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

Ahad Zare Ravasan · Taha Mansouri

10 citations0/0 files storedIntelligent Systems

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

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.

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References

1965Fuzzy sets 1996Applications of the extent analysis method on fuzzy AHP 2008Application of data mining techniques in customer relationship management: A literature review and classification 2004Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management 2006Decision Support and Business Intelligence Systems 1995The One to One Future: Building Relationships One Customer at a Time 2002Customer Relationship Management: Emerging Practice, Process, and Discipline 2004A fuzzy optimization model for QFD planning process using analytic network approach

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