Firebase Analytics reports data w.r.t. Daily/Weekly/Monthly Active Users.
Few questions:
(1) Dashboard:
Projecting the Daily Active Users to a month, does not match the value shown in Firebase Dashboard.
For e.g. if Daily Active Users is 30K, then Firebase shows the corresponding Monthly Active Users as 150K.
Does it imply that there were 30K users in last 7 days, and 120K in the preceding 21days?
Not sure why isn't it 30 days x 30K = 900K.
(2) On selecting Firebase > Events > Select_Content > App version
Last 7 days: shows approx 100K
Last 30 days: shows approx 140K
Does it imply that in the 21 day period only 40K User sessions occurred, while the App usage went up drastically in last 7 days?
Please help clarify.
thanks in advance,
The Active Users report in the Firebase dashboard is showing counts of users in the past 30, 7 and 1 day. The values are not projected, but rather based on user engagement that has been measured over those periods. The other thing to keep in mind is for each of those periods, it's the count of unique users over the entire period.
So, for example, if your seeing 150K Monthly Active Users (which is defined here as 30-day active users), that tells you you've had 150K unique users engage with your App in the last 30-day period. If you're seeing 30K Daily Active users, that tells you you had 30K unique users yesterday, and 120K different unique users from the 29 days before yesterday.
If the same user engages with your App more than once in the period, they only count as one. Out of your 30K users from yesterday, a number of those would have presumably engaged in the 29 days before that, so it's expected that your Monthly Active Users would be less than your Daily Active Users x 30 days. How much lower would depend on the specifics of your app, but the closer those numbers are, the more frequently the same users are returning to your App over the 30 days, which is positive in terms of user engagement retention.
Related
I use BigQuery to get data from Firebase, and I'm having some issues with data accuracy. Figures I get from the data in BigQuery are not matching the ones on Firebase dashboard.
I recently understood that Firebase does not look at all users, instead it considers only active users. So now I have filtered for active users by putting a filter for engagement_time_msec>0. My active users number is matching up with Firebase dashboard now (just 1-2 digits difference occassionally).
But my main problem is with the average engagement time!
Firebase (and GA for Firebase) shows average engagement time metric under engagement overview. When you hover over it, it gives this definition.
"Average Engagement Time per active user for the time period selected"
However, when I get data through BigQuery and calculate this manually, my numbers are off.
I am calculating Active Users as Distinctcount of user_psuedo_ID where engagement time>0, and engagement time is being summed up where event name = user_engagement. (I have converted engagement time msec to mins)
Average engagement time = SUM(Engagement time mins)/Active Users
This should give me an average engagement time per active user, but this figure doesn't match the one in Firebase console. I have tried so many methods, and I fail to understand what Firebase is doing at the back end to come up with these values.
P.s: I have also tried summing up engagement time without a condition on event name and that gives me an even greater average, making the difference between it and Firebase even bigger.
Please help!!
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.
Can someone explain me the difference between the 2 APP metrics, Active Users and Retained Users. I am looking at these metrics through flurry API and I am grouping my data at month level.
I am not able to understand what is my Retained users number mean at a month level.
Retained users plots two lines. The blue line is the number of users who installed the app in a given month, week or year. The green line identifies how many of these users had at least one session with your app in the past week.
The two lines will generally converge for the current month so use weekly or daily views to analyze the current month.
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.
I check my Google analytics on a regular basis to obviously see my daily hits and for some reason my UNIQUE VISITORS count dropped from 1770 to 1730 over a day. How this is possible?
I started to notice this about a week ago when I saw that my UNIQUE VISITORS count wasn't going over 1800 (which it should have considering the visits I receive). I receive an average of about 60 unique VISITS a day but even if it was 0 unique visits a day, it doesn't sound logical that my overall UNIQUE VISITORS count would drop.
Now I can't take GA seriously anymore ...
Anyone have this problem before and / or could shed any light on the matter?
The statistics are period based. When a day passes, the period (begin and end) advances a day as well. So is perfectly normal your total unique visits changes from one day to another, because the period changed too.
For example: lets suppose your site receive 60 unique visitors every single day. You check your Analytics today (13-08-2012) and see 1860 visits. That amout is the total unique visitors for the period from 13-07 until 12-08.
But lets say that your site receives only 10 visitors today. Tomorrow (14-08) you will see your total unique visitors drops from 1860 to 1810, because the period will be from 14-07 until 13-08.