Why it is necessary to integrate Google Analytics 4 with BigQuery

According to some data, 56% of all websites use Google Analytics to track their website traffic and data.

It is certain that your UA analytics property now is sunset and with the transition to Google Analytics 4, it can be confusing to understand the purpose of features such as BigQuery export integration. Thankfully, it can help make advanced data analysis possible and give you an easy way to look at all of your data. Implementing a data warehouse should fall as part of all organization’s goals no matter their size as the plethora of data sets and data tables become available and analyzing this data separately can pose some challenges. From advertising identifiers to multiple event parameters, traffic source data, and endless Martech and Adtech data types that need to be analyzed and blended, the need for the data processed will require actual data storage. This is where the streaming export option of Google Analytics 4 and BigQuery comes into play.

Keep reading for a complete guide on how to use the new GA4 with BigQuery.

Why Use BigQuery With GA4?

There are many benefits of BigQuery being integrated with GA4. One of them is that you have more options for exports, and you can fully customize the data sets that you want to export. 

To connect BigQuery with GA4, it’s free, and you’ll get unlimited access to all of the raw GA4 data. In addition, you’ll be able to filter out some of the incorrect data while also being able to work with historical data. You can export Google Analytics 4 google analytics data to the BigQuery sandbox free of charge ( sandbox limits apply). BigQuery also allows you to do advanced data manipulation and fix any data reporting issues that Google Analytics 4 might present.

However, there are many more benefits you’ll be able to enjoy.

Reporting Options

With BigQuery, you’ll get access to all of the unsampled data in your Google Analytics. This gives you more options for reporting, and you’re also not limited to the GA4 interface. 

It’s only a drag-and-drop interface, so the options and reports that you can run are actually limited. With the BigQuery export limits, you can write a report for exactly what you need. Your BigQuery export data queries and Google Analytics 4 user interface may differ slightly depending on various definitions.

No More Limits

While BigQuery does have some limits (we’ll cover that later), there are no API limits. If you are trying to use a GA4 connector with Free Looker Studio, the limits will be enforced, and this will break any reports you try to run.

However, with BigQuery, their limits are so high that most people won’t even have to worry about them. Instead, you can actually point Looker Studio to BigQuery. This will result in a workaround around the limits, and you’ll have a better reporting system as well.

Better Insights 

BigQuery gives you so much power that you wouldn’t have been able to have with just GA4 alone. you’ll be able to run any type of query with SQL, and you can customize and organize the data any way you want.

This gives you more insights than GA4 can offer. When you have these better insights, then you can also understand what is working on your website and what isn’t. Your web data then can be enriched and joined with other marketing/CRM/contextual data

You can also do better segmentation with this type of tool. For example, if you’re running paid Google Ads, you can get more insight and data into who came to your site off of those ads. You’ll also be able to see what people purchased something, and how much they spent for each transaction.

This data will help you understand if your Google Ads are actually a return on investment. It can also help you optimize them so that you better target your audience. 

BigQuery not only gives you all the answers, but it also helps you figure out what questions you should be asking Google Analytics.

Better Data

While GA4 does give you a lot of data, BigQuery will make it even better. You’ll access all of the raw data, and when you combine it with other sources and analyse it, you’ll have a comprehensive picture of all your marketing efforts. 

With a better sample data set, you’ll have a stronger analysis. On top of that, you’ll also be able to hold onto your sample data set forever.

If you just keep it in GA4, then the data will only stay there for up to fourteen months; however, the default setting is two months. If you use BigQuery, you can store and keep the data as long as you want.

Cardinality

Cardinality is when you have too many rows in a table that came from a report. If this happens, you may not trust the data as much anymore. 

Standard reports have different limits for the table, so if you aren’t using the right metrics and table dimensions, then you could get a different result from the data. 

This can make it confusing to explain to other people who don’t fully understand the data set. However, if you generate a report using BigQuery, it won’t have the same data set representing the same data set represents, schema, issues, and limits.

What Is the Difference Between UA and GA4?

Universal Analytics is sunsetting due to cross-platform browsing and other privacy concerns. It’s not able to do what people need it to, so companies are switching to Google Analytics 4. So what is GA4? 

GA4 is more flexible and can better meet business needs now. It’s a comprehensive and more advanced approach to analyzing how users interact on mobile apps and websites. 

Google Sunset Universal Analytics in July 2023, and now users need to use Google Analytics 4. There are many differences between the two platforms, and you’ll notice it right when you log in. 

Instead of viewing page views, you’ll now see data streams. Both platforms let you track the same metrics, but they’re measured differently now. In some ways, Google Analytics will simplify everything. You’ll have more automated reports, combining app and web analytics. Web and app analytics allow you to have a holistic view across your organization of mobile app streams and web user data.

Google Analytics 4 has some preset views and reports, and you’ll automatically be able to track site searches, links, page views, scrolls, and more. You can also change all of this in settings.

With a new data schema there are corresponding data type to consider but here are some of the major differences between the two platforms: 

• Session calculations
• Data retention and privacy
• Machine learning
• Bounce rate
• Engagement rate
• Event parameters-based tracking
• Attribution modeling
• BigQuery schema

Using GA4 after being used to Universal will take some getting used to, but the integration of BigQuery will give you a further degree of control and enhanced capabilities.

How Much Does Google Analytics 4 BigQuery Cost?

BigQuery does charge to use its software, and there are two components that affect the pricing. They are query processing and storage. BigQuery has a pricing table so you can compare and pick the best option for you. 

If you don’t want to pay, then BigQuery does have a free version that’s a sandbox. However, the sandbox limits how many queries you can run. 

What Is BigQuery in Google Analytics?

