Avinash Kaushik has written the book on Web Analytics (two actually).   His most recent book is called Web Analytics 2.0.  Its worth reading the whole thing but, given our focus on tribe-building, I wanted to give a summary of the chapter on Social Web Analytics.

BTW – please buy the Web Analytics 2.0 book if you have an interest in this topic.  Avinash is donating 100% of his income from this book to charity.  I would like to commend Avinash on this generous action and the oustanding challenge he provides to all of us to consider how we can pay it forward. 

The chapter on Social Media Metrics is called “Emerging Analytics: Social, Mobile, and Video”

In the first few pages, he provides an overview of how analytics have been impacted by the Social Web.   User Generated Content and off-site conversations are an essential part of our online brand experience.  He advocates that we need to think more about “Conversation Rate” than “Conversion Rate”.

Avinash dives deep into the options for tracking analytics.  He starts with Mobile analytics and describes the challenges of determining if your blog or content is being consumed on a mobile device.   He uses a tagging solution from PercentMobile in his blog, but mentions Bongo Analytics and Mobilytics as options.  This lets him track the amount of mobile traffic, devices used, networks, countries and mobile via WIFI.  He points out that the world of measuring mobile is just getting started, but will be crucial  if we are going to segment our data and understand what is happening properly.

Blog Analytics are tackled next.  Avinash advocates two metrics to measure “Raw Author Contribution”: posts per month and average words per post.   From there, he focuses on “Holistic Audience Growth” and discusses how you should use a measure of RSS subscribers to see how your blog is growing.   Reach will measure how often your content is accessed and “Conversation Rate” for blogs is measured by # of Visitor comments / # of posts.   He goes on to discuss the use of Technorati and Tweet Citations to measure your ripple effect.

Finally, he treats the cost of blogging by examining the expenses of Technology, Time and Opportunity Cost.   From there he examines the metrics of value including Comparative Value (blog valuation tools), Direct Value (monetizing through ads and affiliates), Nontraditional Value (savings on PR, offline ads) and Unquantifiable Value.   These factors can, arguably, be used to compute ROI for your blogging efforts.

With Twitter, he starts off with the basic tracking of growth in the number of followers and churn rate.  He then moves to “message amplification” in which the world of retweets is handled.   Pointing to tools like TwitterCounter, Retweetist and Retweetrank, he suggests that you find out which tweets are most effective with your audience.

Beyond these initial metrics, he starts to examine click-through rates for Conversion Rate and Twitterfriends for measuring Conversation Rate.   He wraps this section with a discussion of emerging Twitter Metrics including Engagement, Reach, Velocity, Demand, Network Strength and Activity.

The last section of this chapter tackles Video analytics.   While embedding tracking codes into player-specific modules is the way to get the most granularity, it is also IT-intensive.  He spends some time looking at YouTube Insights to provide metrics on any videos you place in your YouTube channel.   You can discover your top videos, regional data and attention throughout a given video.  [NOTE: - I discovered that this only works with videos that have a relatively high number of visits – in the thousands.]  This can help you identify which parts of your videos are most appealing to viewers.  You can also determine where your video has been embedded to measure the “viralness” of a video.

He wraps up the chapter by discussing the need to compute “Contextual Influence” or rather, the value of each feature relative to others.  Kaiushik is a big advocate of capturing “Voice of the Customer” to determine exactly why people are coming to and using your web site and other properties.   His answer to this problem is simple.  Ask them.  He advocates a tool called 4Q for on-site surveys.   You can also run A/B or multivariate tests to determine the preferences for certain material.

The chapter is filled with technical details, tools to support your analysis and insights into how you can take actionable steps in response to your collected data.

I hope that is a helpful overview that will allow you to decide whether this book might be helpful to your Social Media Analytics efforts.   Avinash tackles the topic of Social Media Metrics in his own blog in a lot more detail.

Have you implemented any Social Media metrics for your company?  Which ones are most helpful?   Please continue the conversation by commenting below.