The sum of total users in rows one and two is 2,274(988+1286), but for some reason, Google is returning 1,956 as the total number of users, how is that possible?
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I'm trying to show the % of users who completed a task on our website, there are four possible outcomes, so I want to break down the % of total for users who completed each. Looking at my table in Data Studio, the % add up to >100%, yet the actual User numbers are the same numbers I'm seeing in Google Analytics. I also have Total Events and % of total for Total Events in the table, everything runs smooth there, the four outcomes % adds up to 100% like it should.
What can I do to fix this? My metric is Users and the Type is "percent" and the Comparison Calculation is "percent of total".
Found the solution. Data Studio dedupes the Users. So in this case, users were able to fail and then succeed in these instances, so Data Studio was actually accounting for those who had failed and then gone on to succeed, hence the >100%. It took the one Users event # and divided by the total to then get the Users %.
I have a Google Analytics event label with high cardinality that I'd like to implement - it is a string that can take on any combination of a finite-but-large number of names in a comma-separated list.
I'm worried mainly about losing data - I found this Analytics Help support page:
https://support.google.com/analytics/answer/1009671?hl=en
...which states:
Reports containing high-cardinality dimensions may be affected by
Analytics system limits, resulting in the creation of a rolled-up
(other) entry in the report to contain the data that exceeds these
limits.
...and am wondering if that would also affect reports without the label included, i.e., reports just looking at unique category/action pairings - would GA still roll-up otherwise-identical into "other" entries if the (undisplayed) labels are different?
Also, am wondering if there would be any hits to performance for similar report types (not looking at labels, just category/action pairings).
Maybe this is just bad practice out of the gate? :)
Google Analytics stores daily, in the processed tables, up to a maximum of 50,000 rows (in Google Analytics 360 the limit increases to 1,000,000 rows, making the problem of data aggregation less frequent). As a result, many combinations of unique dimension values are stored for each table processed every day. If a given table has a larger number of combinations of values of dimensions, Analytics stores the top N values and creates a row of type (other) for the remaining combinations of values.
https://www.analyticstraps.com/valori-raggruppati-in-other-nei-report/
Anyway, I tried a custom report with label and without (same time period) and with label I got (other) while without that dimension I got the actual values.
So the problem you fear does not exist (unless the event action is also high cardinality) :)
I am building one BI over analytics data. Users Tracking give different numbers over different dimensions (Hour, Day, Week, Month) in the same period of dates.
How do I explain this? Is this wrong?
Users is not a metric that you can sum for different time ranges as one user can have sessions in multiple time ranges and so simply summing the number of users each day won't equal the total number of users in a week etc.
I was trying to figure out how many page views each of my Ad Words campaigns are generating to my website via Analytic s.
I followed this path: Traffic Sources > Sources > Campaigns. But there I can only see how many visits, pages/visit, avg. time on site, % New visits and Bounce rate.
To find the number of page views I have to multiply the number of visits by the number of pages/visits, right? But I want to know if it's possible to get this number straight away.
We've noticed lately that as our site is growing, our data in Google Analytics is getting less reliable.
One of the places we've noticed this most strongly is on the "Realtime Dashboard".
When we were getting 30k users per day, it would show about 500-600 people on line at a time. Now that we are hitting 50k users per day, it's showing 200-300 people on line at a time.
(Other custom metrics from within our product show that the user behavior hasn't changed much; if anything, users are currently spending longer on the site than ever!)
The daily totals in analytics are still rising, so it's not like it's just missing the hits or something... Does anyone have any thoughts?
The only thing I can think of is that there is probably a difference in interpretation of what constitutes a user being on line.
How do you determine if the user is on line?
Unless there is an explicit login/logout tracking, is it possible that it assumes that a user has gone if there is no user generated event or a request from the browser within an interval of X seconds?
If that is the case then it may be worth while adding a hidden iframe with some Javascript code that keeps sending a request every t seconds.
You can't compare instant measures of unique, concurrent users to different time-slices of unique users.
For example, you could have a small number of concurrent unique users (say 10) and a much higher daily unique users number like 1000, because 1000 different people were there over the course of the day, but only 10 at any given time. The number of concurrent users isn't correlated to the total daily uniques, the distribution over the course of the day may be uneven and it's almost apples and oranges.
This is the same way that monthly unique and daily uniques can't be combined, but average daily uniques are a lower bound for monthly uniques.