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3 years ago
Supporting Content Decision Makers With Machine Learning

 
Originally published in The Netflix Tech Blog, Dec 10, 2020.

Netflix is pioneering content creation at an unprecedented scale. Our catalog of thousands of films and series caters to 195M+ members in over 190 countries who span a broad and diverse range of tastes. Content, marketing, and studio production executives make the key decisions that aspire to maximize each series’ or film’s potential to bring joy to our subscribers as it progresses from pitch to play on our service. Our job is to support them.

The commissioning of a series or film, which we refer to as a title, is a creative decision. Executives consider many factors including narrative quality, relation to the current societal context or zeitgeist, creative talent relationships, and audience composition and size, to name a few. The stakes are high (content is expensive!) as is the uncertainty of the outcome (it is difficult to predict which shows or films will become hits). To mitigate this uncertainty, executives throughout the entertainment industry have always consulted historical data to help characterize the potential audience of a title using comparable titles, if they exist. Two key questions in this endeavor are:

  • Which existing titles are comparable and in what ways?
  • What audience size can we expect and in which regions?

The increasing vastness and diversity of what our members are watching make answering these questions particularly challenging using conventional methods, which draw on a limited set of comparable titles and their respective performance metrics (e.g., box office, Nielsen ratings). This challenge is also an opportunity. In this post we explore how machine learning and statistical modeling can aid creative decision makers in tackling these questions at a global scale. The key advantage of these techniques is twofold. First, they draw on a much wider range of historical titles (spanning global as well as niche audiences). Second, they leverage each historical title more effectively by isolating the components (e.g., thematic elements) that are relevant for the title in question.

To continue reading this article, click here.

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