Why the Nerds should be Marching into Movie Marketing

posted on Dec 19 2012

There has been an avalanche of articles about the lessons marketers can learn from Barack Obama’s 2012 presidential campaign.

There has never been a political digital campaign of such scale and scope, and the technology and data mining techniques applied by Obama’s team should be of special interest to Hollywood’s marketers. There are striking similarities between campaigning for the next box office hit and for the election of the next president of the United States.

by Tobi Bauckhage co-founder and CEO moviepilot.com

Let’s begin by observing some similarities:

First Weekend vs. Election Day

There is no other industry where the rules of market economy condense with such brutality and drama into one single moment of truth as film. Years of financing and development rounds, months of production, weeks in the cutting room, and then one opening box-office weekend that determines much of the success or failure for a several hundred million dollar investment.

The whole film industry is watching the first numbers coming in on Friday evening from the east coast, first exit polls analysis are evaluated and predictions for the weekend’s box-office results and overall success of a movie are made on Saturday.

The election-day experience is of noteworthy similarity. Years of campaign fundraising, months of campaigning, weeks of unremitting voters mobilization by campaign staff and then one moment of truth — election day. First, numbers from the east coast come in, exit polls, first predictions, calculations based on electoral college and popular vote numbers, all to measure the success of a campaign investment. The exact same experience.

Swing Audiences vs. Swing States

Over half of movie tickets are bought annually by only 10 percent of the population. Many of these frequent moviegoers decide on their movie choice very early, and some of them decide what they want the moment they see their options at the ticket booth (“Swing Audiences”).

Additionally, there are the people who normally do not go to the movie theater at all but when they do, make a significant impact on results (“Sluggish Moviegoers”).

All of these audience segments are important for the studios. And every movie marketer must achieve two goals to see success with these groups: to get them off the couch and into a theater seat for opening weekend, and to get that prospective audience to make the right choice at the ticket booth.

The same applies to presidential campaigns: The nominees and their staff must get people to make the correct, favorable choice and then mobilize them to actually leave their homes and fill out the ballot on election day.

Needless to say, there are major differences. While political campaigning is based on programs and hard facts that will directly affect the future life of voters, the only selling proposition in movie marketing is creativity and storytelling: to build a strong emotional connection with potential audiences based only on a narrative, casting, artwork and a trailer.

It is by far the most advanced industry in creating this magic connection with its audience. Data and technology will never change that.

But it might help to deliver the magic in a more impactful and efficient way. In this sense there might be a few interesting lessons to draw from this year’s Obama campaign:

Use Data to Develop a Holistic View

The biggest change in Obama’s ‘12 campaign was that his CTO, Harper Reed, and his staff integrated the existing databases from prior elections, polling, fundraising activities, field work and much more into one massive database. Only by doing this, was the data analytics team able to develop a holistic view on voting and non-voting America.

By cross-validating the patterns, they were able to draw conclusions from the insights the organization was getting across the United States. The profiles of people signing in to www.barackobama.com with Facebook Connect could be cross-referenced with the profiles of followers on Twitter and pre-registered voters, then further with the fund-raising supporter lists, and door-to-door surveys conducted across the US.

With this holistic view, it was possible to segment American citizens not only by location, ethnicity, household income and political interests but also by two scores that were calculated in the system: a score between 0-100 on how likely these segments were to vote for Obama, and a second identical scale gauging how likely these segments were to actually show up at the polls come election day.

The movie industry does not have this holistic data view yet; there is precious little data on moviegoer’s profiles per title. The awareness and intent of audiences to watch a specific movie are measured in small samples by age and gender, and even then only weeks before opening day.

Further, they do not cross-reference with the other angles and data insights the marketers are able to access, so they are difficult to validate in terms of ethniticity, location, household income, interest, etc. The studio’s tracking data do not cross-reference with their Facebook fan relationships, Twitter data, advertising click through rates, TV consumption patterns, home-entertainment purchase data, media affinities, and so on.

From a data standpoint, the movie industry is pretty much blind.

Test, Measure and Iterate

From the beginning, Obama’s campaign manager Jim Messina had promised a totally different, metric-driven kind of campaign in which politics were the goal, but political instincts might not be the means. With this approach, the Obama team transformed marketing into engineering. Political instincts were used as hypotheses that could be tested with real time data.

The process of campaigning became an iterative process with detailed feedback metrics, making investment and resource allocation decisions much more accountable.

While the movie industry has its tracking techniques that respond to big TV campaigns, this tracking data is rather coarse and un-detailed. And since it is mainly conducted via telephone landline calls, it is not monitoring some of the most important age groups and ethnicities that do make a difference at the box office.

If you only measure and monitor a small fraction of the market with a very rough methodology, it is very hard to allocate your marketing budgets effectively and efficiently.

