Continuing our series exploring fascinating data analytics use cases, we uncover how data science helps the film industry create box office hits.
Have you ever wondered how movie studios like Marvel and Warner Bros. churn out hit after hit, with only the occasional flop? Spoiler alert: it’s not by chance, it’s thanks to advanced data analytics in entertainment industry.
Long gone are the days when movie analytics data studio were driven by creative visionaries who could predict what audiences wanted to see. Now, film is an industry that’s built on algorithms designed to reveal the perfect combination of ingredients needed to make a blockbuster success.
The boom of streaming services like Netflix, Amazon Prime, and Now TV mean film studios have thousands more data points to help them understand today’s audiences, previously being limited to just cinema ticket and DVD sales.
The consumer and market insights generated from these data points are used to inform teams across every part of a film’s development, from the initial pitch to the marketing upon release – ensuring every step is executed in the most efficient, profitable way possible.
Here’s a look at the many roles data analytics plays across the lifecycle of a film.
The initial pitch: Identifying what audiences love, and what they don’t
Data analytics can be used as early as the initial pitch to start a film on the road to success. In recent years, leading film studio 20th Century Fox has revealed it’s using machine learning models to identify the types of films audiences will want to see, and using the results to guide its script purchasing.
The studio uses granular labels to categorise films based on the qualities within the movie. For instance, the 2017 hit Logan was categorised by labels like “tree”, “facial hair”, “car”, and “man”.
These labels might sound too simple to be effective, but once they’ve been put through the studio’s custom neural network, they translate to accurate predictions for what Logan’s audience are likely to want to watch next.
And it’s not just the purchasing stage where data analytics has an influence – it’s even helping scriptwriters to create winning stories. An interview with Ex-Chief Executive of Worldwide Motion Picture Group, Vinny Bruzzese revealed studios use data analytics to compare in-progress scripts to past blockbuster hits, identifying the plot points and story beats that guarantee success.
Vinny’s examples show how the results produced by data analytics models create actionable insights that guide scriptwriters: “Demons in horror movies can target people or be summoned. If it’s a targeting demon, you’re likely to have much higher opening weekend sales than if it’s summoned.”
Similarly, films with bowling scenes often tend to flop in the box office – cult classic The Big Lebowski, notable for its multiple bowling scenes, barely made back its $15 million budget at the domestic box office.
Production: Finding the most cost-effective way to film
After guiding studios towards the perfect script, data analytics can play a critical role in the production process, helping producers make smarter decisions during filming.
During pre-production, producers can use predictive analytics to identify which actors they should hire in which roles, and which parts of the plot they should emphasise during filming. These results are procured by analysing the interrelationships between the box office draw held by certain stars, the budget, and the social media buzz surrounding a film.
One of the most common analytics use cases during production is determining optimal filming locations, looking at where will provide the most efficient, cost-effective filming process. Using predictive analytics models that rely on data from previous productions, producers can identify ideal locations- based key variables like daylight hours and climate, and calculate budgets from the results.
Of course, these models rely on filming locations having existing data sets. For locations like Central Park in New York City, a spot that’s been used in more than 530 films and TV series, producers will be able to predict budgets with a high degree of accuracy. But for lesser-used locations, like Mount Ngauruhoe (known to audiences as Mount Doom of Mordor in Lord of the Rings), predictive models won’t be as useful.
Distribution and marketing: identifying the right audience for the film
Distribution and marketing are perhaps the most obvious opportunities for data analytics, as studios have a wealth of data points at their fingertips to help them find the ideal audience for their films.
Marketers can break audiences down into micro-segments – like parents who live in Alaska that are interested in horror films – to better understand which demographics and geographic areas they should be targeting, and whether they should do so with physical billboards or adverts on social media.
This form of data analytics has been frequently used in the film industry i.e. film analytics for decades, evolving as studios gain access to more data points from their audiences. But more recently, streaming services like Netflix have taken data-based marketing predictions to new levels.
In the past few years, Netflix’s recommendation algorithm has developed a reputation for its impressive accuracy. It’s so successful in fact, that 80% of all streaming time on the platform is thanks to its data-driven recommendations.
The algorithm is also closely integrated with its user interface, with recommendations split into high-level genres and categories like, “similar to what you were watching previously”. Behind the scenes though, this algorithm actually digs much deeper, capturing data points like the time elapsed since the user viewed a piece of content, the points at which users abandon content, the devices they watch on, and much more.
Analytics aren’t just reserved for the big players
You’d be forgiven for thinking the cards are stacked in favour of the industry giants with resources to invest in advanced data analytics – but there are some tangible opportunities for independent filmmakers, too.
As the independent film festival Raindance makes clear, data analytics doesn’t need to be expensive, and filmmakers on lower budgets can find vast amounts of freely available big data in film industry online. Soon, we’ll start to see helpful analytics tools like budgeting and scheduling optimisers developed specifically for independent film producers – unlocking the doors to a wider audience and potentially limitless success.
Analytics at The Smart Cube
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