Information Technology & Tourism

, Volume 21, Issue 1, pp 45–62 | Cite as

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

  • Wolfram HöpkenEmail author
  • Tobias Eberle
  • Matthias Fuchs
  • Maria Lexhagen
Original Research


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.


Google Trends data Search word analysis Online search pattern Tourist arrival prediction Autoregressive time series forecasting Big data 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Business Informatics GroupUniversity of Applied Sciences Ravensburg-WeingartenWeingartenGermany
  2. 2.European Tourism Research Institute (ETOUR)Mid-Sweden UniversityÖstersundSweden

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