Social Media Analytics - expanding the envelope

Avinash...Avinash...AVINASH!

I'm not turning into Steve Balmer (thank goodness) just enthusiastic and inspired by a recent blog post by Avinash (general Web analytics Guru and all round good chap).

In his blog post Avinash points out a fine set of actionable insights that can be gleaned from metrics generated by social media. For example, beyond the 'x followers' type metric on Twitter, Avinash describes how we can look at # of retweets per 1000 followers. Indeed - the impact of one's Twitter stream goes beyond the simple action of following a Twitterer because of an occasionally amusing or interesting 140 char missive. What happens when it turns out the Twitter stream in question is lame? Unfollow...sad though it may be.

Fastbloke...Fastbloke...FASTBLOKE!!!

Ahem, sorry about the outburst there...moving swiftly on:

So, looking at social media (blogging, Facebook, Twitter etc.) from a Web Analytics 2.0 perspective we can treat a simple retweet or new follower/feed subscriber/friend as 'outcome proxies'. What might be called 'vanity metrics' actually have more use than you might think.

One outcome we are interested in from our social media marketing efforts is the delta in long term subscribers/followers/friends. Assume that we can see with our classic Web Analytics analysis techniques that social media 'customers' add value to our business...given the meager spend on Tweets and blogs, the cost of acquisition of a loyal friend on Facebook is really quite slight compared to say a PPC or email click. There is insight to be had here!

In other words, a significant delta in our outcome proxies needs to be understood so it can be fully capitalised. What if I suddenly get a massive leap (OR DROP!) in followers/friends/subscribers? What were the influences? Was it the last post or update? Can external/offline marketing influences be attributed?

Social media outcomes need to be tracked and analysed

In the same way as one analyses the performance of entrance and exit pages on a website we can consider a tweet that yields new followers as the 'entrance tweet' and a corresponding 'exit tweet' for the converse situation.

Did a certain blog post cause subscribers to unsubscribe? We consider 'unsubscribe rate' as an email marketing effectiveness metric - does the same apply to other forms of social media? Can we consider segmented friends lists? Consider (read test) tweeting and blogging at a certain time of day or day of week or frequency?

Always consider content first though!

Viral quotient is an awesome metric but the basic building blocks of analytics 1.0 can still add value.

I'll be starting with the new annotation functionality in Google Analytics real soon but I will also be experimenting with techniques to integrate friends/followers/subscriber metrics with current web analytics data sources.

Maybe Google/Yahoo/Omniture or some other web analytics vendor will beat me to the solution. I look forward to it.