[Pham]

It sounds like the problem could be arising from setting up the Google Tag Manager (GTM) incorrectly for your properties. In GA4, each property should have its own distinct ‘Measurement ID’, which should be used in your GTM tag for each corresponding website. If you are using the same GTM container for all properties, you may be inadvertently firing all tags across all sites, which would cause all properties to record all the data, regardless of origin. To negate this issue, ensure each property tag in your GTM is triggering only on its assigned domain. Adding a new domain in the data flow tab or creating new properties won’t solve this problem if the underlying issue is with your GTM setup. Check your GTM container and the firing rules for your GA4 tags. It’s crucial to ensure that each tag is assigned to fire only on the appropriate domain.

[Pham]

It’s quite possible that this is happening because gtag is ignoring the hash part of the URL when you’re using HashRouter. It’s not something I’ve seen explicitly stated in the docs, but from what I’ve observed, as well as a few other sources I found (you can check them out here and here), it does seem to be the case.

[Pham]

Unfortunately, as the query and data table are blurred out for privacy reasons, it’s challenging to provide an exact solution without knowing the structure of the tables and how you’re calculating each metric. However, I can suggest a few potential reasons and strategies you may want to explore to identify the source of discrepancy.

Firstly, you may want to ensure that your engagement rate formula in BigQuery is equivalent to how GA4 calculates this metric. Some systems consider only unique users in the calculation, while others may include every interaction, possibly leading to an artificial inflation of your results.

Secondly, consider whether all the underlying data is included in your GA4 result and in your BigQuery results. For instance, any data filtering, selective inclusion, or exclusion you’re unintentionally doing either in GA4 or in your BigQuery query could result in differences.

Lastly, examine the timing of your queries. If your query is not in real-time, you might be factoring in more recent data in BigQuery that hasn’t been accounted for in GA4, hence resulting in the difference.

Hope these points guide you towards fixing the discrepancy!

[Pham]

Hey, I totally feel you! I’ve had the exact same issue with GA4 data. After a ton of research, what I found is that you can actually select the column and then go to the transformation tab and click on ‘Analyse JSON’. This will give you an option to expand the column.

But, I won’t lie – despite doing this, dealing with the resulting header columns can be a bit tricky and might not offer the best solution you’re looking for.

[Pham]

Sure, let me explain it as simply as possible.

When you are using Google Tag Manager and you see a value in the data layer that you want to use (like sale price, for example), you can manually link this to your tag.

Just imagine that you’re processing a purchase. You’d first click on the custom event for the purchase. In the data layer, you’ll see multiple fields with specific information on the transaction, including the price of the purchase under a custom value.

The next step is to create a new data layer variable in Google Tag Manager using the same name as the custom value from your data layer. Once you’ve created this, you can then manually include this value in your tag.

Does that make sense now? Even though it’s a multi-step process, it’s definitely doable and can allow you to use a custom GA4 event to carry revenue information.

[Pham]

Absolutely, you can manage Google Tag Manager (GTM) in your React application so it only runs when and where you need it, without needing to remove or disable the actual GTM script.

To unload a script like GTM, you’d typically manipulate the HTML and this can add unnecessary complexity to your app. Overriding script namespaces could lead to unpredictable behavior down the line.

Instead, an efficient way forward is using GTM’s in-built blocking triggers. Use these to prevent certain tags from firing after a user logs in, which would make GTM behave as though it’s been unloaded, without actually doing so.

This strategy aligns better with operations advisable in a production environment. QED – less mess, less stress!

[Pham]

Sure thing, here’s the scoop:

So, it seems that the key to making your AppFlow connection with GA4 work might actually be hiding in the Google Analytics Admin API. We too faced the same hiccup you’re currently dealing with, and we got some insight from the folks over at Amazon.

Here’s the thing: When you’re setting up your GA4 connector, you have to make sure that BOTH the “Google Analytics API” and the “Google Analytics ADMIN API” have access enabled. It’s like a two-for-one sort of deal for this to work.

So, just double-check and make sure you’ve got both these bad boys up and running.

By the way, don’t sweat if you overlooked the Admin API part. The AWS documentation for setting up GA4 with AppFlow currently only lists the “Google Analytics API” – they kinda left out the other half of the equation. No worries though, we’ve notified them about this little hiccup, so hopefully, they’ll get it sorted out soon. Sorry for the confusion and best of luck!

[Pham]

Absolutely, I understand your struggle. What’s usually the case with these kind of error messages is that some dimensions and metrics just don’t play well together. It’s like trying to mix oil and water, they just don’t combine. This is usually because the data needed to generate the report across these dimensions and metrics simply doesn’t exist.

The solution? You would typically need to remove one of the offending dimensions or metrics that is causing the conflict. It’s a bit of a hit-and-miss process to find what works best for your needs, but once you figure it out, you’ll be able to generate the reports you need.

[Pham]

It could be possible that the transactions are being sent twice from your website to Google Analytics, hence the duplicates. This could occur if, for example, a user refreshes the order confirmation page, causing the data layer to be reshared. To resolve this issue, you may need to implement a solution that prevents the data layer from being shared again once the transaction is initially processed. Additionally, if you are unable to identify an issue with your data layer setup, it might be beneficial to seek assistance from someone well-versed with Google Tag Manager as they could better identify if your tags, triggers or variables are set up incorrectly.