" was a distillation of his blog posts. The book is a best seller even although much of its contents is available for free on the web. His second book "Web Analytics 2.0
" came out recently.Avinash is an excellent communicator with a strong personal style. One aspect of that style, quite obvious from his blog posts, is the urge to create lists of ideas. For his presentation, Avinash offered us a list of simple ideas on web sites metrics and analytics. Here are some of the ideas that he presented.
The first idea is simple and direct - Don't Suck. The suckage of a web page can be measured by a metric called Bounce. This is a relatively new metric that we had not previously heard discussed at the Business Intelligence SIG. Bounce measures the users whose experience of the web page and site is, as Avinash put it "I came. I puked. I left." and he showed us some pretty pukey pages that might back up this behavior. A typical analysis is to look at the pages with the highest bounce rate, determine why they cause that behavior and what can be done about it.
His next idea is Segment or Die. Analytics is about aggregating data to make sense of large datasets, however over-aggregation results in a single number and nothing to compare it with. Segmenting the data gives us a number of data items that we can compare. Avinash showed us a simple example where he took a hospital web site and classified the content into 8 segments and then compared the amount of content against the number of page views in each segment. It was immediately apparent where the effort should go into adding and improving content.
Analyzing your web logs only tells a part of the story, you also have to worry about what the analytics cannot tell you. Ex Defense Secretary Donald Rumsfeld is infamous for having talked about "the known knowns, the known unknowns, and the unknown unknowns". The unknown unknowns are the things that you don't even know that you don't know and therefore the thing you should be most worried about. You can start to get a handle on what you do not know by looking at your performance relative to your competitors. This is known as Benchmarking, or in the case of a deep study as Competitive Intelligence. For an example of what can be done, a recent post on the Occam's Razor blog discusses 8 sources for Competitive Intelligence data.
Most web site analytics only looks for the one big conversion from a web site, however there are many other small conversions that are tracked and worth evaluating because there may be hidden value lurking in the long tail. For example, recently Avinash wanted to know what his blog was worth so that he could defend taking time away from the family to write it. After determining a value for each reader, he started adding up all the other micro-conversion like people who subscribe to the RSS feed and advertisements for his books and the non-profit organizations that he supports. Overall he came up with a figure of about $26000 per month. Now Avinash does not make a penny from his blog, so this is notional money that adds to his personal brand value, but that value seems to make the effort of writing the blog well worth the time spent.
The next idea is Fail Faster. By this Avinash means do lots of different experiment, many of which will fail, to find out what works. He led us through an example from the Obama Presidential campaign. President Obama raised huge amounts of money from many small donations on his web site. The initial web page worked well. The experiments were to try some variations on the theme. Pages with video, and stirring video at that, did very badly. A simple picture of Obama with his family did a little better than the initial picture, so that one was chosen.
Avinash showed us this example to make a number of points. The Obama analytics team was tiny. Often the best work is done by a small agile team that has the freedom to experiment. The team used free tools. Avinash believes that that good people are much more important than good tools. His suggestion for dividing up the analytics budget is to spend 90% on people and 10% on tools. Sometimes, a web site design feature starts from a HiPPO (Highest Paid Persons Opinion), which can be destructively bad, and difficult to get around because in all organizations the highest paid persons opinion is taken very seriously. The best way to counter a HiPPO is to show that other ideas work better through the results of experiments that produce hard evidence.
While some may think that web analytics is a mostly solved problem, Avinash believes we are just starting to figure out what can be done, and that there is plenty of room for more innovation. I will continue to read Occam's Razor to find out where he takes us next.
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