It’s worth a read and it sparked off a lengthy twitter exchange.

I think we have a huge talent supply problem in the web analytics industry.

Web analysts are very specialized in terms of their understanding of the Internet, websites, tracking technologies and reporting methodologies. And necessarily so.

There really aren’t that many of them.

Sure, there are plenty of people who have Google Analytics on their blog. And I’m glad that they do. It’s great to have so many people interested in Web Analytics. But there’s a gap between the interpretation and the turning of that data into actionable insight. In fact, many of the things that look easy really aren’t: such as interpreting ‘time spent on site’.

It’s quite another thing to talk about the leap into statistical web analytics. It’s a different world.

Currently, 90% of web analysts are not asked heavy statistical questions. The industry just isn’t there yet.

To be sure: the demand is growing, and will continue to grow.

We already have a talent shortage in web analytics. Baseline web analytics.

The coming revolution, as Peterson puts it, will put unprecedented demand on the existing talent.

I welcome it. I don’t know how many people will really be able to respond to the challenge (immediately).

Markets have time lags.

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.