In this second part of the conversation (part I), Ben Schwarz, CEO, CTOiConsulting, 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?
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.
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 remainrelevancy 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.
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.
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 verypowerful 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.