Using averages for things like website wide data gives a very misleading picture and can result in poor decisions. Average is a nice useful word but average does not always mean what we might naturally think it means. For example it has a clear meaning when we talk about someone being slightly above or below average height – here most people are close to the average. But for many kinds of data where the range of values is very wide our idea of average does not give an accurate picture, in fact it often actively misleading. For example if there are 9 people in a room with typical incomes and somebody vastly wealthy comes in the average income of the 10 people in the room suddenly rises. But basing a decision on the new average income of the occupants would be pretty foolish.
Interestingly this widely uneven distribution of data is quite common. Sometimes know as the power law distribution, the Pareto principle or the 80/20 rule, etc it is often cited as a `natural ` cause of the 99% /1% wealth distribution we hear so much about these days. Web site data is a classic power law distribution. Site wide averages such as Average Time on Site or Average Visits per keyword for the whole site give us very little useful information. That is because a huge number of visitors bounce while a small number account for a large percentage of the page views. Rather than site wide averages you want to know more about those who stay, those who come back, etc. So what to do?
1. In Google Analytics use Advanced Segments to isolate groups of visitors who together exhibit interesting behaviour – where they came from, what pages they viewed, etc.
2. Put Goals in for Time on Site at say 1min, 3 min and 5 minutes. This allows you to identify those who liked your content enough to stick around. Those who stay 5 minutes should be of real interest.
The only way to provide data that individual web site contributors can make use of is to break the overall site data into smaller more understandable pieces. So what is the most ( and least) popular section of your site ? How do the visitors who stay the longest find the site ? via search, social media ? But beware any number that is an`average` for the whole site.