Revolutionizing Tourism Marketing: Big Data Analytics and Machine Learning for Predictive Accuracy

Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Ioanna Kalliampakou, Panagiotis Velissaris and Constantinos Halkiopoulos ()
Additional contact information
Leonidas Theodorakopoulos: University of Patras
Alexandra Theodoropoulou: University of Patras
Ioanna Kalliampakou: University of Patras
Panagiotis Velissaris: University of Patras
Constantinos Halkiopoulos: University of Patras

A chapter in Innovation and Creativity in Tourism, Business and Social Sciences, 2025, pp 321-349 from Springer

Abstract: Abstract In the digital age, the integration of big data analytics and machine learning into marketing strategies signifies a profound transformation toward more predictive and personalized marketing, especially in the tourism industry. This paper investigates how these advanced technologies enhance digital marketing by enabling in-depth analysis of vast consumer data to identify patterns and forecast future behaviors. By employing machine learning algorithms, regression analysis, and clustering methods, tourism marketers can create highly targeted campaigns that predict customer needs and preferences with exceptional accuracy. The findings demonstrate the potential of predictive marketing to not only respond to but also anticipate tourist demands, thereby enhancing engagement, customer satisfaction, and bookings. Through extensive case studies and data analysis, this research highlights the transformative impact of these technologies on digital marketing and e-commerce in the tourism sector, proposing a future where data-driven and predictive approaches dominate marketing strategy development.

Keywords: Big data analytics; Machine learning; Digital marketing; Predictive marketing; e-Commerce (search for similar items in EconPapers)
JEL-codes: L86 M31 O32 O33 Z32 Z33 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-78471-2_13

Ordering information: This item can be ordered from
http://www.springer.com/9783031784712

DOI: 10.1007/978-3-031-78471-2_13

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().