We have all heard stories about those famous scripts and books that companies passed on and thought, “what chumps.” But really, they were probably shrewd. They knew their team, they knew their reach, and they knew what they could do well. A good story without a way to reach the right audience is not going to get made. But with data analytics, marketers can predict the ideal audience down to precise detail and understand the best approach and timing to reach them.
Data analytics can help marketers tailor campaigns based on geographic data; there is even research suggesting a correlation between the number of Facebook likes a given film gets in a location and its chances of selling out at a nearby movie theater. Marketers can also use social listening to optimize release date based on geography, subject matter, or topic. The most successful example of this is 2016: Obama’s America, which debuted on different dates in different regions based on localized political trends.
Integrating Audience Data
A semi-technical explanation of how textual analysis can be used to determine success.
Movies are all about storytelling. Storytelling is all about conveying a specific experience, and successful analytics have to work to drive that experience, not push an agenda or message down someone’s throat. “The crux of predictive analytics,” says Target Marketing Magazine, “is understanding how the customer will respond to your offer.”
We’ve got to understand how each element of a movie impacts the viewer. Take a moment to think about how you as a consumer--not you as a media professional--interacts with any given movie. Each action gives something away about how you are interacting with that story and the way that story was marketed to you. The delivery method, when and where you buy your ticket, when you pause, if you keep watching, if you skip ahead, if you read the description before hitting play: each is a point that tells a story about you. And the facts of those moments--the music, the dialogue, the way the scene was cut--possibly correlate to performance. By collecting data to understand those driving forces, marketers can identify and target even the most specific groups.
AdAge reported in 2015 on a new spin to the “For your Consideration” print ads seen before Awards season: data-driven social targeting of the Academy of Motion Pictures members. Through third-party companies, studios use geographic and demographic information to find and send tailored messages about given movies. Disney takes it even further by using AI to analyze viewers in real time. And by analyzing the social behavior of those users, the messages themselves can be customized based on their style and engagement.
At the movie and television post-production stage, data analytics can improve elements of editing and budgeting, turning eye-crossing spreadsheets into useful information and actionable insight.
We’ve all been to a movie that gave too much away in the trailer and left us feeling like we’ve wasted money and time. Odds are you’ve also been to a movie expecting one kind of experience based on the promotional trailers only to realize the movie was not at all accurately represented. Sometimes this can be a pleasant surprise, but it can also be a huge disappointment.
Taking advantage of sentiment analysis, predictive analytics, and visual analytics, editors can create trailers that are more enticing and more representative of their films. More satisfied viewers are more likely to recommend their friends see the films, increasing your movie’s chance for success.
By using data analytics platforms to advance test trailers and promos, studios learn more about market reception and can adapt accordingly or gain validation on chance decisions, as Stan Entertainment did in anticipation of its 2016 release of remake Wolf Creek. Stan knew that name recognition alone wouldn’t carry the ten-year-old B Australian slasher movie, so they cast Lucy Fry as the female lead, gambling that she would broaden the show’s appeal. At that point, Fry's most prominent role was her portrayal of Lee Harvey Oswald’s wife in Hulu’s adaptation of 11.22.63 by Stephen King.
To test their theory, before release they distributed a version of the trailer that placed more emphasis on Fry than other characters in a 20-second spot. The results supported their casting and developmental decisions.
Turning to Netflix and a similar story emerges. When embarking on their first attempt at original content production, the streaming giant was not going to leave anything to chance. From casting to the concept and promotional trailers, data led the charge. Netflix made five times as many unique trailers for their debut original series House of Cards as most studios make for prime-time television shows or feature films. Starring Kevin Spacey and Robin Wright and directed by auteur filmmaker David Fincher, each version targeted a specific audience with particular preferences, all based on historical viewer behavior. “If you watched a lot of Kevin Spacey films, you saw a trailer featuring him,” explained Kissmetrics. “Those who watched a lot of movies starring females saw a trailer featuring the women in the show. And David Fincher fans saw a trailer featuring his touch.”
Learning from Past Mistakes
One of the most significant strengths data analytics gives decision-makers is the ability to take a comprehensive but detailed view of the past and apply the lessons found to the present and future. A script could test positively, the story and topics correspond with trends observed in social analytics, and the movie can still fail. However, by using the savant-like recall that data analysis gives us, a movie can be compared to past performance of similar movies based on storyline, budget, star-power, release timing, etc. With those features identified, comparable distribution can be analyzed to surface both successful strategies to replicate and pitfalls to avoid.