A rainmaker with klout

One from the archives: circa 2010

I was coerced a few weeks back into watching the local reality TV show "Celebrity Apprentice" and I thought I would spend a few minutes sharing my observations.

I don't normally find the prefabricated entrepreneurial antics of this genre of reality TV particularly appealing however it did provide me with yet another insight into the disconnect between the overriding mythology of the So.Me zeitgeist and the reality of So.Me influence.

Here for all to see was the value of the perennial Rainmaker with a Mobile vs the Facebook Celebrity. Publicist Extraordinaire Max Markson vs Reality TV celeb: "Polly from the Block".

Needless to say Max and his mobile proved to be a significant competitive edge while Polly discovered the value of her 24,000+ Facebook Fans to be negligible when put to the "real time" test of raising money for charity on reality TV.

While Max's mobile delivered tens of thousands into the coffers Polly's virtual fan club delivered zilch.

The overriding message was Max and his Mobile = Significant Influence. Polly and her Facebook = Insignificance.

The question then becomes was this Polly's fault or does it simply highlight the fact the whole idea of the value of Facebook and the Social Graph has been significantly oversold? and, if it has been oversold, what does this tell us about the trend towards measuring social influence based mapping, monitoring and measuring social media (Think Peer Index, Kred and Klout)?


What the Celebrity Apprentice Rainmaker experiment clearly illustrated was the chasm in value between the public and private dimensions of the social graph.

The real message here isn't that Polly failed but that the true measure of social influence is to be found in the very private social graph of the Mobile Phone network data records and not in the very public expressions of interest to be found on Facebook and Twitter.

The question then becomes one of why have we overestimated the value of these social networks?

The simple answer to that as we have seen before is we make this mistake of describing the value of these networks using 2-D concepts rather than 4-D concepts.

Let me show you what I mean.

This diagram is the one most social media gurus use to describe the value social networks.


The simple message being that the bigger the social network the bigger the number of connections.

This is the Facebook illusion. Facebook is important because there are 800 million people in the network. It is also the Internet illusion. The Internet is important because there are now 2 Billion people on the network and 230+ Million web sites... and so it goes.

The next stage in the story is of course the simple fact some nodes in the network are more influential than others.


When you compare the two diagrams one thing becomes immediately apparent. The level of noise (i.e. Connections) is significantly reduced because of the influence of the super nodes. Theoretically the super nodes make the network more efficient. Hence the simple idea that if you can map the network, explore the patterns and monitor the influencers you can predict and perhaps even influence the network.

The problem of course is any study of how messages become viral on the Internet suggests there is no correlation between the involvement of the super node and the ignition point where the message goes viral. Indeed some studies suggest the super nodes are rarely early adopters of the viral trend.

Put simply super nodes may make the network appear more efficient on a point to point basis but they don't necessarily help the network become more efficient as a mass communications platform.

This raises the question of why?

I would suggest the answer to that question can be found in the way we interact with the network (i.e. The Network Experience). As I have explained elsewhere we tend to describe our network experience in terms of navigating patterns where in actual fact our network experience (i.e. our Network Reality) is that of navigating lists from which we bounce backwards and forwards to explore messages.

The original list engine was Google. Facebook, Twitter and the other So.Me properties represent the next step towards a world of "real time" information flow in which these lists scroll and updated 24/7(i.e. More News Ticker than Web Page).


When we compare the original diagram of the hyper connected social network with the endless real time list we discover very quickly that the introduction of time into the network reality significantly distorts the size, shape and purpose of the network. What appeared to be a noisy network in the first diagram of 12 friends busily interacting becomes a real time feed in which each node is monitored and acted upon in isolation as a unique message as opposed to a relationship.

This in turn means the value of "sending" node to the monitoring (i.e. receiving) node has less to do with its connections than with the message being sent. This in turn help to explain why messages can go viral in the absence of the super nodes. It is the message that is primary to the activity not the referring source (This is not to say the source is with out trust value). This of course casts significant doubt on the value of influencers across the network (and by extension the activities associated with mapping and monitoring the social graph) and surprisingly repositions the message, and not the influencer, as being central to the networked media experience.

In a endless real time list the key differentiator that determines influence becomes frequency rather than reach (i.e. connections).

It also goes someway towards explaining how 24,000+ Facebook Fans translates, unfortunately for "Polly from the block", into zilch.

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