I encountered this error and couldn't find a solution. Not sure if it's the same for you, but then I checked my messages and realized I just needed to accept access as an admin. The error was showing up because the request had already been made.
I encountered this error and couldn't find a solution. Not sure if it's the same for you, but then I checked my messages and realized I just needed to accept access as an admin. The error was showing up because the request had already been made.
If the users are unable to locate the invitations in their email inbox, they can directly access GTM by logging in as the user to whom the invitation was sent. On the top of the account list page, they should see a pending invitation for the account.
The gtm.formInteract event is not an officially supported event by Google Tag Manager (GTM). It appears to have been a test event briefly available in some GTM containers but has since been removed.
While third-party developers might create custom events like gtm.formInteract, it's crucial to understand that these events are not officially endorsed by Google. Consequently, they may lack reliability and consistency across different containers.
If your GTM implementation relies on a custom event like gtm.formInteract, it is advisable to transition to an officially supported event by Google, such as the built-in Form Submit event or the Click event. Doing so ensures the reliability of your tags and their continued functionality in the future.
Your understanding is on-point. As per Google's documentation, the "gcs" parameter serves to indicate the user's granted consent type. When consent mode is either not configured or not mandatory, the default setting for the parameter should be "gcs=100." This signifies that neither ad consent nor analytics consent has been granted.
To summarize:
By incorporating this parameter in the request, you ensure that the information is utilized in accordance with the user's specified consent type.
To temporarily address the problem, you can alleviate it by removing the GTM container snippet from your website's source code. Identify the GTM container code within your website's HTML files and either delete it or comment it out until the pixel issue is resolved.
We have examined the julibees.com/ domain and noticed that the "Begin_Checkout" event is not consistently triggered every time we click on checkout and land on the checkout page. It appears that the event fires for new users during checkout, but when adding a new product to the cart and proceeding to checkout again, the event does not fire.
Could you please review the implementation to ensure that the "Begin_Checkout" event fires each time a user clicks on checkout and lands on the checkout page?
Additionally, we are unable to verify if the "Add_Payment" event is firing when clicking "pay now" as we lack the testing credentials.
At present, Google Analytics 4 does not automatically display the bounce rate as a default metric in its reports. It needs to be added manually. Here's a step-by-step guide on how to do it:
Step 1: Access your GA4 property by logging in, and on the left-hand side, choose "Reports."
Step 2: While on the "Reports" tab, choose "Pages & Screens."
Step 3: If you have the appropriate permissions, you'll notice a pencil icon located at the top right-hand side. Click on it to edit your report.
Step 4: Select "Metrics" and then choose "Add metric."
Step 5: Locate "Bounce Rate" in the drop-down list and choose it. Arrange the order in which you want your columns to appear, then click "Apply."
Step 6: Bounce Rate should now be incorporated into your reports. If you want this to be a lasting modification, be sure to click the "Save" button.
And there you have it! Keep in mind that bounce rate remains accessible in GA4; you just need to know where to find it!
Yes, modifications in GTM can have an impact on the insights derived in Google Ads. Altering the GTM configuration can influence how data is gathered and transmitted to Google Ads, potentially affecting the algorithm's capacity to optimize and make informed decisions based on historical data.
In the specific situation you outlined, changing the pageview trigger in GTM from landing page A (/thank-you) to landing page B (/thank-you12) has the potential to reset the learnings in Google Ads. The system may treat the new landing page B as a distinct entity, and the historical data linked to the previous landing page A might not be retained. Consequently, this could disrupt the algorithm's ability to comprehensively understand user behavior and conversion patterns.
The observed significant decrease in recorded conversions, following the switch from thank-you page A to B for your conversions, indicates a negative impact on the performance of your Google Ads account due to the change.
Have you updated the permissions on Google Marketing Platform?
marketingplatform.google.com/
Steps to integrate CRM with Google Analytics -
Step #1 Choose a key
The main requirement for integrating CRM and Google Analytics together is the availability of a common key that can map the two data sets together.
Here are a few things that you need to consider when picking out a key to merge data from CRM and Google Analytics -
1. The key should be unique — now and always. If there is a chance your key might not stay unique for users in the future, you run the risk of overwriting and corrupting your data.
2. The key cannot be a personally identifiable information (PII) like a phone number, email address, or user login ID because it goes against Google’s Terms and Services and it can also put your customer data in jeopardy.
