Intelligent Marketing Model with a Focus on Artificial Intelligence in the Banking Industry

Abstract

In today's world, emerging technologies, especially artificial intelligence, have brought significant transformations to various industries, particularly in banking. Banks need to adopt artificial intelligence-based intelligent marketing models to maintain competitiveness and enhance customer experience. Therefore, the aim of this research is to design an artificial intelligence-based intelligent marketing model for the banking industry. The research method employed in this study is a descriptive survey, and from the perspective of its objective, the research is developmental and applied. Given the qualitative nature of the study, its credibility was evaluated and confirmed through suitability and applicability criteria. Sampling was carried out using the snowball sampling method. The research population consisted of university professors, managers, and experts in the banking industry, and the sample size was determined based on the principle of theoretical saturation. Ultimately, data for this study were collected through semi-structured interviews with 17 participants. Based on the results from qualitative analysis, 17 themes were extracted, including artificial intelligence and emerging technologies, customer needs and expectations, advancements in information and communication technology, competition in the banking industry, and changes in consumer behavior. Other identified themes were the formation of intelligent marketing, recognition of the competitive environment in the banking industry, regulatory and legislative systems, massive data volumes, organizational culture, information technology infrastructure, organizational support, investment in technology, and legal and ethical limitations. Additionally, themes such as intelligent marketing in the banking industry, implementation of artificial intelligence systems, market trend prediction, marketing automation, customer-centric service development, customer satisfaction improvement, financial performance improvement, customer experience enhancement, targeted marketing, and risk and security management were identified. Finally, to determine causal relationships and hierarchical levels among these themes, Interpretive Structural Modeling (ISM) was used, resulting in the classification of themes into 12 levels.