Time on site seems like a straightforward metric, story but it can be very misleading. At first glance, store one would expect this metric to tell exactly how much time a user spends on my site. However, dosage due to the way Google can calculate this metric, this is almost never the case.
I’m not trying to scare you away from using the metric, but I want you to understand how it is calculated so you can make more informed decisions using it.
Why can this metric be misleading?
Google Analytics calculates time on site with this formula: the time of the first hit on the last page minus the first hit on the first page. Sound a little confusing? Don’t worry, this confused me, too – let’s break it down using visuals.
In the visual, a user lands on page 1 and spends 5 minutes there before moving on to page 2. The user spends 5 minutes on page 2, then moves to page 3. The user spends 15 minutes on page 3 before leaving the site (clicking an outbound link, closing the browser, or typing in a new URL).
So the time of the last hit on the last page is at 7:10PM. The time of the first hit on the first page is at 7:00PM. This means the total visit will be recorded as 10 minutes long. But we know that the user spent an additional 15 minutes on the last page.
Why isn’t this counted?
Google Analytics needs something to latch on to. Usually this is a pageview (it can also be an event). Since there is no pageview following the final page, there isn’t anything for Google Analytics to calculate this metric.
Use time on page instead
Time on page will give you an estimate for an individual page. Keep in mind, it has the same problems that time on site has. You can combat this by considering the exit rate of the page.
When the exit rate of the page is low, you should have more confidence in this metric. When the exit rate of the page is high, you should have less confidence in this metric.
Put simply, when users frequently exit from the page, time on page will be unreliable. When visitors generally don’t exit from the page, time on page is more reliable.
Did you find learning more about this metric to be helpful? What other questions do you have about understanding the flow of traffic and amount of time your readers spend on your blog? Let us know in the comments below! Maybe you’ll see your question answered in an upcoming AmpliFound post!