Theoretically speaking, you can come up with your own unique keys for both of the platforms and then integrate them together. But it will only require more effort and time at your end. An easier way is to use a key that already exists in the two platforms.
Google Analytics has a preset parameter called Client ID (CID) which seems like an obvious choice. But here’s the issue with CID — Google Analytics generates a new CID for every new browser or device. That means if the same user uses two different browsers to access your website, they will be assigned two different CIDs and they will seem like two different users which can directly affect your data integrity.
A way out is to use Google Analytics’s UserID override feature to create your own user ID (UID) and then connect it to your CRM.
In case your CRM doesn’t have a unique UserID, you can generate one by hashing the customer email address or the phone number and then passing it through Google Analytics. Note that, you can’t directly use an email address or phone number as ID since that will be a privacy violation.
Step #2 Prepare Google Analytics for integration
Before you send data to Google Analytics, you need to give it a bit heads up. After all, if Google Analytics doesn’t know what to do with all the incoming data, it will just drop it and you won’t get any results.
1- Install Google Tag Manager on your website (or any other tag manager, but GTM works the best since it’s already closely integrated with Google Analytics and you don’t have to go through any extra work). We need the Tag Manager in order to set up the variables which are required to capture and send UserID to Google Analytics.
2- Enable and capture User-ID reporting in your Google Analytics
To enable User-ID in Google Analytics, go to Admin → Select the Property for which you want to integrate CRM data → Tracking Info → User-ID
Activate User-ID and set up a new one to create a User-ID view.
Once you enable User-ID in Google Analytics, you will start seeing a new view of analytics reporting.
There are a few ways to capture UserID in Google Analytics -
With the help of DataLayer: Push your UserID into the DataLayer of Google Analytics and then create a DataLayer variable in the Google Tag Manager. At this stage, you should just make sure the name of the UserID in Google Analytics and variable in Tag Manager are the same so that it is easier for you to track
By incorporating User ID in cookie: You can pass the UserID in the form of a cookie which you can either do through a custom JavaScript code or the DataLayer itself. By storing the User-ID in a cookie you can ensure that the user session persists and you can still track the user behavior even after the user closes the website or accesses the website again without logging in. (Though it only stays applicable until the user clears the cookies)
A JavaScript variable without a cookie: In this case, the UserID is passed to the Google Tag Manager as a custom JavaScript variable which is then directly sent to Google Analytics.
The right step will depend on the type of CRM and the reason behind integration. Though all of these steps can be rather complicated, overwhelming, and often prone to errors. With no dedicated support from Google Analytics, you will need technical support from someone who is well versed in Google Analytics integration.
Step #3: Upload CRM data into Google Analytics
Now that you have unique keys and your Google Analytics is ready to receive data, it is now time to upload CRM data.
There are two ways you can do it:
1- Data Import
With the Data Import option, you can upload your CRM data as CSV files directly to Google Analytics. You can do this either manually through the user interface or through automatically with the Google Analytics API.
Though it’s worth noting that users who don’t visit the website up to 30 days before the data upload are only added to remarketing lists after their next visit. While this might not be an issue if you regularly upload your CRM data in an incremental manner, but if you don’t have the time or the resources to upload CRM data periodically, this option may not be a viable choice.
2- Measurement Protocol
With the Measurement Protocol, you can directly send raw data to Google Analytics. While it’s similar to uploading your own CSV data, this method is more customizable and powerful. Measurement Protocol allows you to make HTTP requests to send all the raw user data from CRM to Google Analytics servers.
You can use measurement protocol for a multitude of things, including:
The challenges in integrating CRM with Google Analytics
The best way to go about here is using EasyInsights - it automates the entire process, and provides both ETL and reverse ETL capabilities to the user
Even though you do not get Views in GA4, you can use standard GA4 reports and filters to create something similar to Universal Analytics’ View option. To do that, head to the navigation bar on the left side of your screen and click on the Reports section. You will see options like Realtime reports, Life cycle reports, etc.
Now, expand the Life cycle reports section and then select Engagement reports. The Engagement reports section has two reports – Overview and Events. And all reports you create that have filters appear below them.
For instance, let us create a view to filter our website traffic that comes only from the United States.
You can also create your own collection of custom reports for quick access. Just click the Edit collection button and move your new custom report to a preferred place in your collection.
