Benjamin Schwarz is an old friend since the early days of IPTV in 2004, and also one of the best professional in digital TV I know. Recognized international expert on converging media and technologies, he bloggs at CTOIC.net.
We often chat about the future of Television: this time we’ve put our conversation in writing, starting by state-of-the art and current challenges of Social TV and Content Recommendation.
Hi Ben, you’re a specialist in the field of Content Discovery, and you’re currently updating a “recommandation engine” study. Tell us more about it: where does the need for recommendation come from?
Ben Schwarz: Together with my colleague Sebastian Becker from thebrainbehind we are putting together an updated guide for operators, to speed up their RFI/RFP processes. It’s all hush hush until it comes out, but anyone interested can always give me a shout or keep an eye on my web site.
The traditional reason that we have been grappling with for years, when it comes to content recommendation, is the ‘Content Explosion’, but a newer challenge is brought by companion screens, multiscreen or the TV-Anywhere user experience, which is linked to the emerging social TV phenomenon. As always, challenge also means opportunity.
The Content Explosion has been with us for well over a decade, at least since the “long tail” became a challenge and an incredible opportunity. It’s no longer newsworthy to note that almost all of iTunes’s millions of items have been sold at least once, or that Amazon’s critical mass of both catalogue and users enable it, unlike brick & mortar shops, to make a significant proportion of revenue from back-catalogue. But when you now look at Netflix’s catalogue on a TV in the US, or for example the Orange VoD store in France, you can only state the obvious: there’s just so much to choose from that the traditional TV navigation paradigms don’t deliver a satisfying user experience anymore (if they ever did). And if you could fix the issue within your walled garden, the OTT offerings will just shift the goal posts again.
For many user situations the goal may simply be to filter out enough of the stuff viewers don’t want, leaving them with a simpler choice.
The biggest remaining challenge for the traditional approach to recommendation is in knowing who is actually in front of the TV. User logon systems just don’t work in a sitting room TV environment. This is where the companion screen may come and save the day for recommendation vendors.
The recommendation challenge changed along with TV usage. Finding what to watch on a tablet in your lap is a different problem to finding something using a gesture based UI on a games console when with friends or on a smartphone in the train. Users are in a different state of mind, with very different interfaces, yet the TV-Everywhere operator has to provide some form of content recommendation consistency. So the recommendation problem is now even more an integral part of the user experience and EPG challenge. It can no longer be treated as a separate feature with a “click here to see our recommendations” button.
But Nicolas, as you like numbers, here are seven strong reasons for an operator to introduce a recommendation engine.
- Breaking through the clutter: Assort endless information to choose from on TV screen.
- Revenue up: Increase usage by having users watch more content instead of browsing.
- Churn down: subscribers that get what they want faster will be happier.
- New customers: nothing beats a demo at the neighbour’s house.
- New revenue streams: some content that was gathering dust is valuable if it can be targeted to the right users.
- Serving all screens: Recommendation offers a good opportunity to provide consistency across multiple devices where the user’s context is different.
- First mover advantage: we are still early enough in the recommendation game and there are some innovations available that will let any operator with the vision and boldness to really differentiate.
How can recommendation engines best fulfill these needs?
Content recommendation solutions are usually classified with technical criteria depending on how they work under the hood. Without going into the details these include approaches like Semantic Analysis, where the recommendation engine uses the information contained in the content metadata to create clusters of similar or related programs.
Collaborative Filtering is the technique Amazon has made commonplace with the “people who like what you like also like this” feature. Other distinguishing features include a declarative approach where the recommendation system uses information that the viewer gave willingly, i.e. “I like action thrillers & sport but I don’t like romantic comedy”.
One of the trickiest techniques to implement in an IPTV setup is Behavior Analysis, where the system learns what you like more from what you actually watch than from what you say you like.
But the most important success or failure criteria are in how the end-solution is integrated so that it:
- Is seamlessly and non-obtrusively part of the user experience, without users having to go to special recommendation pages;
- Simply exploits the available metadata properly before doing anything too sophisticated;
- Fully exploits all available user data (this is usually an OSS/BSS challenge);
- Interacts with other systems like the “Social TV Intelligence” you are working on;
- Is transparent enough for users to understand why a recommendation is made to them;
- Adapts to the screen being used;
- Is flexible so operators can influence the system to promote content with better margins, or for which they have already paid a minimum guaranty.
Note that interacting with OTT and open information sources seems very attractive, but may not apply in all situations. TV usage scenarios always retain some lean back elements, even if a lean forward experience is less rare. Viewers welcoming technology under the hood to simplify navigation won’t necessarily welcome a detailed Wikipedia page to help them choose what movie to watch. Indeed, as we said earlier, one of the main reasons of recommendation was information overload, so be weary of presenting more information to viewers.
Nicolas, you’ve designed a “social TV intelligence” engine Blended TV; could you introduce us to this market?
Nicolas Bry: Social TV is digital interaction between people about television content or their digital interaction with that content, as defined by Futurescape.TV.
In my opinion, Social TV covers 3 main domains:
- Firstly is the domain of Content discovery where the EPG is enhanced with Internet information web sites, and social recommendations through Twitter feed and Facebook. Social reviews nurture social curation (“Social TV essentially makes everyone a curator“), empowering viewers to filter and voice their opinions, and then to participate.
- Secondly there is Participative TV where viewers interact with the program for voting, betting, polling, playing, converse with characters and TV presenter, Live Tweet or Facebook chat, and buy things related to the program.
- Finally the domain of Device and cloud control is where you enable channel flicking from a smart phone, flinging stored or bookmarked content around the home, or the world, one-click options to bookmark or save shows to cloud storage (universal queue).
Social TV corresponds as well to the emergence of the Companion App on smart phone & tablets, making all the connections, discovery, participation, control and giving access to all sorts of TV viewing as well: “broadcast” TV, VoD, catch up, and streaming media.
Smart phones and tablets are called the second screen in this context. A second screen brings many benefits: it is convenient (“big picture on TV, Facebook on second screen”), intuitive, frictionless, personal, and of vital importance to many operators, it is already paid for and can be monetized!
While Social TV meets high usage growth, competition is fierce: more than “50 apps currently socialize your TV“!