisher

    File URL: https://eurasianacademy.org/index.php/busecon/article/view/1253
    Download Restriction: no
    File URL: https://libkey.io/10.17740/eas.econ.2023-V32-04?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
    ---><---

    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:eas:buseco:v:32:y:2023:i:32:p:47-56. 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: Kutluk Kagan Sumer (email available below). General contact details of provider: https://www.eurasianacademy.org/index.php/busecon .

    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.