[Isaiah]

It seems like your attribution report is not properly capturing the various channels that drive traffic to your site. There could be several reasons for this. Perhaps your tracking setup is not configured correctly, or there might be issues with the cookies that the visiting users have enabled. It could also be that your marketing initiatives are not properly tagged, causing the system to not understand where the traffic is coming from. To rectify the issue, you may want to review your tracking setup and ensure you have the correct tagging in place for all your marketing channels. If you’re not sure how to resolve this on your own, consider reaching out to someone with expertise in these analytics tools.

[Isaiah]

Sure thing! So, yes you can totally see the number of times that ‘map_grid_view’ event happens in GA4, no need for creating a custom dimension. And if you register ‘click_text’ and ‘page_path’ as custom dimensions, that information will flow nicely into GA4 once your GTM container is live. You’re all good to go!

[Isaiah]

It seems like the date filter in Looker might not be linked up correctly to your BigQuery data. I’d recommend checking how you’re passing date parameters from Looker to BigQuery, just to make sure they’re matching up. Another thing is to consider if Looker is interpreting your dates correctly. Sometimes, apps can misread date formats, which can result in strange data output.

[Isaiah]

Analyzing active users across BigQuery and GA4 might not always have a perfect match due to several reasons. First, Google Analytics and BigQuery measure things slightly differently. GA might filter certain bot traffic or adjust for known issues. Also, understand that The BigQuery export for GA4 only includes raw, hit-level data (not processed) and summarizes them differently.

Secondly, user_pseudo_id in GA isn’t always consistent. For example, when a user clears cookies or accesses your site from a different device, a new user_pseudo_id is created. BigQuery would consider each as a separate user, thus showing a higher number of active users.

Lastly, timezone differences could be a reason. Be sure to check if GA4 and BigQuery are set to the same time zone.

To match Bigquery and GA4 data, adjust your BigQuery logic to filter similar to GA4, be mindful of user_pseudo_id inconsistencies across devices and cookie clears, and confirm data is compared in the same time zone.

[Isaiah]

In Google Tag Manager, you can add additional parameters like “value” and “currency” to your gtag events by modifying the function calls that send data to Google Analytics. For instance, for a purchase event, the code might look something like this: gtag('event', 'purchase', { 'value': 23.07, 'currency': 'USD' });. In this code, ‘event’ refers to the type of event that’s being tracked, ‘purchase’ is the specific event, ‘value’ represents the monetary value associated with the event, and ‘currency’ is the type of currency associated with the ‘value’. Alternatively, if you’re using Google Tag Manager’s interface, you can use the ‘Fields to Set’ section when setting up a GA tag to add these parameters. You’d enter ‘value’ and ‘currency’ as the field names on individual lines and then put the corresponding values in the ‘Value’ column.

[Isaiah]

The issue you’re experiencing appears to be originating from the Firebase Google Analytics 4 dependency. When you add this to your project, it causes error messages from ProGuard, which is likely failing to recognize certain libraries within your project. The dependency version and ProGuard rules could be contributing to this issue. You may need to consult the documentation again in order to find the best settings for your project.

[Isaiah]

Yes, using Google Analytics’ Explore report, you can actually see what percentage of your site’s visitors are using mobile versus desktop. Here’s a basic outline of how you can do this. Under the ‘Audience’ section in your Google Analytics account, navigate to ‘Mobile’ and then ‘Overview’. Here you will be able to see the breakdown of visitors using desktop, mobile and tablet. To find out the ratio or percentage, you just need to divide the number of mobile visitors by the total number of visitors (the sum of mobile, desktop and tablet users). So for instance, if you have 500 mobile visitors out of a total of 2000, your mobile visitor percentage would be 25%. Remember to adjust the date range for the specific period you are interested in analyzing.