You neither know who you activated through your advertising nor what was the approximate cost per activation.

While instincts and experience always will be important in the movie industry, a more data and structured signal-echo process would reduce a lot of wastage and lead to a higher marketing efficiency.

Concentrate on Scope, Not Only Scale

Once Obama’s team had modeled reliable probability scores within their big sample set, they could extrapolate the findings to a broader population and prioritize their target segment. Cross-referenced with latest polling numbers, Obama’s team could estimate which target segments of non-voters or undecided voters might actually make a difference in terms of winning the election in particular states, regions or even neighborhoods.

With this differentiated probability map the team could much more efficiently allocate their media budgets and campaign activities.

They could hone in in targeting those segments and driving up their probability scores with the most effect per spent campaign dollar or hour of volunteer work. To be fair, this is not at all a new approach, and on a macro level, Gov. Romney’s team did a similar job. They did not spend their media budgets on unwinnable states like California, where media budgets would have had a very low return on investment.

The key is, Obama’s team optimized this targeting technique on the street level, rather than simply the state level.

The movie industry does not have enough data to model its audiences by probability scores in a differentiated way. It concentrates parts of its campaigns to reach particular audience segments like “moms” or “gamers” where the strongest affinities are supposed. But this is done in a very undifferentiated way — and more importantly, it does not have any feedback process to ensure the effectiveness of these more targeted parts of the overall campaigns.

Start Early

With a thorough, data-centred approach that monitors the market in an elaborate way, marketing becomes a metric-driven feedback loop that can be used to hone in on the right positioning, and to deliver the most effective message to different market segments.

The key in this process is to begin early. Collect data as soon as possible, derive hypotheses from early population samples, test those hypotheses against broader populations, optimize, and verify the hypotheses. The value of early iterations of campaigning is not to win early votes but to gain data insights that can be used for the following campaign iterations.

Although the numbers were not accessible at this time, one would safely assume that Obama spent bigger portions of his budgets earlier than Romney, moving away from a “Total Awareness Buyout” Strategy one or two weeks before election towards a more engineering-typical approach of Agile Advertising.

The vast majority of the marketing budgets in the movie industry are spent within two weeks before the theatrical release. While that is understandable — and probably similar to Obama’s timing of spends, it means that by the time the big chunks are invested into TV advertising, the marketers do not yet have a clear picture of their most important audience segments and on the best technique to win them on opening weekend.

By spending a bit earlier movie studios would enter the metric-driven feedback loop earlier and could improve their market understanding. Every penny spent months before release would — in a data-driven approach — make every dollar spent closer to release more efficient and effective a thousand times over.

You can also follow me on Twitter here.

This article was first published at TheWrap.

Some great articles about Obama’s nerds and the influence of big data and tech on his campaign 2012.

One year older, one year wiser

posted on Nov 27 2012

We recently celebrated our one year birthday and we couldn’t be more pleased with how we’re rounding off our first year.

We’re happy to announce we just passed 10 million fans across our Facebook pages and we now have over 1,000 movies and TV shows on Moviepilot. Both huge milestones for us to end a great year on.

We’d like to thank you all for joining us on this journey. We have much more on the way, particularly in terms of empowering you to get even more involved on Moviepilot. In the mean time, get stuck in discovering a ton of upcoming movies and we will see you soon!

Onwards, Moviepilot

Don’t diss me!

posted on Nov 26 2012

In our last two posts we concentrated on proving that increasing PTAT has a direct effect on the organic reach of your Facebook page. In this and the next update, we want to analyze the second value that now has an effect on the Edgerank algorithm since the change in September 2012: negative feedback. In this post we will show that this value is relatively unlinked to organic reach. Next time we will show how you can improve your frequency by careful monitoring of negative feedback. In a third and last post on this, we will give our opinion on whether the usage of this value drives a better user experience.

by Jon Handschin
Co-Founder moviepilot.com

There has been a heated debate on how negative feedback affects Facebook pages performance since the algorithm update. A central piece was the article “Killing rumours with facts” by Techcrunch writer Josh Constine. Summarizing his position, he argues that the reduction in organic reach experienced by many pages in the last week of September is due to the roll out of the new negative feedback feature on the news stream rather than based on Facebook’s intent to push the promotion feature in an attempt to make page owners pay for additional reach. While Techcrunch’s article concentrates on the reason why the organic reach is decreasing, we instead want to understand which values are driving this change in order to gain insight on how page managers can improve their page’s performance.

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The Story So Far

posted on Nov 15 2012

We founded Moviepilot with the goal of helping movie fans find great movies, and helping those great movies find their true audience. As filmmakers ourselves we understand the importance, and difficulty, in finding and engaging the right audience for a movie. As true cinema geeks, we also understand the influence fans have in helping a hidden gem turn into a surprise success.

