Advertisement

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

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 data 

References

  1. Baddeley MC, Barrowclough D (2009) Running regressions—a practical guide to quantitative research in economics, finance and development studies. University Press, CambridgeCrossRefGoogle Scholar
  2. Bangwayo-Skeete PF, Skeete RW (2015) Can Google data improve the forecasting performance of tourist arrivals? A mixed-data sampling approach. Tour Manag 46:454–464CrossRefGoogle Scholar
  3. Box GE, Jenkins GM (1970) Time series analysis, forecasting and control. Holden Day, San FranciscoGoogle Scholar
  4. Carrière-Swallow Y, Labbé F (2013) Nowcasting with Google Trends in an emerging market. J Forecast 32(4):289–298CrossRefGoogle Scholar
  5. Chekalina T, Fuchs M, Lexhagen M (2018) Customer-based destination brand equity modelling—the role of destination resources, value-for money and value-in-use. J Travel Res 57(1):31–51CrossRefGoogle Scholar
  6. Cho V (2001) Tourism forecasting and its relationship with leading economic indicators. J Hosp Tour Res 25:399–420CrossRefGoogle Scholar
  7. Divisekera S, Kulendran N (2006) Economic effects of advertising on tourism demand. Tour Econ 12:187–205CrossRefGoogle Scholar
  8. Edgell DL Sr, Del Mastro Allen M, Smith G, Swanson JR (2008) Tourism policy and planning—yesterday, today and tomorrow. Routledge, New YorkCrossRefGoogle Scholar
  9. Fesenmaier DR, Xiang Z, Pan B, Law R (2010) An analysis of search engine use for travel planning. In: Gretzel U, Law R, Fuchs M (eds) Information and communication technologies in tourism. Springer, New York, pp 381–392Google Scholar
  10. Fitzsimmons JA, Fitzsimmons MJ (2001) Service management—operations, strategy & technology, 3rd edn. McGraw Hill, New YorkGoogle Scholar
  11. Forni M, Hallin M, Lippi M, Reichlin L (2000) The generalized dynamic-factor model: identification and estimation. Rev Econ Stat 82(4):540–554CrossRefGoogle Scholar
  12. Frechtling DC (2002) Forecasting tourism demand. Butherworth-Heinemann, OxfordGoogle Scholar
  13. Fuchs M, Rijken L, Peters M, Weiermair K (2000) Modelling Asian incoming tourism—a shift-share approach. Asia Pac J Tour Res 5(2):1–10CrossRefGoogle Scholar
  14. Fuchs M, Höpken W, Lexhagen M (2018) Business Intelligence for Destinations: Creating Knowledge from Social Media. In: Sigala M, Gretzel U (eds) Advances in social media for travel, tourism and hospitality: new perspectives, practice and cases. Routledge, New York, pp 290–310Google Scholar
  15. Granger CW (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3):424–438CrossRefGoogle Scholar
  16. Granger CW (1988) Some recent developments in a concept of causality. J Econom 39(1–2):199–211CrossRefGoogle Scholar
  17. Grönroos C (2008) Service logic revisited—who creates value? And who co-creates? Eur Bus Rev 20(4):298–314CrossRefGoogle Scholar
  18. Hill RC, Griffith WE, Lim GC (2011) Principles of econometrics, 4th edn. Wiley, New YorkGoogle Scholar
  19. Höpken W, Fuchs M, Menner Th, Lexhagen M (2016) Sensing the online social sphere—the sentiment analytical approach. In: Xiang Zh, Alzua A, Fesenmaier D (eds) Analytics in smart tourism design—concepts and methods. Springer, Berlin, pp 129–146Google Scholar
  20. Höpken W, Ernesti D, Fuchs M, Kronenberg K, Lexhagen M (2017) Big data as input for predicting tourist arrivals. In: Schegg R, Stangl B (eds) Information and communication technologies in tourism, Springer, Cham, pp 187–199Google Scholar
  21. Höpken W, Eberle Th, Fuchs M, Lexhagen M (2018) Search engine traffic as input for predicting tourist arrivals. In: Stangl B, Pesonen J (eds) Information and communication technologies in tourism 2018. Springer, New York, pp 381–393CrossRefGoogle Scholar
  22. Hurst HE, Black RP, Simaika YM (1965) Long-term storage: an experimental study. Constable, LondonGoogle Scholar
  23. Kim S, Kim A (2016) A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast 32(3):669–679CrossRefGoogle Scholar
  24. Kristoufek L (2014) Measuring correlations between non-stationary series with DCCA coefficient. Phys A 402:291–298CrossRefGoogle Scholar