Here's the guide -
Method 1: Using PostgreSQL Connector to Connect Google Data Studio to PostgreSQL
Follow the steps below to connect your Google Data Studio to the PostgreSQL database.
Step 1: Create an account with Google Data Studio if you don’t already have one. Log in to your account if you have one.
Step 2: Click the “+Create” button in the top left corner to add a new Data Source.
Step 3: As shown in the image below, select the Data Source option.
Step 4: Look for and install the Google Data Studio PostgreSQL connector. It’s visible in the window below.
Step 5: Configure the Data Server credentials. After selecting the PostgreSQL connector, the Database Authentication window will appear. It has the following features.
Host Name/IP Address: The Data Server’s IP address.
Port: The PostgreSQL connector’s port on the Data Server. By default, it is 5432.
Database: The database’s name. It is a PostgreSQL database in this case.
Username: The PostgreSQL database’s username.
Password: The PostgreSQL database’s password.
SSL Connection: The SSL connection is shown here. By checking it, you can enable a secure SSL connection.
Fill in the details.
Step 6: Choose a data table.
In Data Server, you can select any data table.
Google Data Drive introduced a new feature in 2018 that allows you to use a query to transform your PostgreSQL data into a preferred format before connecting it to your Google Data Drive. You can use this feature to import aggregated data from your data table rather than the entire data table.
Step 7: Create a data table.
You must give the new data table a name. You can also change the names of the columns, as well as the data type and aggregate function.
Step 8: Produce reports.
After connecting the Google Data Studio PostgreSQL database, you can generate reports for the zoo dataset by creating charts.
You can also display the number of animals in your zoo-data-counted dataset. Any changes made to the PostgreSQL table will be reflected in Google Data Studio. You can see them by refreshing the above-mentioned charts.
Method 2: Using CData Connect Cloud to Create Google Data Studio PostgreSQL Data Reports
You can create reports with visualizations for your clients using the Google Data Studio PostgreSQL connection. You can also connect to CData Connect Cloud to get immediate access to PostgreSQL data for visualizations.
Using CData to Connect to a PostgreSQL Database
Follow these steps to create a virtual PostgreSQL database and generate reports from it using Google Data Studio.
Step 1: If you don’t already have an account, sign up for a free trial at CData Connect. Proceed to the next step if you have one.
Step 2: Sign in to CData Connect Cloud and navigate to Databases. In a new window, it will display the available data sources.
Step 3: Choose the PostgreSQL database.
Step 4: Fill in the authentication details for connecting to the PostgreSQL database, such as the connection name, username, password, and security token.
To connect to the PostgreSQL database, use port 5432 as the default. Set your server’s username and password. The database property is then linked to the default database.
Step 5: Navigate to the Test Database tab.
Step 6: Now, go to the Privileges tab and either add a new user or use an existing user with appropriate permissions.
Using Google Data Studio to Visualize PostgreSQL Data
Here’s how to use Google Data Studio to visualize PostgreSQL data.
Step 1: Open Google Data Studio and navigate to Data Sources.
Step 2: Create a New Data Source and select CData Connect Cloud Connector from the drop-down menu.
Step 3: To connect with the external service, you must authorize the CData Connect Cloud connector.
To connect to your Connect Cloud instance, enter your instance name as myinstance in myinstance.cdatacloud.net. You must also enter your username and password to connect to your Connect Cloud instance.
Step 4: Select the PostgreSQL database and press the Next button.
Step 5: Select the table and press the Next button.
Step 6: In the top right corner, click the Connect button.
Step 7: Now, you can change the columns. After that, click Create Report.
Step 8: Incorporate the data source into your report.
Select the visualization type and insert it into the report. You can customize the visualization by selecting the dimensions and measures.
Method 3 - Automating this task with EasyInsights
You may need to import data from various ad platforms to create a comprehensive marketing report. Here's the sample of a guide in our knowledge base - the article is titled "Creating GA4 Dashboards on Google Datastudio"
The majority of BI solutions support multiple APIs and databases. Microsoft Power BI is a popular business intelligence tool, and PostgreSQL is a popular database. Here’s how to connect them
A. Using Npgsql
Download and install the latest version of Npgsql as Administrator on your computer while enabling GAC Installation. Restart your computer and launch Power BI Desktop. Select ‘PostgreSQL database’ from the ‘Get Data’ menu. Enter the Server and Database names and the User and Password. Select the table you require in the Navigator window and Load it. However, if this does not work, you can try an ODBC connection.
