Artificial intelligence in tourism marketing: an exploratory factor analysis of the 7Ps of marketing mix

Purpose The present study aims to make these three contributions. First, it reconceptualizes the traditional marketing mix by discussing how artificial intelligence (AI) has changed it from static framework into more dynamic and data-driven system. Second, the study highlights the diminishing boundaries across different marketing mix elements. With AI and its associated strategies, there are simultaneous impact on multiple Ps, rather than each element of marketing mix functioning independently. Third and most importantly, this study will develop a measurement scale that empirically captures the impact of AI across the marketing mix elements in tourism context, providing a robust tool for future research and practical application. Design/methodology/approach A self-administered survey was conducted using convenience sampling, with data collected directly from respondents in person. The data collection for this study was carried out between 20 Jan 2025 and 13th Feb 2025. The respondents for the study were Indian tourists and the point of data collection was across Sikkim, India. The questionnaire underwent face validity evaluation by two experts, leading to a final version with 46 items after refinement. A total of 210 questionnaires were distributed and after screening for incomplete responses, missing values, outliers, etc. 149 valid responses were considered for further analysis. Findings The study confirms that the 7Ps marketing mix remains relevant in AI-enabled contexts but requires reinterpretation as an interconnected, dynamic system rather than independent elements. AI-driven personalization simultaneously influences product, price and promotion, blurring traditional boundaries. The findings highlight the evolving nature of marketing mix frameworks under rapid AI advancements. Additionally, the study develops a reliable and validated measurement scale, offering a strong methodological foundation for future research on AI’s impact on marketing mix within the services sector. Research limitations/implications Tourism firms should shift from standardized packages to AI-driven recommendation systems that enable dynamic, personalized offerings and modular itineraries for real-time customization. Integrating these systems into digital platforms can tailor services to individual customer profiles. Additionally, adopting dynamic pricing based on demand, seasonality and behavior can enhance competitiveness and profitability. AI-powered chatbots and virtual assistants should support employees to improve service speed and accuracy, while automation can streamline operations and reduce friction. Overall, firms should develop integrated AI-enabled systems linking all 7Ps to enable real-time, data-driven decisions and continuous service optimization. Originality/value This research work contributes to the theoretical advancement of AI and marketing frameworks by providing an empirically validated model for assessing the services marketing mix in the tourism industry.