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Information Technology Journal
  Year: 2012 | Volume: 11 | Issue: 9 | Page No.: 1193-1201
DOI: 10.3923/itj.2012.1193.1201
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The RFM-based Institutional Customers Clustering: Case Study of a Digital Content Provider

Spring C. Hsu

The RFM (recency, frequency and monetary) model has been widely applied for personal customers’ analysis, but limited for the institutional customers. Therefore, the study takes a digital content provider with institutional customers of a variety of Small and Medium Enterprises (SMEs) in Taipei city for RFM clustering and deploys an innovative method combining RFM model and fuzzy c-means (FCM) for analysis. Clustering results suggest that September and October are the two busiest months of transactions for major customers. Moreover, major customers have twice of transactions and total transaction amount of 6,462 USD; i.e., an average transaction amount of 3,231 USD. Consequently, for a digital content provider, the enough staff and equipment preparations must ready before September and October to make sure a high quality service for major customers. The average transaction amount implies that the approximate cost of 3,250 USD is the upper bound of willing to pay for many SMEs. Therefore, to deliver the customized service packages with the charges close to 3,250 USD are the first priority of services development for a digital content provider.
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How to cite this article:

Spring C. Hsu , 2012. The RFM-based Institutional Customers Clustering: Case Study of a Digital Content Provider. Information Technology Journal, 11: 1193-1201.

DOI: 10.3923/itj.2012.1193.1201






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