Every month, 46 million viewers in the U.S. generate 153 million social signals about TV shows. That’s 11% of all social activity across the entire web. It’s not all Red Wedding reaction videos and spoiler rants, either; viewers share reviews, recaps, and discuss plot points, contributing to meaningful conversations in a variety of ways. Earlier this year, we took a dive into our data set to map out these behaviors and build a quantifiable link between social and viewership.

How we did it:

  • Collected social data from our sharing tools, which sit on more than 3 million sites and apps.
  • Over the course of 4 months, we built a data set of 62 million unique users generating 612 million social signals about TV series.
  • Layered TV audience segments from Nielsen into our first-party social data set, then segmented TV viewers out by genre, frequency and viewing platform (on-screen vs. digital) and built social profiles on each segment.

What we found:

  • On average 16% of viewers share about the shows they watch – reality & variety shows driving the most engagement from their fans
  • Timing of social engagement varies drastically by genre – for drama, sharing is heaviest after broadcast, while reality sharing is heaviest during the show.
  • Timing of device and channel usage also vary drastically – tablet and smartphones spike enormously during a broadcast, as does Twitter.
  • Social engagement has a strong correlation with higher tune-in rates – users who click on shared content about a TV series are 2.4x more likely to become viewers.

What it all means:

TV viewers are a highly engaged audience across the web, and are therefore a prolific source of valuable social data. This data is actionable in three ways:

  1. It serves as an additional barometer for the success of a series. This is especially relevant given recent trends toward non-linear programming, where viewership metrics are nebulous and hard to come by.
  2. It provides some powerful insights into audience behaviors that transcend viewership, which could potentially inform content, targeting, and messaging strategies for networks.
  3. If used correctly, it can significantly amplify tune-in and is especially effective in driving discovery for new viewers and new series.

Visit this page to download the study.


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