Alexander
Forum Replies Created
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Unfortunately, GA4 doesn’t provide demographics data such as age and gender in BigQuery directly. The demographics data, including age and gender, isn’t included in the GA4 data that’s exported to BigQuery because it is against Google’s policy to store personally identifiable information. You can, however, gather this data indirectly by utilizing the user explorer in Google Analytics, but remember to abide by Google’s privacy policy when doing so. If you need these details for marketing or analysis purposes, you might need to consider integrating with another data source that is compliant with privacy regulations.
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This seems like a complex issue and there could be a few reasons why this is happening. The issue could be related to the domain settings of the cookies. Cookies are domain-specific and they can’t be shared or accessed by different domains by default. If the parent page and iFrame have different domains, they would be treated as separate users by Google Analytics, even if the _ga and ga cookie values are the same.
Second, there might be issues with the way you’re passing the client_id and session_id from the parent page to the iframe. If the parent page and iframe are on different domains, you might need to use postMessage API to send the client_id and session_id to the iframe.
Lastly, Google Analytics uses a variety of data sources to identify users, which are beyond just client_id and session_id. Issues like ad-blockers or privacy settings in user’s browser can interfere with the ability to track users accurately across pages.
So, you might want to recheck the domain settings, how you’re passing the identifiers between parent and iframe pages, and potential impact of browser privacy settings or ad-blockers.
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Sure! This situation can be caused by several factors. First, you could be experiencing high levels of single-visit users, possibly due to a specific marketing campaign or fluctuations in your target audience behavior. Second, something could be causing the users’ cookies to reset, making them appear as new in your GA360 account. This could be caused by users who frequently delete their cookies, use cookie-blocking software, or visit your site in private browsing mode.
Considering these issues, one way to dig deeper would be to cross-reference the data with other metrics. Check to see if you’re having an increase in traffic from specific referral sites or advertisements. Another way would be to take a detailed look at the user journey. Are these ‘new users’ behaving differently compared to your regular users?
Keep in mind that it’s normal to see some degree of variation in a website’s new user percentage, especially when changes are made to the site or its tracking methods. If you’ve recently implemented the dual tagging with Adobe Launch, allow some time for the systems to calibrate and see if the numbers stabilize. Finally, don’t forget to check for any changes in your site’s cookie policy, as this can negatively affect the accuracy of your user tracking.
However, if you’re still puzzled, it would be wise to have someone proficient in web analytics look into the situation more in depth.
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The discrepancy may be due to various factors that affect data processing between Google Analytics 4 (GA4) and BigQuery. GA4 has additional features, such as engagement_time_msec, that are based on front-end JavaScript events. These events may not be logged with as much precision due to network latencies or system performance variations.
The API might filter or adjust data for consistency before reporting, which is not reflected in the raw data. This can also be influenced by time zones of the servers where data accumulated, and the specific instant data is exported, which could lead to minor discrepancies.
Lastly, be aware of the data latency. BigQuery’s data freshness can vary and can take up to 24-48 hours to sync fully. So, if you’re pulling data and comparing it with what’s being shown in the GA4 API at this moment, you might see some differences.
Given these complexities, the 3% you’re seeing is within an acceptable margin of error. To align BigQuery data more closely with GA4’s API numbers, consider cross-checking your date range, app instance ID, and user_pseudo_id. If the problem persists and the discrepancy is major, you might want to consider contacting Google support.
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Hey! So, the issue you’re bumping into is pretty standard. Every user, including service accounts, has a cap on the number of accounts they can access. Last time I checked, this limit was somewhere in the hundreds, which is generally more than enough for most users. You must have a lot going on if you’re hitting those numbers!
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The discrepancy is probably because Google Analytics API and BigQuery handle data sampling differently. Google Analytics samples your data when it gets too large, while BigQuery gives you all the raw records. That’s why BigQuery may show more data than API. It’s like watching a movie in 4K where you see everything (BigQuery) or in SD where some finer details are lost (Analytics API). You may want to use smaller data segments or switch to GA360 for unsampled data.
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You might have added event code on your site and in GTM causing duplicates. Go through your GTM settings and website code to find and remove the duplicate. You could also trace your events using GA’s debug mode or browser extensions like Tag Assistant. Keep digging, you’ll find it!
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As of now, in GA4 properties, the audience data isn’t present in the BigQuery event export. So, you would not be able to use it to filter in Looker Studio.
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Hey! So here’s the thing – Google launched GA4 with the idea of saving on servers used for supporting Analytics. In its earlier avatar – Google Analytics Universal Analytics – it was just too feature-packed as a free product.
With GA4, things have changed a bit. We’re talking harsher data retention rules, limitations on dimensions cardinality, stricter sampling, you get the drift. It’s a different ballgame.
Coming to your question, remember that one session can have only one associated channel. What’s likely happening is, GA4 isn’t just adding up the session numbers. That would be too costly processor-wise. Instead, it uses complex algorithms that make the process speedier but less precise. It’s similar to this technique called ‘hyperloglog’ used in data aggregation.
Oh, did I mention? The GA4 interface can be buggy at times.
But hey, there’s a workaround! To make those numbers add up, you can carry out your analytics in a BigQuery export from GA4. Or even better, use a Business Intelligence tool of your choice on the exported data. Hope this helps!
