Big Data: Defying Gravity

March 31, 2016 Defying Gravity How MTV uses machine-learning algorithms so its promos will reach more viewers in a fragmenting audience. By Dennis Kneale Network television is at risk of getting caught in a vicious cycle. As the audience fragments in a million different directions, smaller subsets of that audience see promos for new shows. Then, as new shows draw smaller crowds, even fewer viewers see promos for other programs. The reach of television networks (the total number of viewers who watch for a minute or more once a day) is down a daunting 12 percent in one year. Yet a six percent larger audience has seen the promos for MTV's Viacom networks—even though they're using fewer spots. That is a stunning gap of 18 percentage points. "When network reach is down, increasing your reach six percent is actually a huge accomplishment," says Oktay Arifkhan, senior VP of analytics and measurement science for Viacom Media Networks. READ MORE» Related News Big data projects on the rise (but data use could be better)» Big Data and the Future of Business» How The Super Bowl Uses Big Data To Change The Game» Big data is the new ground game: How Ted Cruz won Iowa» Cloud, Mobile, And Big Data Analytics Drive Latest Rounds of Megadeals»

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