Abstract


The aim of the present study is to design a customer relationship management model based on artificial intelligence in digital marketing of services in the health tourism industry. The research method is applicable in terms of its purpose, and mixed (qualitative-quantitative) in terms of its implementation method. The statistical population of the qualitative part of the study includes 14 experts and scholars in the field of marketing and artificial intelligence selected by the snowball sampling method. The statistical population in the quantitative part includes experts and marketing managers related to health tourism in Tehran. Given that their exact number cannot be calculated, a maximum number of 384 people was considered based on the Morgan and Cergesi table. Data collection in the qualitative part was carried out through semi-structured interviews, and in the quantitative part through questionnaires. The coding method was used in the qualitative part data analysis, and SPSS and Lisrel software were used in the quantitative part. The results of the study showed that the causal conditions in the study include improving market competition, improving relationships, automated data analysis, and empowerment; and the background conditions include customer data management and intelligent services. Also, the intervening conditions include efficient planning, saving resources, and managing customer behavior. The strategies in the study include solving the integration problem, solving the information management problem, and solving planning problems; and the outcomes include increasing customer satisfaction, increasing financial strength, customer loyalty, and saving time. The results of the structural equations show that the dimensions are well loaded on the research variables and can provide a suitable description of the variables.


Introduction


The activity of customer relationship management includes collecting, managing, and intelligently using data with the support of technology solutions to develop long-term customer relationships. Data obtained from all customer touchpoints, if managed well, can support companies in creating personalized marketing responses, generating new ideas, tailoring products and services, and thus delivering high customer value and gaining competitive advantage (Agarwal et al., 2021). In the digital age, the increase in the volume, velocity, and variety of data, as well as its processing capacity, has led to new technological solutions, including the advancement of artificial intelligence techniques, which refers to the ability of a system to correctly interpret large amounts of data, learn from this data, and use this learning to achieve specific goals and tasks (Ahmed et al., 2020). Artificial intelligence seems to be the future of the industry, and the focus of this technology on putting consumers at the center of health and well-being and ensuring that patients’ daily patterns and healthcare professionals’ needs are observed to provide improved guidance, support, and feedback, and ultimately customer relationship management in the health tourism industry will be of great importance in the future (Al Sayed, 2024).


A review of the role of data in the health tourism sector shows that the data required for the impact of AI on service delivery, especially in health tourism, is still limited (Dalkıran, 2023). Artificial intelligence can provide opportunities for health tourism service providers (Cubric, 2020). New customer relationship management features, such as personality insight services, website formation, chatbot services, programmatic advertising, and facial, image, and face recognition technologies, require significant data to be collected in real time, which is almost impossible to implement without advances in AI. Along with the relevance of AI in the business world, universities also claim that AI is the next step towards a new and more powerful customer relationship management (Rabbi, 2024).


Therefore, this research seeks to answer the question: How does the design of an AI-based customer relationship management model in digital marketing of services in the health tourism industry look like?


Theoretical Framework


Digital Marketing


Digital marketing means using the Internet, mobile devices, social media, search engines and other channels to reach customers. Some marketing experts consider digital marketing to be a completely new endeavor that requires a new way of approaching customers and new ways of understanding how customers behave compared to traditional marketing (Barone, 2021(.


Customer Relationship Management


Customer relationship management involves the intelligent collection, management and use of data supported by technology solutions to develop long-term customer relationships and exceptional customer experiences (Ledro et al., 2022).


Artificial Intelligence on Customer Relationship Management


Artificial intelligence is impacting customer relationship management. Artificial intelligence has revolutionized customer relationship management by automating tasks, providing deeper insights, and creating more personalized experiences for customers. Artificial intelligence enhances the automation process in customer relationship management, freeing up human resources to focus on more complex issues in the work (Ahmed, 2025).


Ponomarenko et al. (2024) in their research on the topic “Application of Artificial Intelligence in Digital Marketing” reported that it is important to identify the main directions of using artificial intelligence to optimize marketing strategies of companies in the digital environment in conditions of intensifying competition on the Internet. Artificial intelligence is considered as a tool for qualitative transformations in the use of digital marketing tools based on various information generated in the global network. The methodological basis of this study is a comprehensive analysis of scientific approaches to the implementation of artificial intelligence in the field of digital marketing, the formation of a database for modeling and identifying optimal machine learning algorithms to ensure the competitiveness of brands. A scheme of the main sources of information that should be used by the company to implement artificial intelligence algorithms in the process of increasing the effectiveness of the use of digital marketing tools is developed on the Internet. Digital marketing tools are presented to be utilized to communicate with the target audience in the long term and ensure the economically feasible level of conversion. The main stages of interaction of companies with audiences on the Internet using modern machine learning algorithms are presented. The main directions of using artificial intelligence in digital marketing have been identified, which enables the company to achieve a high level of loyalty among users based on personalized interaction models.


Baran et al. (2023) in their research on “Next Generation Technologies in Health Tourism” reported that the developments in the field of digitalization in health tourism were initially focused on e-health technologies and suggested that managers and employees should be prepared for the profound transformation created by technology.


Research Methodology


The research method is applicable in terms of its purpose, and mixed (qualitative-quantitative) in terms of its implementation method. The statistical population of the research in the qualitative section includes 14 experts and specialists in the field of marketing and artificial intelligence, selected by the snowball sampling method. The statistical population in the quantitative section includes experts and marketing managers related to health tourism in Tehran, and given that their exact number cannot be calculated, the maximum number was considered to be 384 people according to the Morgan and Gergesi table. Data collection in the qualitative part was done through semi-structured interviews, and in the quantitative part through questionnaires.


Research findings


Coding method was used in the qualitative part data analysis, and SPSS and Lisrel software were used in the quantitative part. The research results showed that the causal conditions in the research include improving market competition, improving relationships, automatic data analysis, and empowerment; and the background conditions include customer data management and intelligent services. Also, the intervening conditions include efficient planning, saving resources, and managing customer behavior. The strategies in the research include solving the integration problem, solving the information management problem, and solving planning problems; and the outcomes include increasing customer satisfaction, increasing financial strength, customer loyalty, and saving time. The results of structural equations show that the dimensions are well loaded on the research variables and can provide a suitable description of the variables.


Conclusion


The present study aimed to design an AI-based customer relationship management model in digital marketing of services in the health tourism industry. The results of this part of the study are consistent with the findings of Abdollahi (2021), Ghasemi (2019), Ribeiro et al. (2021), Ramon Saura et al. (2021), Al Sayed (2024), and Ponomarenko et al. (2024). AI in customer relationship management involves the integration of intelligent technologies to analyze customer data, predict behaviors, and automate interactions. This integration enhances the capabilities of traditional customer relationship management systems, making them more efficient and responsive to customer needs. AI-based customer relationship management systems provide the tools needed to achieve these goals and provide insights and automation that were previously unavailable.


According to the research results, the following suggestion was made:


Based on the role of artificial intelligence in customer data management and automated data analysis, it can be suggested to develop artificial intelligence in tourism service marketing because with artificial intelligence, businesses can use historical data to predict customer behavior and anticipate customer needs.