The dedication it takes to stay ahead of the game as a movie buff is no easy feat. So, we created a platform that delivers all the movie information you are interested in from the upcoming movies you want to hear about, and gave it a beautiful home on the web. We believe that the magic of cinema can only unfold if a great movie meets its great audience. We’re here to help make that magic happen.

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Just in: TV Shows

posted on Nov 13 2012

Greetings, friends!

We recently crossed the thousand movie mark, meaning that we now have over a thousand movies waiting to be discovered and followed on Moviepilot. As we continue to grow towards our goal of being the world’s best movie community, today we are excited to announce we have a new area of discovery to be explored: TV Shows!

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KEEP TALKING!

posted on Nov 8 2012

This is our second post in our Facecook Insights Disclosed Blog. We share ideas, insights and thoughts derived from running 13 pages around movie topics on Facebook with over 10M likes. Last time we analysed whether or not an increased impact of PTAT (People Talking About That) really led to a better user experience. We formulated some doubts, at least from certain perspectives such as those published here. Today we will take a closer look at the interaction between the PTAT value and Organic Reach.

by Jon Handschin
Moviepilot Co-Founder

Facebook wants us to understand the importance of PTAT and Organic Reach. PTAT again being the amount of times in a 7 day period somebody comments on one of your page posts, shares or likes them, thus creating a story about you in the respective user’s newsfeed. Whereas organic reach is the amount of individuals from your fans that actually see at least one of your page posts within a 7 day period. In contrast to organic impressions, in organic reach each fan your posts get displayed to will only count once a week, even if more of your stories are being shown to that fan. You can tell how much Facebook stresses the importance of these two values at first glance in your Insights section. PTAT and Reach are the first and foremost curves displayed to you there. The central question for you as page manager should be: “Can I drive the organic reach of my page by increasing PTAT? And if so, what effect do I expect?” In this blog entry we come up with some answers to these questions.

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November news

posted on Oct 31 2012

Hello Pilots,

November brings with it some new features that we’re rather proud to show you. First off – Profiles. Your Moviepilot Profile features all the movies you follow and gives you your very own home on Moviepilot. To register your username (and thus, your profile URL) head to moviepilot.com/settings. Friends, fellow pilots and curious onlookers can check out your profile at moviepilot.com/USERNAME. We’d love to hear what you think, so please do let us know.

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Facebook Insights Confidentials

posted on Oct 31 2012

Hello Facebook Mavens, Social Media Marketeers, Friends.

We here at Moviepilot are strong believers in the Facebook platform. Currently, we operate 12 pages celebrating upcoming movies from Superheroes to Horror; Fantasy to Legendary Directors and through them we are connected with 9 million Likes in the U.S. today.

We are thankful for the opportunities that Facebook has given us during the past few years. There would not be a U.S. version of Moviepilot if it hadn’t been for the simultaneously influential and disruptive impact Facebook has had on the web overall.

But we also feel that there is so much more potential out there for case studies and business scenarios, if only so that the knowledge about content strategies and their effects on KPIs on Facebook is more readily available to a wider audience.

This is why we want to disclose our insights and the lessons we’ve gained from our daily work on Planet Facebook with you.

We hope you enjoy and we’re thankful for every note of encouragement or criticism you throw our way.

All the best,

Jon Handschin
Co-Founder

THE TIMES THEY ARE A-CHANGING

posted on Oct 30 2012

Changed Edge Rank I: Why “Talking About” is not “Engagement” and why this ultimately leads to a deteriorated user experience

We have wanted to publish information on our Facebook insights for quite some time now. But the changes made to the Facebook Edge Rank algorithm in the past two weeks have also altered everything we wanted to talk about. So in this note and in the consecutive ones we will only concentrate on the affected changes and what page owners can do to anticipate them.

The Edge Rank Algorithm determines which story makes it into your personal news feed. You may recall that your feed is not sorted chronologically by default but according to relevancy. When this was introduced a couple of years ago there was an uproar in the community, but pretty quickly everybody seemed to appreciate that Facebook was fishing out those cat pictures from old high school classmates you hadn’t talked to in years and instead highlighting stories from people closer to your daily life.

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The Rise Of Superheroes At Cinemas

posted on Sep 18 2012

Ever since the release of _Spider-Man_ in 2002, superhero movies have been feverishly churned out and dominated the box office. The majority of superhero movies are released for the summer blockbuster season and attract huge numbers at the cinemas.

The biggest box office draws have come from America’s largest comic book publishers, Marvel and DC Comics. Marvel has brought us Spider-Man, Iron Man, Captain America and The Hulk, among others. DC Comics has given us the likes of Batman, Superman and the Green Lantern.

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