[Sophia]

The person is experiencing an issue with the reporting of data from their database. They’re trying to categorize user information according to certain criteria such as the medium and source, along with measures like total users, engaged sessions, and engagement time. Using a specific query, they’ve managed to pull some data, but when they compare this data in a visualization tool called Looker, they notice a significant discrepancy between the query results and the GA4 data. They’re looking for reasons that might explain why the data from their query doesn’t match their GA4 data.

[Sophia]

Firstly, don’t worry as this is quite common. The issue may be related to the way data is processed in GA4. To check if the event is firing correctly, you can use Google’s Tag Assistant. Install it on your Chrome browser, enable recording on your site, and perform the actions the same way a user would. When you come to the payment step, the extension will show whether the payment event has been fired successfully. If it’s not firing as expected, you can then fix the issue in the website’s GTM or at the event’s source. Make sure to double-check the event setup in GA4 for mistakes or discrepancies. Finally, remember that it might take a while for the data to show up in GA4. If still the problem persists, contact the GA4 or GTM support for further assistance.

[Sophia]

In GA4, user interactions are tracked primarily through events. Therefore, setting up each individual question as an event could provide you with the granularity you need to understand at what point users leave the process. It’s a similar concept to how you used event categorizations in Universal Analytics (UA). Each question would be an event, and you can map out a funnel based on these events using GA4’s Funnel Exploration report. However, keep in mind, the implementation would require tagging each question with its specific tracking code which can be a bit of work upfront. An alternative option is to leverage the ‘engagement_time’ event which is automatically captured by GA4. This event tracks active engagement time on a screen, and could provide some insight into user behavior on the questionnaire. However, it may not be as specific or granular as setting up individual events for each question.

[Sophia]

In your situation, it appears you are trying to update the webStreamData.defaultUri using Google Analytics Admin API. However, you are receiving a 400 error indicating that the update_mask.paths_list is invalid. The update mask in Google’s Protocol Buffers is used to specify which fields you wish to update within a resource. Therefore, it requires the exact field name from the resource. In this case, make sure the naming is correct according to the API documentation. Generally, it’s common to use camelCase when specifying fields in an updateMask. Therefore, try using ‘webStreamData.defaultUri’ as your updateMask. If that doesn’t solve the issue, there’s a possibility that updating the defaultUri may not be supported, you should check the API documentation or reach out to Google’s API support for clarification.

[Sophia]

In order to transition from analytics.js to gtag.js when you are using Google Tag Manager (GTM), you would need to create a new GA4 property in your Google Analytics. After that, inside GTM, you will have an option to install GA4 Setup Tag where you can enter your GA4 Measurement ID. You should also replace the existing GA3 tags with GA4 as well. After these changes, be sure to test and validate that your analytics are working correctly. It is recommended that you keep your Universal Analytics running in parallel with Google Analytics 4 for the time being, till GA4 matures, as GA4 is fundamentally different from the previous version.

[Sophia]

It’s common to find discrepancies when data is broken down by specific metrics like ‘source’ or ‘source/medium’ because these only report on sessions where this information is available or tracked. If a session didn’t have a source (for example, if it was a direct visit), it wouldn’t be included in this specific breakdown. Another reason may be due to applying a filter that excludes some sessions, or incomplete data collection in your Google Analytics. The difference can also be due to data sampling. Google Analytics often uses data sampling in its reports when you have large volumes of data, as in your case. So, the data you see is just a subset, or sample, of your total data. To ensure more accurate reporting, you might want to consider using smaller date ranges or, if you’re using Google Analytics 360, increase your data sampling size. It’s important to carefully interpret these numbers knowing the parameters and limitations of the tool you are using.

[Sophia]

Hey there! So, you’re worried that adding more scripts (like the one for GA4) could slow down your page? No need to stress about it, it’s a common misconception. The reality is, these small, non-blocking scripts like gtag usually don’t really affect your page load time in a noticeable way when loaded asynchronously.

Now, if you’re into keeping things neat, you might want to look into using something like Google Tag Manager or another tag management system. They’re designed to handle this kind of stuff, only loading scripts as they’re needed. It’s kind of like having one ring to rule them all, without the whole destroy-the-world thing.

And, just a little food for thought, if loading scripts is a big concern, have you considered not loading the analytics.js at all? You could include everything directly in your source or even rewrite everything to send data directly through the measurement protocol. This would apply to GA4 as well. All these libraries do is make the process of sending network requests to their endpoint easier to manage. Just a thought!

[Sophia]

Absolutely! You’ve linked your GA4 property to BigQuery and are dispatching custom events from your app, right? Events sent either through the standard JS library or the back-end via the Measurement Protocol API should all make their way to BigQuery, no worries there.

Now, you’ve mentioned that all of your data is showing up in the Realtime report on your GA dashboard – that’s brilliant! However, you noticed that only front-end events are making their appearance in BigQuery. So, where are those also important back-end events sent via Measurement Protocol API? They should be showing up too! You’ve done everything right by not stirring up any custom events or dimensions in the GA dashboard, as it’s not essential for BigQuery data.

So, without a doubt, those events tracked by the Measurement Protocol API should be making their grand entrance in BigQuery. No extra steps needed on your part. Turns out, I was sending events to the wrong property, which is why they pulled a disappearing act in BigQuery! Once I had that sorted, everything fell into place. Always remember to double-check those property settings!

[Sophia]

Once UA properties stop collecting data, if you continue to use the old tracking code (like the window.ga method), it’s not going to send data to Google Analytics anymore. This might not necessarily show up as Javascript errors in your console, the event information just won’t be passed along anymore. So, you won’t see these events in your Analytics data. It is recommended to transition to Google Analytics 4 (GA4) and update your custom event tracking to suit the GA4 system.