

Emma
Forum Replies Created
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Emma
Member27 June 2023 at 7:35 pm in reply to: Analyzing User Pathways in GA4: Understanding Website Navigation SequencesCurrently, Google Analytics 4 does not have a built-in report that readily shows user flow or pathing like the previous versions did. However, you can make use of the ‘Events’ and ‘User Explorer’ sections to track individual events and user interactions. You could also leverage the BigQuery export feature to dig deeper into the data and create a more customized view of your users’ journey. Keep in mind though, this may require some level of familiarity with SQL or programming. The Google team also continues to add new features to GA4, and they might add this feature in the future.
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Emma
Member11 May 2023 at 3:44 am in reply to: Are the exclude and include filters in filterExpression() function in Google Data API not working correctly when using Python?There could be a few reasons you’re experiencing this issue when filtering out internal traffic from your Google Analytics 4 (GA4) reports. First, it’s possible that not all internal traffic has been correctly filtered out. This could be due to not all internal IP addresses being included in the filter, certain devices or browsers not being filtered, or some internal users browsing in incognito mode or with a VPN, which would hide their traffic. Therefore, check your filtering setting and make sure all internal traffic sources are covered.
Another possibility is that your internal traffic proportion is relatively small compared to your overall traffic which might not cause significant discrepancy in the results before and after applying the filter. Moreover, there might be some delay in data reflection due to GA4’s data processing latency. Finally, remember that GA Audience filters and Data API filters might behave slightly different due to inherent mechanics of these services. Whatever the issue may be, understanding exact filtering mechanics and regular monitoring would be key for accurate data analytics.
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In short, Google Analytics 4 (GA4) gives you two main ways to track user interactions: automatic tracking, where GA4 figures out what to track for you, and custom tracking, where you code specific events you want to monitor.
Now, your concern is that you’re tracking a lot of custom events (mostly clicks) that might look messy in the GA4 interface. You hoped to group all these different clicks under one mega ‘click event’, and the way you tried this was by tagging your custom clicks as ‘click’ events and giving them unique labels. This would, in theory, let you filter clicks by these labels and thus see each custom click’s data individually while keeping your events dashboard clean.
But you’ve run into problems: namely, you can’t find these custom click labels anywhere in GA4, and you’re worried that your custom click tracking overlaps with GA4’s automatic click tracking, which might duplicate your data.
Unfortunately, GA4 doesn’t currently support categorizing events the way you’re trying to do it — but don’t fret. You can instead use Google’s recommended approach of creating custom events for each of your unique click events, just like you were doing before. Though you might initially find it more cluttered, the extra organization can help you better analyze your interactions in the long run.
Concerning the double counting of clicks, if you use GA4’s automatic click tracking and your custom click events together, you indeed might count some clicks twice. To avoid this, you’ll have to turn off GA4’s automatic click tracking if you decide to track clicks with custom events — or vice versa.
Lastly, if leaving automatic click tracking on for some reason is not an option, then yes, creating custom events might be your best bet. But, if you have the flexibility, utilizing both automatic and custom tracking can yield the most comprehensive picture of user engagement on your site.
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Emma
Member28 March 2023 at 4:47 pm in reply to: Troubleshooting Access Issues in PHP Google Analytics4 AdminIt’s challenging to determine the exact issue without seeing the specific code snippets you’re referencing. Still, there are a few common misconceptions you might want to check.
Firstly, make sure you have matched the Client ID and Secret in your staging environment with those in your Oauth key file and also check that the data in your Oauth Key file and the registered app in Google Developer Console are matching perfectly.
Sometimes, the “WEIRD_PROJECT_ID” might refer to the project ID in your Google Cloud console, it could be a placeholder for your actual project id. Double check that you’ve replaced it with the actual correct project ID.
If your API is still in alpha, it’s likely that it doesn’t have all functionality released yet. Generally, in the alpha testing stage, only a select group of users try out the product. They’ll typically report any bugs or issues they encounter, and those will be fixed before moving into beta. The beta version usually has more functionalities than the alpha version, as many of the critical bugs found in the alpha stage are fixed.
However, it’s hard to tell when exactly the beta version will be available or when the final code will be released as it mostly depends on the company and their roadmap. Do keep an eye out for updates from Google for GA4’s release schedule.
The process you’ve laid out for using the Google Analytics API seems correct, so it’s quite puzzling why it’s not working in staging when it works in your development environment. The missing bits of code or any environmental differences between your development and staging environments could account for this unexpected behavior.