Social TV metrics: where this path starts

Written on 08 June 2012
By: Emanuela Zaccone

This the first post of the blog. And it is going to be legendary.

Not because of the topic - which I find interesting anyway - but because of the role it is going to have. Since a couple of years, I thought I would have loved to start a blog about Social TV.

Finally I found time and motivation thanks to a wider project that we are going to discover together in the next few months. This post is, one word, the beginning of a path.

Since a path, as a road or highway, needs to be measured, I'd like to start this Social TV focused blog talking about metrics.

In the last years, the main criteria used to measure the impact Social TV has been represented by the Twitter engagement (mentions, reply, hashtags used) observed around a TV shows, sport event etc. While networks began to use official hashtag or started to use these hashtags in order to run contests (as HBO did with True Blood yet in 2010, as I explained here), they also began to create Twitter accounts for their programmes, the characters of their TV series and the networks (or leagues, in case of sports) themselves. Which means that people who watch TV are able not only to talk "around" it but "with" it. Which further means that they are able to produce an interesting and observable volume of conversations which finds a common ground on Twitter, more than of dedicated platforms.

Indeed these often represent just a sort of collector of what comes from Twitter and Facebook, offering also chat systems but mainly focusing on conversations arisen on social media (so does for example HBO Connect). In other cases - just think to Bravo Talking Bubble or Comedy Central Roast (it is impressive to read about the engagement produced by Charlie Sheen Roast) - Twitter conversations become the show itself and actively contribute to build it and its storytelling.

That's why considering mainly Twitter volume of conversations could be an interesting metrics for Social TV, even if not the only one.

In my opinion the best examples in this area are represented by Bluefin Labs and Trendrr activities.

Bluefin Labs is a MIT Media Lab startup founded by Deb Roy. They realized and recently launched a custom product called Signals Brand Edition which aims to measures the Social Media impact of TV ads, along with Signals Networks Edition which simply measures the Social media impact of a show based on the analysis of conversations and engagement around it. 

Their analytics and KPI demonstrated to be so important that could also "save" a Fox Sports marketing campaign. They usually combine sentiment analysis, topics analysis, volume KPIs (such as impressions generated and total of conversations) to evaluate performances.

Trendrr focuses more on the reputation and awareness of TV shows by identifying the moist trended and discussed programmes. It is really interesting that Trendrr takes in count activities produced on: Twitter, Facebook, GetGlue, Miso and Viggle. As Bluefin Labs does, Trendrr offers a customer solution with complete analytics and insights. 

Other websites - such as Mashable and Social TV Guide - publish periodical charts about the most buzzed TV shows.

In general, for many networks the main problem is represented by the necessity of "monetizing" the Social Media engagement and finding the best way to measure it (just give a look to the new chapter of Nielsen vs Comscore history): how much is the ROI of the awareness around my show? How can I transform it in rewards for the TV network?

While - for well comprehensible reasons - TV networks keep worrying about these questions, social TV are changing the paradigm and modifing the boarders of storytelling: they are part of the story and they create aside stories.

Using the correct metrics to identifying them could lead to the creation of better tools and applications to collect and "re-use" these conversations creating a concrete value for networks.

While the "old TV metrics" considered audience as a whole, aggregated group, new TV metrics approach seems more similar to the big data approach based on the observation of complexity, of the greater net of relationships stemming from common interests, or form viewing a TV shows "together online".

I think there's lot more to say about it.

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