It might be the showcase of a filter bubble, though I would say that we all agree that if you want to do anything with communities, social platforms are anything related to that, that you need a community manager. You need a community manager to make it into a success, since without a community manager luck is your biggest success factor. Though do you already have a data scientist?

However an upcoming trend I see is data driven community management. Currently most community managers are great in social skills, are great connectors and sometimes have a traditional internal communications background. They might be great in doing some reporting either via standard reports int he community platform itself or via data dumps in excels. However that is not science, that is reporting, that is simplifying data so it fits a pie chart or bar chart.

The data scientist

The data scientist takes a higher order view of things: for example, for example they might correlate weather data with community activity and looking at their relationship to the overall customer satisfaction.  It’s understanding the relationships between data and how they interact with each other.

It is a whole different mindset. The community manager sees a certain issue arise (a declining participation rate) and will start to solve it by creating more engaging content or by engaging members of the community more actively. A data scientist will first question if there really is an issue and will try to see if there is a correlation or causation that is causing this perceived issue and will based on that information provide next steps (or no steps in case it is a non solvable issue).

However if I would only provide that example, I would do the real data scientist and community manager a lot of injustice. Since these people do more than solving issues. They spot trends, things that are common, things that arise, that are useful. The community manager often uses ‘gut’ to identity these things. The data scientist uses ‘ head’. The community manager often only uses the information on what is happening in the community (not limited to the platform, though limited to a group). A data scientist will use all the sources he can use.

A data scientist spot trends based on combining different data sources and discover previously unknown insights such as that community members that like content more than average buy more bottles of soda in one go than other members. Or that a decline in community participation means a run a barbecue meat, since the weather correlates both to dinner choices and online activity.

You need head and gut

You need both ‘head’ and ‘gut’  in your community. Community management isn’t an exact science. Yet! A good data scientist will test the assumptions of ‘gut’  and will provide new insights and recommendations based on his analysis of various data.