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Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations

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  • Xiang, Zheng
  • Pan, Bing

Abstract

Given the growing importance of search in online travel planning, marketers need to better understand the behavioural aspect of search engines use. Built upon a number of previous studies, the goal of this research is to identify patterns in online travel queries across tourist destinations. Utilizing transaction log files from a number of search engines, the analysis shows important patterns in the way travel queries are constructed as well as the commonalities and differences in travel queries about different cities in the United States. The ratio of travel queries among all queries about a specific city seems to associate with the “touristic” level of that city. Also, keywords in travelers' queries reflect their knowledge about the city and its competitors. This paper offers insights into the way tourism destinations are searched online as well as implications for search engine marketing for destinations.

Suggested Citation

  • Xiang, Zheng & Pan, Bing, 2011. "Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations," Tourism Management, Elsevier, vol. 32(1), pages 88-97.
    • Handle: RePEc:eee:touman:v:32:y:2011:i:1:p:88-97
    DOI: 10.1016/j.tourman.2009.12.004
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    Cited by:

    1. Zheng Xiang & Qianzhou Du & Yufeng Ma & Weiguo Fan, 2018. "Assessing reliability of social media data: lessons from mining TripAdvisor hotel reviews," Information Technology & Tourism, Springer, vol. 18(1), pages 43-59, April.
    2. Gerard Loosschilder & Jean-Pierre I. Rest & Zvi Schwartz & Paolo Cordella & Dirk Sierag, 2017. "From OTA interface design to hotels’ revenues: the impact of sorting and filtering functionalities on consumer choices," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(2), pages 125-138, April.
    3. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    4. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    5. Katerina Volchek & Anyu Liu & Haiyan Song & Dimitrios Buhalis, 2019. "Forecasting tourist arrivals at attractions: Search engine empowered methodologies," Tourism Economics, , vol. 25(3), pages 425-447, May.
    6. Dinis, Gorete & Costa, Carlos & Pacheco, Osvaldo, 2019. "Composite Indicator for measuring the world interest by Portugal’s Tourism," Journal of Tourism, Sustainability and Well-being, Cinturs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve, vol. 7(1), pages 39-52.
    7. Lei Li & Xue Song & Shujun Liu & Kun Huang, 2021. "Defining High-Quality Answers on a Chinese Tourism Q&A Platform in Terms of Information Needs," Sustainability, MDPI, vol. 13(24), pages 1-21, December.
    8. Vinaitheerthan Renganathan & Amitabh Upadhya, 2021. "Dubai Restaurants: A Sentiment Analysis of Tourist Reviews," Academica Turistica - Tourism and Innovation Journal, University of Primorska Press, vol. 14(2), pages 165-174.
    9. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    10. Yuanfang Fu & Zhenrao Cai & Chaoyang Fang, 2024. "Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data," Sustainability, MDPI, vol. 16(3), pages 1-24, January.
    11. Theologos Dergiades & Eleni Mavragani & Bing Pan, 2017. "Arrivals of Tourists in Cyprus: Mind the Web Search Intensity," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 107, Hellenic Observatory, LSE.
    12. Matthew Krawczyk & Zheng Xiang, 2016. "Perceptual mapping of hotel brands using online reviews: a text analytics approach," Information Technology & Tourism, Springer, vol. 16(1), pages 23-43, March.

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