پیام رسان بله
The losses forecast of the insurance industry customers based on customer lifetime value and data mining techniques
The losses forecast of the insurance industry customers based on customer lifetime value and data mining techniques
Nasibeh Johari1 Davod Jafari2
1) Graduate student of Department of Industrial, Parand Branch, Islamic Azad University, Parand, Iran
2) Assistant Professor of Department of Industrial, Parand Branch, Islamic Azad University, Parand, Iran
IOI Code
:
XYCG-AEAFK
Publication :
international conference of Management Elites(icme.ir)
Abstract :
From the modern management point of view in the competitive atmosphere of the business market processing the data and analyzing the information expressed the changes of the customers, providers, beneficiaries and the organizations. Moreover ,the insurance companies by concentrating on the process of the decision making in order to identify the advantageous targets ,evaluating the risk of the policy holders and the competitive insurance premium those companies will earn a complex of the growth of market shares and benefits and also by identification of the low risk and high risk customers the above mentioned companies tried to propose the proper insurance premium base on the characteristic of each group. So, focusing on the necessity of referring to the results of data processing in designing the long- term strategies on the base of differentiation of profitable and disadvantageous customers with concentration on the customers value and competitive advantageous as a key factor of organization in order to persist in today’s competitive markets with the respect to increase of the insurance services companies in order to earn more benefits and better position of the markets. The aim of this research is getting the clear idea of distinction of the customers’ base on some elements such as lifetime value estimation and presenting a model on order to predict the looses of customers by using the methodology of Crisp-Dm and the techniques of data analysis. Moreover, for validation of the model , the split holdout method was used too.
Keywords : customer relationship management loss insurance data mining