Document Type : Research Paper

Authors

1 Ph.D. Student in Marketing Management, Department of Business Management, Rasht branch, Islamic Azad University, Rasht, Iran

2 Associate Prof., Department of Business Management, Rasht branch, Islamic Azad University, Rasht, Iran.

10.22054/ims.2025.81499.2507

Abstract

In terms of the purpose of this study, it is an applied-developmental study that seeks to provide a model for reducing customer churn using artificial intelligence-based insurance industry. From the point of view of the data collection method, . To achieve the goal, a mixed exploratory research design (qualitative-quantitative) was used. In the qualitative part, the theme analysis method was used, and in the quantitative part, the partial least square method was used. The community of participants of the qualitative part included the managers of Iran Insurance Company, 17 of whom were selected by purposive sampling method. In the quantitative part, the statistical population consisting of managers and experts of Iranian insurance and managers of Iranian insurance agencies in Gilan province, with the method of effect size and power analysis, 130 people were selected by cluster-random sampling method. The data collection tool in the qualitative part was semi-structured interview and in the quantitative part, researcher-made questionnaire. Research findings showed that technical factors of artificial intelligence, managerial factors of artificial intelligence and relationship marketing affect the management of relationship with customers. Customer relationship management improves customer experience by influencing service personalization and customer orientation. This factor by influencing customer loyalty, customer satisfaction and customer participation leads to the reduction of customer churn. Therefore, it was found that artificial intelligence is an infrastructure structure that, from a technical and managerial point of view, can help to improve customer relationship management in Iranian insurance agencies and reduce customer turnover and loss.

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