Aubrey
Forum Replies Created
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This may not necessarily be a glitch. The discrepancy could be due to a variety of reasons. One common reason for such variation is the difference in the way data is processed and aggregated in Google Analytics and Azure SQL. Also, it’s possible that there might be duplicate data in your Azure SQL tables. Google Analytics deduplicates your data, whereas Azure SQL does not.
Moreover, the data in Google Analytics can be sampled, especially if you’re querying a large amount of data. This could result in lower numbers in Google Analytics compared to Azure SQL.
There could also be a time lag in data reflection. Data pushed to Azure SQL might contain the latest events not yet displayed in Google Analytics reports, hence the higher numbers in Azure SQL.
Remember, the data pulled via API might also contain more detailed raw data compared to pre-aggregated report metrics you see in Google Analytics interface.
Do double-check your pipeline settings and all configuration details. If everything still appears to be configured correctly, you should consider reaching out to Azure or Google Analytics support for assistance.
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The user is trying to create a report that organizes data by total users, engaged sessions, and engagement time. They’ve written a code, or “query”, to extract this information, but what they’re finding is that the data from their query does not match the data from Google Analytics 4 (GA4) once it’s imported into Looker, a business intelligence software. They have included their code, but are unsure why the discrepancy between the GA4 data and their query results is happening.
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This issue seems to be a limitation within the GA4 API, particularly in relation to how dateHourMinute dimension interacts with the Default Channel Grouping dimension. Currently, it doesn’t appear that the GA4 API supports a dimension filter for Default Channel Grouping when dateHourMinute is selected. The official documentation provided by Google within their Dimensions and Metrics Explorer doesn’t list any workaround either. It might be helpful to continue monitoring Google’s updates for any added functionalities in the future. For now, you might consider manipulating the gathered data post-extraction or using other APIs that allow for more flexibility with dimensions. It is also possible that creating a custom dimension that categorizes channels like the Default Channel Grouping may solve the issue.
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Yes, you can still track ‘mailto’ clicks as conversions in Google Analytics 4, but the process is a bit different. You’ll need to set up a custom event for the mailto link click in Google Tag Manager and then mark that event as a conversion in Google Analytics 4. Similarly, in Google Ads, you can set up a conversion action for the same.
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In GTM, instead of creating a new RegEx table for each trigger, you can create a single trigger that looks at the ‘Page Path’ (or similar variable) against your RegEx table. Check if the output matches with the specific group name or page path you are interested in. This would trigger when someone lands on one of those specific group of pages and keeps things clean in your container.
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No, it’s not possible to retrieve a user’s Google Click ID (GCLID) using the Measurement Protocol. The Measurement Protocol is designed for sending data to Google Analytics, rather than pulling data from it. You can, however, send the GCLID in a hit to Google Analytics with the Measurement Protocol using the “gclid” parameter, as long as you are able to capture the GCLID from the landing page URL when a user arrives at your website from a Google Ads click.
