@article{MAKHILLIBM2016101826652,
    title = {Identifying and Segmenting Customers of Pasargad Insurance Company Through RFM Model (RFM)},
    journal = {International Business Management},
    volume = {10},
    number = {18},
    pages = {4209-4214},
    year = {2016},
    issn = {1993-5250},
    doi = {ibm.2016.4209.4214},
    url = {https://makhillpublications.co/view-article.php?issn=1993-5250&doi=ibm.2016.4209.4214},
    author = {Karim and},
    keywords = {Customer classification,customer segmentation,vehicle insurance,clustering,probability},
    abstract = {Customers&#146; segmentation helps organizations in targeting their services customization and prioritizing products on the basis of its profitability. Organizations&#146; success depends on attracting and keeping loyal customers. Since, most vehicle insurance policies are issued in one year period and it would be expired after the period,end of insurance periodmay lead toend of customer&#146;s loyalty. Therefore, recognition and identification of customers whom are more likely to repurchase or renew their insurance policies isimportant for insurance companies and it helps themin targeted marketing and advertising, keeping existing customers and identifying future customers. In this study, we aimed to provide a framework forcustomers&#146; (vehicle insurance clients) segmentation in Pasargad Insurance Company based on the factors affecting customer lifetime value. For this purpose, a series of transactions related to the 384 customers of Pasargad Insurance Company in the spring of 2015 were considered. Transaction data included customers&#146; purchaserecency (R), frequency of insurance policies renewals in the 6 years period (F) and the monetary amounts paid by each customer for vehicleinsurance policy in the last purchase (M). According to the results of clustering, customers were divided into 4 segments; the first segment; golden customers, the second segment: valued and loyal customers, the third segment; steady customers, the fourth segment; invaluable customersand withthe probability to disaffirm.</P><P><B>Key words:</B> Customer classification}
    }