Making a "compared to site average" measure for Google Analytics queries

[Daniel]

We recently noticed that Google's Sessions, users, and other metrics are extremely inflated when querying audience affinity category, in-market segment, or other category dimensions.  If you query sessions by affinity category for instance, Google will report hundreds of sessions per affinity type, yet the sum of those sessions might be 10x or 20x the actual total session count.  Since this discrepancy exists right in Google Analytics's own dashboard, it is definitely a GA issue and not a supermetrics issue, but there is a workaround in GA that I would like to replicate with Supermetrics and I'm wondering if someone can let me know how I would do it...or if I should stop trying because it's not possible.

I believe that the reason for this discrepancy is that a single user may fall into multiple affinity categories, but GA does not allow you to breakdown the affinities by user id in most cases so they just count the associations and hope that nobody notices that they don't add up properly. I have not found a way of fixing this yet, but I can select the "compare to site average" option in GA to create a less confusing data model which shows the average propensity of visitors to fall within specific categories rather than a bunch of numbers that just don't seem to add up properly.

Here is an example of the user count discrepancy in GA along with the "compare to site average" data model:

So the question is, is there a way for me to query the "compare to site average" metric in supermetrics, or is there a way for me to calculate this metric manually so that I can recreate the view shown in the above example?

[Leo Wang]

Have you gotten a reply to this question? I'm interested too.

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You may view and respond to the ticket here https://support.supermetrics.com/helpdesk/tickets/10516 

 

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