{"id":5010,"date":"2017-04-05T11:53:46","date_gmt":"2017-04-05T15:53:46","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/legendary-applied-analytics-making-movies-the-moneyball-way\/"},"modified":"2017-04-05T11:53:46","modified_gmt":"2017-04-05T15:53:46","slug":"legendary-applied-analytics-making-movies-the-moneyball-way","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/legendary-applied-analytics-making-movies-the-moneyball-way\/","title":{"rendered":"Legendary Applied Analytics: Making Movies the \u2018Moneyball\u2019 Way"},"content":{"rendered":"

Hollywood believes more and more in big budget \u2018tentpole\u2019 movies, and Legendary Pictures is one of the group, making many blockbusters such as Jurassic World ($1.7 billion box office revenue) and two The Dark Knight movies ($2.1 billion revenue in total). Legendary may spend up to $100 million promoting a new movie, a process it goes through four to eight times a year[i]<\/a>. Big data and analytics became a secret weapon for Legendary to improve its economic performance in this risk-undiversified sector.<\/p>\n

In 2012, Matt Marolda sold his sports player analytics business Stratbridge, which established six months before the publication of book Moneyball and focused applying the same analytics to predicting future performance of athletes to XOS Digital, joined Legendary building and heading the firm\u2019s Applied Analytics team.<\/p>\n

 <\/p>\n

Value Creation and Capture<\/strong><\/p>\n

Marolda\u2019s team uses deep analytics against multiple data sources to inform decisions from choice of actors, content of trailers to media buying.<\/p>\n

First of all, the team assembled a database containing information including name, email address, demographics, interest, social media activity and movie viewing and game playing histories from different sources, such as Twitter, Wikipedia, blogs and vertical websites\/platforms. The group surveyed samples from this database and scored them for their interest in different genres of entertainment. Then they analyzed what predictors in the database could best explain these scores and applied the result to score the whole database.<\/p>\n

This proprietary database helped the team to micro-segment potential customers to thousands of groups, accordingly deploy different combinations of promotion elements on different media (majorly digital), and continuously monitor, test and optimize results on a daily basis during a movie\u2019s campaign window.<\/p>\n

By doing so, the Applied Analytics team made As Above So Below, a low budget thriller film, Legendary\u2019s most profitable movie in 2013. The big data and analytic strategy also helped mitigating losses, for example, the team successfully predicted the disappointing box office revenue of Blackhat, and thus Legendary timely cut media spending before movie release and saved cumulatively over $20 million.<\/p>\n

 <\/p>\n

Competitive Advantages<\/strong><\/p>\n

The conventional movie production is done by alliances of relatively small independent producers who brought together stuff on a project-by-project basis, though ordinary audiences can only recognize names of the six dominant studios (Warner Brothers, Walt Disney, NBC Universal, Sony, Fox and Paramount), whose role is financer, marketer and distributor. And the studios\u2019 standard marketing approach is to concentrate spending most of their budget 6 weeks before the opening weekend, of which the box office revenue is proved to be as an indicator for ultimate revenue performance.<\/p>\n

Two major problems exist in this conventional practice. Primarily, customers usually don\u2019t buy tickets till they go into cinemas for movie watching, this is especially true in the U.S market. By then, most of the promotion budget was spent by the studios, thus too little could they do to change ultimate performances.<\/p>\n

Furthermore, movie marketing budgets in the US tended to concentrated in non-addressable traditional media such as TV and radio, which makes it difficult to measure marketing effectiveness and make timely adjustment.<\/p>\n

US Marketing Budget Allocation<\/p>\n\n\n\n\n\n\n\n
\n

Trailers<\/p>\n<\/td>\n

5%<\/td>\n<\/tr>\n
Television<\/td>\n60%<\/td>\n<\/tr>\n
Internet<\/td>\n10%<\/td>\n<\/tr>\n
Other Media<\/td>\n10% (includes radio, magazine, billboard)<\/td>\n<\/tr>\n
Others<\/td>\n15% (market research, publicity, creative services)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Data source: HBS case: Legendary Entertainment: Moneyball for Motion Pictures, John Deighton, May 2016<\/p>\n

 <\/p>\n

Compared with the conventional approach, Legendary applied analytics strategy and tactics is a source of competitive advantage for the company. In January 2016, the Chinese investment group Dalian Wanda acquired Legendary, presented greater opportunities for the team to play in more markets. The industry believes that the analytics capability is an important strategic rationale behind Wanda\u2019s acquisition. Moreover, thanks to the alliance between Wanda and two internet giants in China, Tencent and Baidu, Legendary is able to access huge amount of high quality data of Chinese customers. The application of big data and analytics contributed a lot to Warcraft\u2019s success in the China market, according to Marolda (when visiting HBS).<\/p>\n

 <\/p>\n

Challenges<\/strong><\/p>\n

Even though Legendary is ahead of competition, it is still facing challenges. One major challenge is that in the U.S. market, due to above noticed ticket purchasing behavior, there is limited transaction data that can be collected timely. The Applied Analytics team can only optimize on something less than purchase, aka referral data rather than descriptive data. And this constraint poses a discount on the team\u2019s work.<\/p>\n

What\u2019s more important, Legendary still needs to deal with disappointing box office revenue for movies like The Great Wall. This reveals the fact that movie production and marketing is essentially a matter of art and science, in which data and analytics can only go that far. In this sense, big data and analytics is not a magic mirror that can answer every question.<\/p>\n

\n

Related reading:<\/p>\n

https:\/\/www.bostonglobe.com\/business\/technology\/2016\/03\/31\/making-movies-moneyball-way\/Uzgwh2cdGthA1N3nZHqz0N\/story.html<\/p>\n

\n

[i]<\/a> http:\/\/data-informed.com\/big-data-takes-a-star-turn-at-legendary-entertainment\/<\/p>\n","protected":false},"excerpt":{"rendered":"

How big data and analytics can change the traditional movie industry, and how far it can go?<\/p>\n","protected":false},"author":1049,"featured_media":5012,"comment_status":"open","ping_status":"closed","template":"","categories":[134,29,1020],"class_list":["post-5010","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-analytics","category-big-data","category-movie"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-and-analytics-as-digital-assets\/","yoast_head":"\nLegendary Applied Analytics: Making Movies the \u2018Moneyball\u2019 Way - Digital Innovation and Transformation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/legendary-applied-analytics-making-movies-the-moneyball-way\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Legendary Applied Analytics: Making Movies the \u2018Moneyball\u2019 Way - 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