Social networks online, such as Facebook and Myspace are becoming more and more important. Increasingly, marketing through these online social networks will become ever more prevalent. For example, web apps / application onto Facebook’s open API (which means external programmers can add programs and applications to facebook – not just facebook employees). Another example might be someone with a lot of social capital (ie contacts and influence online), in turn influencing their friends, contacts and others exponentially in theory due to the fluid nature of a social network to buy a product or service. Related to this but slightly different, Blogvertise is a service which pays bloggers to promote a product or service related to the blogger’s field of expertise.

Onanalytica does online buzz monitoring. Measurement of influence is key!
Influence is the “weight” of each voice. Alternatives: Treat all voices equally, use gut-feeling or equate popularity and influence – all are really bad. Onanalytica’s methodology has been used in the academic community for more than 30 years to measure the influence of academic journals and is a huge mathematical challenge known as an input-output model in econometrics.

You get the gist. This is all seriously fascinating marketing.

The 2 things that absolutely fascinate me are:

1. The major challenge facing marketing strategists in how to increase the effectiveness of social network based marketing strategies.
2. The future marrying of web analytics and social network analysis and resulting improved marketing effectiveness and business intelligence.

Measuring the influence of myspace visitors
MIT Media Lab / Social Media Lab designed a flexible tool for the content driven exploration and visualization of a social network. Building upon a traditional force-directed network layout consisting of nodes (profiles) and ties (friend-links), the system shows the activity and the information exchange (postings in the comment box) between nodes, taking the sequence and age of the messages into account.

In the myspace service network-only visualization methods are no longer sufficient to meaningfully represent the community structure. Numerous commercial profiles, fake/spam/celebrity profiles and tools such as automated friend adders result in a huge numbers of connections, many of which carry little information about a person’s actual social ties and behavior. The average myspace user has more than 130 friends, but there are also profiles with over a million “friends”. By going beyond the “skeleton” of network connectivity and looking at the flow of information between the individual actors, the authors hope to create a far more accurate portrait of online social life.

What is social networking analysis:
Social network analysis has emerged as a key technique over the last century in modern sociology, anthropology (good thing I am a qualified anthropologist), sociolinguistics, geography, social psychology, information science to measure what individuals do and the many types of relationships between one another. And now social network analysis becomes important in web analytics. Social network analysis sees social relationships in terms of nodes and ties. Nodes are the individuals (visitors) within the networks, and ties are the many types of relationships between the individuals.

Two Social networking analysis metrics (an introduction merely):
Read Judah’s thought provoking post for more social networking analysis metrics and opinions.
Betweenness:
Degree an individual lies between other individuals in the network i.e it’s the number of people who a person is connected to indirectly through their direct links.
Centrality Closeness:
The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the “grapevine” of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network.

Marketing in social networks
Jason Ethier has written a good paper on social networks. He tells us that the main questions for researchers in social network theory are which types of social networks can be used as a basis for marketing strategy, how to identify and measure social networks, how to mobilise and manage social networks, and which marketing decisions can benefit the most from social network concepts and methods? Researchers have found that consumer networks that are not under the control of a corporation work best for marketing purposes which is why networks such as facebook and myspace are so successful. Corporations identify and measure social networks by collecting information from their customers. One method of doing this is by distributing loyalty/discount cards (large retailers do this) in exchange for customer information.

To conclude:
Social network theory and analysis and their marrying with web analytics is certain to become ever more prevalent as more companies learn of the marketing potential of social networks. And hopefully will become more mainstreamed into web analytics as a whole and into the web analytics solutions themselves. (hopefully anyway)
I welcome your feedback, thoughts or complete disagreement – so please share your thoughts and most importantly, THANKS FOR READING!