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Analyzing Client IDs in Google Analytics: Comparing Data Across Time Periods
Hey there, I was trying to check on Google Analytics if a user who was there on 13/08/2020 came back at all between then and now. I used the “User Explorer” and tried to compare two time periods but it only shows me 10001 rows and I am pretty sure there’s more than that. Is there a workaround for this?
And, is there a way to download user data or better still, load it into R using an API? I should let you know though, I’m not using GA4. Any ideas? Check out the pics below for more clarity.
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2 Replies
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Sure, let me explain this in a simpler way. If you want to check if a particular user came back to your website, you’d probably have to export your Google Analytics data to BigQuery. That way, you can analyze your data thoroughly and detect returning users more easily.
In case you didn’t know, Universal Analytics (which I assume you’re using) allows you to create custom reports. This could be a convenient tool for you, especially because it lets you use the “Client Id” dimension. However, just a friendly heads-up, the Client Id dimension in Universal Analytics can be a bit tricky to work with. I’ve noticed that it doesn’t always work perfectly when filtering or searching. So don’t worry if it feels a little clunky, you’re not alone!
I hope that helps make things a bit clearer! If you have any more questions, feel free to ask.
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Google Analytics UI indeed has a limit of 10,001 rows for data tables, it is to ensure optimal performance. For workaround, you can try a couple of ways:
1. If you’re doing an analysis, you can try to reduce the size of your dataset by narrowing down your date range.
2. If it is necessary to handle a large dataset, you can make use of the Google Analytics API. The Google Analytics API does help to extract a significant amount of data which can be more than the limited rows.As for the part of downloading user data and loading it into R, yes, this is doable. You can use Google Analytics Reporting API v4 to query and retrieve data. A library called “googleAnalyticsR” can be used in R to interact with Google Analytics Reporting API. This will save all the data to data frames in your R environment.
Please remember that due to privacy and security reasons, GA does not provide identifiable user-level data. Therefore all data extracted would be aggregated or anonymized.
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