Increasingly, companies are harvesting their data to understand relationships between customers. Customer’s word of mouth promotion or denunciation of a product or company can be a vital piece of knowledge for organizations. Companies that can identify key influencers within a network are capable of utilizing those influencers to promote the product and affect the communication of information in the shortest path possible, in stark contrast to simply broadcasting information over traditional media.

Social network analysis (SNA) is the student of social structure using graph theory. A network of the structure consists of nodes identifies as actors, which can be people or things that act in the network. These nodes are connected, or have a relationship, in some way to each other.  For example. For example, let’s say you wish to examine who might be key influencers inside your organization.

Companies that can identify key influencers within a network can gain a competitive advantage.

Every person in your organization would be a node. Then, if you wanted to examine the relationships between every person based on their email communications, e.g. number of emails sent to each other, you would create an edge, line between two nodes, and give it a weight, which corresponds to the number of emails between the two nodes, or employees. From this network, patterns of centrality, i.e. who is a central figure, can emerge. You can identify people who act as gatekeepers of the network in which the flow of information seems to be funneled. In fact you can actually analyze how fast a piece of information might get from one node to every other node through emails, assuming one doesn’t send an email blast to the whole company.

Social network analysis is very useful in areas such as analyzing Facebook or Twitter connections. It can also be used with survey data or data collected from sales in which you can connect groups of shoppers to identify collective buying habits or influencing habits.

Social network analysis isn’t very difficult and many applications such as R and SAS can handle social network analysis. Other tools such as NodeXL and Gephi, are great tools to get you started using SNA, and with many online resources it should be be long before you can really dive into networks that are within your company.