How Well Do You Know Your Social Network? Probably Poorly

I had a great time at MarketingProf’s Social Tech 2010 conference today, getting a lot out of every session I was in.  I have a rather diverse client base, so pretty much name the challenge and I have a client dealing with it.  Getting so many different perspectives seriously helped.

The biggest eye opener for me today had to be the session on “Using Social Network Analysis to Leverage the Dynamics of Social Media Interactions Inside and Outside Your Organization.” In addition for winning the award for longest and most awkwardly named session, I’m sure it was also probably the most baffling for anyone trying to follow on Twitter. But the message I took away is that most of us have been flying practically blind when it comes to understanding our social networks, and that’s sure not going to cut it in the future.

Tweets of #futureofbirth by marc_smith created in NodeXL

Talk about a brainy line-up of speakers: Dr. Marc Smith is chief social scientist for the Connected Action Consulting Group and the author of Analyzing Social Media Networks With Node XL. Lawrence Liu is a product manager for Cisco’s Enterprise Collaboration Platform business unit. Thomas Lento is a data scientist for Facebook where he builds statistical models to understand the behavior of Facebook networks. And Michael Wu is principal scientist of analytics at Lithium Technologies and a Ph.D. in biophysics from U.C. Berkeley. I was in awe of these people but really had to concentrate to keep up with them.  They know each other well and the interplay among them was fun.

Social network analysis (SNA) is very comparable to past references I’ve made to what our partner Community Analytics does to map influence networks.  In this context, we’re talking specifically about influencer maps in an online social media setting. You can read the Wikipedia definition here but what we’re talking about is a network map per the image here of nodes, which are typically people, but sometimes ideas, and edges or ties that connect them, which might represent advocacy or simple education but are very topic-specific. Software like the open source and free Excel add-on NodeXL lets you take social media data sources and create these maps (give it a try if you’re geeky enough). Ultimately, they enable you to spot the real influencers in your network and identify the roles they are playing. One example from Marc Smith:  Let’s say you have a node, an influencer, with lots of other people connecting to that influencer. That’s good, that’s a key person. But if none of those other people are connected to each other, then if the influencer were to withdraw, the whole network would collapse.  Another example from Lawrence Liu: You can find a cluster of connections suggesting a virtual “echo chamber,” which is notable. But if someone in that echo chamber is also well-connected with others outside the echo chamber, that makes them the key to extending your reach beyond your usual suspects. 

Smith warns that it’s not enough to just count the number of connections. As in real estate, where it’s about location, location, location – in the social network world, it’s about those critical people who have the connections others don’t, who can be the gatekeepers to key new relationships. He described the job of the community manager as a park ranger. You’re really talking about nurturing a social network ecosystem. Continuing to add more bears doesn’t necessarily make a nature ecosystem healthier and just ramping up connections doesn’t make a social ecosystem automatically better either.

There’s a lot more to this and I won’t go on about it but the fact is we no longer need to just guess about what these influencer networks look like and as social media programs continue to scale up, we’re really going to need to take a hard look at these more sophisticated, automated ways to analyze what’s happening out there. Gut feel won’t be enough.

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2 thoughts on “How Well Do You Know Your Social Network? Probably Poorly

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