Beacon was pulled due to privacy concerns. The goal for Beacon was to create an ad/affiliate network that would allow Facebook to make specific recommendations based on users’ actions off-Facebook. Beacon was a way to capture users’ off-Facebook actions and broadcast those preferences (implicit recommendations) to users’ friends. The problem Beacon hoped to solve was one of “intent”: people go to Facebook to see what their friends are doing, not to research with the “intent” to make purchases (as when searching on Google). Without “intent”, Facebook users don’t click on many ads and CPCs are low. Alas, Beacon’s attempt to remedy the intent problem failed, but that may have been the best thing to have happened to Facebook.
Enter Open Graph
The failure of Beacon forced Facebook to rethink how to solve its “intent” problem and Open Graph is a brilliant if not worrisome solution. Basically, Open Graph allows publishers to put “Like” buttons next to articles, products, blog posts, etc. If you’re signed into Facebook, you can “Like” something on the publisher site and that gets posted on your Facebook page with a link back to the publisher. Users will also see their friends’ actions on that publisher site. Unlike the soon-to-be retired Facebook Connect, your Likes will not only be displayed in your Activity Stream, but also persistently stored against your Facebook profile. Since Open Graph supports semantic markup of objects using RDF, Facebook will know that what you like is a book, song, band, etc. and not just a web page (as of today, the API doesn’t support multiple objects per page). So, the idea is that Facebook learns and stores what you and your friends Like across the entire web. The Open Graph API not only writes this information to your Facebook profile, but also allows a publisher to read your profile’s Likes in order to customize your experience on the publisher’s site. As CEO Mark Zuckerberg explains, this would be pretty useful to a concert site looking to tap into the data FB stores against its users:
“…if you like a band on Pandora, that information can become part of the graph so that later if you visit a concert site, the site can tell you when the band you like is coming to your area. The power of the open graph is that it helps to create a smarter, personalized web that gets better with every action taken.”
Why Open Graph will Succeed Where Beacon Failed
The implications of Open Graph are extremely important. Through user-generated “Likes”, Facebook will become the central repository for your and your friends’ preferences and that information will be used by FB and its partners to make recommendations (sell things) to you on- and, more importantly, off-Facebook. Like Beacon, Open Graph attempts to leverage users’ off-Facebook actions so that FB can be there when the user has the “intent” to buy that concert ticket. But Facebook learned its lessons from Beacon’s failed attempt to (some might say) surreptitiously track and broadcast users’ actions. Unlike Beacon, Open Graph will succeed by giving “control” to users. Namely, the Like button will get users to voluntarily share their Likes with friends. Facebook will then use this information off-Facebook at the concert site when the user’s intent is to purchase tickets. There is no lack of cunning in this Beacon pivot.
It seems Open Graph has all the ingredients for success. Publishers will implement it to generate more traffic and improve monetization. Users will enjoy seeing what their friends Like and will generally appreciate a more customized browsing experience. Furthermore, it’s easy. Users have been trained to click Like buttons all over the web and since these buttons are the lowest-common-denominator contribution (vs. rating, tweet, comment, review, picture, video, blog post, etc.), the barriers to participating are low.
Implications for the Taste Graph
Open Graph may also have implications for sites such as Hunch that provide recommendations (on- and off-Hunch) based on what other people like you enjoy (what Hunch calls the “taste graph“). While less sophisticated (and less fun), Facebook’s Like button is similar to the “Teach Hunch About You” questions that give Hunch the data it requires to make recommendations. Facebook’s clear advantage over Hunch is Facebook’s massive installed base of 500 million users that will attract publishers to implement Open Graph. Nonetheless, publishers should consider the long-term implications of implementing Open Graph for a couple reasons. First, by supporting alternatives to Open Graph, it preserves competition and will help drive continued innovation. Second, Facebook doesn’t have much experience building collaborative filtering systems and it’s not clear whether a simple “Like” system can generate the type of data necessary to deliver effective recommendations (that drive conversions, etc.).
Regardless, Facebook is positioned extremely well. They provide an increasingly compelling product to a huge and rapidly growing user base. While it’s not clear whether Open Graph will be widely adopted, more thought and resources should be directed towards initiatives such as OpenLike and XAuth that could counter-balance Facebook’s awesome success.