Modelling Tourism Demand Using Google Analytics: A Case Study of Portugal’s Alentejo Region

Abstract

The development of information and communication technologies, specifically the Internet, has changed the way tourists plan their trips, being one of the most important information sources for tourism decision-making. However, a limited number of studies has been developed to analyse the causal relationships between the web interaction and tourism demand. Therefore, this paper intends to shed light on the usefulness of big data analytics to understand the tourism demand of a destination. More specifically,it aims to examine the causal relationship between website’s visitor interactions and the tourism demand of a destination and verify whether there are differences in this relationship according to the visitors' country of origin. In order to achieve the research objectives, the Alentejo region in Portugal was selected as a case study. Monthly data for the period between 2007 and 2017 was used to examine the long-run causalrelationship between the sessions of the users to the official website of the Destination Management Organization of Alentejo (measured through Google Analytics) and tourism demand of this region (measured trough the number of guests in tourism accommodation establishments). To analyse if there are differences in this relationship according to the country of origin of visitors, the most important tourism markets for this destination were selected. Cointegration (Johansen´s maximum-likelihood method), Granger causality test, Vector Autoregression Model, and Vector Error Correction Model were used to examine the relationship. The results reveal a causal relationship between Internet search and the tourism demand. However, this relationship varies among the tourism market analysed.

Digital footprint for tourism research

“…Unlike Google Trends, this information is only available to the website owner, who can access the traffic monitoring service via a free plan, or a paid plan which includes more advanced features. Google Analytics is the most frequently used service, with authors utilizing a wide variety of data, such as the number of visitors (Daniel and Gaceu, 2009), the source of traffic (Plaza, 2011;Dinis et al, 2016), the number of new and returning visitors (Gunter and Önder, 2016;Plaza, 2011), the average time spent on a page or a session (Dinis et al, 2016;Gunter and Önder, 2016) and the visitor country of origin (Dinis et al, 2016).…”

“…Access to this data can be obtained through the user interface or through the API, but most authors choose to access it through the user interface (Daniel and Gaceu, 2009;Dinis et al, 2016;Gunter and Önder, 2016;Plaza, 2011).…”

“…Google Analytics enables the collection of data on website visitors, including the age, gender, country of origin, and more. In this vein, Dinis et al (2016) study the visitor profile of the Portuguese region of Antalejo using Google Analytics data from the official promotional website. However, the most common method for obtaining information about the profile of tourists is through the study of their social media profiles.…”

“…Most of these studies rely on descriptive analysis (Tenkanen et al, 2017;Wood et al, 2013;Ahas et al, 2007Ahas et al, , 2008Raun et al, 2016;Tiru et al, 2010;Palazzo et al, 2021). However, other methodologies such as machine learning approaches (Yu and Egger, 2021) or the analysis of reports from Google Analytics (Dinis et al, 2016), have also been employed.…”