New users vs returning users never decrease - google-analytics

You would expect that the number of 'new users' would decrease over time, and the number of 'returning users' would accumulate. This should be the case because in our marketing we only target 'known' users. However, even when measured over a longer period of time (2 years) the number of new visitors never decreases, why is that?

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What's Google Analytics user/active user/new user/define?

I have a question regarding the active users definition in the active users report.
According to the official explanation (https://support.google.com/analytics/answer/6171863?hl=en)
1-Day Active Users: the number of unique users who initiated sessions on your site or app on January 30 (the last day of your date range).
7-Day Active Users: the number of unique users who initiated sessions on your site or app from January 24 through January 30 (the last 7 days of your date range).
Can I interpret sessions here as "at least one session"(one or above)? If so, the 7-Day Active Users can be users who only viewed one session during the last 7 days. How can this metric indicate the "returning users"?
Should I sessions as " more than one" (two or above), which seems to make more sense?
Another question: As 7-Day Active Users counts into the active users from the last 7 days (including today), so it should include all 1-Day Active Users . By the same logic, the 14-Day Active Users should include all 7-Day Active Users, and the 30-Day Active Users should include all 14-Day Active Users. Am I correct?
If I am correct, then it will never happens that 1-Day Active Users are more than 7/14/30-Day Active Users.
What does the below sentence from the explanation page mean?
"In cases where you have a lot of 1-Day Active Users but the numbers drop off for longer term users"
Does it mean that 1-Day Active Users stabilizes/increases while the long term users decrease? So it's about comparing the trend, not the absolute active user number?
Users reports are bit tricky to understand in GA, basically it depends on the date range you are selecting.
Q1: GA considers a user as active if he had at least one session for that day irrespective of whether the user is new or returning or he had more than a single session.
Q2: No, all 1-Day Active Users are not included in 7-Day Active Users. For example a user had a session today and also on the 7th day then he'll be counted only once because in the selected date range at least one session is only considered.

How to differentiate active users from non active ones

I built an app and I would like to differentiate the behaviours of my users regarding their activity levels.
Objectives : make monthly users become daily users by understanding how daily users use the app vs monthly users and trying to narrow the gap between them.
I am well aware of the Daily / Weekly / Monthly active users Firebase offer but it is still a snapshot at a specific time.
Basically, if someone open a session at least one time during 20 days / month => highly active users, if someone opens it at between 7-20 times a month => medium active user, if someone opens it less than 7 times => low active users.
Do you have any clue on how to split these to then understand their behaviour?
because you are tagging your question firebase database that means you want to do it programmatically.
you can make a field in user node name it counter and every time the user login to the app you just increment the counter and make a query to bring the count that's it.

Google Analytics: Making an Experiment respond to earnings

I'm currently in the process of creating an A/B test in Google Analytics (aka an "Experiment"). It's for a ticket purchasing page where there are several levels of tickets for sale (eg. Standard, Premium, First Class, etc).
We want the winning page variant to be the one that earns the most money, not necessarily just the one that triggers the most Goals (ie. number of successful transactions).
For example:
Variant A
Sales: 20
Total Sales Value: $100
(20 x Standard tickets)
Variant B
Sales: 5
Total Sales Value: $1000
(5 x First Class tickets)
We would want Variant B to win, even though the number of events triggered (aka "Goals") was higher with Variant A.
I cannot find anywhere discussing this issue, but surely the average value of an Event should factored into the success of a Variant?
We have the Goal configured like this at the moment:
One thing I've considered is increasing the Value threshold to be larger than the average sale, so the Goal doesn't trigger unless the page performs better than average. This isn't ideal for obvious reasons, though.
Is there another way?

Track count of events unique by user rather than session

We have a way to fetch the number sessions unique per device and the number of The New Feature uses, this can be done with a public API and requires implementation of two events to be sent by mobile applications to Google Analytics server. It will give us a statistics of the sessions when The New Feature was used, although it doesn't directly reflect individual users activity.
Ex: the app was opened 1000 times among all unique users (devices), The New Feature has been opened 200 times, the resulting value is 200/1000 or 20%. The drawback is that at this particular case we have no way to tell that is wasn't one user who has opened The New Feature 199 times and another one who has opened it just once, the real retention rate is low to none.
The secondary statistics that we are aiming to be able to calculate is the percentage of unique users who have used The New Feature at least N times during the given period. This statistics should be a closer representation of the real The New Feature retention as it will both show the share of users who were using the feature and the dynamics of frequency. For that we are not clear of which events are needed to be set up.
Ex: the app was opened 1000 times: user A used The New Feature 10 times, user B 5 times, user C 4 times, most of the other users who used The New Feature opened it 2 times - The New Feature was opened 200 times in total. The resulting percentage of users: 10% have opened The New Feature at least once, 8% used it at least 2 times, ..., 1% used it at least 10 times.
The numbers from the second example are giving us more useful information about how often the new feature is being used, but it isn't clear how we can set it up. We would need a kind of the event that shows a number of uses of The New Feature unique by the users (not just sessions) and I think the event values might be used to distinguish the users, will it be possible to get the number of unique users who has triggered the event at least N times this way ? Any other suggestion is welcome.

How does collection sampling affect the "live" stats for Google Analytics?

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.

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