Concerning life insurance, penetration rate is one of the main goal of every developed insurance industry. In this sense systematic marketing is a significant component in strategic plan of insurance companies. To achieve the goal insurers need to group their client into different groups in which some common features are shared and people demonstrate a similar pattern. This paper utilizes K-means clustering as an unsupervised learning algorithm in order to divide customers into number of clusters. The clusters are constructed based on two independent variables namely; car and life insurance premiums. Then the descriptive statistics of other determining features are provided with which the most willing group in purchasing life insurance is presented.
Khanizadeh, F., Khamesian, F., Bahiraie, A. (2021). Customer Segmentation for Life Insurance in Iran Using K-means Clustering. International Journal of Nonlinear Analysis and Applications, 12(Special Issue), 633-642. doi: 10.22075/ijnaa.2021.22324.2350
MLA
Farbod Khanizadeh; Farzan Khamesian; Alireza Bahiraie. "Customer Segmentation for Life Insurance in Iran Using K-means Clustering". International Journal of Nonlinear Analysis and Applications, 12, Special Issue, 2021, 633-642. doi: 10.22075/ijnaa.2021.22324.2350
HARVARD
Khanizadeh, F., Khamesian, F., Bahiraie, A. (2021). 'Customer Segmentation for Life Insurance in Iran Using K-means Clustering', International Journal of Nonlinear Analysis and Applications, 12(Special Issue), pp. 633-642. doi: 10.22075/ijnaa.2021.22324.2350
VANCOUVER
Khanizadeh, F., Khamesian, F., Bahiraie, A. Customer Segmentation for Life Insurance in Iran Using K-means Clustering. International Journal of Nonlinear Analysis and Applications, 2021; 12(Special Issue): 633-642. doi: 10.22075/ijnaa.2021.22324.2350
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