Using Data to Make Profitable Film Acquisitions

Posted on Aug 3, 2020
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Background

Streaming services have a content problem. Subscription-based streaming services like Netflix must consistently acquire new content to keep users engaged; otherwise they risk losing subscribers. However, acquisition of this content is an expensive gamble and can lead to large cash burn. For example, in 2019, Netflix had a cash burn of -$3.3 billion, in total having invested over $14 billion in content, mostly original [1]. Based on data on an increasingly competitive market, streaming services must focus on lowering these acquisition costs. 

Examining data from crowdfunding services may offer a potential solution. Crowdfunding platforms like Kickstarter offer streaming services an opportunity to discover new projects while they are still relatively unknown and therefore cheaper to acquire.

The Data

For this project I explored a Kickstarter dataset from Kaggle spanning from June 3rd, 2009 to March 3rd, 2018.  I scraped Kicktraq, a third party site that tracks the progress of Kickstarter projects, to update the Kaggle dataset with more current projects. Kicktraq has an excellent archive of previous campaigns that spans back about three months. At the time I scraped the site, I had data from April 5th 2020 to July 24th 2020.

Ranking the Types of Kickstarter Projects

 

Fig 1a. Total number of Kickstarter campaigns

Fig 1b. Number of successful Kickstarter campaigns

First I checked the total number of Kickstarter campaigns for films and movies. Figure 1a shows the total number of campaigns in each campaign category, and figure 1b shows the number successful campaigns per category. Film and video ranks first in total number of campaigns with a count of over 57,000, and second in terms of number of successful campaigns with over 23,000. We can use this wealth of data to help services like Netflix identify popular genres and even potential acquisition targets.

Most Popular Film and Movie Genres Based on Data

Fig 2a. Number of successful Kickstarter campaigns in film
data
Fig 2b. Percent campaigns successful in film

Then I examined which film genres were the most popular and most likely to succeed.  Figure 2a shows documentaries are funded most often by far, followed by narrative films and webseries. However, documentaries might have high numbers simply because a large number of documentaries were submitted. If we look at success rate as a percentage (figure 2b), documentaries are still among the top five, along with narrative film, comedy, drama, and science fiction. Documentaries are the most popular projects in the film category and have a relatively high success rate.

Identifying the Ideal Film Acquisition Based on Data

Backers

Web streaming services like Netflix measure a film’s success on their platforms by calculating the film's viewership relative to its acquisition cost [2]. Netflix wants films that are well watched and liked on their platform to keep their subscribers from churning. Therefore I used backer number, the total number of people that backed a project, to approximate the level of public interest in a film. 

 

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Figure 3. Number of backers and funding goals of successful Kickstarter campaigns

In figure 3 above, we have a scatterplot where the x-axis is the number of backers and the y-axis is the project’s funding goal in thousands of USD. The majority of the points cluster around the lower right with fewer backers and lower funding goals. In the upper right corner, there are outliers, projects that had high goals and over 20,000 backers.

An ideal movie acquisition would have a large number of backers and mid-to-low funding goals. The thinking here is that the ultimate acquisition cost will be a multiplier of the film budget, similar to how Hollywood studios acquire films for distribution. Figure 4 ‘zooms’ into an ‘ideal’ range of acquisition costs for movies. Here, projects with potential high acquisition costs are filtered out.  The limit used is the median funding goal of $6,000, but this upper bound can change, depending on the streaming site’s budget.

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Fig 4. Ideal acquisition candidates (red)

The red points highlight the projects where the backer number is greater than the overall mean. These projects are more popular than average and within our budget. Projects that fall in this range may be especially attractive for acquisition, as they have demonstrated both a high public awareness and potential for low acquisition costs. 

One such example is the 2019 political documentary, Knock Down the House. Its Kickstarter had a large number of backers (424) and a relatively small funding total of $28,000. After a successful Kickstarter campaign, it showed at the Sundance film festival. Shortly after, Netflix acquired it for $10 million after a bidding war [3].  Had they acquired this movie before it showed at festivals and gained traction, the acquisition cost could have been much lower than $10 million. 

Kickstarter is a thriving marketplace for independent film talent, and can be a viable option to find profitable candidates for acquisition. By investing in Kickstarter data, streaming services can pinpoint and acquire promising content before it goes to auction at film festivals, thereby lowering costs.

Sources

[1] Trefis Team Contributor. “Netflix  One Question: Is It Making Money or Losing Money?” Forbes, 5/1/20, https://www.forbes.com/sites/greatspeculations/2020/05/01/netflix-one-question-is-it-losing-money-or-making-money/#1738cee029a6

[2] Patel, Neil. “How Netflix Uses Analytics to Select Movies, Create Content, and Make Multimillion Dollar Decisions.” NeilPatel.com,  https://neilpatel.com/blog/how-netflix-uses-analytics/

[3] Sakoui, Anousha. “Ocasio-Cortez Campaign Film Draws Top Dollar From Netflix.” Bloomberg, 2/7/19,  https://www.bloomberg.com/news/articles/2019-02-07/ocasio-cortez-campaign-film-draws-top-dollar-from-netflix

 

About Author

Judy Chung

Data Scientist with five years of lab manager experience in microbiology and ecology. Strong research professional with a Bachelor's degree in Integrative Biology from University of California, Berkeley. Excited to generate data-driven insights to drive business value.
View all posts by Judy Chung >

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