Legendary Entertainment is a large media company with branches in film, television, digital media, and comics. Films produced or associated with Legendary Pictures have grossed over $12 billion at the international box office, including monster hits such as Jurassic World and The Dark Knight Rises. In 2013, CEO and founder, Thomas Tull, hired Matthew Marolda, an analytics guru in the sports world, to apply the same “Moneyball” techniques to the film industry. As a result, Legendary is one of the few studios that uses data at the core of their decision making strategy.
Legendary creates value using data in the following key ways:
- Make marketing spends more efficient. Competitor studios tend to macro-target with the “four quadrant” targeting technique (male/female/young/old). Because of this, and the pressure to make sure tent-pole films open, they use a “blanket the market” technique when it comes to marketing – spending tens of millions of dollars just to make sure they covered all their bases. Legendary, on the other hand, looks to target “micro-segments” based on readily-available data collected about potential movie-goers. So they only pay for ads going to the consumers they’ve deemed most likely to buy a ticket.
- Creating better trailers. Competitors usually make only a few different cuts of a trailer – typically varying only by platform. Legendary, on the other hand, tests hundreds of trailer elements with their microsegments. This helps them to create the best possible trailers tailored for each unique segment of the market.
- Identifying bombs earlier. Using the capabilities discussed above also allows Legendary to realize when they have a bomb on their hands much earlier. That way, they can stop spending on marketing and cut their losses. On the other hand, competitor studios without data to help them track the market might continue to overspend to no avail up until its release.
- Directing creative decisions. Legendary also uses data to aid in making decisions on concepts and casting. While other studios might rely exclusively on gut and creative taste of executives.
Legendary captures value from data and analytics mainly from reducing their marketing spends by 15 to 20 percent. This could amount to up to $15 million or more in decreased expenditures, improving margins across all films. Their micro-targeting helps eliminate wasteful spends and increase the amount of “persuadable” audience that pays for tickets, thus increasing revenues. Their data strategy also reduces the risk of each film by making sure there is a market for the concept and the talent most likely to pull in audiences is cast.
History and Challenges
Hollywood is reluctant to use data. Their culture largely views data as the death of art, and there is also an incredible number of variables that would need to be identified and studied in order for a “model of making movies” to actually work. However, Thomas Tull saw an opportunity to use data to shrink wasteful marketing spends, and his vision and commitment was key to making data catch on for this company. Also key to his data strategy was keeping the analytics department separate from Hollywood culture by physically locating the branch in Boston instead of Los Angeles. This way, the analytics team could maintain an unbiased stance instead of being influenced by industry executives.
Key challenges for Legendary data moving forward include:
- Changing Hollywood perspectives on the usefulness of data. This will allow Legendary to grow their data consulting business for other studios and explore new applications of data within their own production capabilities.
- Rectifying historical vs predictive data. Data gathered on past tastes of potential audiences doesn’t necessarily predict future tastes. Will relying on historical data favor incremental innovation over artistic leaps that could potentially pull in much higher profits?
- Identifying important variables. In order for data to actually be useful in terms of creating better movies on the front end, variables at each stage of production need to be identified, tagged in a data set, and deemed statistically significant in terms of increasing box office revenue. This will take a ton of work and data in order to create a useable model.