Big Data: Applications in the Media Industry

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Big Data: Applications in the Media Industry

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Technology is transforming entire industries, and the media industry is no exception. Advances in technology have triggered the rapid evolution of customers from passive partakers of whatever the media dishes out to empowered, cord-cutting multi-taskers who prefer consuming their media content on multiple platforms---often while on the go.

All of this customer activity creates massive data trails via intelligent video analytics. And as media organizations scramble to cater to new consumer behaviors and preferences, embracing big data analytics will serve as a powerful tool to create competitive advantage.

In a recent article on Forbes.com, Charles Kim and Rasmus Wegener---who both work with Bain’s Global Media practice---discuss how big data is becoming Media's Blockbuster Business Tool.

Here are some key points gleaned from the article that media execs looking to leverage big data analytics should take note of.

The media industry is ripe with rich data

While executives in other industries look to reap value from analytics, the kinds of data associated with the media industry make it uniquely positioned for big data success. Speaking of media execs, the authors state that, “Every song listened to, every minute of video viewed, every online page that is clicked contributes to the mountains of data that tell them what audiences want.” For media, the challenge isn’t about getting more data, the authors explain. It’s about doing a better job at sifting through data and, “…blending together the multiple streams of data---some from new digital sources, others from reliable analog ones---to understand what their audiences like so they can deliver more of it.”  

Big data analysis creates competitive advantage

Being able to understand and predict the media content that audiences want is a major advantage for media companies in a very competitive industry. To illustrate this point the authors refer to the bidding war between Netflix, HBO and AMC to secure the rights to the political drama House of Cards. Although all three networks understood that the show would be a money maker, Netflix, as the authors explain, “brought superior data to the bidding, based on its in-depth and fine-grained analysis of viewer’s habits over many millions of showings.” Armed with the understanding of what types of shows best engaged and held the interest of viewers, Netflix executives were able to base their bidding for House of Cards on data-driven decision making, which, as the authors point out, “…allowed Netflix to make a bolder bid and win the show―as well as three Emmy awards.”

Big data insights help inform new products

The insights gained from big data ad-hoc analysis  can also help media executives make more informed bets on new products. A good use case mentioned in the article is The Weather Company, which the authors explain is in the process of, “building up its capabilities to sell weather data and insights as new services.” One promising new service developed by The Weather Company which could prove to be very lucrative is WeatherFX, which the Forbes authors describe as, “a marketplace service that allows advertisers to correlate their display ads with weather events, based on determining which products are most likely to sell under different weather conditions.”  

Big data hurdles for the media industry

As media giants such as Netflix invest more in analytics talent and come to rely more heavily on data-driven decision making, the authors caution that, “Media companies that lack competitive advanced analytical skills will increasingly find themselves outpaced by the better-informed, quicker business moves of those that excel in analytics.”  

In order to stay more competitive going forward, authors Kim and Wegener  suggest that media company execs need to commit to analytics as a tool to boost business performance. Once that ambition is declared, a chief data officer should be appointed to help orchestrate and implement the big data initiative. Instead of partitioning analytics talent to IT, companies need to seat them close to the decision makers so that analytics goals and business goals stay constantly aligned and that the critical question, “What data do we need to understand to build our business?” remains top of mind.  


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