I was playing around with InMaps a moment ago. Actually I was trying to make sense of the different coloured groupings in my network. InMaps has been created by Linkedin to help you visualise your connections. Mine currently looks like this.

InMaps got me thinking about how data visualisation can be applied to our own or wider networks in terms of ‘help and support’. InMaps seems to group the people in my network according to where they work or worked, the associations or clubs they belong to, and then everyone else who is left over and I can’t quite work out (conveniently coloured grey in my InMap). But what if we could tag people not just by the knowledge they possess about a given subject or subjects, but actually how helpful they are in terms of their willingness to share that knowledge, how relevant their help was to me, how timely their help was?

So perhaps in my network, from a customer service perspective there might be those who were the willing and active helpers in a particular subject, others who were helpful informers (acting like signposts to where the answers might be), the niche experts, the ‘connectors’ (those people who could put you in touch with someone who could help in another network)… there might even be the ‘Twelpers’ (perhaps one day the true power and value of Twelpforce will be unleashed when it goes beyond just BestBuy), those employees from companies who roam freely to offer relevant help where it is needed.

Each of these types or roles might also have their equivalent ‘customer service’ Klout or PeerIndex score, or whatever it might be called. Each time a person provided some kind of help, advice or assistance, they would be rated by whoever they had helped. They might be awarded badges depending on the type of help they provided, their attitude, their knowledge…

For people seeking help, they would be able to find the specific help network they require, and then find someone within the network to help them by simply scanning across the specific nodes within the help network (as you might scan across your Linkedin InMap and hover over any name to find out more information about them), or reading through the recommendations.

Advertisements