I´ve created an google analytics segment with the demographic feature "location" equals "France". (in the menu behavior -> all pages)
Now I want to use this for the period 2014-2018 but I only get data for the last two years.
If i analysis without a location filter, I get data for the hole period.
Is there a way to get older data per country?
EDITED based on the answer from Ms. Easy:
The condition in the segment:
It's because of Data Retention period in your GA property settings.
Admin -> Property -> Tracking Info -> Data Retention
I guess there are 26 months.
Data for "Country" is not available for the period before 26 months.
Related
I am getting the average engagement time in my analytics dashboard. I want the session time to be displayed in categories based on time:
Sessions less than 1 min
Sessions with 1-3 mins of engagement time.
2 options to accomplish that:
Create Segments for each Session length
Under Segments > Behaviour "Session length" > 3
Aggregate your Data in Excel or Google Data Studio
I am trying to get the average number of users or sessions per month for GA custom reports. In Google Analytics, i can get total num of users or sessions, however there is no way to get total number of months in time dimension to calculate average users per month, and I was not able to find time dimension data show up in calculated formula any way. Any suggestions? Thanks, Hong
You can use 'Month of the year' dimension:
Or if you want you can use 'Month of year' dimension to get the months in YYYYMM format.
It looks like my country wise data in google analytic is wrong or else I'm unable to understand the way that GA track Geography data.
My total monthly users is 1.3 million and when I go for country wise data then India has 1.5 million unique users.
Why my total website data is less than India data?
Make sure you are looking at the same metric User and User
Make sure you are not under sampling (there are no pre-aggregated report in GA with geo location) - try selecting only one day (not today) and see if you get the right number
Before Tuesday, March 14th, we saw the data lag in Google Analytics at approximately 1-2 hours. (It was never immediate.) You can see this effect on the Conversions > Ecommerce > Overview page if you search by date and select "today" to "today" (1 day's worth of data)
As of Tuesday, March 14th, we started seeing the lag for this overview report anywhere from 8-12 hours, with an inconsistent aggregation time. For example, it is now 4 PM here on the east coast (EDT), and here is a screenshot of our GA overview tab (I have obscured the revenue number for our privacy). As you can see, there are no numbers after 6:00 AM.
We saw this same effect yesterday (about 8-10 hour lag), and the following day the overview report seemed to fix itself (catch up with all of the aggregated data).
Now, what's more interesting, is that if we either A) Add a "Secondary Dimension" or B) use a "Custom Report", we can see all our data near real-time. For example, if I switch into the Ecommerce > Sales Performance report, then add a Secondary Dimension of "Hour of Day", I can see all my data through 2 PM today (about a 2 hour lag as it is now 4:22 pm as I am writing this)
[
Note that to replicate this I sorted the "Hour by day" column by descending order (showing most recent first.)
Our questions are:
(1) Does anyone know why searching by Secondary Dimension or Custom Report shows us the data in more real-time than just looking at the overview report?
(2) Can anyone else confirm that what used to be a 2-3 hour delay now appears as if it is a 8-12 hour delay, starting on or around March 14th (possibly a few days earlier, this is the first day we can remember seeing this effect)
We are using Universal Analytics (with Enhanced E-commerce) implemented via the newer analytics.js. We are NOT using the older ga.js (we moved away from that about a year ago.)
We are not a GA 360 customer, just a regular free account.
From Google Analytics Help Center article.
Processing latency is 24-48 hours. Standard accounts that send more than 200,000 sessions per day to Analytics will result in the reports being refreshed only once a day. This can delay updates to reports and metrics for up to two days. To restore intra-day processing, reduce the number of sessions your account sends to < 200,000 per day. For Analytics 360 accounts, this limit is extended to 2 billion hits per month.
What it means is that for Standard accounts up to 48h delay is normal, if you have more data it can take more if you have less data it can be faster.
Regarding your observation that certain reports load faster than others this is linked to the design of Google Analytics Backends. Google will generate pre-aggregated tables with common reports to speed up consult and that sometimes can takes longer to process. Other non-common reports can't be answered by aggregated reports so it can be responded by a different backend that already has fresher data. So it is considered normal to see different levels of freshness in different reports.
Google Analytics 360 has fresher data of course.
This other table from the HC article highlights some of the differences and has more info.
Suppose I have 65 people that register on January 1, 2012.
I want to find out how many of those 65 people returned to the site that same week. (More generally, if n people signup on date A, I want to be able to find out how many of those n people return in a given date range.)
Is there a way to do this using Google Analytics? If so, how? I am currently getting the user's username for each page hit.
If you only need to track people who sign in then you don't need to get very fancy. You can copy the relevant user attributes, such as sign up date, from your DB to GA using events or session level custom variables.
But if you want to track everyone, including those who don't sign up, then you'll need to use visitor level custom variables (GA cookies).
I explain how to set this up in detail in this post so I'll just highlight the key points here:
First, decide how to layout the data in Google Analytic's custom variables based on your requirements. For example, are you storing retention dates for daily, weekly or monthly tracking? Do you also want to track cohort goals? Partition this data into the available custom variable slots.
Write the cohort data to these custom variables when visitors arrive or achieve goals using Google Analytic's _setCustomVar function. Setting the fourth parameter of that function to 1 indicates you want to do visitor-level (cookie) tracking.
For each cohort you wish to analyze, create an advanced segment in Google Analytics. Using a regex expression in the condition will give you the flexibility to segment for interesting cohorts. ex: "All users whose first visit was the week before Christmas".
Analyze the results with reports by specifying a date range and the corresponding cohort-sliced advanced segments. Another option is to extract the data using the Google Analytics Data Feed Query Explorer or their API.
Once you've put in the work your new visitors will be stamped by their first visit date and nicely fall into each daily or weekly retention bucket. This is what it might look like if you were tracking weekly retention, for example:
This is not a full solution, but here are some points on how I would approach this problem with the help of Google Analytics:
You have to make sure that you somehow store the registration date of each user, either in your database or in a cookie. Then have a look at Google Analytics Event Tracking. You could for example set up a new category based on the registration date. On every page load in your page, you then have to set up this event tracking call, for example like:
_trackEvent("returns", "2012-01-01", "UserId:123123123")
This way you will receive all page views for users that registered on that particular date. To add a date range in this, you have to make sure that these events only get fired for the number of dates after the signup (e.g. 7 days).
After your date range, you will be able to see how many page views and how many users returned - you even know which users came back.