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A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation in Auto Insurance Sector

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  • Ahad Zare Ravasan

    (Department of Industrial Management, Allameh Tabataba'i University, Tehran, Iran)

  • Taha Mansouri

    (Department of Industrial Management, Allameh Tabataba'i University, Tehran, Iran)

Abstract

Data mining has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the raw data. This study has proposed a brand new and practical fuzzy analytic network process (FANP) based weighted RFM (Recency, Frequency, Monetary value) model for application in K-means algorithm for auto insurance customers' segmentation. The developed methodology has been implemented for a private auto insurance company in Iran which classified customers into four “best”, “new”, “risky”, and “uncertain” patterns. Then, association rules among auto insurance services in two most valuable customer segments including “best” and “risky” patterns are discovered and proposed. Finally, some marketing strategies based on the research results are proposed. The authors believe the result of this paper can provide a noticeable capability to the insurer company in order to assess its customers' loyalty in marketing strategy.

Suggested Citation

  • Ahad Zare Ravasan & Taha Mansouri, 2015. "A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation in Auto Insurance Sector," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 7(2), pages 71-86, April.
    • Handle: RePEc:igg:jisss0:v:7:y:2015:i:2:p:71-86
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