Graph Search’s Dirty Promise and the Con of the Facebook “Like”

Steve Cheney:

The numbers are shocking in magnitude: e.g. over the past several years AmEx actually spent about half of its ad spend on buying likes—tens of millions of dollars. Your friends didn’t just go to the American Express fan page and “like” the company for no apparent reason. They did so because they got something.

Across the board big advertisers were told to spend 50% of their ad buy solely on fan acquisition. This is a dirty little secret in ad agency land. Trust me. I’ve seen it firsthand from the marketer, advertiser and agency side.

One direct effect of all this passive liking is an ugly messy data set with a bunch of implicit signals… that are wrong. What happens when your girlfriend types in “restaurants in San Francisco” into graph search and P.F. Chang’s gets spit out because it’s the most-liked restaurant. Was a bad Chinese chain the kind of serendipity you were looking for on your date? Didn’t think so.

Sure FB places check-in feature is another signal (beyond the like) I get it… but this isolated piece of structured metadata means almost nothing without massive scale and structure. So… someone in your social graph went to a restaurant and checked in. Wow! Stop the presses. Thank you FB for saving my night, I could not have eaten without you…

And basic math backs up how weak FB’s structured data is in spades. At launch yesterday FB claimed that one trillion connections have been made on the network to date. Great, that’s a lot of restaurant recs, right? Uhm, not really…

FB has 1 billion users. 1,000 signals x 1 billion is 1 trillion. So each user has logged on average 1,000 events/photos/places/things etc. IN TOTAL. And that’s gonna somehow predict where I want to eat? Most of that 1,000 pieces of data are your actual photos and friends.