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Does Google Analytics Improve the Prediction of Tourism Demand Recovery?

Author

Listed:
  • Ilsé Botha

    (School of Accounting, Department of Accountancy, College of Business and Economics, University of Johannesburg, Aucklandpark Campus, Johannesburg 2006, South Africa)

  • Andrea Saayman

    (School of Economic Sciences and Tourism Research in Economics, Environs and Society (TREES), North West University, Potchefstroom Campus, Potchefstroom 2531, South Africa)

Abstract

Research shows that Google Trend indices can improve tourism-demand forecasts. Given the impact of the recent pandemic, this may prove to be an important predictor of tourism recovery in countries that are still struggling to recover, including South Africa. The purpose of this paper is firstly, to build on previous research that indicates that Google Trends improves tourism-demand forecasting by testing this within the context of tourism recovery. Secondly, this paper extends previous research by not only including Google Trends in time-series forecasting models but also typical tourism-demand covariates in an econometric specification. Finally, we test the performance of Google Trends in forecasting over a longer time period, because the destination country is a long-haul destination where more lead time may be required in decision-making. Additionally, this research contributes to the body of knowledge by including lower frequency data (quarterly) instead of the higher frequency data commonly used in current research, while also focusing on an important destination country in Africa. Due to the differing data frequencies, the MIDAS modelling approach is used. The MIDAS models are compared to typical time-series and naïve benchmarks. The findings show that monthly Google Trends improve forecasts on lower frequency data. Furthermore, forecasts that include Google Trends are more effective in forecasting one to two quarters ahead, pre-COVID. This trend changed after COVID, when Google Trends led to improved recovery forecasts even over a longer term.

Suggested Citation

  • Ilsé Botha & Andrea Saayman, 2024. "Does Google Analytics Improve the Prediction of Tourism Demand Recovery?," Forecasting, MDPI, vol. 6(4), pages 1-17, October.
    • Handle: RePEc:gam:jforec:v:6:y:2024:i:4:p:45-924:d:1501894
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    References listed on IDEAS

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    1. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    2. Liu, Yuan-Yuan & Tseng, Fang-Mei & Tseng, Yi-Heng, 2018. "Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 123-134.
    3. Maximo Camacho & Matías José Pacce, 2018. "Forecasting travellers in Spain with Google’s search volume indices," Tourism Economics, , vol. 24(4), pages 434-448, June.
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