[Ethan]

It sounds like you’re encountering issues related to scope and data sampling in Google Analytics 4. The total revenue might be changing because of the way GA4 calculates metrics based on the dimensions you’ve selected. When you added new dimensions like “new users” or “Date,” GA4 calculates the metrics again for each unique combination of dimension values. This process might lead to internal data sampling or exclusion of some data points leading to the change in total revenue. This typically happens when the dimension added has a different scope. For instance, session-scoped dimensions, like ‘device category’, relate to data within individual sessions, while user-scoped dimensions, like ‘new users’, relate to data across all sessions by individual users. A mix of these can result in discrepancies.

To identify the correct total revenue, you may want to test your metrics in different configurations to see how they are impacted. Also, consider the scope of each dimension and metric you’re using. For a robust approach, you could pull your raw data into a database and query it directly, bypassing potential GA4 sampling issues.

Remember, the key is understanding that adding more dimensions essentially forces Google Analytics to finer segmentations, potentially lowering some aggregated numbers due to different scopes. So the total revenue isn’t necessarily decreasing, but is rather being re-distributed across the smaller, finer-grained segments of data.

[Ethan]

Vue-gtag should be compatible with both Vue3 and Vue2 applications. However, if you encounter issues using vue-gtag with Vue2, you may consider using vue-gtag version 1, which has been found to be more compatible with Vue2. In any case, it should be noted that gtag (the global site tagging by Google for analytics and advertising) is not Vue-specific – it is a JavaScript-based solution. Therefore, you can use it directly in your Vue2 application without needing a Vue-specific package if you so prefer. Just follow the implementation guide provided by Google analytics.

[Ethan]

You’re absolutely right. Backfilling was something we could do for Universal Analytics (UA) properties if you were a GA360 customer, and unfortunately, the Data API doesn’t spit out event level data. But as of now, trust me, there’s no official way to backfill BigQuery event data for GA4 properties. Hopefully, they might add this feature in the future.

[Ethan]

It seems like there might be a potential issue with tracking conversions due to the switch in Google Analytics versions, which can certainly be frustrating. The GCLID or Google Click Identifier helps track where your Ad traffic comes from, and it is necessary for it to be set up throughout all your booking engine pages, not just the starting page when the gtag event is triggered. This helps track the user’s journey from start to finish, thus attributing the conversion properly. Also, you may want to double-check if your conversions are properly set up in Google Analytics 4, as this version operates differently from Universal Analytics in regards to conversion tracking. Lastly, it could be worth reaching out to Google Support to investigate the issue further as the problem could stem from a multitude of places including the new Analytics setup, Google Ads, or even the way your website handles these tags.

[Ethan]

Sure, but it would be useful to view your current query to give a more precise direction. Generally speaking, in SQL, if you want to implement an UPDATE operation based on a selection, it would look something like this:

`sql
UPDATE your_table
SET page_location = ‘new value’
WHERE event_name = ‘page_view’ AND page_location LIKE ‘%specific pattern%’
`

Please replace ‘your_table’ with the name of your table, ‘new value’ with the new page_location value you want to set, and ‘%specific pattern%’ with the specific pattern you mentioned. This would set the ‘page_location’ to ‘new value’ for all records where ‘event_name’ is ‘page_view’ and ‘page_location’ contains the specific pattern.

[Ethan]

In case of the remove_from_cart event in GA4, the item you put in the array should be the one you just took out of the cart, not the stuff that’s still in there. Think of it like a ‘lost items’ list for your cart, where you’re noting down what’s been removed. This happens to be the official take from Google too. Check their support link, it’s got a lot of pointers on stuff like this.

[Ethan]

Yes, it’s possible to set up a dataLayer push to fire an event when a specific div element appears on page using a custom HTML tag in Google Tag Manager. You do this by writing a script that listens for changes in the DOM (the structure of the webpage), and then pushes an event into the dataLayer when it detects that the specific div has been added. Since the element you’re concerned with is encapsulated in a Shadow DOM, your JavaScript will have to penetrate that to access the element. This method gives you the flexibility to specify exactly what conditions must be fulfilled for the event to be fired. After setting this up, you can then configure Google Tag Manager to fire a Google Analytics 4 event tag each time it sees the custom event you’ve specified.

[Ethan]

There could be a few reasons why you’re seeing differences in active users between BigQuery and GA4. First, make sure you’re comparing the same time periods in both. If your BigQuery query is using UTC time zone and GA4 is set to another, this could cause discrepancies.

Additionally, data in BigQuery is recorded on an event basis where multiple events may be produced by a single user, whereas GA4 aggregates data before displaying it. Thus, you might be counting duplicate users when directly counting user_pseudo_id from the raw BigQuery dataset. You must ensure to count distinct user_pseudo_id over a certain time period to get the unique active users.

Lastly, it’s also important to note GA4 filters out bot traffic and applies its own data thresholding for privacy reasons while BigQuery does not, which could cause a discrepancy.

However, if these adjustments still don’t get your numbers to match, you might want to reach out to the Google Analytics and BigQuery support teams for additional help.