A tight group of friends will tend to overlap in terms of product adoption and preferences. Like people clump alike.
I hypothesize that the social graph is partially-fractal. I use the word ‘hypothesize’ because I don’t have the technology to prove it. Moreover, at this point, I don’t think I could write the proof to prove that it’s partially-fractal.
By fractal, I mean that at the most basic level, the individual with a circle of friends, they’re all alike. If you zoom out, treating each group as though it’s a person, they’re all linked together in a similar way, and if you zoom out again, treating each groups of groups…the structure is the same. In other words, the further you zoom out, the same essential pattern bears out. (I could see Maven’s clumping together in some way, even though Mavens might organize in groups of acquaintances – and it’s that pattern that replicates.)
There are times when ‘forward to a friend’ actions are important: intensity plays are one example. If a group of people enjoy wines, frequent talking about wine (and brands) will bring ideas to the front of mind, and I hypothesize that you’ll have a higher intensity of use.
There are times when ‘forward to acquaintance’ actions are important. It might very well be that you’ve achieved 90% penetration within one set of social groups, and you need to leap out.
In a way, the same rules that should apply at the micro-level should be possible at the macro-level. I suspect that there’s a law in there: perhaps a predictable step-function, that could be used to predict market penetration. I wonder if it’s really been embedded all along in our traditional S curves.
The takeaway from all this is that it’s worth considering which behaviours you want to encourage at which times in your customer lifecycle.