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Artificial intelligence-based predictions of movie audiences on opening Saturday

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  • An, Yongdae
  • An, Jinwon
  • Cho, Sungzoon

Abstract

Marketing activity by distributors is a significant factor in attracting audiences to theaters before a movie is released. Importantly, audience numbers on opening weekend are highly affected by marketing activity before the release, and these numbers determine how many screens will be allocated to the movie. Therefore, distributors need to predict audience numbers on opening weekend and develop marketing strategies in order to gain a competitive advantage over other films being screened at the same time. However, as distributors make predictions based on their experiences and intuitions, it is difficult to quantify the reliability of predicted values and deliver the correct marketing strategy. In this study, we propose a model that predicts audience numbers on the opening Saturday using market research data obtained through online and offline surveys to help distributors develop better marketing strategies.

Suggested Citation

  • An, Yongdae & An, Jinwon & Cho, Sungzoon, 2021. "Artificial intelligence-based predictions of movie audiences on opening Saturday," International Journal of Forecasting, Elsevier, vol. 37(1), pages 274-288.
    • Handle: RePEc:eee:intfor:v:37:y:2021:i:1:p:274-288
    DOI: 10.1016/j.ijforecast.2020.05.005
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    References listed on IDEAS

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    1. Dan Lovallo & Carmina Clarke & Colin Camerer, 2012. "Robust analogizing and the outside view: two empirical tests of case‐based decision making," Strategic Management Journal, Wiley Blackwell, vol. 33(5), pages 496-512, May.
    2. Lepori, Gabriele M., 2015. "Positive mood and investment decisions: Evidence from comedy movie attendance in the U.S," Research in International Business and Finance, Elsevier, vol. 34(C), pages 142-163.
    3. Márton Mestyán & Taha Yasseri & János Kertész, 2013. "Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    4. Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
    5. Karniouchina, Ekaterina V., 2011. "Impact of star and movie buzz on motion picture distribution and box office revenue," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 62-74.
    6. M. Bagella & L. Becchetti, 1999. "The Determinants of Motion Picture Box Office Performance: Evidence from Movies Produced in Italy," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 23(4), pages 237-256, November.
    7. Thorsten Hennig-Thurau & Mark Houston & Shrihari Sridhar, 2006. "Can good marketing carry a bad product? Evidence from the motion picture industry," Marketing Letters, Springer, vol. 17(3), pages 205-219, July.
    8. John Sedgwick & Michael Pokorny, 1999. "Movie Stars and the Distribution of Financially Successful Films in the Motion Picture Industry," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 23(4), pages 319-323, November.
    9. Enrico Moretti, 2011. "Social Learning and Peer Effects in Consumption: Evidence from Movie Sales," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(1), pages 356-393.
    10. Sunčica Vujić & Xiaoyu Zhang, 2018. "Does Twitter chatter matter? Online reviews and box office revenues," Applied Economics, Taylor & Francis Journals, vol. 50(34-35), pages 3702-3717, July.
    11. Liran Einav, 2007. "Seasonality in the U.S. motion picture industry," RAND Journal of Economics, RAND Corporation, vol. 38(1), pages 127-145, March.
    12. Marshall, Pablo & Dockendorff, Monika & Ibáñez, Soledad, 2013. "A forecasting system for movie attendance," Journal of Business Research, Elsevier, vol. 66(10), pages 1800-1806.
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