  25. Kronenberg K, Fuchs M, Salman K, Lexhagen M, Höpken W (2016) Economic effects of advertising expenditures—a Swedish destination study of international tourists. Scand J Hosp Tour Res 16(4):352–374CrossRefGoogle Scholar
  26. Li X, Wu Q, Peng G, Lv B (2016) Tourism forecasting by search engine data with noise processing. Afr J Bus Manag 10(6):114–130CrossRefGoogle Scholar
  27. Liu B (2008) Web data mining—exploring hyperlinks, contents, and usage data. Springer, HeidelbergGoogle Scholar
  28. Liu Y, Lv B, Peng G, Yuan Q (2012) A pre-processing method of Internet search data for prediction improvement. In: Proceedings of the data mining and intelligent knowledge management workshop, New York, ACM 2012:3:1–3:7Google Scholar
  29. Menner Th, Höpken, Fuchs M, Lexhagen M (2016) Topic detection – Identifying relevant topics in tourism reviews. In: Inversini A, Schegg R (eds) Information and communication technologies in tourism 2016. Springer, New York, pp 411–423CrossRefGoogle Scholar
  30. Mukherjee C, White H, Wuyts M (1998) Econometrics and data analysis for developing countries. Routledge, New YorkGoogle Scholar
  31. Önder I, Gunter U (2016) Forecasting tourism demand with Google Trends for a major European city destination. Tour Anal 21:203–220CrossRefGoogle Scholar
  32. Pan B, Wu C, Song H (2012) Forecasting hotel room demand using search engine data. J Hosp Tour Technol 3(3):196–210Google Scholar
  33. Pan B, Li X, Law R, Huang X (2017) Forecasting tourism demand with composite search index. Tour Manag 59(1):57–66Google Scholar
  34. Pearson CMG (2017) Internet and search engine use by country: global search engine marketing. http://ptgmedia.pearsoncmg.com/images/9780789747884/supplements/9780789747884_appC.pdf. Accessed 20 Feb 2018
  35. Peng B, Song H, Crouch G (2014) A meta-analysis of international tourism demand forecasting and implications for practice. Tour Manag 45:181–193CrossRefGoogle Scholar
  36. Peng G, Liu Y, Wang J, Gu J (2017) Analysis of the prediction capability of web search data based on the HE-TDC method—prediction of the volume of daily tourism visitors. J Syst Sci Syst Eng 26(2):163–182CrossRefGoogle Scholar
  37. Pike A, Rodríguez-Pose A, Tomaney J (2017) Local and regional development, 2nd edn. Routledge, New YorkGoogle Scholar
  38. Podobnik B, Jiang Z-Q, Zhou W, Stanley HE (2011) Statistical tests for power-law cross-correlated processes. Phys Rev E 84(066118):1–8Google Scholar
  39. Schmunk S, Höpken W, Fuchs M, Lexhagen M (2014) Sentiment analysis—implementation and evaluation of methods for sentiment analysis with Rapid-Miner®. In: Xiang Ph, Tussyadiah I (eds) Information and communication technologies in tourism 2014. Springer, New York, pp 253–265Google Scholar
  40. Song H, Li G (2008) Tourism demand modelling and forecasting: a review of recent research. Tour Manag 29:203–220CrossRefGoogle Scholar
  41. Song H, Li G, Witt StF, Fei B (2010) Tourism demand modelling and forecasting: how should demand be measured? Tour Econ 16(1):63–81CrossRefGoogle Scholar
  42. Turner LW, Witt SF (2001) Factors influencing demand for international tourism: tourism demand analysis using structural equation modelling. Tourism Economics 16(1):63–81Google Scholar
  43. Varian H (2014) Big data: new tricks for econometrics. J Econom Perspect 28(2):3–28CrossRefGoogle Scholar
  44. Weiermair K, Fuchs M (1998) On the use and usefulness of economics in tourism: a critical survey. Int J Dev Plan Lit 13(3):255–273Google Scholar
  45. WTTC (2016) Travel & tourism: economic impact 2016—world. World Travel & Tourism Council, LondonGoogle Scholar
  46. Yang Y, Pan B, Song H (2014) Predicting hotel demand using destination marketing organizations’ web traffic data. J Travel Res 53(4):433–447CrossRefGoogle Scholar
  47. Yang X, Pan B, Evans JA, Lv B (2015) Forecasting Chinese tourist volumes with search engine data. Tour Manag 46(3):386–397CrossRefGoogle Scholar
  48. Zebende G (2011) DCCA cross-correlation coefficient: quantifying level of cross-correlation. Phys A 390:614–618CrossRefGoogle Scholar

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

Personalised recommendations