There is little doubt that Web analytics is the key to effective Internet advertising. Are you getting the most out of the time you spend looking at all the charts, graphs, and endless tables of data that Google gives you?
Most of our customers fall into two categories of Web analytics users: “hit and run” and “obsessive compulsive.” The hit and run user looks at the number of clicks, checks for conversion rates on some top campaigns, and never looks below the surface. This approach can leave behind the most useful information that is often buried deep in the data.
The other user is the obsessor who spends too much time scrolling through tables of data and pondering over why one word is converting more than another, or why a referrer gets lots of time on the site but no conversions. While hunting through the data for nuggets of information can be fun (like looking for a ruby in a mine shaft), it is not the most productive use of your most precious resource, time. The obsessive compulsive method can also lead to a common Web advertising ailment we call “data insanity” (staring at all those numbers for too long can make even the most committed statistician go mad).
We can tell a customer is suffering from data insanity when they are having trouble taking action on their campaign. There is just so much information – much of it is contradictory – that unless you have a system of consistent measurement and analysis tools, it can be very difficult to know which way to turn.
We ask a series of analytical questions that the Web analytics data can shed light on. Then with some quick manipulation of the data, we can get answers and take actions. This approach prevents data dementia and consistently pulls actionable data from an otherwise incomprehensible series of numbers.
One example of this approach is to ask the question: is natural search traffic or paid search generating the highest ROI? As a campaign ages, you want to be investing in natural search strategies, but only when it makes sense. Some campaigns simply do not perform well on natural search, and analytics will help you sift through this data as long as you have set up your filters correctly.
This is just one of many systematic analyses that we use to troll your data looking for opportunity. We can run these measurements regularly and quickly so that, rather than spending hours just staring at your data, we can quickly pull out the key elements that will allow us to maximize your return.
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