[Oliver]

While the image description mentions a “500 status code”, it’s crucial to determine the exact issue for a successful debugging. The HTTP 500 Internal Server Error is a very general HTTP status code that indicates something has gone wrong on the website’s server, but the server could not be more specific on what the exact problem is.

To get a more detailed error, you should temporarily enable debug mode in your .env file by adjusting the APP_DEBUG value to true. Then try to trigger the error again. Be sure to change it back to false once you’ve solved the issue, as leaving debugging enabled in production can lead to the exposure of sensitive information.

Also, ensure that you have properly set up the credentials for the Google Analytics API in your config/laravel-google-analytics.php file. Verify that you have the correct values for the view_id, service_account_credentials_json, etc. Check to see if the service account linked to the service_account_credentials_json file has the necessary permissions in your Google Analytics account.

Lastly, don’t forget to clear the configuration cache using the php artisan config:clear command after making changes to the config/laravel-google-analytics.php. The Laravel framework will cache configurations to improve performance, so changes made won’t take effect until the cache has been cleared.

[Oliver]

There can be many reasons for this discrepancy. One reason could be caching or some delay in data reflecting in the UI; it might not be real-time. Try clearing the cache or refreshing, or give it some time to see if it updates itself. Another possibility is if the UI is filtered in any way, i.e., it might be excluding some kinds of users, while the API counts all users. To solve this, check for any pre-applied filters or conditions on the UI that might be limiting the number of users it’s showing. Lastly, there might be a bug in either the software displaying the UI or the API. It might be a good idea to engage the professionals who developed these platforms and bring the discrepancy to their attention.

[Oliver]

It sounds like you’ve done everything right in setting up the tracking, but sometimes there can be a delay between when the data is collected and when it appears in your reports. Also remember to check if you’re looking at the right date range that includes when your data was collected.

[Oliver]

The difference in session counts between ‘A’ and ‘B’ could be due to a couple of reasons. One possible reason could be the way the session count is calculated in Google Analytics 4 (GA4) and BigQuery. Google Analytics may be counting sessions differently, such as including instances where multiple sessions are counted as one due to a user leaving a website and returning within a short timeframe. Alternatively, how BigQuery processes data might be different. Depending on how the data is collected and processed, it could potentially result in discrepancies. Another potential reason could be the way Looker connects and fetches data from GA4 and BigQuery. Even though you didn’t apply any filters while conducting your analysis, it’s worth checking the data extraction and transformation process. You may want to recheck the configuration settings of both ‘A’ and ‘B’ connectors and make sure there’s no unintended data transformation on the Looker side. It’s advised to thoroughly investigate to know the exact reason. Differences in data can sometimes arise due to very nuanced reasons that require a detailed walk-through of your data processing pipeline.