Cover Story: What’s On?
TV Techs Solve Viewers’ Oldest Angst With Recommendations
By Todd Spangler -- Multichannel News, 5/10/2010 12:01:00 AM
Talk about a one-sided affair.
Each month, Americans spend upward of 150 hours gazing longingly at their TVs. Yet most TV services, surprisingly, don’t provide any feedback to their human companions. They just sit there passively, obediently changing channels on command.
Until now. Pay TV services are starting to get smarter about suggesting movies and shows to viewers. Dish Network, AT&T and Verizon Communications are among those pursuing new automated recommendations today, and cable operators are actively exploring the technology as well.
For the most part, they’re following the lead of other services — TiVo, Netflix, Apple’s iTunes Store and Amazon.com, to name a few — which have been recommending video selections based on someone’s preferences or order history for years.
TV providers are banking on the fact that an intuitive guide, like a great concierge, can induce you to watch more TV. And that can affect business in several ways: by cutting churn, differentiating services, driving up revenue from video-on-demand purchases and upselling programming tiers.
The need for more personalized recommendations is evident, considering VOD libraries are already approaching 20,000 titles and could grow five times as large. Add to that the prospect of millions of Internet video clips accessible on TV, and it’s obvious subscribers need a new tool to find content.
“If you think about it, for someone to say, ‘Gee, there’s nothing on TV tonight,’ is kind of nonsensical,” said Mark Hess, Comcast’s senior vice president of advanced business and technology development. “Especially on the VOD side, as we increase the number of titles, recommendations become more and more important.”
As simple as the concept is — recommending a title — the execution can be surprisingly difficult. For starters, set-top capabilities are limited. Meanwhile, several people in a single household often use the same account, diluting the effectiveness of a personalized suggestion. “The recommendation might not really reflect what the person who’s sitting in front of the TV wants,” Hess pointed out.
Privacy issues may trump recommendations. Some MSOs, under their existing subscriber agreements, may not have the ability to monitor a viewer’s habits and then provide recommendations.
Dish Recommends feature
And some viewers say the
idea of a company tracking their
choices — and saving them — is,
well, creepy. When TiVo first introduced
its recommendation
service, the running joke was “My
TiVo thinks I’m gay.”
Nonetheless, pay TV operators need to do something on this front to keep TV services relevant and valuable. “We get stuck in this ‘paradox of choice’ when we’re presented with massive numbers of options,” said NDS Americas sales director Paul Ranger.
By the end of 2010, several cable operators will have implemented recommendations with VOD the first target, predicted Corey Ferengul, Rovi executive vice president of product management and marketing. “One of the big ways of driving more VOD is telling people what’s available,” he said.
Content recommendations fall into four main forms: based on content (“if you liked Avatar, you’ll like these shows”); based on popularity (“here are the top TV shows being watched right now”); based on user profile or behavior (more tricky — see the aforementioned TiVo gaydar); or through social networks (e.g., “most of your friends love The Closer”).
Several technology companies are pitching solutions that make smarter TV recommendations to viewers, including Rovi, Jinni, ChoiceStream and ThinkAnalytics.
Each offers a different take. Jinni — whose content-discovery system was voted “best product idea” at the CableLabs Winter Conference 2010 in February — groups content by genre, which is one of the most typical ways to organize content. But the startup’s system goes well beyond that, providing additional linkages and recommendations based on the emotional attributes of a movie or TV series (like “mind bending,” “gloomy” or “race against time”).
“You can create for yourself a semantic genome that shows you exactly what you like,” Jinni CEO Mike Pohl said.
Initially, most operators are opening the door on content-based recommendations and those based on aggregated-viewing metrics.
Comcast is kicking around the idea of introducing an interactive TV application, based on CableLabs’ Enhanced TV Binary Interchange Format specification, which would display the top 10 shows being viewed at the time or suggest content related to the TV show or movie a subscriber is watching. “I think it’s better to have the wisdom of the crowds rather than trying to predict what an individual viewer wants,” Hess said.
Dish Network in April debuted a recommendation feature with the launch of its ViP 922 Sling-Loaded digital video recorder.
The system uses viewing data from Dish’s 14 million subscribers to determine four related TV shows and movies, airing within the next nine days, and provides recording options. “The business goal is definitely to get a stickier service, to give customers an easy method to find the shows they want,” said Keith Gerhards, director of software engineering at EchoStar Technologies, which supplies Dish with set-tops and related software.
It took time to refine the Dish recommendation filters, which are updated once per day. EchoStar also found that the system needed to be contextually aware — so as not to make an inappropriate suggestion. As Gerhards put it, “We had to make sure that only G-rated content gets recommended with G-rated content.”
The “Dish Recommends” feature uses a filtering algorithm developed by Cambridge, Mass.- based ChoiceStream.
AT&T’s U-verse TV also employs ChoiceStream for its VOD “top picks” feature, based on a subscriber’s past video-rental history. For example, if you’ve rented comedies or children’s movies in the past, the app will recommend the latest titles in those categories.
The telco’s app also provides a list of the top 10 on-demand titles U-verse TV customers are renting and the ability to rate a movie you have previously rented, which is averaged into other U-verse TV customer ratings and displayed next to the title in the on-demand recommendations list.
