Representing a strategic about-face, Netflix says it is currently baking Facebook into its entire service -- in large part to encourage a segmenting of household accounts into multiple personal accounts. "We're working on an extensive Facebook integration, which will further the notion of a personal Netflix account," Netflix said in a shareholder letter issued this week. Netflix accounts have traditionally been affiliated with individual home addresses, but more screens per household -- along with more diverse offerings -- gives Netflix the opportunity to mine households for multiple accounts. "When we were primarily a DVD-by-mail service, we measured our market in terms of households," Netflix said. "Households subscribed to Netflix and members of the household watched the DVDs as they wanted ... Online streaming video, however, is more naturally individual, since it is watched on personal screens like phones, tablets, and laptops, as well as on shared large screen televisions." Netflix previously launched social tools, but scrapped them last year after they failed to take off. Mike Hart, previously Netflix's director of engineering for APIs, is now director of engineering for social. "Our long-term goal is to evolve the Netflix service so that it feels more natural to have a personal account," Netflix said this week. "This evolution from household to personal relationship will take several years, and there will always be some households that only have one account." In what may prove a challenge to Netflix's social strategy, a recent study found that the TV watching isn't as "social" an experience as one might assume. Rather, just 25% of consumers expressed an interest in sharing what they watch with friends, according to SideReel, which helps users find content and TV shows online. Going forward, however, that may be the least of Netflix's problems. Its relationship with media companies could soon change when a deal with pay-TV channel Starz to stream movies from Sony and Disney expires. Indeed, Richard Greenfield, an analyst at BTIG research, estimated that the cost of the deal could go up from $25 million a year to more than $250 million a year. Overall, according to research firm Screen Digest, Netflix revenues for 2010 were expected to reach $2.2 billion. Netflix's snail-mail business was expected to account for 35% of disc-rental spending in the U.S. in 2010 -- up from 26% in 2009, according to Screen Digest.
No stranger to high-wattage partnerships, AOL has tapped Heidi Klum to co-create original programming for women. Klum and Full Picture Entertainment, the producers behind "Project Runway," have signed on to create video, articles, blogs, and photo galleries focusing on fashion, beauty, parenting, arts and crafts, and relationships. "Our objective in 2011 is to make AOL the first choice online destination for women," AOL CEO Tim Armstrong said Wednesday. Regular content posted to Klum's home page on AOL will revolve around her and her handpicked lifestyle experts. "Right now, advice seems so scattered online," said Klum. "I have created a place on AOL that's really one-stop-shopping" for female focused content. Today's announcement follows a number of recent acquisitions and partnership deals from AOL including the acquisition of tech-centric TechCrunch, the social software startup Thing Labs, and video platform 5min Media. Continuing to formulate its content strategy, AOL recently appointed Amber Lawson to the newly created position of Head of Video Programming. With a special focus on the company's consumer audience, Lawson is now responsible for procuring, programming and promoting all of AOL's original video content. Earlier this month, meanwhile, AOL and Endemol USA announced a production agreement to co-develop and co-produce new Web programming. Initially targeting female audiences, the partners aim to produce unscripted digital video content, which exploits the Web's real-time, interactive and increasingly social nature. The first series to be produced under the new agreement is "Re-Dressed By America" -- an interactive Web series where online users make over subjects facing a life-changing event, whether it be a high school reunion, a first date, or a sex change. The series will be featured on Stylelist.com and MyDaily.com. Closely aligning video with its overall content push, AOL has aggressively sought to establish itself in the space over the past year. Between July and November, AOL video streams increased from 192 million to 566 million -- an increase of 195%, according to comScore. In particular, "You've Got..." -- the video series that launched on the new AOL.com in November -- generated nearly eight million views in its first month. Appearances by President Obama, Matt Damon, and Kelly Ripa helped the show's cause.
I read The New York Times every weekend. I wake up, retrieve it from my doorstep, make coffee and I read it, glancing up once in a while at the Manhattan skyline. It's a great experience for me. It's not that I can't get similar content on the web instead, or read it comfortably on my iPad, it's just that heft of the print edition reconfirms to me -- in a familiar way established over years of habit -- that someone I trust curated what I read, and by positioning where each story runs, real people are making recommendations on what they think I might find interesting. Moreover if I have someone over, we can share by trading sections of the paper, something that can't happen on my iPad or PC. As much as I appreciate the depth of experience behind each "recommendation" I get from the editors -- as I move away from the homepage or front page of a website where I totally get the value -- and I dive into the various articles -- the recommendations I get are often broad, category-based, and not necessarily targeted to me. That's because they were never meant to be for me to begin with, but rather for the majority of their audience .Not an easy task for anyone. Even for machines. And therein lies the challenge. How can a handful of editors "curate" to satisfy millions of very different readers? Now, on the web as opposed to the print version, editors could potentially go article by article, and match them with links to videos and other articles based on some parameters, and keep fine- tuning those every day. However, that might be costly. Mind you, this is essentially matching what millions of different people want to watch or read after reading every single story. As an example with video recommendations -- on a site with 10 million users, where the average user reads 3 stories, with 1,000 available videos to be recommended per story -- we're talking about a range of 30 billion options of recommendations during users' sessions. A lot. Machines on the other hand, are suppose to be able to make those decisions by looking at what readers are spending time on, what they've read and watched in the past, and instantly recommend other stories or videos they may like. And machines can do this for a hundred readers, a thousand, a million or tens of millions, each individualized to the reader's behavioral patterns, predicting what will most likely engage them to read or watch further. Think of it like a smart TV network, that instead of showing you the same prime-time lineup of shows, serves you up programs it knows you like based on your past viewing history. With the right data, the network could even introduce you to new shows that it has "learned" over time you will probably like. There is big value in having editors put items such as videos in front of you that you never knew you might like. It broadens your horizons and gives the news dimension. It also helps you relate to the brand and the people behind it. So, in a perfect world, the editors and the machine coexist on every site. If you're a publisher and you wonder how to combine the two, here are some tips: (1) You can combine human and machine intelligence in a way that helps add revenue. A nice example is how the New York Times is presenting other videos you may like, and right below it a similar UI of featured content curated by editors, maximizing users' experience. (Disclosure: The New York Times is a customer of my company.) (2) Make sure you're labeling your recommendations in a way that works with users' expectations. If a recommendation is machine-generated, let the user know. (3) Much like the car you drive, aim to get the best machine-learning engine you can for your site. Your users are important. It's easy to disappoint, and it's hard to rebuild trust that is broken by underpowered recommendation functionality. (4) Some machine-learning vendors offer capabilities to bias their recommendations, and input some guidelines. Those can make your editorial team feel more comfortable. In some cases, publishers can allocate certain recommendation-slots and promote videos or categories of their likings. This is usually used if a publisher needs to promote a video they've sponsored, etc. We are at an inflection point in the media world where machines can take over much of the back end tasks that maximize the user experience and result in more revenue for the publisher. But, as I am affirmed every Sunday morning with my coffee and print edition of the Times, there will always be a place for human editorial judgment. Also, on the Internet, editors and machines can indeed coexist.