Google Analytics Fight Night: No Metric is useless, not even Average Time on Site/Avg. Time on Page

… however, use made of otherwise worthwhile metrics can be incorrect, inappropriate, unfortunate, unnecessary, excessive, etc. So how do we use Average Time on Site and Average Time on Page, taking their “shortcomings” into account? How is time measured and how is ‘Average’ calculated …

This post is in reply to and quotes from Kent’s “Analytics Fight Night: Average-Time-On-Site and Average-Time-On-Page are Useless Blog Metrics”. linked to below.

Note that Count does NOT include exit pages, so the average is correctly calculated, for non-exit, and therefor, non-bounce pages… and here’s the proof – note how the Avg. Time on Page is 1:46 for the All Visits and for Non-bounced Visits Advanced Segments:

See below how the Total TOS for All Visits is used to calcuate ATOS for All visits.
Surely, the ATOS for all visits and only those visits with >1 page view should be 367 mins?

The data is taken from ATOScalculationProof.JPG linked to below. (note that “Time on Site” in the GA Overview report should read “Average on Site”)

Kent, your theory is right and wrong and the situation is better and worse than you thought but not necessarily in that order!

ATOP is not useless if …

“your time for each post is going to be a flawed sample (the people who read then clicked to the next article, or didn’t read and clicked back to the index)”

• No, ATOP is not flawed because it is correctly calculated (compare the 2 formulae above) and because it’s an average. If it were a measure of Total Time on Page, then it would be wholly incomplete and seriously flawed.
  But as an average, it is valid for comparisons and trends.
• In fact, one might get a more useful indication of a post’s engagment because bounced post views are excluded. Do we want to include post views by visitors who found the post to be irrelevant to their search or beyond/below the level of discussion they are looking for or do we want to measure the engagement of relevant visitors?
• The worst that one can say of ATOP is that the sub-set of a post’s views included in the calculation is not representative of all the post’s views. However, it should be used as a metric to measure the engagement of visitors who found the post relevant. As such, if post A is more engaging than post B, would that not apply both to visitors who came to read it and left as well as those who came to read it and viewed another?
• The use of the metric may be flawed if one does not use it or adjust it to compare like posts. ‘Like’ refers to posts of similar length, complexity, and structure. (By structure I’m thinking of posts that have links close to the begining resulting in a 2nd post being opened within a few seconds of the first loading.)
• So ATOP is a valid engagement metric when used appropriately – as is the case with all metrics.
• If we need to know total time, we can fire an event every 20 seconds or whenever the visitor scrolls. We can also measure Vertical % scrolled (which Adobe/Omniture’s SiteCatalyst now does).

ATOS is not useless if …

“Your average time on site is going to be TOTALLY off, since the people who landed on a blog post, read it, then left are not going to be counted at all.”

• This is where things really get bad because they are counted but their time is not – see the table above and ATOScalculationProof.JPG – Average time on site will always be less that it should be because it divides by all visits, including bounced visits for which it has no elapsed times!!!
• So, if we want Average Time on Site, we need to (force GA to) calculate it on only non-bounced visits. When we do that, we can see that the site has a Non-Bounced-ATOS of 6:07 mins, not 1:44.
• Now that we have a N-B-ATOS metric that we can use to compare engagement over time or between blog sections or between technical topics and “fluffy” topics (eg on Privacy, etc)

So, not only are ATOP and ATOS metrics not useless mietrics but, when used correctly, they can deliver the actionable insights with the best of metrics.