Stemato - Stephen Tompkins Writes About Digital Marketing and Social Networks

A recent report, from OfCom of the UK, about social-networking shows their prolific growth and deep saturation in the UK. I first read about it in a MSN UK story located here and I found its classification system to oversimplify these users. Surely, we can come up with a more profound classification than: Alpha Socialisers, Attention Seekers, Followers, Faithful and Functionals. It seems to only scratch the surface of what is a much more complex eco-system driven by many different types of users and scenarios.

Lets first take a look at scenarios that could evolve as a result of shifting user profiles and maturation of the space. As my company reported last month, Facebook’s numbers have slowed in recent months but its not endemic of the death of social-networking in the UK. The fact is the numbers were growing at a rate that could not have been endured much longer.

But, have they reached critical mass?

This is an interesting question, but with 23% penetration in a country that has only 30 million people total online, it would seem social-networking is still red-hot in the UK. Certainly with that kind of reach, users would fall into more than a handful of types and morph from one classification to another. In fact, I believe that user intentions on social networks are so varied and amorphous that any attempt to classify must be primarily organic.

Lets take deeper look at my organic classification system.

Instead of a linear zoological approach to classes, it should appear more as a hexagon with overlapping interest and a sliding scale. Something like this:

Using this hexagonal approach, you could then further define user personality traits based on aggregate sentiment analysis. What does this mean? If you could take a predefined number of UK social network users evenly dispersed across the three majors and parse out there profiles into text. Using that text you could then score the sentiment into different buckets (eg. dating, networking, spammer) based on keyword recognition.

Further refining your chart to something like this:

Building out these finite profiles, you get a clearer picture of social networking users and how they interact and relate to one another. The more data ascertained the better the profile. Time of day, age and other demographics can also enhance the map to show more in-depth details of how people engage.

In a very general sense OfCom gets it right, they just leave out a big part of the picture. User interactions and how they effect user profiles. My father said it best when he said “you cannot be, all things to all people.”