[Taylor]

Yes, you’re correct. It seems you’re having trouble seeing ‘view_item_list’ event details in your Monetization/Ecommerce Purchases report, despite them appearing in Realtime and Engagement reports. While there is a possibility of modifying your DataLayer pushes to get this data, it may be more beneficial to identify why these specific events aren’t appearing in your report. Look into possible filters or settings in the Monetization/Ecommerce Purchases report that might be preventing the information from coming through. This way, you would get the ‘view_item_list’ count for each product without modifying your existing DataLayer setup.

[Taylor]

Google Analytics 4 (GA4) and Universal Analytics (UA) handle and calculate data differently which might explain the discrepancies you’re seeing. GA4 may take longer to process the data or calculate active users in a different way compared to UA. This might be why the count for active users today in GA4 is lower than in UA and also lower than the count for the last 15 minutes in GA4. If you’re getting the same figures on the analytics site as through the API, it’s less likely to be an API issue. To confirm, you could cross-check if the counting of users in GA4 and UA aligns with your understanding. It could also be beneficial to check these figures over the course of a few days and at various times of the day to account for potential processing delays and differing calculation strategies.

[Taylor]

It’s highly probable that the issue is occurring due to the unnecessary creation of a custom event in the GA4 configuration. The GA4 interface allows for the creation of ‘new events from existing events’, a feature that might inadvertently result in duplicated event fires when incorrectly configured. You might want to check if you’ve actually configured such a custom event which could be causing multiple triggers. This may explain why the ‘view_faq’ event triggers multiple times for a single button click.

[Taylor]

Each GA4 property has a daily limit of 500,000 core tokens, and yes, we are talking about the same GA4 properties that are used within Google Analytics to separate data from different websites, apps, and other digital platforms.

[Taylor]

From what I understand, you’re trying to structure your data to match the Google Analytics 4 (GA4) raw sessions event action format. Currently, with your query, you’re creating an array of structs with three fields (ec, ea, el). This isn’t matching the complex nested structure you’re aiming for which seems to be key:value pairs for each event action and label.

To shape your data like in GA4, you will need to adjust the structure of your data to have separate key-value pair arrays for “event_params.key” and “event_params.value”. This structuring is not straightforward as the method to create this structure in Bigquery is not built-in.

In Google BigQuery language (Standard SQL), you may need to use a more complex query to create the same structure. The STRUCT and ARRAY functions are certainly useful for creating nested data, but for creating the specific schema required by GA4, more complex querying may be needed that involves key-value pairing and nested subqueries.

You may need to consult Google BigQuery documentation on complex data types and nested and repeated data or consider involving a developer who is familiar with structuring data as per the GA schema.

[Taylor]

Hey there! It sounds like you have quite the Matryoshka doll of data on your hands! Just a heads up, the best practice for this kind of situation is to flatten your BigQuery GA4 export before you even export it. Basically, you want to simplify that json data in your event property/custom dimension column so you don’t have to worry about it later. A common approach is to create a unique column for each event property or custom dimension.

Then, once everything’s all organized and simplified like that, you can go ahead and shift your data into the SQL server tables. It’s a relatively straightforward move at that point. If you decide to transform the data any further, it’ll mostly depend on your specific requirements. Just keep in mind that ETL logic can get a bit hefty as you make modifications over time. But no worries, it’s all part of refining and improving your data, right?

I hope that helps! Feel free to reach out if you have any other data dilemmas you need a hand with.

[Taylor]

It’s possible that a custom dimension named “price” is causing interference with your ecommerce data. Since you’ve already archived it, let’s see if it solves the issue. The number format for the price should be alright, as GA4 accepts it. We’ll know more soon after the fresh data starts coming in!