Book Review: Web Analytics 2.0 The Art of Online Accountability & Science of Customer Centricity

Web Analytics 2.0 Cover

Web Analytics 2.0 – Copyright Wiley Publishing 2010

What is Web Analytics 2.0?

In his book, Web Analytics 2.0 The Art of Online Accountability & Science of Customer Centricity, Avinash Kaushik defines what he calls the five pillars of second generation web analytics:

1) Collecting, storing, processing, and analyzing click level data (aka, The What),
2) Measuring increased revenue, reduced cost, and improved customer satisfaction & loyalty (aka, The How Much),
3) Experimenting and testing to determine what works (aka, The Why),
4) Listening to the voice of the customer (aka, The Why again), and
5) Performing competitive analysis (aka, The What Else).

As Community Managers, moving to the second generation of web analytics requires several shifts in mindset. First, we must dig much deeper than simply measuring “clicks”. We need to choose new quantitative measurements that measure things the business really cares about (i.e. revenue, expenses, and customer engagement) while at the same time gathering as much qualitative customer data as possible (i.e. via customer surveys, etc.). We need to automate decision and create continuous feedback and learning loops which measure customer sentiment and drive improvements in our behaviors and customer interactions. Finally, we can’t stop with just looking at our own position; we need to look at our position in relationship to our competitors.

Use the 10/90 Rule to Make the Right Investments

One of Kaushik’s reoccurring themes throughout his book is the use of his 10/90 rule. When investing in web analytics, many companies make the mistake of believing the primary investment is in the tools themselves. Instead Kaushik argues, 90% of our investment in analytics needs to be in the people who are interpreting the results of the tools. The investment needed in tools and professional services to get the tools up and running is only about 10% of the total investment in web analytics. Investing in expensive tools will yield little benefit if we don’t invest sufficiently in one or more brilliant people to interpret the results of the tool. Community managers need to be one of those people with a “planet sized brain” as Kaushik puts it. While the community manager may not need the technical depth of a web analytics professional, he or she needs to understand the techniques used by the web analytics professional, be able to ask the right questions, and provide direction for prioritizing deeper analysis by web analytics professionals. Of course in some cases the Community Management, Social Media Manager, and Web Analytics guru is the same person…..(sigh). Good luck with that. You’ll need a “galaxy sized brain”.


Using segmentation is another recurring theme in Kaushik’s book. Segmenting your metrics makes it possible to gain deeper insights into your website’s data and drive new actions which will improve your website’s effectiveness. By using analytic tools to break the data into different segments you may be able to determine that the rogue video your colleague put on YouTube has drawn more customers to your site than the latest more expensive marketing campaign. A community manager may find that new community members are passionate about certain topics that long-time members are not and seed community discussions with unique content that will appeal to each audience.


Kaushik’s book helps Community Managers understand that Web Analytics is not just important for hard core technologists. Community Managers can benefit from understanding web analytic techniques and using them to grow the size of their community, differentiate themselves from similar communities (through competitive analysis), develop better business oriented metrics for their communities, and of course, listen to the voice of their customer.

How much do you think Community Managers need to understand about Web Analytics? Are you a Social Media Manager, Community Manager, and Web Analytics person all rolled up into one? If so, please share how you do it all.

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