Data-mining application for country segmentation based on the RFM model

Authors:
Mehdi Ghazanfari;Samira Malek Mohamadi;Somayeh Alizadeh
Affiliations:
Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.;Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.;Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
Venue:
International Journal of Data Analysis Techniques and Strategies
Year:
2008

Citing 3
Cited 1

Clustering techniques

Future Generation Computer Systems - Special double issue on data mining
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Building Data Mining Applications for CRM
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Expert Systems with Applications: An International Journal

Mining important association rules based on the RFMD technique

International Journal of Data Analysis Techniques and Strategies

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Abstract

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.