VOD recommendations encourage consumers to buy more on demand and are effective at converting non-VOD or free-VOD subs into paying customers, said ChoiceStream chief technology officer Mike Strickman. In the first year after recommendation features are deployed, an operator should expect VOD revenue and usage increase around 10%, according to Strickman.
“We have really tried to focus on the transactional applications generally because the revenue you’re generating is measurable,” he said. “It’s harder to tell how much impact you’re having on the subscriber-retention front.”
AT&T also uses ChoiceStream to deliver personalized upgrade offers. The telco’s promos select from around 50 shows—which are available only in a higher-priced programming package— that an individual subscriber would likely be interested in given his or her past viewing history, such as Showtime’s The Tudors or HBO’s True Blood. Strickman said the personalized offers are up to five times more effective than generic upsell offers.
Verizon, for its part, offers “Recommendations for You,” which provides personalized VOD recommendations for customers based on programs they have previously viewed. Its “More Like This” feature recommends VOD titles that are similar to others recently selected by a customer, and a “What’s Hot on FiOS TV” widget lists the most popular programs and VOD titles currently being accessed in a viewer’s ZIP code.
But there are stumbling blocks for many cable operators. Set-top box platforms may be one of the biggest gating factors.
“There’s no place in the interface today where recommendations exist,” said Brian Kahn, director of engineering for VOD vendor SeaChange International. “It tends to take a long time to redesign the guide, and get tested and deployed.”
Changing the interface in a set-top guide is a much bigger project than adding a new feature to a Web site, Strickman said. “Web development is pretty mature, whereas these set-top stacks are much more proprietary, more complex, running on devices that have very limited resources,” he said. “And politically, there are all the decisions about what goes on the set-top.”
In addition, if an operator were to start recommending content from a linear channel, that could create conflict with programmers.”Convincing NBC or TNT to make suggestions based on their programming to drive viewership to another channel is going to be tough,” ActiveVideo Networks senior vice president of marketing Edgar Villalpando said.
And privacy looms large on the content-recommendation front. For the most part, providing recommendation features based on personal data can be delivered on an opt-in basis and fall within accepted use, said David Jacobs, chief technology officer of Amdocs' broadband, cable and satellite division “Most subscribers do trust their service provider.”
Another tough problem for TV content recommendation is that television programming is harder to analyze than movies. Let’s say you religiously follow the Chicago Cubs. That doesn’t mean you would necessarily have any interest in, say, watching a Mariners- Royals game.
“Just because I like sports doesn’t mean I should record every sports program,” Ferengul said. “Movies have kind of a known criteria.”
Questions abound: Should TV recommendations cover only primetime? How are news programs recommended— should potential political slant be factored in? Is The Daily Show With Jon Stewart a news show or a comedy show?
What’s also difficult about TV is that often the newest content is the most relevant. But if it’s brand-new, it won’t have any viewing metrics yet, Strickman noted. To address this, ChoiceStream’s recommendation algorithm takes into consideration metadata such as actor, genre and release date for a piece of content that has not been rated.
Perhaps the most vexing challenge is how to handle multiple viewers in a household with different likes and dislikes. This week, Cox Communications takes the wraps off Trio, its new interactive program guide that, among other features, lets up to eight different family members set up their own preferences. The initial release of Trio, however, doesn’t include any content-recommendation features.
The multiuser-rating problem has been around for years. TiVo, on its customer-support site, provides this notice: “If some TiVo Suggestions do not reflect your preferences, it is possible that other members of your household are pressing ‘Thumbs Up’ on shows you do not care for, and vice versa.”
Even Netflix, which runs the granddaddy of video-recommendation engines with a database of more than 3 billion ratings, hasn’t cracked the code on accounting for different tastes of people in a single household. “Anything that comes highly rated with Adam Sandler, I know it’s a rating from my teenage daughter,” Netflix vice president of corporate communications Steve Swasey acknowledged.
It’s important to keep in mind that recommendations — while increasingly vital — are one of several tools to promote content. In Demand Networks is using multiple forms of search, navigation and promotion to drive up VOD usage, said CEO Bob Benya. Those include In Demand’s “online storefronts” with up to 20,000 titles available for download-to-own or rent; using Web portals to let subscribers create VOD playlists; promoting VOD assets using a pop-up prompt on a linear channel; and integrating on-demand into linear search.
At some point, smarter suggestions
will find their way to the set-top,
Benya said: “Personalization
and recommendation tools are
very powerful.”
VID-PICKERS
Key video-recommendation players:
ChoiceStream (choicestream.com): Powers recommendation
features of AT&T U-verse TV and Dish Network
Jinni (jinni.com): Content-discovery system uses “moods” and
emotions to recommend movies and TV shows
Rovi (rovi.com): Acquired content-recommendation firm
MediaUnbound in March 2010
ThinkAnalytics (thinkanalytics.com): Uses real-time analytics
and behavioral-modeling techniques applied to linear TV, video-on-demand, games and music
TV Genius (tvgenius.net): London firm’s customers include Sky,
AOL, ITV.com
SOURCE: Multichannel News research
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