- \n
- Wide range of returns highlights difficulty of getting it right<\/strong><\/li>\n<\/ul>\n
Stephen Follows analyzed 29 blockbusters with an average production budget of $150mm and their average profit was only ~$14mm. In fact, an oft-cited rule of thumb is that ~50% of movies make money while ~50% lose money. In a follow-up, Stephen demonstrated that half the money earned from profitable ventures in recent times came from the top 6% of movies and half the money lost came from the bottom 6%.\u00a0[1]\u00a0\u00a0<\/a>[2]\u00a0<\/a>New technologies can help studios both move the profitability curve upwards and shed the right tail seen in Figure 4.<\/p>\n
Figure 1. Illustrative Movie Studio’s Revenue Streams<\/strong><\/h5>\n
![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Revenue-1.png\")
<\/a><\/p>\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Figure 2. Average Blockbuster Revenue Breakdown\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Figure 3. Average Blockbuster Cost Breakdown<\/strong><\/h5>\n
![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Revenue-2.png\")
<\/a>
\n![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Cost-1.png\")
<\/a><\/h6>\nFigure 4. Profit Dispersion for 279 Movies<\/h5>\n
![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Dispersion-1.png\")
<\/a><\/h6>\n- \n
- Downside risk in dollars increasing as costs\u00a0<\/strong>are increasing<\/b><\/li>\n<\/ul>\n
Movies are increasingly expensive as studios have been concentrating on big bets. According to Eric Buchman, “Hollywood is no longer in the business of hitting singles. They now swing for the fences almost every time they step to the plate […]” After adjusting for inflation, 45 of the 50 most expensive movies ever made were released in the last decade or so\u00a0[3]<\/a>. By concentrating their bets, studios increase the chances of large returns in dollar terms but also increase the chances of large losses, so they seek every advantage to minimize downside risk.<\/p>\n
Figure 5. # Movies Released per Year Costing $100mm+
![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Movie-Costs.png\")
<\/a><\/h5>\nHow Fox Leverages Machine Learning to Inform Decisions<\/strong><\/span><\/p>\n
Fox has been at the forefront of using machine learning to match stories and audiences. Historically, studios employed a \u201cspray and pray\u201d approach: show many ads and hope for the best\u00a0[4]<\/a>.\u00a0Fox\u2019s A.I. project began when the studio realized it was promoting \u201cThe Greatest Showman\u201d to the wrong audience; instead of appealing to musical lovers, it was a hit with those interested in characters shunned by society\u00a0[5]<\/a>.\u00a0Since then, Fox has partnered with Google Cloud to develop a learning program, called Merlin, that tries to identify the basic patterns in audiences\u2019 preferences for different types of movies through trailer analysis\u00a0[6]<\/a>. Initially, the team loaded Merlin with customer data, film synopses and trailers to build its pattern recognition abilities.<\/p>\n
Figure 6. Merlin Architecture Flow Diagram<\/h5>\n
![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Merlin-overview.png\")
<\/a><\/p>\nThe first step in a new analysis is for Merlin to tag all the elements in a trailer.<\/p>\n
Figure 7. Merlin Tagged Labels for Logan’s Trailer
![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Labels-by-Frequency.png\")
<\/a><\/h5>\nThen, Merlin feeds the information it has into a very complex custom neural network that learns over time and produces a current movie attendance probability for the audiences of prior movies.<\/p>\n
Figure 8. Merlin Prediction Model Process
![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Regression-Model.png\")
<\/a><\/h5>\nThe result is a prediction of audiences from prior movies that are most likely to be interested in the current movie, i.e. target audiences. On a weekly basis, the team gets a scorecard that compares Merlin\u2019s predictions to actual results so it can close the loop and have Merlin improve over time.<\/p>\n
Figure 9. Merlin Accuracy Scorecards<\/h5>\n
![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Comp-Results.png\")
<\/a><\/p>\nPotential Next Steps<\/strong><\/span><\/p>\n
Currently, Fox seeks to integrate Merlin Video with Merlin Text, a script analyzer, into one framework that leverages both methods to produce better predictions. Fox will likely also be focusing on building out tools that allow it to reach those audiences through targeted advertising, a complex task as explained by Matt Marolda of Legendary Entertainment\u00a0<\/em>[7]<\/a>. On the production side, while Merlin may help provide some broad thematic insights, there is still a lot of white space to use machine learning to help make decisions, such as with casting choices. Another exciting avenue is the use of facial biometrics by Disney to track & predict audience reactions on a scene-by-scene basis, helping film-makers understand in advance the impact a scene may have on their target audiences\u00a0[8]<\/a>. Such technologies can help Fox grow Merlin from a marketing optimizer to an analytics platform that provides significant value throughout the entire lifecycle of a production.<\/p>\n
Key Considerations<\/strong><\/span><\/p>\n
One of the key issues for Fox is the balance between data-driven decision-making and creativity. By copying what has worked well in the past, it can kill innovation by only iterating on existing ideas instead of taking risks on new ones. For example, Avatar altered the movie landscape in 2009 through its use of 3D technology, but a machine learning algorithm would likely have struggled to predict its success. How can movie studios best combine technology & creativity to produce movies that are not only profitable, but also define and inspire generations?<\/p>\n
(780 words)<\/p>\n
References:<\/p>\n
[1]<\/a>\u00a0Stephen Follows.\u00a0How movies make money: $100m+ Hollywood blockbusters<\/a>. Stephen Follows: Film Data & Education<\/em> (July 10, 2016).<\/p>\n
[2]<\/a>\u00a0Stephen Follows.\u00a0Do Hollywood movies make a profit?<\/a>\u00a0Stephen Follows: Film Data & Education<\/em> (July 25, 2016).<\/p>\n
[3]<\/a>\u00a0Eric Buchman.\u00a0Why are movies more expensive than ever when tech makes them easier to make?<\/a>\u00a0Digital Trends<\/em> (October 12, 2014).<\/p>\n
[4]<\/a>\u00a0Michael Krigsman.\u00a0Moneyball for movies: Data science and AI in Hollywood<\/a>\u00a0ZDNet<\/em> (February 20, 2018).<\/p>\n
[5]<\/a>\u00a0Brent Lang.\u00a0How Mobile Phones Are Giving Hollywood a Better Understanding of Its Audiences<\/a>\u00a0Variety<\/em> (March 22, 2018).<\/p>\n
[6]<\/a>\u00a0Miguel Campo-Rembado & Sona Oakley.\u00a0How 20th Century Fox uses ML to predict a movie audience<\/a>\u00a0Google Cloud<\/em> (October 29, 2018).<\/p>\n
[7]<\/a>\u00a0Matthew Marolda & Michael Krigsman.\u00a0“Moneyball” for Movies: Data and Analytics at Legendary Entertainment<\/a>\u00a0CXO Talk<\/em> (February 9, 2018).<\/p>\n
[8]<\/a>\u00a0Disney Research.\u00a0Neural nets model audience reactions to movies<\/a>\u00a0Phys.org<\/em>\u00a0(July 21, 2017).<\/p>\n
- Downside risk in dollars increasing as costs\u00a0<\/strong>are increasing<\/b><\/li>\n<\/ul>\n