23 Apr Cinelytic: Using Predictive Analytics to Evaluate Content Packages and Financing Options
Rapid advances in technology have changed the way we consume, work and live. On the business side, new technologies like predictive analytics have transformed nearly every industry in the last 10 years. While the movie industry has eagerly embraced new technology on the production side, one can make the case that business methods and intelligence, in the film industry, have been relatively stagnant and do not yet benefit from the operational excellence that advanced technologies can provide.
Technological advances have enabled a major shift in consumer viewing behavior in the last ten years. As a consequence, streaming revenues will likely overtake theatrical revenues in 2018 forcing a major change in the competitive landscape (Chart 1).
The driving factor for this dramatic shift in viewing habits has been Netflix, Amazon, and other relatively new content providers (Chart 2) that are able to distribute content so that at viewers can watch at any time, and on any device.
The wider movie industry has been slow to adapt to this new approach to viewing content, and as such, the new tech companies, like Netflix, Amazon, etc., are not only competing with the major studios, but overtaking them in terms of content spend and number of films and TV shows produced.
Chart 1: US Movie Industry Revenues – Advance of Streaming
Source: DEG 2018, MPAA
Chart 2: Share of Total US OTT Viewing Hours
Netflix alone plans to produce 700 TV shows and 80 films in 2018. One of Netflix’s keys to success is that they effectively use data & advanced analytics prior to green-lighting projects putting them at a great advantage regarding what content to create and how best to market it.
New analytics technologies, such as predictive analytics, and cloud-based project management systems can help entertainment companies to better identify opportunities, improve risk assessment and improve operational effectiveness.
Chart 3: Yearly Estimated Content Spend (in billions of US $, excluding live TV and sports)
Source: 2016 Annual Reports, Bloomberg, Google
In this article we focus on predictive analytics applied to content at the greenlighting stage.
What is predictive analytics?
Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened, to provide a best assessment of what will happen in the future.
How predictive analytics can help the entertainment industry
Today, data analytics in the movie industry mostly starts at the marketing phase – a point when the product has been packaged, financed / greenlight and produced. Using analytics to target audiences is important but starting to apply predictive analytics during the packaging stage helps to better understand content potential and financial risk at the outset when major decisions can still be informed.
The greenlighting decision, which is the most important decision junction and informs all further decisions about a piece of content, still heavily relies on gut feeling. Data informing that process with predictive analytics to faster and better understand the financial risk/return potential of content, as well as audience size and demographics, will help companies to operate with better information and hence reduce risk, and increase profits.
A concrete use case
At Cinelytic we apply predictive analytics to provide probabilistic revenue forecasts for filmed content in the packaging stage, to inform the greenlighting decision. Our forecasts help our users understand the likelihood of their project’s performance in different content release scenarios, including per-territory, and over all release windows.
Our system allows users to evaluate packages in real-time, to enable them to get robust forecasts in minutes, or hours, rather than days, or weeks. Our predictive forecasting tool takes in key parameters of the project into account, including production budget, genres, source material, runtime, talent and release strategy.
Once input fields are populated executives can run the predictive analysis to instantly forecast revenues scenarios. Our aim is to step away from base, high, and low forecasts to provide more substantial probabilistic scenarios that flow into an integrated financial model to calculate ROI. This way executives can rapidly assess project risks/returns, especially the percentage likelihood of breaking even.
Chart 4: Cinelytic Predictive Forecast – Output Page
In addition, predictive audience analytics can improve the understanding of the potential audience size, demographics and consumer behaviors at the greenlighting stage. As Stacey Snider, 20th Century Fox CEO & Chairman put it “For a really long time, we have been disintermediated from our customer base, because we sell to exhibitors. We had been flying blind.” – Variety, 9/20/17. Advanced audience analytics at the greenlighting stage can provide very valuable insights to better audience understanding early on.
This new level of transparency and risk management at an early project stage will help attract new sources of financing that can range from brands to sophisticated institutional investors as these investors can model out each investment case in detail.
Predictive analytics at the greenlighting stage is an enormous opportunity for the entertainment industry to better leverage talent, experience and core assets (high quality IP & libraries) for producing great content that has an audience.
Tobias Queisser is Co-founder of Cinelytic.