Once the dust of Social TV hype settles, content recommendation will be changed forever (2/2)

In this second part of the conversation (part I), Ben Schwarz, CEO, CTOi Consulting, and Nicolas Bry, Senior VP, Orange Vallee, continue their detailed analysis of how to harness Content Discovery and Social TV, and introduce the concept of Blended TV.

Ben and I try then to draw some perspectives related the the mix of Social TV and Content Recommendation.

Why is there so much buzz about the rise of Social TV?

Social TV challenges the paradigm of  TV ratings:

  • Nielsen has analysed the relationsghip between social media buzz and TV ratings. It has shown “significant relationship throughout a TV show’s season among all age groups, with the strongest correlation among younger demos (people aged 12-17 and 18-34), and a slightly stronger overall correlation for women compared to men”.
  • Social recommendations encourage interactivity, meaning stickiness to a program, and provide strong user behavior data, that can further processed to target users for advertising purpose and specific offerings.

Some predict an even stronger impact, amending the story telling:

  • It’s a major issue for broadcasters and networks. “The future isn’t either traditional or digital: it’s a feedback loop between the two. It’s how creative we are in engaging those fans – and keeping them connected – that will determine how potent and profitable we will be in the future.” says Kevin Reilly, President of Entertainment, Fox Broadcasting.
  • “Content will then be created with social interaction in mind”, adds Anne-Marie, “the audience will be able to interact with the storyline”.
  • Voting online for some game shows, and affecting the outcome of the show is just a start: welcome to the era of Transmedia!

What innovative way did you choose to design your social TV intelligence engine, Blended TV?

Nicolas Bry: We laid our design on 3 pillars: belief, metaphor, and model following Prof. Nonaka’s framework.

  • Belief = our starting point was the belief that there is great value in social conversations around TV, but that this value is difficult to capture with the tools available to us, especially for non frequent users.Our idea was to filter out the noise so as to enable content discovery in real-time, by providing TV buzz and clean content trends, social TV computed data and metrics, and in-depth sorted conversations across different programs, to give viewers the power to connect with each other and build relationship.
  • Metaphor = Our metaphor was that of a filter, or a funnel.
  • Model = from the outset, we based our approach on collaborative design. Rather than completing an end-user application, we focus our innovation endeavor on a social TV component, an underlying enabling technology, which could be embedded in various end-user applications and devices, letting others make value out of our data and build services on top of our platform through an API.

This component, called Blended TV is a semantic engine scanning social conversations, harnessing comments and filtering them. It was developed within an open innovation  framework: we partnered with a social media intelligence specialist called Mesagraph and benefitted from the precious overview of designer jean-Louis frechin (@nodesignfrom NoDesign agency. We also cooperated with Social TV consultants (Thibault Celier @kindoftvMarc-Emmanuel Foucart) and customer observation specialist (@oliviermokaddem), harnessed accurate insights  (@gip89@_advid_, @laouffir@cgiorgi), and leveraged on HTML5 interactive video skills from Djingle.

Our bet starts to win-back: developed in very short time, Blended TV is currently used or in the process of being used by various applications within Orange (Orange Sports web portal, Rendez-Vous TV / Le Mag TV companion app, Roland Garros app, Orange France web portal), and outside Orange (Broadcasters, TV metrics provider, TV guide).

Is it possible to merge recommendations from dedicated engines and social media? What is the challenge to meet success from a customer point of view?

Nicolas Bry: the main challenge and the main objective of this endeavor remain relevancy and simplicity.

Mixing engine-based and social-media recommendation should bring the best of both worlds to end-users. But we’ll have to respect the specific cultures of each world to provide a straightforward and accurate suggestion.

Furthermore, consumers use a variety of sources to discover what’s personally relevant. Richard Edelman distinguishes 4 main spheres in “Media Cloverleaf”:

I believe the user interface has to screen the complexity of the engine, reflected in the various spheres, and the range of data that could be processed by a recommendation tool, such as program metadata and consumer behavior.

Emma Wells, marketing guru at TV Genius, believes it should “combine the different types of recommendations together and come out with a perfect mix“, presenting a very simple proposal of “what’s up/recommended tonight”, learning to know the viewer better everyday (“the system recognizes me!”) and enable him to refine settings if he wants to engage more.

Cory Bergman founder of Lostremote, a web site dedicated to social TV, speaks of “a smart social guide” displaying 4 kinds of recommendations :

  1. new episodes of shows you customarily watch;
  2. current shows your friends enjoy;
  3. trending shows across the larger population, and
  4. what your friends are watching now.”
Maybe like the four sides of a cube?

I also see social curation as creating an opportunity for a second loop for recommendation, exposing the suggestion to the social network of the user, and starting viralization of the content service … so lots to explore and I certainly think it’s worth testing and iterating!

Ben Schwarz: All the recommendation engine vendors already claim to be implementing social recommendation, but most of that is vaporware so I agree with you Nicolas that there is an exciting opportunity for experimentation. Social TV will probably change the TV landscape forever. However, I don’t yet know if it’s just another feature, albeit an important one, or a real paradigm changing disruption. Issues remaining include the fact that I simply don’t want to broadcast all of what I watch to my whole social network, so I’d say that two key challenges and success criteria are a seamless integration, and a very powerful filtering mechanism.

Who is in front of the TV?” has proven to be an obstacle that many recommendation solutions couldn’t satisfactorily overcome. Social TV has a great side effect: it brings personal second screens into the living room.

The 50 competing apps you mentioned at the beginning of our discussion Nicolas, are all in the early hype phase. But even if they never truly deliver on their fantastic promises of a new social TV paradigm, they will at least enable plain-vanilla recommendation to at last work fully i.e. personally.

Additional readings:

Is there a place for Social TV recommendations?

Social TV latest news

Great Tweets about Social TV

Beyond TV, social TV

The future of Social TV (Mashable)



Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s