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

Ravasan, AZ and Mansouri, T ORCID: https://orcid.org/0000-0003-1539-5546 2018, 'A fuzzy ANP based weighted RFM model for customer segmentation in auto insurance sector' , in: Intelligent systems : concepts, methodologies, tools, and applications , IGI Global, pp. 1050-1067.

Full text not available from this repository. (Request a copy)


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.

Item Type: Book Section
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Intelligent Systems: Concepts, Methodologies, Tools, and Applications
Publisher: IGI Global
ISBN: 9781522556435 (print); 9781522556442 (online)
Related URLs:
Depositing User: T Mansouri
Date Deposited: 09 Jun 2021 08:42
Last Modified: 27 Aug 2021 21:54
URI: http://usir.salford.ac.uk/id/eprint/60883

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)