Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden

Abstract

Accurate forecasting of tourism demand is of utmost relevance for the success of tourism businesses. This paper presents a novel approach that extends autoregressive forecasting models by considering travellers' web search behaviour as additional input for predicting tourist arrivals. More precisely, the study presents a method with the capacity to identify relevant search terms and time lags (i.e. time difference between web search activities and tourist arrivals), and to aggregate these time series into an overall web search index with maximal forecasting power on tourism arrivals. The proposed approach enables a thorough analysis of temporal relationships between search terms and tourist arrivals, thus, identifying patterns that reflect online planning behaviour of travellers before visiting a destination. The study is conducted at the leading Swedish mountain destination, Åre, using arrival data and Google web search data for the period 2005-2012. Findings demonstrate the ability of the proposed approach to outperform traditional autoregressive approaches, by increasing the predictive power in forecasting tourism demand. Keywords Google Trends data • Search word analysis • Online search pattern • Tourist arrival prediction • Autoregressive time series forecasting • Big dataThis is an extended version of a conference paper entitled "Search engine traffic as input for predicting tourist arrivals" previously published in the proceedings of Information and

Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach

“…Therefore, deep learning might constitute a promising future forecasting approach, especially when adding multiple big data sources as additional input to the prediction of tourism demand. Finally, the identified search queries and their corresponding time lags with a high correlation with future tourist arrivals are an excellent input to analyze tourists’ online search behavior (Höpken et al 2019). Thus, identifying concrete search terms, actually preceding tourism demand, would constitute valuable input to destination marketing generally and search engine marketing and optimization specifically.…”

“…Most important, search engine query volumes are successfully used for tourism demand forecasting and modeling (Artola, Pinto, and de Pedraza García 2015; X. Yang et al 2015; Padhi and Pati 2017; Siliverstovs and Wochner 2018; Höpken et al 2019).…”

“…Yang et al 2015; E. Kim et al 2016; Höpken et al 2019).…”

A systematic literature review on the use of big data for sustainable tourism

“…Understanding target-market tourist is also important to adjust the destination management objects (DMO) marketing strategies based on tourist's perspectives about destination (Deng et al, 2019;Mariani et al, 2016). Tourists' origin, nationalities, domestic vs. international, will influence their perceptions, choices and behaviour about destinations (Hopken et al, 2019;Liu, Huang, et al, 2019). Table 9 presents a more complete picture about the use of big data analysis for DMO, competitiveness and tourist destination.…”

“…Purposes Methodology Data Sources Inversini et al (2010) To promote destination among tourist and internet users Content analysis UGC Chao and Lai (2015) To promote destination among tourist and internet users Consumer-oriented intelligent support system Web service incorporated with tourism domain Marchiori and Cantoni (2015) To deliver information of products and services to tourists and specific audience Sentiment analysis UGC Deng et al (2019) To adjust DMO marketing strategies based on tourist's origin and perspective on destination Content analysis Geo-tagged UGC Hopken et al (2019) To understand tourist's origin, nationalities, domestic vs. international which influence perspectives and choices Regression analysis UGC Liu, Huang, et al (2019) To understand different characteristics of domestic tourist compared to international visitors Sentiment analysis UGC Raun et al (2016) To identify which places are popular among different nationalities in Estonia…”