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 How Machine Learning Algorithms Ensure Promos Reach More Viewers in a Fragmented Audience

 

 


As the television 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.

Machine-learning algorithms are helping MTV, and its siblings, Comedy Central, VH-1, 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.

Viacom networks are using a “promo optimization platform” developed by RSG Media, resulting in a six percent larger audience seeing promos.  MTV was able to reach 28.3 million people with promos on MTV for the 2015 MTV Video Music Awards, despite the fact that they used 17 percent fewer spots than they did a year ago.  RSG Media’s machine-learning algorithm learns from past successes, but more importantly, past failures, and automatically adjusts to those success and failures when moving forward.  The tool runs through ratings data 24/7, produces a trend report in two days, and adjusts accordingly.  A startling 47 percent of viewers who saw VMA promos across multiple networks went on to watch the awards show, as compared to just 20 percent of viewers who saw a promo only on MTV. 

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