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Big Data Insights: Defying Gravity

Mar 29, 2016

By: Dennis Kneale

Kneale 3

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.

Credit goes to the machines. Machine-learning algorithms are helping MTV and its siblings, Comedy Central, VH1, Spike, and others, show their promotional ads to the “right” viewers (the most impressionable and like-minded). They target new recruits with better aim and achieve broader exposure by mixing and matching the right networks and shows. Overall, they’re able to reach more viewers while using less ad inventory to do it, and they’re achieving some stunning increases in “conversion rates”—the portion of viewers who see a promo then tune in to watch the promoted show.

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