Recently, I read an interesting article from MIT Tech Review which diagnosed why the social network Friendster failed – An Autopsy of a Dead Social Network.
The research done by David Garcia and colleagues at the Swiss Federal Institute of Technology in Zurich concluded that networks with a significant percentage of participants who had two friends were highly vulnerable to collapse. On some level, this sounds intuitively right. In my research into why social projects fail (Social Projects Require Project Managers to Think Differently), we identified a critical mass of participants as a critical success factor. It’s nice to see some research which backs up the point and explains the observation.
Coincidentally, I also had the opportunity to speak with Remi Kirche from Skyrock.com. Skyrock.com describes itself as a social network of blogs: a blogging solution which includes native communication tools and social features. What I found interesting about our discussion is the way Skyrock.com uses predictive analytics to give value back to its members. They find that people who have friends on the network get more value from the communities they join and they participate more. When someone comes into the Skyrock.com network, they can quickly develop friends. Predictive analytics lets Skyrock.com can see the communities and understand the interactions. This allows them to make recommendations to advertisers about which communities are a potentially valuable market for them.
There are a couple of points worth considering for those people tasked with rolling out social software solutions inside their organizations. Based on our research, we know that adoption of social tools is still a struggle. Simply giving employees social tools and waiting ‘to see what happens’ doesn’t work.
If we buy the premise that gaining active participation in a network is essential for its survival, then we need to help the participants figure out who might be worth ‘meeting.’ It’s quite similar to the real world counterpart of creating ways to ‘break the ice’ at meetings, parties and other face-to-face events. The sooner we get people engaged with like-minded people, the more likely they are to stay engaged and view the event positively.
This seems like an increasingly useful way to employ analytics: use the knowledge of affinity to quickly create connections. Give the network of workers with an affinity (aka work to get done) a place to meet and a reason for meeting. In addition to setting up the meet, use analytics to monitor the health of the interactions looking at measures such as frequency, duration and increase in connections.
I’m looking forward to continuing my research into this exciting topic.
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