[Sophie]

It sounds like a complicated situation, bud! It’s possible that there’s some kind of conflict with the Universal Analytics conversion tracking that’s still running. It could also be a small hitch with the way the tags are set up or perhaps it’s just a waiting game. I’d definitely double-check your setup though.

[Sophie]

It appears you’ve identified a solution through trial and error. Here is the corrected syntax for your request for v1beta:

`ruby
{
:property=>”properties/”,
:dimensions=>[{:name=>”date”}, {:name=>”customUser:type”}],
:metrics=>[{:name=>”activeUsers”, :invisible=>false}],
:date_ranges=>[{:start_date=>”2023-01-01″, :end_date=>”today”, :name=>”date”}],
:order_bys=>[{:dimension=>{:dimension_name=>”date”}}],
:dimension_filter=>{:filter=>{:field_name=>”customUser:type”, :string_filter=>{ :match_type=> “EXACT”, :value=>””}}},
:keep_empty_rows=>false,
:return_property_quota=>true
}
`

This should resolve the syntax errors you’ve been encountering in your GA4 Custom Dimension request. The changes mainly pertain to how the dimension filter is structured. The field_name should match the name from the dimensions array (e.g. customUser:type) and the match_type and value will determine the records to include in the results.

[Sophie]

From your query, it is clear that there are several elements involved, likely relating to Google Analytics 4 (GA4) permissions, user authentication and data access. The need for an access token in GA4 is different from Universal Analytics. Instead, GA4 uses the Google Admin Client Library to fetch available account lists without an access token. I realize this may not be suitable for your goal of needing individual user authentication.

Regarding the access to profile data in GA4, it is accurate that GA4 does not use profiles or views as UA does. Instead, GA4 uses Google Ads data sources and data streams for each property. As an alternative to profiles, you may have to give your users a list of accounts/properties to pick from.

For obtaining referral, organic search, user, and session data, finding these within GA4 might differ slightly from Universal Analytics. For instance, sessions are now called “engagements” in GA4. GA4 provides extensive built-in reports and the option to create your custom analysis. Depending on the specifics of what you want to track, you might indeed require additional setup, use a different endpoint, or another library to get the appropriate data.

[Sophie]

Sure, I’d be happy to give you a more everyday breakdown.

Essentially, it’s a bit of a hassle to configure Google Tag Manager (GTM) the way you want to, primarily because it doesn’t allow using just JavaScript objects to send as event parameters, with the current exception of Ecommerce DataLayer.

However, the apparent good news is your developer can decide on the event parameter themselves. This means they can share their new implementations with whoever is handling the GTM at your end, so it can be adjusted accordingly.

There might be a little more efficiency if changes are frequent and need to be automated. The GTM API would come into play here. But it’s worth noting that there would be some extra time for developers to integrate with it.

Speaking from experience, I was once juggling over 10 properties and adding more than 30 new events per week. I found Google Sheets, in combination with Google App Script, very convenient for managing GTM.

The takeaway is there surely are ways to make the process more automated. The decision comes down to whether it’s worth the time and effort necessary for implementation.

[Sophie]

Yes, you can tweak how the Landing page dimension shows in GA4. While GA4 does not provide out-of-the-box functionality to simplify URLs, the powerful feature of BigQuery Export provides a great platform to perform such modifications. This can be carried out by using REGEXP functions in SQL to extract the first two components of a URL.

You can also use this extracted data to form a new ‘shortened’ dimension, in effect creating a new ‘streamlined’ view of your data without making changes to your original Landing page dimension. This will help you manage the high variability (“high cardinality”) in your page paths without losing detail in your original data. Do note that some technical know-how is needed for this process. You might need a developer or someone with knowledge on BigQuery and SQL to implement these changes.