… 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.
ATOP = | |
Total Time on Page | |
Count of pages having a T.O.P. value |
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:
ATOS = | |
Total Time on Site | |
Count of ALL visits |
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?
# of visits | ATOS (mins) | Total TOS (h:m:s) | |
All visits | 105603 | 104 | 50:45:12 |
Non-bounce Visits | 29902 | 367 | 48:20:34 |
bounced visits | 75709 | 0 | 0:00:00 |
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!
“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)”
“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.”
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.
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