The second in our series on understanding Google Analytics focuses on time on page / time on site and how it is calculated. There’s more to it than you might think.
Unfortunately, site visitors do not clock in and clock out at the beginning and the end of their shift – although without doing a bit of digging under the surface, you could be forgiven for thinking they do.
(Source: Public domain, via Wikimedia Commons)
So let’s take the ficticious site www.ilovecashmeresocks.com (as it’s currently tipping it down with rain and I’d quite like to be at home, all cosy in front of a fire.) Having searched for ‘buy cashmere socks’ I land on the homepage of this site at 9.30am and immediately register a page view.
I browse the content and then at 9.31am click a link to page 2 – an overview of different styles of socks. Having read the merits of various styles, at 9.34am I move on to page 3. Frustratingly for the socks company, I then leave the site as they are more expensive than I thought – who spends £26 on one pair of socks? I could ‘leave’ in many different ways: by closing the browser, clicking through to an external link, typing in a new URL or just sitting on the page for 30 minutes or more.
You’ll notice I don’t comment about which time I leave the site – and this is exactly Google Analytics’ problem. In fact Google Analytics doesn’t really track the time you spend on each page, it actually tracks the time in between clicks through to different pages.
In the scenario above, I may have stayed on page 3 for just a couple of minutes, for 20 minutes or kept the tab open in the browser all day but the result would be identical in all cases. Google Analytics would record that I spent 4 minutes (the time between 9.00am and 9.34am) on the site. It can have no idea when I actually left page 3 because I didn’t click on to a page 4, and thus it is not able to do the calculation. Google Analytics will record that 3 pages were visited but only the time spent on two.
A spanner in the works can occur when a site visitor opens a page on a new tab halfway through their visit. Different analytics programmes tackle this in different ways but Google Analytics tends to treat this as one visit and logs time in linear order – regardless of which tab you’ve got open.
So is this not-quite-accurate logging of time concerning?
Not really. As long as you are consistent in your inconsistency then that is all that really matters.
Other points to note:
Always remember that single page views are not always low quality ‘bounces’ even though they will be recorded as ‘bounces’ in GA. If someone watches a video, reads an entire blog post over several minutes, shares something or transacts, this is effective engagement with your audience. However unless you specifically set up event tracking to monitor this, it will disappear in to the ‘bounced’ ether. This high bounce rate could also lead you to believe your average time on site stats are poor, when in actual fact people are spending a considerable amount of time on site but without moving on to view a different page.
The average time on site calculation has its flaws but if you monitor like for like over a period of time, seeing this metric rise is testament to the fact that people are finding useful information on your site and engaging. It shouldn’t be taken in isolation and certainly needs interpreting alongside other Google Analytics measures.
More to follow on Bounce Rates and other Google Analytics metrics in the coming weeks…