From Moviepilot With Love: A Data Set Of Movie Buffs for Studios

MoviepilotMovie advertising timelines are rough. Over 80% of a campaign spend occurs within two weeks of a movie’s premiere. If something isn’t working, it’s usually too late to fix it.

Moviepilot -- part publisher, part platform, part agency – is helping them change that. The secret of its insight into moviegoers’ habits and intent: data.

The US site, just two-and-a-half years old, attracts 30 million monthly unique visitors and has amassed 28 million Facebook fans. This summer, Webedia bought the five-year-old German version of the site for $20 million. Founder Tobi Bauckhage said the sale allows him to fund and focus on the US site.

The audience skews young and male, with strong content offerings in superhero, horror and young adult verticals.

For Moviepilot, display buys and site takeovers take a backseat to information. It cares more about selling its data to others than selling ads on its own site.

The goal is to “make money outside your own platforms” using your own data, said Bauckhage, citing Amazon as a company with a similar approach to audience-extension offerings.

To aid in media buys both on its properties and off, Moviepilot gathers information about what its users are reading, saving and following. Did a reader watch a movie trailer all the way through? Did he share it? How many articles about the next Avengers movie has the user read?

Moviepilot marries these insights with the profiles of the 2 million users connected to the site via Facebook Connect. Through those users, it can map age, gender, location and “likes” against users already demonstrating interest in an upcoming film.

It then executes media buys on Facebook, Twitter and YouTube, targeting interest groups shown to have a strong affinity for an upcoming movie. It's experimenting with adding Google's display network to its audience-extension offerings.

It does so with a nod to traditional movie marketing, which is focused around the “four quadrants” (age is one axis, gender the other). Through its proprietary interface, Q, Moviepilot organizes users’ affinities into four quadrants. If a studio wants to reach young males, Moviepilot will look for common interests among young males who expressed interest in a movie.

For “The Hunger Games: Catching Fire,” Lionsgate used Moviepilot to find potential viewers outside the young female quadrant, where the studio already expected strong turnout.

“To move from $400 to $600 million [box office], they would need to go into other quadrants and find those crossover audiences,” Bauckhage said. “We defined a few segments they could grow into, like males over 25 who liked adventures like Indiana Jones and Lara Croft.

Sometimes studios use these insights to inform buys in other channels: For the 2012 Paramount movie “The Devil Inside,” it added “Walking Dead” to its TV media plan after finding out existing fans indexed high for the show.

When working with clients, Moviepilot advises studios to start early and buy media around the trailer release to start getting results.

“Spending against their trailer release was not commonly done until a couple of years ago, but it’s an efficient, cheap way to spend money. You can use the echo and data you get back to inform the campaign later on.”

When a studio releases a trailer, Moviepilot will run a campaign targeted against 15 or so segments for which the company wants to test click-through rates. Based on the results, some segments will be cut and others will be added to the next test.

Besides handling buys for a studio’s Facebook or Twitter page, Moviepilot is exploring letting studios buy against its own content, which often has huge organic reach.

“On Facebook, we generate a few million trailer views. For [2015 teen comedy] 'The Duff,' we had three times as many trailer views as the official channel,” Bauckhage said. That organic reach has spurred Moviepilot to experiment with native posts for studios, which can take advantage of its organic reach.

Despite the wealth of data Moviepilot has amassed, it longs for more. “For me, the closing loop would be ticketing data,” Bauckhage says. “Finding out who is ten times as likely to buy a ticket. Fandango is sitting on it, but they won’t share it.”

 

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