Some Google Analytics metrics confuse the world of online marketing and are often misinterpreted. This article clarifies these and explains precisely how the individual values are calculated and how they should be interpreted.
1. Average time on page / Average Session Duration
Google Analytics calculates the time spent on a page as the difference between the page load of one page and the page load of the next page. For this reason, Google Analytics cannot calculate the time spent on the last page of a session. To compensate for this, calculating the average visit time of a page excludes all page loads that involve viewing the previous page of a session. As a result, the calculation of the average session duration of a page with a high exit rate is based on relatively few pages.
The “average session duration” metric, on the other hand, does not take the time spent on the last page of a session into account. Here, the total amount of time spent on the website up until the previous page is loaded is used, and this entire time is then divided by the number of sessions. For this reason, the measured average session duration tends to be reported lower than it actually is.
The following two examples show the calculation of the two metrics:
For this session, the two metrics, “average session duration per page” and “average session time”, look like this:
- Time on Page A = 5 Min.
- Time on Page B = 5 Min.
- Time on Page C = The Time on page C cannot be calculated, and this is therefore excluded from the average time on page calculation. (If GA is unable to calculate an average time on Page C from the remaining page views, this is displayed as 0).
- Session Duration = 10 Min.
This session results in the following values for the two metrics:
- Time on Page A = 2 Min.
- Time on Page C = 2 Min.
- Time on Page B = The Time on page B cannot be calculated, and this is therefore excluded from the average time on page calculation. (If GA is unable to calculate an average time on Page B from the remaining page views, this is displayed as 0).
- Session Duration = 4 Min.
While here, the time spent would look like this:
- Time on Page A = The Time on page A cannot be calculated, this in therefore excluded from the average time on page calculation. (If GA is unable to calculate an average time on Page A from the remaining page views, this is displayed as 0).
- Session Duration = 0 Min.
The average time on the various pages and the average time on site for all the visits would thus be:
- Average Time on Page A = (5 Min. + 2 Min.)/2 = 3.5 Min.
- Average Time on Page B = 5 Min.
- Average Time on Page C = 2 Min.
- Average Session Duration = (10 Min. + 4 Min. + 0 Min.)/3 = 4.67 Min.
It is imperative to keep this in mind when dealing with websites and pages that have high bounce and exit rates. This is when Google Analytics has to rely on small sample sizes, which may result in misleading or skewed information.
As a result, the number of visitors in Google Analytics is almost always higher than the effective number of visitors to your website.
The graph shows how easy it is for two different people to be listed as five unique visitors in Google Analytics.
Dieses Problem können Sie umgehen, wenn Sie mit User-IDs arbeiten. In diesem Fall haben Sie genauere Angaben, wie viele verschiedene Personen Ihre Webseite besuchen.
3. Direct Traffic
When first learning about the various traffic sources, many people are taught that direct traffic is a result of someone entering the URL directly into the browser or clicking on a bookmark. While both of these do result in direct traffic, it is essential to note that the direct bucket also catches a lot more traffic. The actual definition of direct traffic is any traffic where the first page of the session does not contain a referrer within the headers of the HTTP request.
This can have multiple reasons:
- A referral from https to http will not include referral information.
- Certain apps do not pass on referral information.
- Untagged emails and links from an untagged promotional pdf, excel or word document will not contain referral information.
- You also get browser extensions that can modify or remove referrer information.
- And finally, some websites mask or remove referral information when sending visitors to another site (for security purposes).
So while clicking a bookmark or entering the URL directly into the address bar will be counted as direct traffic, it is important to remember that many other traffic sources fall under direct traffic. This is mainly because GA doesn’t know where that traffic is coming from most of the time.
4. Pageviews vs. Unique Pageviews
Google Analytics defines the term pageview as a view of a web page that is captured by the Analytics tracking code. Each time the page is reloaded, a new pageview is counted. If you view other pages in the meantime and then come back to the original page, the page is calculated with one pageview again.
Unique pageviews are registered at the level of a session. These represent an accumulation of pageviews generated by the same user during the same session. For example, if you visit this blog post ten times, ten pageviews will be counted, but only one unique pageview. For this reason, pageviews will always be higher than unique pageviews.
Pageviews show you how users interact with your website. Since individual pageviews ignore page updates and multiple pageviews in a single session, they give you a more accurate view of the amount of user traffic. This makes it easier for you to see which of the pages are effectively attractive to your users. You can then figure out how best to design content on your website to generate more unique pageviews.
5. Bounce Rate
Google Analytics defines a bounce as a session to a single page on your website. The bounce rate, therefore, includes the number of bounces divided by the number of sessions. The problem here is with the bounce rate for individual pages. Here, the bounce rate is calculated as the number of bounces on this page divided by the total number of sessions where this page is the entry page.
The bounce rate at the page level is often confused with the exit rate. The exit rate also shows how many times a page was left, similar to the bounce rate. The exit rate is calculated as follows: How many times the page is the last page of a session divided by the number of times that page is viewed.
It is important to understand the difference between these two metrics. The following example illustrates the difference a little better:
Imagine that there are only these four sessions on a web page:
As a whole, there are only two sessions that count as bounces, Session 2 and Session 4. Thus the website bounce rate is 2/4 = 50%
- Page A has three page views (Visit 1, 2, and 3) and two exits (visit 2 and 3); this equals an exit rate of 2 / 3 = 66.66%.
- Page A was also the landing page twice (Visit 1 and 2) and has one bounce (Visit 2); this equals a bounce rate of 1 / 2 = 50%
- Page B also has two page views (Visit 2 and 3); it, however, has no exits, so an exit rate of 0%. It was also the landing page once (Session 3), but as this was not a bounce, it has a bounce rate of 0%.
- Page C has three page views (Visit 1, 3, and 4) and one exit (Visit 4), so an exit rate of 1 / 3 = 33.33%
- Page C was only the landing page once, though (Visit 4), as this was also a bounce, Page C has a bounce rate of 100%
- Finally, Page D had only one pageview (Visit 1), where it was also the exit page, resulting in an exit rate of 100%. As Page D was never the landing page, it had no bounces and has a bounce rate of 0%.
As this example shows, the bounce rate of the website, the bounce rate of the individual pages, and the exit rate of the pages can achieve very different results and thus also provide additional explanations. It is crucial that the three metrics are interpreted correctly and that the results are incorporated accordingly into the assessment of the website.
If you have any questions about these metrics, please feel free to contact us.