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Social’s Impact on TV Still Small, but Growing

by MarketingCharts staff
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Among the study participants, 6% said they watched a new show because they had seen something about the show on social media, compared to 1.5% who reported watching any show due to a social impression. Still, traditional commercials and promotions on TV bring in far more viewers: 31.5% counted these traditional promos as a reason for watching a new show, and about 5% pointed to them as a factor in watching any show.

A study released early last year by TV Guide similarly found social impressions having far less of an influence on TV program decisions than ads.

Interestingly, the CRE study shows that among the 37% of participants who interact with social media about TV on at least a weekly basis, they’re as likely to do so while not watching a show as while watching. (The 37% was split into: 12% who interact while watching; 12% who interact while not watching; and 13% who do both.) The potential for social media to drive sustained conversations about TV before and after programming could have significant ramifications for program engagement and ratings. (Nielsen has already found a correlation between Twitter buzz about live TV and TV show ratings.)

The CRE study unearths a series of findings, some of which are listed below:

  • Smartphone and tablet owners are more likely to interact with social media about TV than the average study participant, a fairly unsurprising result considering that many consumers are using their mobile devices in front of the TV.
  • Sci-fi, sports and news shows overindex other genres in social media chatter both concurrent to watching and while not watching.
  • Social chatter about sports and reality shows are most likely to occur during a show, while chatter about comedies and dramas are more likely to occur after the show.
  • The most common topic of conversation on social media while watching a show is discussing what’s happening (55%). Compared to face-to-face communication while watching a show, social media chatter is more heavily slanted towards recommending the show (34% vs. 15%) and reminders about the show (23% vs. 12%).
  • TV “super connectors” (who communicate about characters on shows they watch, follow shows they watch on TV, or follow actors/personalities they watch on TV) number only 12% of public, and are more likely to be female (65%) than male (35%). Compared to the total sample, they’re also more likely to be aged 18-34.
  • On a race/ethnicity basis, Hispanics over-index as TV “super connectors,” while Blacks under-index.
  • “Super connectors” are more likely than the general public to interact with social media about sci-fi, documentary/specials, reality, and comedy shows.
  • 25% of Spanish-language program viewing also includes a social media interaction.

About the Data: The study describes its methodology as follows:

“The research comprised four component studies, employing a mix of methodologies in order to provide a complete picture of social media behavior related to TV viewing.

A quantitative study was conducted, by the Keller Fay Group, among a sample of more than 1,700 adults, ages 18 to 54. Respondents were asked to participate in a two-phase research exercise, first completing an online profiling survey and then a seven-day diary via use of a mobile-app from Nielsen Life360. They used the diary to “check in” and report on content, motivation and engagement levels, every time they came across a touchpoint relating to primetime television or late local news – while they were watching as well as when they were not watching. More than 25,000 “check-ins” were recorded by the respondents over this seven-day period.

Ethnographies were then conducted by Nielsen Life360 among a sub-segment of 200 heavy social-media users who charted their “Day in the Life” interactions via mobile app for an extra seven days, recording social media activity simultaneous with other media activity. To complete the ethnography, a group of 40 respondents from the primary quantitative phase charted all their social media and TV behavior for seven days using video cameras.

The social media analytics phase, under NM Incite, combined listening, analytics and insights to determine volume, trends and topics of conversation relating to nine different genres of TV programming.

The academic team has just concluded statistical modeling to determine the relative impact of social media on program viewing. The team integrated complex, extensive data sets and employed a choice modeling approach to develop additional learning.”