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Leveraging analytics to produce compelling and profitable film content

Abstract

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user-generated content and original content produced by subscription video on demand platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications.

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Notes

  1. 1.

    Parrot Analytics (2018). Global digital original SVOD production trends. https://www.parrotanalytics.com/insights/global-svod-digital-originals-production-trends/.

    PwC (2017). Consumer intelligence series: I stream, you stream. PwC. https://www.pwc.com/us/en/advisory-services/publications/consumer-intelligence-series/i-stream-you-stream/pwc-videoquake-i-stream-you-stream.pdf.

  2. 2.

    McDonald (2018). Ampere: SVOD to overtake box office revenues next year. Digital TV.com. https://www.digitaltveurope.com/2018/12/17/ampere-svod-to-overtake-box-office-revenues-next-year.

  3. 3.

    Fuselier (2017). Use these data-transparent companies to amplify your film distribution strategy. Sundance Institute Creative Distribution Initiative. https://www.sundance.org/blogs/creative-distribution-initiative/use-these-data-transparent-companies-to-amplify-your-film-distribution-strategy..

  4. 4.

    Bloore (2009). Re-defining the independent film value chain. UK Film Council. https://www.bfi.org.uk/sites/bfi.org.uk/files/downloads/redefining-the-independent-film-value-chain.pdf.

  5. 5.

    Prange (2018). Big data revolution. JCH Media Inc. https://www.mediaplaynews.com/big-data-revolution/.

  6. 6.

    Theories of film production may be viewed at Bugaj (2013). The Production Pipeline Series. Private Blog. http://www.bugaj.com/?category=pipeline. Also see Marolda and Krigsman (2018). “Moneyball” for movies: data and analytics at Legendary Entertainment. https://www.cxotalk.com/episode/moneyball-movies-data-analytics-legendary-entertainment.

  7. 7.

    Smith et al. (2018). Inequality in 1100 Popular Films: Examining portrayals of gender, race/ethnicity, LGBT and disability from 2007 to 2017. USC Annenberg Inclusion Initiative. http://assets.uscannenberg.org/docs/inequality-in-1100-popular-films.pdf.

  8. 8.

    Del Vecchio et al. (2018). The data science of Hollywood: Using emotional arcs of movies to drive business model innovation in entertainment industries. Cornell University E-Prints Computer Science Computation and Language Resource document. https://arxiv.org/pdf/1807.02221.pdf.

  9. 9.

    Practitioners’ use of academic methodologies is exemplified by Miguel Campo and his team at 20th Century Fox, who deploy methods from computer science, such as interactive intelligent systems, neural networks, and pattern recognition, to predict movie audiences using trailers. See Hsieh et al. (2018). Convolutional Collaborative Filter Network for video-based recommendation systems. Cornell University Computer Science Computation and Language Resource document. https://arxiv.org/abs/1810.08189. Another example is Navaratjhna, Carr, and Mandt at Disney, who alongside their colleagues at Simon Fraser University and California Institute of Technology, utilize matrix and tensor factorization methods to examine facial expression data to model movie audience reactions (Deng et al. 2017). A third example is Movio, a third-party analytics provider serving STX Entertainment and the major studios. Movio stipulated that its causal inference methods were validated by Professor D. Rubin at Harvard University.

  10. 10.

    Such innovations are covered in the trade press, such as Roxborough (2016). Alibaba Pictures buys into ‘Mermaid’ Financier Hehe Pictures. Hollywood Reporter. http://www.hollywoodreporter.com/news/alibaba-pictures-buys-mermaid-financier-hehe-pictures-950659.

  11. 11.

    New York Times (2013). Giving viewers what they want. https://www.nytimes.com/2013/02/25/business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html.

  12. 12.

    Smith and Beguely (2017). Movio view: propensity, machine learning, automation, and why it matters. Movio Blog. https://movio.co/blog/movio-view-propensity-machine-learning-automation-and-why-it-matters/.

  13. 13.

    For example, StoryFit (2018) caveats its proposition to “using big data to improve greenlight” with the proviso that “it is not possible to ‘predict success’ in the way that most people mean.”

  14. 14.

    See Mosisoglu (2017). Netflix content data: From script to screen. Netflix Data Presentation 8/6/2017. https://www.youtube.com/watch?v=qXo9jTxfqJ8&t=2s.

  15. 15.

    There are examples of established market leaders with in-house practices, such as Legendary Entertainment. There are also emerging research-led endeavors. For example, ETC’s Theatrical Demo Data Project tests a new in-theater technological solution to gather large-scale viewing data in real time (cf. Bergquist 2017. How my team and I are trying to revolutionize Hollywood. Medium Corporation. https://medium.com/@punkstrategy/how-my-team-and-i-are-revolutionizing-hollywood-9930e28937d).

  16. 16.

    Riley et al. (2018). AVA: The Art and Science of Image Discovery at Netflix. Technology Blog. https://medium.com/netflix-techblog/ava-the-art-and-science-of-image-discovery-at-netflix-a442f163af6.

  17. 17.

    VRVCA (2018). VR/AR global investment report and outlook 2018. Resource document. VRVCA. https://static1.squarespace.com/static/575e5cd62b8ddeb3fba63f79/t/5aa963d4e2c483ff9d60f3c9/1521050602030/VRVCA_Global+Investment+Report+2018_vF+%28EN%29.pdf.

  18. 18.

    IAB, and MARU/Matchbox (2017). The changing TV experience: 2017. Online report. Lab and maru/matchbox. https://www.iab.com/wp-content/uploads/2017/05/The-Changing-TV-Experience-2017.pdf.

  19. 19.

    EFM Horizon (2019). Blockchain in Motion. https://www.efm-berlinale.de/en/horizon/programme/blockchain/blockchain.html#!/accordion1085974=accordion-item-start-module+accordion-item-start-module-1.

  20. 20.

    Dixon (2019).Why recommendations aren’t better, yet! NScreen Media.

    http://www.nscreenmedia.com/boost-metadata-better-video-recommendations/.

  21. 21.

    For example, Caranicas (2018). Artificial intelligence could 1 day determine which films get made. Variety. https://variety.com/2018/artisans/news/artificial-intelligence-hollywood-1202865540.

  22. 22.

    Klossa (2019).Toward European Media Sovereignty. https://ec.europa.eu/commission/sites/beta-political/files/guillaume_klossa_report_final.pdf.

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The authors contributed equally and were listed alphabetically. The authors thank the co-editors of this Special Issue, Thorsten Hennig-Thurau, S. Abraham (Avri) Ravid, and Olav Sorenson, and all other participants of the 2018 Mallen 20 Conference for their valuable comments. The authors also thank Josh Eliashberg, Sam Hui, and Ann-Kristin Kupfer for their valuable input.

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Behrens, R., Foutz, N.Z., Franklin, M. et al. Leveraging analytics to produce compelling and profitable film content. J Cult Econ 45, 171–211 (2021). https://doi.org/10.1007/s10824-019-09372-1

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Keywords

  • Entertainment analytics
  • Big data
  • Content production
  • Film producer
  • Film industry