news analysis Facebook is now valued at US$15 billion by many estimates. Soon the young company will have to prove it's really worth it.
Next week in New York, Facebook is expected to unveil its new advertising strategy on the heels of an expanded advertising partnership and US$240 million investment from Microsoft. The company will likely talk about selling targeted ads to members, but experts say that to build its own ad serving system, Facebook must figure out how to serve the right ads to the right people in real time.
To prove its estimated value at US$15 billion, experts say Facebook, with its new ad partnership with Microsoft, must figure out how to serve the right ads to the right people in real time.
"That's a very difficult problem at large scale, with so many ads and millions of people," said Greg Linden, founder of Findory and an architect of Amazon.com's early product recommendation and personalization engine. "And the data's not well tied to purchases like at Amazon, or even like Google, where so many Web searches are about products."
The predominant question on everyone's mind is: Can Facebook build an ad system clever enough to keep pace with the passing fancies of its social-networking members? Facebook will have to get really good at processing all of the data it has collected on its reported 49 million members--demographics, personal preferences, and social histories--to predict what advertisements they might actually like and respond in their "news feed" or next to their "wall," according to industry executives.
That's no small task. In fact, it's a massive computing problem and one that very few companies apart from Google and Amazon have mastered. That's why Facebook--a company known for its young, fun culture--has been trying to hire more seasoned experts in so-called machine learning who can develop the right algorithms for a new generation of targeted advertising, people familiar with the company say.
One tech executive characterized the challenge like this: "The company that can process the most data will win." That maxim has proven true of Google in Web search and Amazon in e-commerce sales and product recommendations. Now Facebook must figure out how to take billions of data points about its members and turn that into an automatic ad machine.
A Facebook representative declined comment for the story.
There's no question Facebook is sitting on a data goldmine, with an exhaustive amount of information on people's preferences, backgrounds, and social histories--all given voluntarily by members. Facebook has profiles that include people's favorite music, television shows, books, and hobbies; their job history, education, birth date, and marital status; as well as daily activities, social networks, and interest groups. Traditional ad networks would kill for all that information in one place.
Machine learning is a broad term in the field of artificial intelligence. It refers to developing algorithms that can discover patterns in data and learn from them. Google, for example, has used probabilistic Bayesian models to serve results to data searches based on keywords. With advertising, it's all about matching the right person to the right ad. And on an individual level, that's a tall order.
So some technologists focus on lumping people into groups or types, tracking their typical behaviors in aggregate, and then trying to predict what they might want or do next.
Machine learning in online advertising might involve trying many different techniques on affinity groups to figure out which work best. That's because no one obvious technique is the silver bullet for social networks--no one has solved the problem of serving ads in that setting before.
Many thorny issues can arise, too, such as trusting what people post about themselves. Social networks can be noisy in terms of data, meaning that one day a member can say he broke it off with a girlfriend and change everything about his music and film tastes. On social networks, people are prone to misspellings, random statements, and exaggeration. Also, Web surfers are on Facebook to socialize, network, or be entertained, not to buy something. In that kind of social environment, ads can be ineffective or annoying.
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