B. Using an ODBC Connection
Open Database Connectivity (ODBC) is a standard API for accessing DBMS (Wikipedia). ODBC was created to be independent of databases and operating systems. How do you use ODBC to connect PostgreSQL to Power BI Desktop?
To begin, download and install psqlODBC from the official website.
You must now complete two setups. One is from Power BI, while the other is from PostgreSQL.
Open Power BI Desktop and select ‘Get Data’ after downloading and installing psqlODBC x64. Look for and select ODBC. Select Connect.
When prompted to choose a Data Source, select None. Enter the non-credential properties in the connection string (Driver, Server, Port, and Database).
The connection string looks like this:
Driver={PostgreSQL ANSI(x64)}; Server=localhost; Port=5433; Database= my_database
If you have previously entered your credential information, you may want to clear the permissions. To do so, navigate to Options and settings, then Data source settings, and finally Clear Permissions.
After that, select Database on the credential screen and enter the Username and Password. In the data structure, you can now select the table with which you want to work.
You must also configure the remote connection if your database is in the cloud. You must do this by editing the pga hba.conf and postgresql.conf files.
The pg hba.conf file should be edited as follows:
This is how you should edit the postgresql.conf file:
You must restart PostgreSQL after editing these files. You can also restrict who can access the database using the IP address from your cloud source.
C. Using EasyInsights -
Have a look at the knowledge base. And here's a sample article "Creating GA4 Dashboards in PowerBI"
So there are 3 ways to do this -
MANUAL METHOD 1 - Configuring a PostgreSQL Account in Tableau
For this, you must first install PostgreSQL database drivers.
Step 1: First, launch Tableau Desktop.
Step 2: Click the Connect to A Server button.
Step 3: Choose PostgreSQL.
Step 4: Type in the name of the server.
Step 5: Choose Port 8060.
Step 6: Enter the Database: workgroup name.
Step 7: Enter your authentication information, such as your username and password.
Step 8: Select the Sign In button.
Step 9: A live connection between Tableau and PostgreSQL will be established.
Step 10: The Read-Only user will have access to various tables.
Step 11: Choose one or more tables with which to create a relationship.
Step 12: Click the Worksheet button or press Ctrl+M.
MANUAL METHOD 2 - Tableau Server Version Identification
If you want to find out what version of PostgreSQL you have, do the following:
Step 1: Sign in to Tableau Server first.
Step 2: Launch the Task Manager.
Step 3: Navigate to the Details tab.
Step 4: Right-click on the postgre.exe processes and select End Process.
Step 5: Decide on the properties.
Step 6: Determine the PostgreSQL version that is currently installed.
This method of connecting the database to Tableau appears simple. However, in the real world, you are most likely working with multiple data sources to collect data and maintain it in a Postgres database. This process can be automated by using an ETL tool like EasyInsights. Here's the process -
AUTOMATED METHOD - Use EasyInsights
The best way to understand how to go about it is visit the knowledge base and search for a tableau article. Here's a sample article on "Creating GA4 dashboards on Tableau'
Here’s how you can establish a live connection between Tableau Desktop and PostgreSQL using EasyInsights. You may need to install the PostgreSQL database drivers, also known as Postgres.
Follow the steps below to connect Tableau Server and EasyInsights.
Step 1: First, launch Tableau Desktop.
Step 2: Click the Connect to A Server button.
Step 3: Choose PostgreSQL.
Step 4: Enter the following information:
IP address or hostname
Port
Database
Username & Password
Select the Authentication option.
This information can be found on the data destination page in the webapp
Step 5: Select the Sign In button.
Step 6: A live connection between Tableau and PostgreSQL will be established.
Two recommendations for you -
To pull your data from Google Analytics 4 to BigQuery:
Create a Google-APIs-Console project.
Enable BigQuery.
Link BigQuery to a Google Analytics 4 property.
Let’s take a detailed look at all the steps to pull data from GA4 to BigQuery.
Step 1: Create a Google-APIs-Console project as the first step.
To setup an APIs-Console project:
Open the Google APIs Console and log in.
Click on Select a project.
Choose an existing project or start a new one.
