A fuzzy ANP based weighted RFM model for customer segmentation in auto insurance sector

Ravasan, AZ; Mansouri, T

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.

Citation

Ravasan, A., & Mansouri, T. (2015). A fuzzy ANP based weighted RFM model for customer segmentation in auto insurance sector. International Journal of Information Systems in the Service Sector, 7(2), 5. https://doi.org/10.4018/ijisss.2015040105

• Journal Article Type: Article
• Publication Date: Apr 1, 2015
• Deposit Date: Jun 9, 2021
• Journal: International Journal of Information Systems in the Service Sector
• Print ISSN: 1935-5688
• Electronic ISSN: 1935-5696
• Publisher: IGI Global
• Volume: 7
• Issue: 2
• Pages: 5
• DOI: https://doi.org/10.4018/ijisss.2015040105
• Publisher URL: https://doi.org/10.4018/ijisss.2015040105
• Related Public URLs: http://www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=1099