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Search Engine Traffic as Input for Predicting Tourist Arrivals

  • Wolfram HöpkenEmail author
  • Tobias Eberle
  • Matthias Fuchs
  • Maria Lexhagen
Conference paper

Abstract

Due to the perishable nature of tourism services and the limited capacity of tourism firms in serving customers, accurate forecasts of tourism demand are of utmost relevance for the success of tourism businesses. Nowadays, travellers extensively search the web to form expectations and to base their travel decision before visiting a destination. This study 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 corresponding tourist arrivals), and to aggregate these time series into an overall web search index with maximal effect on tourism arrivals. 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, thus, to increase the predictive power in forecasting tourism demand.

Keywords

Tourist arrival prediction Web search traffic Google trends data Data mining 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Wolfram Höpken
    • 1
    Email author
  • Tobias Eberle
    • 1
  • Matthias Fuchs
    • 2
  • Maria Lexhagen
    • 2
  1. 1.University of Applied Sciences Ravensburg-WeingartenWeingartenGermany
  2. 2.The European Tourism Research Institute (ETOUR), Mid-Sweden UniversityÖstersundSweden

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