I’ve been told that I don’t have a very good poker face, and I’m ok with that. I find it actually helps my clients, since they can tell almost immediately when I don’t agree with something or when a point needs more clarification. For example, there are times I see marketers heavily focus on time-based metrics like Time on Page and Time on Site (as if those metrics were 100% accurate). Unfortunately, this isn’t the case and the numbers reported can be far from accurate.
The confusion about how time metrics are calculated usually leads to some interesting conversations (as clients see various times listed in their reporting and want to start comparing pages and visitor segments.) That’s hard to do, since there are serious limitations with the way time metrics are calculated in web analytics (for now anyway). Even worse, people can subsequently make decisions based on the data. This is why I tend to focus on key actions visitors can take versus other measurements of success. I track these actions via conversion goals and events versus focusing heavily on time-based metrics. I’m not saying that time-based metrics are useless, but they need to be taken with a grain of salt (at least until we find a more accurate way to track them).
For example, let’s say a marketing manager analyzed a new piece of content and was blown away with an Avg. Time on Page of 5:26. This is where my (lack of a) poker face leads to a deeper conversation about how the actual Time on Page could be much different. My comment is typically something like, “I think it’s great that visitors who decided to visit another page on your site spent 5:26 on the page.” I often get a strange look at this point. Then I start to explain that Time on Page is actually calculated by page jumps. So, if someone reaches a certain page and exits from that page (or bounces), then Time on Page is 0:00. No, that’s not a typo. Time on Page is 0:00 whether they spent 10 seconds on the page or 10 minutes. I’ll explain more about how time metrics are calculated below, but I wanted to give a quick example now so we’re on the same page.
Note, although I’m focusing on Time on Page and Time on Site in this post, there are ways to calculate time metrics like Elapsed Time for certain processes that occur on a page. For example, check out my tutorial for using TimeTracker to calculate elapsed time (or Time to Complete). It’s an interesting way to extend event tracking to record how long it takes to finish a process like completing an elaborate form.
Access To Data Can Be Powerful But Interpreting Data Incorrectly Can Be Dangerously Powerful
I never want to rain on anyone’s parade, but it can be dangerous to walk around your office telling everyone something that might not be entirely accurate (and can be way off). When I start helping clients with analytics, I typically make a point to grab a conference room with a white board and start explaining how Time on Page and Time on Site are calculated. After I explain how the metrics are calculated, there are times that I hear a level of skepticism, since there isn’t hard evidence that the metrics are really calculated this way. And I’m totally cool with that. I’d be a hypocrite to say that being skeptical is wrong, since I test everything.
In online marketing, I don’t necessarily take anyone’s word without testing it myself. Which brings me to this post… Based on what I explained above, I decided to run an experiment to test Time on Page and Time on Site in Google Analytics. My goal was to document the hard numbers so you can see how the metrics are reported. I think it’s important to understand the limitations of the metrics, so you can provide context in your analysis. If you provide context, then your clients can better understand the analysis you provide.
The G-Squared Lab
When setting up this experiment, my goal was to isolate directories and pages and then document the exact time that I spent while visiting those pages (and sets of pages). If each visit was isolated, then I could drill into the reporting to see exactly how Google Analytics tracked time for each page and each visit. I’ll detail the paths I took in the experiment below.
Quick Introduction: The Formulas for Time on Page and Time on Site:
Before we hop into the experiment, let’s start with a quick introduction to how Time on Page and Time on Site are calculated. Both metrics are based on page jumps (or moving from one page to the next). By calculating the difference between visiting one page and the next page during the visit (via the page timestamp), Google Analytics can determine how long you stayed on the preceding page. Calculating Time on Page via page jumps is where a major limitation is exposed.
That’s because if you visit a page and then exit the site, the Time on Page is 0:00. As mentioned earlier, that’s whether you spent 10 seconds or 10 minutes on the page. On a similar note, if you visit just one page on the site and leave (which is a bounce), the Time on Page is also 0:00. Time on Site is calculated by adding the time that a visitor spent on the site by combining the Time on Page for each page that was part of the visit. Average Time on Site simply divides total time by number of visits and does not exclude bounces. You can see the formulas below.
Time on Page A = Page B Time – Page A Time (based on the timestamp)
Average Time on Page = Total Time on Page A/(Pageviews – Exits)
Total Time on Site (For a Specific Visit) = Page A + Page B (or the sum of Time on Page for each page in the calculation)