Think clusters, not networks, when you’re out to mine the social graph.

When marketers look at the social Web, they do so from altitude – not ground level.  So it’s natural for them to think about it in monolithic terms, because they’re accustomed to delivering monolithic results, right?  But in mining the social graph, that complexity of interlaced networks and sub-networks, they’re better off playing small ball, and looking at the small picture.

The social Web isn’t (obviously) made up of a single, unitary body of users, but it’s infinitely fragmented.  It’s personal, so those relationships are personal, and woe to the marketer who tries to trod on them too blatantly.  It’s actually comprised of clusters — separate micronetworks, individuals who may be grouped around a single anchor or strong influencer.  These clusters not only overlap a lot, but overlay each other: just think of the relationships in your own life on the social Web, and how those relationships are often interwoven with each other, or entirely separate.Chart B

These clusters can be big or small, linked strongly or weakly.  But there’s a commonality of some kind that brings them together, and that glue is the first critical element you’ve got to discern in order to succeed in reaching them — not just what it is, but how strong, how influential in their lives, how approachable?

To target users with really relevant messages, then, and drive response that’s more authentic and engaged with your brand than hitting the “Like” button, marketers need to dig down into this kind of data, and use the best available analytics to get at what some pundits call the Profile of Revealed Preferences, among other terms.  That involves not just their connections, but their preferences and behaviors, too — an aggregate picture of how they act, not just who they like acting with.

Spread the word: