Recently we described an idea called the database of affinity: A catalogue of people’s tastes and preferences collected by observing their social behaviors on sites like Facebook and Twitter. Why are we so excited about this idea? Because if Facebook or Twitter or some other company can effectively harness the data from all the likes and shares and votes and reviews they record, they could bring untold rigor, discipline, and success to brand advertising.
But exploiting the database of affinity won’t be easy. Any company hoping to turn affinity data into something marketers can use will need three things:
Lots of affinity data from lots of sources. The raw data required to build a functional database of affinity doesn’t live in just one place. Facebook controls the most "like" data, recording more than 80 billion per month at last check. But Twitter records more "talking" than anyone else (1.5 billion tweets per month); Amazon collects the most reviews (well over 6 million per month); and Google’s YouTube and Google Display Network have data on how a billion people prefer to spend their time.
The ability to bring meaning to that data. It’s easy to draw simple conclusions from affinity data: If you ‘like’ snowboarding you might like to see an ad for energy drinks. But the real value in affinity data won’t be unlocked until we can find hidden combinations of affinity that work for marketing. That’ll require technologies and teams that can do some serious data analysis — as well as a real-time feedback loop to determine whether people really are interested in the ads targeted to them based on such complex assumptions.
For years, brand marketers have guessed at people’s affinities from the barest of demographic, geographic, and contextual clues. We deduce that Midwestern men prefer pickup trucks and that people watching extreme sports like energy drinks, and then we spend billions advertising to these inferred affinities.
But today, we no longer have to guess. Every day huge numbers of people online tell us what they like. They do this by clicking a ‘like’ button, of course — but there are many other ways people express affinity: talking about things on Twitter and in blogs; reviewing things on Amazon and Yelp; spending time with content on YouTube (and telling us where they’re spending their offline time on Foursquare); and sharing things through both public and private social channels.
People’s rush to post their affinities online recalls another flood of data that began a decade ago: the explosion in online searches. John Battelle once described the data created by search as the “database of intentions,” which I’d define as “a catalogue of people’s needs and desires collected by observing their search behaviors.” In the same way, the result of all these online expressions of “liking” has created the “database of affinity,” which Forrester defines as:
A catalogue of people’s tastes and preferences collected by observing their social behaviors.
The value of Facebook "Likes" is supposed to be clear: My friend likes something, and that is valuable and persuasive information for me. This is the idea behind Bing launching social search — if my friends have liked something for which I'm searching, that will be more relevant and helpful information than just another one-size-fits-all search engine results page. It's also the idea behind Facebook's Open Graph — if you visit a site and see that a friend has "Liked" it, you are more likely to pay attention, spend time, and complete a transaction.
But as we all know, a "Like" (with quotations) does not necessarily signify a like (without quotations). An interesting ExactTarget study demonstrated that people may "Like" a brand for a wide range of reasons: to learn about discounts, to earn freebies, for entertainment, to gain access to exclusive content, and — of course — to show support for the company to others. Just look at the list of companies you follow on Facebook — do you like them all equally? Are there any you've followed even though you really aren't a true fan of the organization or its products? The disconnection between “Like” and like will only grow greater in the coming year, as brands looking to expand their pool of Facebook friends reward new fans and followers (an activity I compared with the “black hat” tactic of buying links in the early days of search engine optimization.)