Your Brain – The Best Social Media Analysis Engine

October 7, 2009 CollabNet VersionOne

There’s a lot of work going on by various companies to build better ‘listening posts’ for the huge stream of social media that is flooding into companies and communities. There are a plethora of listening tools available, such as Radian6, Buzzlogic Insight, Sentiment Metrics, etc. More recently, Microsoft has gotten into the game with a project they call ‘Looking Glass‘.

While details are still sketchy, Looking Glass appears to hook into (and lock into) other Microsoft products, and even do some analysis of trends & stats to automate certain actions. At this point, I diverge from the popular opinion that social media stats, ROI, etc. are the end-all and be-all of the social media world. I’ve argued this point before, but I’d like to reiterate that all of the metrics gathered as part of social media/community really do require a person (be it a community manager or social media sherpa) who can perform the ‘Information Synthesis‘ necessary to build an actionable plan in response to the flood of social media input. We even have a section in our recently released Community Management Cookbook related to this thought. To wit:

Community health can NOT be determined by simple numbers. It takes a community manager, or several, to read through conversations.

For example, a community could have only a few members (relative to some ‘mega-community’), but the conversation/engagement/work produced might dwarf the mega-community. Raw membership numbers might present a skewed picture in this case which could lead to a decision maker thinking they need to shut down a productive community. With the stats, plus analysis by a human, a more educated decision could be made in this case. By the way, there is always the risk of over analyzing the data, and I agree with Jack Repenning, CollabNet CTO, on this point. Providing both the raw metrics, and a reasoned analysis, is the best we can hope to do, but clearly, one without the other doesn’t benefit anyone.

Once the analysis is combined with the numbers, the responsibility for a response to something in the social media stream can be farmed out to the necessary department or individual to deal with appropriately. However, the action item may be to adjust an internal process or business decision in reaction to the stimuli received from the social media feed.

There is enough inherent ‘squishiness’ in social media/community management that you really have to rely on the best social media analysis engine (the one between a talented person’s ears) to make sense of what the metrics are saying. Trying to automate thinking in an attempt to scale the social media listening/response function is doomed to failure if your goal as a company/community is to actually do something useful with what the crowd is telling you, and to be perceived as an organization that understands how social media benefits you.

Bottom line – statistics are but one tool in the arsenal of an effective community/social media strategy.

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