LEVERAGING ANALYTICS TO PRODUCE COMPELLING AND PROFITABLE FILM CONTENT

By Ronny Behrens, Natasha Zhang Foutz, Michael Franklin, Jannis Funk, Fernanda Gutierrez-Navratil, Julian Hofmann and Ulrike Leibfried

In light of the rising availability of big data and the fast evolution and diffusion of analytical methods in the creative industries, content producers are faced with manifold opportunities, but also feel the pressure to leverage those resources to create more compelling and profitable content. Dissecting state-of-the-art research as well as current industry developments and embedding them in theories of value creation and film production, we identified key analytic techniques that producers can utilize to their benefit at various stages of film production.

Our paper gives a brief overview of recent market changes in the film industry, such as the rise of digital technologies for producing, distributing and consuming content. These developments have put heavy pressure on the competitive landscape by lowering entry barriers, leading to a spike in user-generated content, and shifting power to tech companies such as Netflix, which is currently making multi-billion dollar investments into its content library. At the same time, digitalization lead to a major increase of available data, both on the individual- and on the market-level. Although industry players push the usage of advanced analytics to new frontiers in content production, academic research has mainly focused on the downstream activities (i.e., distribution, marketing, etc.). For this reason, our paper tries to capture and systematize the use of analytics and central capabilities producers need to develop, to face the dynamics and challenges in creating compelling and profitable content successfully.

Combining an industry engaged understanding of the film production process (e.g., Bugaj 2013; Marolda and Krigsman 2018)[1] and the literature on value creation (e.g., Bozik and Dimkovski 2019; Teece et al. 1977; Verhoef et al. 2016) builds the foundation for structuring the wealth of available and currently evolving knowledge. Whereas the first stream gives us an overview of the typical production value chain, detailing packaging, production, and exploitation stages, the second allows us to identify important data-related capabilities, ranging from acquiring the right data, utilizing appropriate analytics, integrating insights into established processes, to applying the generated knowledge to create value for consumers and production companies. Layering these two theoretical lenses, we identified such crucial capabilities at each stage of the value chain, which can lead to sustainable competitive advantages over less-tech savvy players. We will briefly outline and summarize them in the following.

Figure 1. Important Capabilities during Film Production

Packaging Stage: The packaging stage encompasses all steps that happen before a production is greenlit and costs become sunk in physical production. Here, producers are mainly concerned with three simultaneous processes: script development (i.e., deciding which script should be brought to life – and how to optimize it), packaging (i.e., which talent to use to produce the film), and financing (i.e., predicting potential demand and assessing risk through audience analytics). We summarized important capabilities for the packing stage in Table 1.

Table 1. Important Capabilities at the Packaging Stage

Production Stage: The production stage covers all steps that happen between packaging and until the film has reached its final, unalterable form. This includes pre-production (i.e., everything between greenlighting and the first day of shoot), production (i.e., principal photography), and post-production (i.e., everything between the last day of shoot to delivering the film for distribution). We summarized important capabilities for the production stage in Table 2.

Table 2. Important Capabilities at the Production Stage

Exploitation Stage:  The exploitation stage comprises all steps concerning the marketing of the film, and most importantly, learning processes that influence future productions. In this context, we mainly focused on leveraging WOM analytics to improve future production decisions by utilizing both structured (e.g., star reviews) and unstructured (e.g., review texts) data sources about consumers’ movie reception. Taking this perspective, we shift the focus from existing research, which provides abundant and valuable insights into the effect of WOM on the sales of a focal movie, to extracting useful insights for producers’ future activities. We summarized important capabilities for this stage in Table 3.

Table 3. Important Capabilities at the Exploitation Stage

Concluding, we can see a trade-off between the potential of decisions to affect the production and economic performance as well as their complexity, and the availability of appropriate methods and their accuracy, respectively. Early on, decisions are impactful (e.g., should we greenlight this script?), but decision criteria are fuzzy, research scarce, and suitable methods still in their infancy. Later on, decisions still have impact (e.g., how should we promote our movie?), but are smaller in scope as previous decisions are not easily reversible. Nevertheless, research offers guidance on crucial aspects and determinants, and appropriate methods make such work eminently achievable.

However, the evolution of digital techniques and analytics has no end in sight, yet. In the last section of our paper, we discuss novel technologies/analytics, potential implementation roadblocks, analytic requirements for producers, and the need for advanced regulatory support – and highlight important gaps in the literature that need to be addressed going forward.

Although we illustrated the great potential analytics offer for producing more compelling and profitable content, producers might face a multitude of roadblocks when trying to implement them. Most importantly, producers often lack access to data (which is frequently not recorded systematically and/or scattered across the value chain) and neither have the necessary understanding of state-of-the-art methods nor access to rare analytics talent that could assist them in reaching sensible conclusion suitable for complementing managerial expertise. Furthermore, installing and maintaining wide-ranging and ongoing data analytic operations raise cost concerns, in cash and time, especially for smaller players. Last in order but not of importance, there are organizational resistances and resentments about the merits of data-supported decision making, especially in the creative realms, where experience and trained intuition mainly drive decisions.

Finally, inherent in the novelty of the discussed topics in combination with potential risks of accumulating mass data about granular individual behavior, there is an increased need for regulatory support and intervention.

As this dynamic field produces new challenges by the minute, we hope that researchers will come forward to use their skills and expertise to address these stimulating research questions and call for industry-academia collaborations

[1] This paper was born from the 20th anniversary Mallen conference (http://themallenconference.org/mallen-20/), which brings entertainment researchers and practitioners together to tackle current industry problems with rigorous research and analytics. In an intense thought leader workshop, we gathered valuable input from practitioners, such as our co-author Ulrike Leibfried, who is a producer and managing director at UFA Fiction. These unique insights from a producer perspective provided us with crucial guidance and a solid basis for our paper.

Additional References:

Bozik, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role of absorptive capacity. International Journal of Information Management, 46, 93–103.

Bugaj (2013). The Production Pipeline Series. PrivateBlog. http://www.bugaj .com/?category=pipeline.

Marolda & Krigsman (2018). “Moneyball” for movies: data and analytics at Legendary Entertainment. https://www.cxotalk.com/episode/moneyballmovies-data-analytics-legendary-entertainment.

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

Verhoef, P. C., Kooge, E., & Walk, N. (2016). Creating value with big data analytics: Making smarter marketing decisions. London: Routledge.

This article is based on:

Behrens, R., Foutz, N.Z., Franklin, M., Funk, J., Gutierrez‑Navratil, F., Hofmann, J., & Leibfried, U. (2020). Leveraging analytics to produce compelling and profitable film content. Journal of Cultural Economics, Online first, 1-41.

About the authors:

Ronny Behrens,

Natasha Zhang Foutz,

Michael Franklin,

Jannis Funk,

Fernanda Gutierrez-Navratil,

Julian Hofmann,

Ulrike Leibfried,

Image source:

The authors.

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