[Nguyen]

The user is having trouble setting up a service client for Google Analytics 4, receiving an error message that says “Failed to build HttpHandler (0)”. They have shared the code they are using to set up this client, as well as the content of a related key file. The user isn’t sure what the error means and if there is something missing in their code or the key file that could be causing this problem. The ultimate aim of the user is to find out about the accounts that the client can access and to transition their existing Google Analytics Universal Admin code to Google Analytics 4.

[Nguyen]

It seems like you’re encountering issues with Google Analytics 4 Data API due to conversions named with spaces. The error messages indicate that a metric name should only contain letters, numbers, or underscores. When spaces in the conversion names were replaced with underscores, another error occurred suggesting that the metric doesn’t exist. Unfortunately, without access to the GA4 account in question, it can be challenging to pinpoint the exact problem. It may be helpful to work with the customer to rename their conversions using only letters, numbers, or underscores to satisfy the naming rules. Alternatively, it’s also worth exploring whether the API provides a way to handle or encode spaces in the conversion names.

[Nguyen]

The issue you’re facing might be because of various reasons related to how the audienName filter and the “exclude internal traffic” implementation are behaving. Hence, any issue related to how these are being implemented could result in the same output. One possibility could be that the “exclude internal traffic” filter is not correctly set up, leading to internal traffic not being actually excluded. Another possibility could be that your AudienceName filter already excludes internal traffic, hence not causing any changes when an additional filter to exclude internal traffic is added. To know the exact cause behind this, these possibilities need to be examined and more diagnostic information would be required.

[Nguyen]

Given the steps you have already taken, this appears to be a very peculiar code issue. You might want to pay attention to how your gtag script associates the purchase event with a user’s session. If the identical ga_session_id and ga_session_number are translating to duplicate events on the first purchase, they may be being duplicated due to session initialization. Consider examining the implementation of your events and session control on your website. Make sure your purchase event isn’t being fired automatically on session start. If it’s not being properly controlled, it could inadvertently fire twice on the first purchase of a new user session. Enhanced e-commerce settings might also be contributing to this issue. Checking how those are set up might also give you more insight. Moreover, consider enlisting the help of a developer skilled in GA4 implementation if the problem persists. They’d be better equipped to sniff out any underlying code errors.

[Nguyen]

That error you’re getting seems to be coming from the VPC Network, but don’t sweat it – there’s a fairly straightforward way to resolve it.

First, you’ll want to remove the VPC Network. After you’ve done that, go ahead and connect GA4 with BigQuery. With that connection established, you can now re-enable the VPC Network.

Next step is enabling the access manager API. This helps manage access permissions.

Last but not least, make sure to set the access level for the GA4 service account. This defines what data the service account can access.

After you’ve gone through these, you should be good! Let us know how it goes!

[Nguyen]

Sure thing! Let me break down what’s happening here. Your data pipeline from Google Analytics 4 (GA4) to Looker Studio via BigQuery is neat. When you directly connect GA4 to Looker Studio, your date range filter works perfectly which shows your GA4 data is correctly configured.

The tricky part is when you add BigQuery to the mix – something’s not clicking right. Now, let’s focus on the BigQuery part.

The data in BigQuery is stored in ‘STRING’ format or ‘Integer’ formats. In order for Looker Studio to recognize it, especially for date ranges, we need it in ‘DATE’ format. That’s probably where the signals got crossed!

Here’s the fun part! Time for a little magic trick to transform all those numbers and characters into a date format that Looker Studio will understand and love:

Step 1: Open Looker Studio. Create a new data source and choose ‘Custom Query’.
Step 2: Use this magical spell (aka SQL code):

SELECT
  event_name,
  PARSE_DATE('%Y%m%d', event_date) as event_date, // This turns strings to Date format
  platform,
FROM
  yourProject.analytics_0000000.events_*

Voilà! Your data is transformed! If you want to pull in more dimensions or metrics, you just need to tweak this query a bit.

Now, with your data transformed into the ‘DATE’ format, select the pie chart again in Looker Studio. Ta-da! Your date range dimension will show up.

Not too difficult, right? Have fun with your newly transformed data!

[Nguyen]

Your issue is occurring due to the problem when aggregating over both metrics and dimensions together. A Looker-based solution would be to create two different measures: one for the sum of ‘application’ events and one for the sum of ‘view’ events. You can do this by editing the code of the measures to be something similar to:
SUM IF([Event Name] = 'application',[Event Count],0)
and
SUM IF([Event Name] = 'view',[Event Count],0)
respectively.

Then, you can calculate the ratio within LookML, using these two new measures and avoiding the issue of mixing aggregations. For example:
[Application Sum] / [View Sum]
Remember to handle cases where View Sum may be zero to avoid division by zero errors.

This solution allows you to avoid creating calculated fields which mix metrics and dimensions, which Looker does not allow. It will calculate the ratios you need without the need for merging or blending multiple tables.