The Advent of Social TV: Commercial and Creative Impacts of Using Twitter Activity Metrics Upon What Audiences Now See

Image by Arti Dandhu

“Sensory Overload”, Image by Arti Sandhu

[This post was originally uploaded on July 31, 2014. It has been updated below with new information on December 19, 2015 and then on March 11, 2015.]

July 14, 2014 Post:

Nielsen is a long-established and industry leading firm in measuring, analyzing and reporting upon media deployment, usage and audiences. Their services also include a similar range of sophisticated services concerning consumer behavior and products.

A fascinating new report appeared on their sites Newswire section on June 2, 2014 entitled This TV Season’s Biggest Moments on Twitter that chronologically mapped which US TV shows from September 2013 through May 2014 generated the greatest volume of traffics and postings on Twitter. The categories included:

  • Greatest Reach
  • Most Tweets
  • Greatest Activity and Reach
  • Most Impressions
  • Most Tweets Per Unique
  • Most Tweets and Tweets Per Minute

Each of these data points is clearly explained and includes the names of the shows, their corresponding data generated by these massive amounts of Twitter activity, and the hashtags and handles involved. In a single screen, this data visualization is a terrific example of how to present so much information that is belied by its elegant and informative design.

Moreover, the value of this data must be highly significant in a multitude of ways to, among others, advertisers, entertainment companies, media planners and producers, content strategists and marketers, and demographers in assessing their respective audiences and clients.

December 19, 2014 Update:

For me, the best story told on TV during the 2014 season was – – in a fictional world where “brains” take on an entirely different significance – –  The Walking Dead on AMC in terms of the extraordinary number of tweets about ongoing adventures Sheriff Ricky and the Grimes Gang. This was covered on Nielsen.com on December 15, 2014 in a post entitled Tops of 2014: Social TV.  TWD averaged twice as many tweets as its next competitor in the ongoing series category. As I read scores of TWD tweets on the mid-season finale myself, everyone will miss you, Beth.

March 11, 2015 Update:

We are now experiencing the emergence of what is being called Social TV, a phenomenon where Twitter and other social media traffic and sentiment data and metrics are exerting significant influences upon on-air advertising campaigns, audience perceptions and creative choices. Just to cite another example of this is the contemporaneous two-screen experience audiences can now join on shows such as The Walking Dead.

Scientific support for the relevancy, accuracy and scalability of Twitter data and metrics on individual TV shows continues to grow. Persuasive new evidence was released on March 9, 2015, in a report authored by Nielsen entitled Social TV: A Bellwether for TV Audience Engagement. An informative article on this report was also published in yesterday’s edition of The New York Times entitled Social Study of TV Viewers Backs Twitter’s Claims to Be Barometer of Public Mood by Vindu Goel. I will sum up, annotate and comment on this article and supplement this with a look at the Nielsen report itself. I very highly recommend clicking through and reading both of them in their entirety.

Nielsen used 300 people in a study of their brain activity while they watched eight selected shows in an effort to find the level of correlation to their volume of tweets about the same content. The results showed a significantly close correlation. The Nielsen report contains a concise graph of the data that  visually  charts this point at 79.5%.*

As a result, researchers can now accurately assess the level of a particular show’s “depth of engagement” of its audience with the events as they unfolds on the small screen. Moreover, this is not only for the show itself, but on a more granular level on a scene-by-scene basis. Thus, this study and report reaffirms Twitter’s assertions that its data accurately represents its platform’s real-time engagement of its users in real-time during a show’s broadcast, as well as a show’s “popularity”.

Most importantly, this data and its interpretations can be used to sell ads to advertisers looking to best maximize their expenditures in their efforts to most effectively reach the audiences they are seeking for their  services and products.

Twitter data and metrics can also be used for predicting potential audiences for new shows even before they premiere, according to a report Nielsen released on January 15, 2015 entitled Must See TV: How Twitter Activity Ahead of Fall Season Premieres Could Indicate Success.

Nonetheless, neither Nielsen nor Twitter have addressed the key issue of the degree to which the volume of Twitter traffic actually increase the size of the viewing audience.

Nielson is planning another study to evaluate the impact of Twitter activity concerning TV ads upon the audiences who view them. (Might I suggest starting with this current TV ad about Mountain Dew Kickstart that has been viewed nearly 6.5 million times on YouTube and makes me laugh out loud every time I see it!)

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*   Issues concerning the distinctions between correlation and causation were raised in two recent Subway Fold posts on November 27, 2014 entitled Minting New Big Data Types and Analytics for Investors and then on January 27, 2015 in a post entitled Studies Link Social Media Data with Personality and Health Indicators.

2014 LinkedIn Usage Trends and Additional Data Questions

A very enlightening report was recently published on Forbes.com in May entitled New Research: 2014 LinkedIn User Trends (And 10 Top Surprises) that I highly recommend for its insights, detail and thoroughness. This professional social and networking sites continues to grow its global use base, services, influence and reputation on a daily basis. The particulars provided within this report about the size ranges of users’ personal networks, the percentages of free versus paid account, the average time users spend on the site, awareness of available feature sets, the utility of special interest groups, and other data points illuminate a truly thriving and extensible services.

What is particularly impressive about format and content of this report is the infographic it contains entitled Portrait of a LinkedIn User 2013 Edition. This expertly limns the who’s who and what’s what of the user base. Moreover, it sets into perspective just how unique LinkedIn is among the other leading social networks such as Facebook, Twitter, Google+ and others.

Having viewed many other infographics online, imho, this one receives an A+ from me for its simple and engaging design which belie such a wealth of useful information. I believe that you will learn much about LinkedIn here while simultaneously viewing a clinic on how to create a highly effective visual representation of data.

The data and graphics presenting a distribution of percentages of the relative sizes of users’ networks immediately grabbed my attention. The largest percentage, 25.2% of the entire user base, of first degree user connections falls between 500 to 999. Okay, I’m in there with my personal network, too. What I would further be interested in knowing is whether dividing the total number of second degree connections by the number of first degree connections, would produce an accurate average number of second degree connections among all of a user’s first degree connections. For example, if I have 100 first degree connections and, in turn, from them 2,000 second degree connections, does that mean that the average number of second degree connections among the first degree connections in my network is equal to 20? If this is a valid number, how can a user apply possibly this figure to advancing the size of his or her network, increase their presence and influence within their network, and assist in business and/or career development?

Another data point I am curious about is whether the degree to which one’s personal network on LinkedIn grows buy itself each day worth examining? That is, if you stop issuing or accepting new invitations to join other personal networks for a period of time, how much does a personal network continue to grow by virtue only of your own first degree connections continuing to send and accept invitations? If, for example, a network will increase its daily second degree connections in this manner by 0.005%, how can a user employ this factor in the same set of questions in the previous paragraph?

Finally, I would like to see some more granular data about networks involving particular professions and job titles. For instance, do attorneys as opposed to web designers have, on average, larger or smaller personal networks? As well, would this evidence direct causation or perhaps just correlation in the data?