| dc.description.abstract | This thesis investigates the impact of AI-driven pricing strategies on customer churn in Iranian banks offering health insurance. The research problem revolves around understanding whether personalized pricing, facilitated by AI, can enhance customer loyalty and satisfaction in the Iranian financial sector. The study employs price discrimination theory and the Technology Acceptance Model as theoretical frameworks to explain the potential benefits of AI-driven pricing strategies.
Exploring the Impact of AI-Driven Pricing on Customer Loyalty and Churn Rates in the Banking Industry
The research methodology includes both quantitative and qualitative analyses, with a survey being administered to gather data on consumer perceptions and experiences with AI-driven pricing. The analysis involves a thematic exploration of the responses and frequency distributions of non-open questions.
The main findings reveal that the majority of consumers place significant importance on personalized pricing and have a favorable perception of AI-powered pricing strategies. Trust in AI-facilitated pricing emerges as a crucial factor for adoption, with most respondents experiencing an increase in trust in their banks due to AI-driven pricing. Additionally, the results highlight the need for improvement in communication to ensure AI-powered pricing mechanisms are comprehensible to all consumers.
In conclusion, the study demonstrates that AI-driven pricing strategies can be a powerful tool for reducing customer churn in Iranian financial institutions offering health insurance. By implementing these strategies, banks can increase client satisfaction and trust, address the particular challenges posed by the Iranian context, and ultimately improve customer retention rates and overall market success. | |
