Designing an Analytical Model to Determine the Factors Affecting Insurer Churn by Neural Network Technique

In today's growing, saturated and competitive markets, the insurance industry, like other industries, is suffering from customer churn. Recent advances in communications and technology have led to increased awareness and ease of comparison of insurance policies and services. Thus, insurers are constantly confronted with new offers from competing companies and easily turn to competitors; in other words, they churn. The aims of this research are actually pursued through the following research question. What are the main factors of churn in the Iranian insurance industry? This study is conducted in two phases: qualitative and quantitative. First, in the qualitative phase, 15 experts in the insurance industry are interviewed to identify the factors affecting churn. Then, based on the identified factors in the research literature and comparing them with the results of the interview, 8 main influential factors are identified and finalized. In the quantitative phase, these indices are first weighted by hierarchical analysis technique. Then, the statistical population is determined in the quantitative phase; they included the insurers of the Iranian insurance company, and by selecting a sample of 120, neural network technique is applied to fit the model. The calculated R = 0.74 proves that the eight identified factors (type of insurance, premium, final result of claims, duration of cooperation, payment method, number of installments, number of policies, and number of claims) can best explain the reasons of churn of policyholders.