Using onbeforeunload event with Google Analytics to record page exits and therefore more accurately record user time on page / site

I have been trying to research the hack proposed by Avinash Kaushik in his book Web Analytics 2.0. He poses the problem whereby most web analytics tools are unable to record the time a user spent on the last page they visit on a website, or on the only page they visit. In other words if user comes to page 1, a timestamp is created showing the time they arrived at the page, when they visit page 2, a second timestamp is created. The time spent on page 1 can be calculated by timestamp 2 - timestamp 1. However if the user closes the browser window or navigates away from the website there is no way to record time on page 2. Here is a link to this problem on Kaushik.net

standard-metrics-revisited-time-on-page-and-time-on-site

One proposed hack is to use the window.onbeforeunload event to call a method and push the time that the page was unloaded to google analytics. So I tried the following code -

window.onbeforeunload = capturePageExit;
function capturePageExit()
  {
    _gaq.push(['_trackPageview', '/page-exit?page=' + document.location.pathname + document.location.search + '&from=' + document.referrer]);
    return("You are about to close this page");
  }

Using firebug I can see that the correct __utm.gif image is requested and the correct params are sent to google analytics. But clearly there is a problem now that this will be called on each page unload and so each visitor will appear to go from page1 -> page-exit -> page2 -> page-exit -> page3 -> page-exit... but I should get a more accurate time on site reading, right?

However this is at the expense of accurate navigation-summary data and so not a good solution. What would be good is if I could tell - if user has clicked the close browser/tab button or is navigating away from my site then record the page-exit.

I cant find a great deal of information about how to solve this problem, plenty of discussion about being aware of this inaccuracy when interpreting google analytics (and most web analytics tools probably), another useful link is time_on_page_and_time_on_site_how_confident_are_you

Just wanted to raise this on stackoverflow as I cant find a similar question and start a discussion about this, but my interpretation is that there isnt really a way around this problem but it is just better to be aware of it.

any thoughts?

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Here is another link that was suggested to me from a blog called Savio.no, is this a good method?

how-to-measure-true-time-with-google-analytics

Web Analytics is not an exact science. Data is always approximate and most of the time sampled.

Web Analytics tools strive for Precision not accuracy. This whitepaper describes why it's more important to have precision and less important to have accuracy when working with Web Analytics.

Once you understand the difference between precision and accuracy and why it matters you will understand that it's not important to get the exact time on site metric, but a precise measure that could clearly express trendings or changes to that metric.

On other words forget about absolute numbers, learn to report using trends and changes.

Another advice, don't bother tweaking GA to render every single metric perfectly if you're never gonna use it. Bother with metrics that you can use. And by use I mean Actionable analysis.

There are, however a few cases were some code tweaking can help you out measuring the time on site. A clear example is a weblog. You may want to implement something like that in a weblog, ince most of your visits will be looking at your homepage, reading your posts and then leaving, all that is done in the same single PageView so it may be a good idea to fire an event when the user leaves to get the correct time on site, or maybe fire an event when the user scrolls past some threshold, in the end you'll be measuring the same ting, if the user scrolls more he reads more, and if the user spends more time then he reads more. So it may not make sense to track those 2 metrics to measure the same effect. Just choose one and stick with it, leave it running for a while to create historical data and then make use of it.