DATA MINING TECHNIQUES IN CUSTOMER SEGMENTATION: A CASE STUDY IN AUTOMOBILE INSURANCE IN AN INSURANCE COMPANY

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HANAFIZADEH PAYAM; RASTKHIZ PAIDAR NEDA

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Paper Information

Journal: IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH) Year:2011 | Volume:26 | Issue:2 (102) Page(s): 55-81

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Information Journal Paper

Title

DATA MINING TECHNIQUES IN CUSTOMER SEGMENTATION: A CASE STUDY IN AUTOMOBILE INSURANCE IN AN INSURANCE COMPANY

Author(s)

HANAFIZADEH PAYAM | RASTKHIZ PAIDAR NEDA | Issue Writer Certificate

Keywords

CUSTOMER SEGMENTATIONQ2

SELF ORGANIZING MAPQ2

K-MEANSQ2

AUTO INSURANCEQ2

Abstract

This paper aims to discuss customer segment ion in AUTO INSURANCE market based on several risk criteria. Many businesses realize that their most important asset is their customer base. In today’s competitive business environment, the ability to identify profitable lines of business and build long term loyalty among customers with expansion of existing profitable lines is a major business strategy. Identifying customers with high default risk is a key competitive advantage to a company.The relatively high share of unprofitable business clients in many sectors leads to the question of how insurance companies should deal with such clients.Auto insurance in Iran is one of the largest lines of insurance business which has a large share in most insurance company portfolios. However, offering similar premiums for all people with different risks, leads to unfair rates and consequently losses. There are important factors such as age, gender, marital status, income and automobile characteristics like vehicle's make and model that can affect risks faced by the insured. Segmentation is one of the most important concepts in marketing which enables firms to target profitable customers, understand customers' desires, allocate resources, and locate against competitors. CUSTOMER SEGMENTATION involves subdivision of customers into smaller groups with similar characteristics. In our analysis we use Self-Organizing feature Maps (SOM) as a neural network to segment AUTO INSURANCE customers, and then use K-MEANS algorithm to compare with the SOM method.

Cites

SEGMENTING AND PROFILING GREEN CONSUMERS WITH USE OF SELF ORGANIZING MAPS

References

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APA: Copy

HANAFIZADEH, PAYAM, & RASTKHIZ PAIDAR, NEDA. (2011). DATA MINING TECHNIQUES IN CUSTOMER SEGMENTATION: A CASE STUDY IN AUTOMOBILE INSURANCE IN AN INSURANCE COMPANY. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), 26(2 (102)), 55-81. SID. https://sid.ir/paper/100895/en

Vancouver: Copy

HANAFIZADEH PAYAM, RASTKHIZ PAIDAR NEDA. DATA MINING TECHNIQUES IN CUSTOMER SEGMENTATION: A CASE STUDY IN AUTOMOBILE INSURANCE IN AN INSURANCE COMPANY. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH)[Internet]. 2011;26(2 (102)):55-81. Available from: https://sid.ir/paper/100895/en

IEEE: Copy

PAYAM HANAFIZADEH, and NEDA RASTKHIZ PAIDAR, “DATA MINING TECHNIQUES IN CUSTOMER SEGMENTATION: A CASE STUDY IN AUTOMOBILE INSURANCE IN AN INSURANCE COMPANY,” IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), vol. 26, no. 2 (102), pp. 55–81, 2011, [Online]. Available: https://sid.ir/paper/100895/en

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