Step 2: Activate BigQuery
Visit the table of APIs.
Select Library by clicking APIs & Services from the Navigation menu.
Select the BigQuery API option under Google Cloud APIs.
When a new page appears, click Enable.
To your Cloud project, add a service account. Check that firebase-measurement@system.gserviceaccount.com is a project member with the editor-role assigned.
Step 3: Connect Google Analytics 4 to BigQuery
Enter your Google Analytics login information. The account should have Edit access to the Google Analytics 4 property you’re working with and Owner access to your BigQuery project.
Go to the Admin section, and find the Analytics property you need to link to BigQuery.
Set BigQuery Linking on in the Property column.
Click Link
After clicking Link, Click to view the projects you have access to
Select a BigQuery project. Click Learn more to begin a new BigQuery project.
Click Confirm after selecting your project.
Choose a location. (You cannot define this option if your project already contains a dataset for the Analytics property.)
Choose Next.
Choose the data streams from which you wish to export the information.
Check Add advertising identifiers for mobile app streams if you need to include them.
Set the Frequency to Daily Export.
Click Submit.
Within 24 hours, Google Analytics 4 data will appear in your BigQuery project.
You will have all raw user behavior data in BigQuery. However, you must include cost data from advertising providers, data from CRM systems, call monitoring services, and mobile apps to do marketing analysis and identify your growth zones and weak points. The data should also be combined into a single dataset and made business-ready so marketers can quickly produce reports using BigQuery data.
Sure. Here's the checklist that can help:
Optimizing Google Ads and Google Analytics Integration:
Linking Google Ads and Google Analytics: - Proper linking between Google Ads and Analytics is crucial to avoid 'not set' values. Ensure correct views in GA and linked accounts in Ads. Refer to this link (support.google.com/analytics/answer/9379420?hl=en#zippy=%2Cin-this-article) for guidance on linking your Google Ads and Google Analytics accounts.
Auto-Tagging:
When a user clicks a Google ad, a Google Click Identifier (GCI) appears in the target URL.
The GCI is stored in the website's Google Analytics cookie file, allowing Google Ads data visibility in Analytics Reports.
Manually tag links if auto-tagging isn't functional. Verify auto-tagging status in Google Ads and enable manual tagging in Google Analytics preferences.
Avoid using manual and auto-tagging simultaneously to prevent inaccurate results.
Invalid Clicks:
Not set' values may indicate invalid clicks, possibly from malware or bots.
Address invalid traffic issues by referring to the Google Help Centre for solutions.
Redirects:
Redirects can cause 'not set' values for GCLID if not saved during URL redirection.
Save the GCLID URL parameter during redirects for accurate tracking.
Ensure the auto-tagging feature is active using Chrome Developer Tools. Visit the [Google Analytics Help Centre] for detailed instructions.
Scripts:
Syntax and GCLID issues may lead to 'not set' values.
Ensure scripts run accurately to process tags correctly.
Check for scenarios where the GCLID parameter is omitted due to URL changes or character limitations.
Https URL:
Ensure destination URLs in Google Ads have the correct http or https prefix to capture click-throughs accurately.
Adding the https prefix is crucial to track activities and conversions effectively.
Settings & UTM Code:
Activate eCommerce settings on the Google Analytics admin page.
Verify and enable the UTM Code in Google Adwords settings to ensure proper tracking.
By addressing these considerations, you can enhance the accuracy of tracking and reporting in your Google Ads and Google Analytics integration.
Google Analytics has an option that allows you to exclude the FBCLID parameters from all the website page URLs and ensure they appear as normal URLs in your report. Though this can only be done for future data that is tracked by Google Analytics. It does not apply to the existing reports and page URLs that already have a FBCLID parameter attached to them.
1- In your Google Analytics account, go to the ‘Admin’ section
2- Under the View column, go to View Settings
3- Go to the ‘Exclude URL Query Parameters and type in ‘fbclid’
4- Click on the Save button and you are done
While it is not possible to remove FBCLID parameter from previous data in GA, you can create a filtered view using advanced search features to view compiled analytics for every page that does not include FBCLID.
1- Go to Behavior → Site content → All Pages.
2- Click on the Advanced Search options
Select Matching RegExp option from the drop down and type ^\/$|^\/\?fbclid in the search field. You should now be able to see page reports without the FBCLID in it.