IDEAS home Printed from
   My bibliography  Save this article

Searching for the picture: forecasting UK cinema admissions using Google Trends data


  • Chris Hand
  • Guy Judge


This article investigates whether Google Trends search information can improve forecasts of cinema admissions. Using monthly data for the United Kingdom for the period 1 January 2004 to 31 December 2008, we examine various forecasting models that incorporate Google Trends search information. We find clear evidence that Google Trends data on searches relevant to cinema visits (as opposed to searches for specific films) do have the potential to increase the accuracy of cinema admissions forecasting models.

Suggested Citation

  • Chris Hand & Guy Judge, 2012. "Searching for the picture: forecasting UK cinema admissions using Google Trends data," Applied Economics Letters, Taylor & Francis Journals, vol. 19(11), pages 1051-1055, July.
  • Handle: RePEc:taf:apeclt:v:19:y:2012:i:11:p:1051-1055
    DOI: 10.1080/13504851.2011.613744

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    File URL:
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item

    As the access to this document is restricted, you may want to search for a different version of it.


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2021. "Online Salience and Charitable Giving : Evidence from SMS Donations," The Warwick Economics Research Paper Series (TWERPS) 1325, University of Warwick, Department of Economics.
    2. Cruz-Suarez, Ana & Prado-Román, Alberto & Prado-Román, Miguel, 2014. "Legitimidade cognitiva, acesso aos recursos e resultados organizacionais," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 54(5), September.
    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. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    5. Palma Lampreia Dos Santos, Maria José, 2018. "Nowcasting and forecasting aquaponics by Google Trends in European countries," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 178-185.
    6. Ateca-Amestoy, Victoria & Prieto-Rodriguez, Juan, 2013. "Forecasting accuracy of behavioural models for participation in the arts," European Journal of Operational Research, Elsevier, vol. 229(1), pages 124-131.
    7. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, Open Access Journal, vol. 8(4), pages 1-23, April.
    8. Ikhoon, Jang & Young Chan, Choe, 2016. "Forecasting Agri-food Consumption Using the Keyword Volume Index from Search Engine Data," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236124, Agricultural and Applied Economics Association.
    9. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    10. Aaron Yelowitz & Matthew Wilson, 2015. "Characteristics of Bitcoin users: an analysis of Google search data," Applied Economics Letters, Taylor & Francis Journals, vol. 22(13), pages 1030-1036, September.
    11. Abay,Kibrom A. & Hirfrfot,Kibrom Tafere & Woldemichael,Andinet, 2020. "Winners and Losers from COVID-19 : Global Evidence from Google Search," Policy Research Working Paper Series 9268, The World Bank.
    12. Jinah Yang & Daiki Min & Jeenyoung Kim, 2020. "The Use of Big Data and Its Effects in a Diffusion Forecasting Model for Korean Reverse Mortgage Subscribers," Sustainability, MDPI, Open Access Journal, vol. 12(3), pages 1-17, January.
    13. Stephen L. France & Yuying Shi, 2017. "Aggregating Google Trends: Multivariate Testing and Analysis," Papers 1712.03152,, revised Mar 2018.
    14. Baranowski Paweł & Korczak Karol & Zając Jarosław, 2020. "Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland," Business Systems Research, Sciendo, vol. 11(1), pages 73-88, March.
    15. France, Stephen L. & Shi, Yuying & Kazandjian, Brett, 2021. "Web Trends: A valuable tool for business research," Journal of Business Research, Elsevier, vol. 132(C), pages 666-679.
    16. Mohammad Reza Farzanegan & Mehdi Feizi & Saeed Malek Sadati, 2020. "Google It Up! A Google Trends-based analysis of COVID-19 outbreak in Iran," MAGKS Papers on Economics 202017, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    17. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    18. Marshall, Pablo & Dockendorff, Monika & Ibáñez, Soledad, 2013. "A forecasting system for movie attendance," Journal of Business Research, Elsevier, vol. 66(10), pages 1800-1806.
    19. Kim, Ho & Hanssens, Dominique M., 2017. "Advertising and Word-of-Mouth Effects on Pre-launch Consumer Interest and Initial Sales of Experience Products," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 57-74.
    20. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    21. Eksi, Ozan & Gurdal, Mehmet Y. & Orman, Cuneyt, 2017. "Fines versus prison for the issuance of bad checks: Evidence from a policy shift in Turkey," Journal of Economic Behavior & Organization, Elsevier, vol. 143(C), pages 9-27.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:apeclt:v:19:y:2012:i:11:p:1051-1055. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.