Data-mining application for country segmentation based on the RFM model
For effective Customer Relationship Management (CRM), it is important to gather information on customer value. Segmentation is the method of knowing the customers and partitioning a population of customers into smaller groups. This paper develops a novel country segmentation methodology based on Recency (R), Frequency (F) and Monetary value (M) variables. After the variables are calculated, clustering methods (K-means and fuzzy K-means) are used to segment countries and compare the results of these methods by three different criteria. Customers are classified into four tiers: Top-active, Medium-active, New customer and Inactive. Then a customer pyramid is drawn and the customer value is calculated. Consequently, the data are used to analyse the relative profitability of each customer cluster and the proper strategy is determined for them.
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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 1 (2008)
Issue (Month): 2 ()
|Contact details of provider:|| Web page: http://www.inderscience.com/browse/index.php?journalID=282|
When requesting a correction, please mention this item's handle: RePEc:ids:injdan:v:1:y:2008:i:2:p:126-140. 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: (Graham Langley)
If references are entirely missing, you can add them using this form.