چکیده:
This study clusters customers and finds the characteristics of different groups in a life insurance company in order to find a way for prediction of customer behavior based on payment. The approach is to use clustering and association rules based on CRISP-DM methodology in data mining. The researcher could classify customers of each policy in three different clusters, using association rules. At the end of study the characteristics are defined and given to the company, so they could implement CRM strategies based on the newly found differences. Attention to the income and cash earning comes before paying attention to other problems. In most of the companies in developing countries, infrastructural problems of the company like earning enough income prevent the company from effective research implementation on advanced strategies. So this study focuses on basic problems. Utilizing data mining approach to classify customers in life insurance is a new approach among insurance companies in Iran. There are some research in relation to the CRM and data mining, but the contribution of this study is to investigate two new attributes plus those common attributes used before in studying customer behavior; the two attributes are "payment type" and the "purchaser". In order to have a framework, all the process is embedded in CRISP-DM methodology.
خلاصه ماشینی:
Mehregan 1 Department of Industrial Engineering, School of Engineering, Alzahra University, Tehran, Iran 2 Department of Information Technology Management, School of Management and Accounting, Allameh Tabatabaie University, Tehran, Iran Received 18 February 2014, Accepted 15 December 2014 ABSTRACT: This study clusters customers and finds the characteristics of different groups in a life insurance company in orderto find a way for prediction of customer behavior based on payment.
There are some research in relation to the CRM and data mining, but the contribution of this study is to investigate two new attributes plus those common attributes used before in studying customer behavior; the two attributes are "payment type" and the "purchaser".
Keywords: Data mining, Life insurance, Customer retention, Decision tree, Segmentation INTRODUCTION a.
In other side, data mining has frequently used in CRM, like classification of customers at risk, creating profitable customers and customer retention (Min and Emam, 2002; Rygielsky et al.
Literature Review There is literature about CRM strategies based on data mining and some models like SAS to determine the level of data mining match with insurance companies (Chen and Hu, 2005).
Table 1: Attributes classification Attribute type of investigations Attributes or independent variables Mean rank Investigated before in several literature First time to investigate The association rules for the L&S with 82% confidence showed if the purchaser chose a monthly payable payment, the customer is not the IB one.
Customer Retention Based on the Number of Purchase: A Data Mining Approach.
An Analysis of Customer Retention and Insurance Claim Patterns Using Data Mining: A Case Study.