Algorithms and producers: an evidence-based approach to target audience definition and revenue prediction for feature film producers
With the rise of streaming platforms and direct-to-consumer business models, big data and audience analytics have emerged as powerful new ways to define target audiences and predict movie revenues in the film industry’s public discourse. Yet while major studios have since launched proprietary intern...
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| Format: | Abschlussarbeit Elektronisch E-Book |
| Sprache: | English |
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| Zusammenfassung: | With the rise of streaming platforms and direct-to-consumer business models, big data and audience analytics have emerged as powerful new ways to define target audiences and predict movie revenues in the film industry’s public discourse. Yet while major studios have since launched proprietary internal data science efforts, the role such technologies could play for the practice of independent producers remains unclear. In an effort to close that gap, this dissertation examines how the way feature film producers define their target audience and forecast box office revenues might be improved upon by the use of large sets of individual user data and digital methods of modeling movie preferences. Drawing from the productions studies approach, we establish in an exploratory qualitative study that German independent feature film producers mostly base their target audience definitions on vague, anecdotal evidence, and use intuitive box office forecasts mainly to win over financiers. We then take a look at scientific studies on target audience definition and revenue prediction for feature films and identify reasons for why their results have not been more widely adopted among practitioners. Subsequently, we propose an approach to evidence-based target audience definitions building on recommender algorithms and movie preference modeling at the individual level. Using several datasets on German moviegoers (n = 2,374, n = 6,564, and n = 700, respectively), we confirm that past movie choices indeed provide reliable information on future behavior that can be exploited for efficient targeting. Targeting by as few as three past movie choices also is on average more efficient than targeting by demographic proxies (gender and age). The increase in targeting efficiency as compared to targeting by gender is statistically significant. Outlining a possible practical application, we go on to show that it is possible |
| Beschreibung: | 1 Online-Ressource (xv, 234 Seiten) Illustrationen, Diagramme |
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| 520 | 3 | |a With the rise of streaming platforms and direct-to-consumer business models, big data and audience analytics have emerged as powerful new ways to define target audiences and predict movie revenues in the film industry’s public discourse. Yet while major studios have since launched proprietary internal data science efforts, the role such technologies could play for the practice of independent producers remains unclear. In an effort to close that gap, this dissertation examines how the way feature film producers define their target audience and forecast box office revenues might be improved upon by the use of large sets of individual user data and digital methods of modeling movie preferences. Drawing from the productions studies approach, we establish in an exploratory qualitative study that German independent feature film producers mostly base their target audience definitions on vague, anecdotal evidence, and use intuitive box office forecasts mainly to win over financiers. We then take a look at scientific studies on target audience definition and revenue prediction for feature films and identify reasons for why their results have not been more widely adopted among practitioners. Subsequently, we propose an approach to evidence-based target audience definitions building on recommender algorithms and movie preference modeling at the individual level. Using several datasets on German moviegoers (n = 2,374, n = 6,564, and n = 700, respectively), we confirm that past movie choices indeed provide reliable information on future behavior that can be exploited for efficient targeting. Targeting by as few as three past movie choices also is on average more efficient than targeting by demographic proxies (gender and age). The increase in targeting efficiency as compared to targeting by gender is statistically significant. Outlining a possible practical application, we go on to show that it is possible | |
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| spelling | Funk, Jannis Verfasser (DE-588)1061543323 aut Algorithms and producers an evidence-based approach to target audience definition and revenue prediction for feature film producers by Jannis Funk Potsdam 2021 1 Online-Ressource (xv, 234 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Dissertation Filmuniversität Babelsberg Konrad Wolf 2021 With the rise of streaming platforms and direct-to-consumer business models, big data and audience analytics have emerged as powerful new ways to define target audiences and predict movie revenues in the film industry’s public discourse. Yet while major studios have since launched proprietary internal data science efforts, the role such technologies could play for the practice of independent producers remains unclear. In an effort to close that gap, this dissertation examines how the way feature film producers define their target audience and forecast box office revenues might be improved upon by the use of large sets of individual user data and digital methods of modeling movie preferences. Drawing from the productions studies approach, we establish in an exploratory qualitative study that German independent feature film producers mostly base their target audience definitions on vague, anecdotal evidence, and use intuitive box office forecasts mainly to win over financiers. We then take a look at scientific studies on target audience definition and revenue prediction for feature films and identify reasons for why their results have not been more widely adopted among practitioners. Subsequently, we propose an approach to evidence-based target audience definitions building on recommender algorithms and movie preference modeling at the individual level. Using several datasets on German moviegoers (n = 2,374, n = 6,564, and n = 700, respectively), we confirm that past movie choices indeed provide reliable information on future behavior that can be exploited for efficient targeting. Targeting by as few as three past movie choices also is on average more efficient than targeting by demographic proxies (gender and age). The increase in targeting efficiency as compared to targeting by gender is statistically significant. Outlining a possible practical application, we go on to show that it is possible (DE-588)4113937-9 Hochschulschrift gnd-content https://nbn-resolving.org/urn:nbn:de:kobv:po75-opus4-3340 Verlag kostenfrei Volltext |
| spellingShingle | Funk, Jannis Algorithms and producers an evidence-based approach to target audience definition and revenue prediction for feature film producers |
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| title | Algorithms and producers an evidence-based approach to target audience definition and revenue prediction for feature film producers |
| title_auth | Algorithms and producers an evidence-based approach to target audience definition and revenue prediction for feature film producers |
| title_exact_search | Algorithms and producers an evidence-based approach to target audience definition and revenue prediction for feature film producers |
| title_exact_search_txtP | Algorithms and producers an evidence-based approach to target audience definition and revenue prediction for feature film producers |
| title_full | Algorithms and producers an evidence-based approach to target audience definition and revenue prediction for feature film producers by Jannis Funk |
| title_fullStr | Algorithms and producers an evidence-based approach to target audience definition and revenue prediction for feature film producers by Jannis Funk |
| title_full_unstemmed | Algorithms and producers an evidence-based approach to target audience definition and revenue prediction for feature film producers by Jannis Funk |
| title_short | Algorithms and producers |
| title_sort | algorithms and producers an evidence based approach to target audience definition and revenue prediction for feature film producers |
| title_sub | an evidence-based approach to target audience definition and revenue prediction for feature film producers |
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