The Evolution of Customer Journey Models in Digital Tourism Markets: A Historical-Analytical Approach

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Business Management, Faculty of Economic and Administrative Sciences, University of Mazandaran, Babolsar, Iran.

2 Associate Prof., Department of Business Management, Faculty of Economic and Administrative Sciences, University of Mazandaran, Babolsar, Iran.

3 Assistant Prof., Department of Business Management, Faculty of Economic and Administrative Sciences, University of Mazandaran, Babolsar, Iran.

10.22059/jibm.2025.391848.4940

Abstract

Objective
The rapid and extensive advancements in digital technologies over recent decades have significantly redefined traditional customer journey models in the tourism industry. Historically, these models were typically simple, linear, and limited to a few distinct stages. However, with the emergence of technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and augmented reality (AR), the customer journey has evolved into a complex, multidimensional, interactive, and non-linear process, where a wide range of technological and social variables influences consumer behavior. Adopting a historical-analytical approach, this study investigates the evolutionary trajectory of customer journey models within the digital tourism landscape from 1975 to 2024. The primary focus is on understanding how customer behavior patterns have shifted in response to technological progress and identifying their roles in shaping new touchpoints, multichannel interactions, and the personalization of travel experiences. Through a historical analysis of these transformations, this research highlights the key trends and driving forces and aims to outline a roadmap for the future development of customer journey models in the tourism domain.
 
Methodology
This study employs a historical analysis approach in conjunction with a systematic literature review, examining 8,733 articles from the Scopus database. The data were analyzed based on six key domains: digital transformation, historical development of customer journey models, consumer behavior, technological advancements, data-driven personalization, and the emergence of digital touchpoints. Co-word analysis was conducted using VOSviewer software to identify research trends and conceptual shifts in customer journey models over time.
Findings
The results reveal that traditional customer journey models, which were primarily based on linear and offline interactions, have evolved into complex, multichannel frameworks due to the advancement of digital technologies. The integration of AI, big data, IoT, and AR has altered customer decision-making pathways and enabled personalized travel experiences. Digital platforms and recommender systems have increased customer engagement and revolutionized the booking and destination selection processes. Additionally, the use of big data analytics has contributed to enhancing customer experiences and improving customer loyalty. Regional analysis indicates that Europe focuses on luxury and sustainable travel experiences, while Asia has experienced a significant growth in smart systems and digital payment infrastructure. In emerging markets, infrastructural challenges remain a major concern. Moreover, digital touchpoints such as mobile applications, social media, and chatbots play a vital role in shaping customer interactions. These technological shifts suggest that the future of customer journey models will center on data-driven personalization, the adoption of interactive technologies, and the expansion of smart tourism ecosystems.
 
Conclusion
The historical trend analysis shows a transition in customer journey models from simple structures to nonlinear, multi-stage, and digitized forms. The emergence of interactive technologies has heightened the need for personalized travel experiences and increased the complexity of decision-making processes. This research emphasizes that optimizing future models requires tourism businesses to leverage big data analytics, AI-driven marketing, and predictive behavioral systems. Furthermore, the identification of research gaps underscores the importance of future investigations focusing on interactive experiences, digital trust, and the integration of customer journey models with emerging technologies such as the metaverse and augmented reality.

Keywords

Main Subjects

References

 
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Journal of Business Management
Volume 17, Issue 4
2025
Pages 1174-1205

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