BigQuery is Google’s service, and it’s fully managed. It enables scalable analysis of data that you can get from Google Analytics. This will make it easier to understand your GA4 analytics. 

You can incorporate BigQuery into Google Analytics and use an SQL query to get all of your data.

Does Google Analytics Use BigQuery?

GA4 is compatible with BigQuery, but you’ll need to set it up and integrate it. You can use Google Analytics without BigQuery, but it might be more challenging and confusing to use. 

BigQuery allows you to export the data and see it in an easier way.

What Is BigQuery vs Google Analytics?

Google Analytics is a tool that allows you to track metrics on your website and app. This allows you to track any marketing efforts and the overall traffic to your website to see how it’s performing. 

Google Analytics 4 (GA4) is the newest iteration of analytics software. It launched in 2020 but replaced Universal Analytics in July 2023. 

BigQuery is a data warehouse that helps you analyze and manage your data. It also has built-in features to help you do this, like machine learning and business intelligence. 

You can use SQL queries to comb through your data and answer some questions without needing any infrastructure management. BigQuery is scalable, and it lets you query huge amounts of data in just a few minutes. 

BigQuery is also a flexible solution that separates the data-combing engine from the storage option. You can analyze and store your data in BigQuery or elsewhere. There are also tools that will help you comb through that data so that you properly understand it. 

BigQuery has the option to interface with Google Cloud, including Google Analytics. 

How Do I Connect Google Analytics to Google BigQuery?

To start, you’ll need to open your GA4 account and navigate to Admin. You’ll need to then go to the property.

In the property column, you’ll see an option that says Product Links. There, you’ll see BigQuery Links. You’ll want to click on that.

From there, choose the right BigQuery project. When you do that, you’ll then see a list of all the BigQuery projects that you have access to. 

Once you’ve found the right project, hit confirm. Next, you’ll want to set a location for the data. If you already have a dataset for a certain property, you won’t be able to configure it. 

If you don’t have a dataset, click Next. The next screen will let you configure data streams and events, and select the one you want to export and include. 

You can include or exclude events from selecting them on the list. You can even pick a certain event by name. Once you’ve configured the list to your satisfaction, click Next. Take one last chance to review all of your settings, and then press Submit.

How to export raw data from Google Analytics 4 to Google BigQuery

If you’ve already had a project in Google BigQuery and the information you need is collected in Google Analytics 4, you can start exporting it.

1. Create a project in the Google API Console

Sign in to the Google API Console. Create a new project or select an existing one.

2. Enable the Google Cloud API

Open the Google API Console project you’ve created and select APIs & Services from the sidebar, then select Library:

Enter BigQuery API in the search bar:

Find BigQuery API in the search results and click on it:

Make sure BigQuery API is enabled and click Manage.

Add a service account to your Cloud project. To do this, click the Create credentials button in the upper right corner:

In the opened window, select Application data to create a Firebase service account. It will be used to export Google Analytics 4 data to BigQuery:

Scroll down the screen, select No, I’m not using them and click Next:

Enter firebase-measurement@system.gserviceaccount.com as the account name and click Create and continue:

Then you can add the role to the account. For example, Editor.

In the third step, add the email addresses of everyone who will use or administer the account and click Done.

After that, you will see your new account in the list.

It’s done! You have successfully enabled and configured the BigQuery API.

3. Link BigQuery to your Google Analytics 4 property

Sign in to your Google Analytics account. The account must have owner access to your BigQuery project and edit access to the Google Analytics 4 property you’re working with.

Go to the Admin tab and select the property you want to link to BigQuery. In the Property column, click BigQuery Linking.

Then click the Link button:

Click Choose a BigQuery project to view the projects you have access to. To create a new BigQuery project, click Learn more.

Select the project you need and click Confirm.

Select a location. (If your project already has a dataset for the Analytics property, you can’t configure this option.

Click Next and select the data streams you want to export information about:

If you need to include advertising identifiers, check Include advertising identifiers for mobile app and server data streams.

Set Frequency: Daily or Streaming (continuous) export (you can choose both as well).

Finally, click Submit.

It’s done! You’ll see new Google Analytics and 4 information in your BigQuery project within 24 hours.

Use Cases of Integrating GA4 to BigQuery

Uncover marketing insights with speed 

For answers to your important business questions, you can use BigQuery to query your Google Analytics data. In seconds, you can get answers to the following questions: 

1. How many transactions do purchasers make on average?

2. In addition to the product that was purchased, what other items did the customer purchase?

3. In the last six months, what were the top ten items added to the cart? 

Here are some sample queries that you can use to answer your top business questions using BigQuery

Build and activate audience segments

Audiences can be segmented quickly based on any combination of events

Improve marketing ROI by better understanding customer behavior and targeting audiences that are valuable to your business.

Segment your audience based on Google Analytics events, such as visitors who added items to their carts, but did not complete their purchase, and customers with high lifetime value. This data can be segmented and shared quickly with Google Analytics using BigQuery.  

Developed strategic audience segments using more advanced queries

Use BigQuery ML for predictive analytics

Create demand forecasts and build predictive audiences

Make better marketing forecasts and plans with BigQuery’s machine-learning capability. In order to better plan for business peaks, you can create accurate forecasts using historical marketing data.

Google Analytics data can be used to create predictive audiences for targeting potentially valuable new customers using BigQuery ML based on propensity to purchase and customer lifetime value. Churn can also be predicted using machine learning-based predictive capabilities. 

Build marketing performance dashboards

With Looker Studio, you can create marketing reports and dashboards

With Looker Studio, you can combine and analyze data from your marketing activities to gain rapid insights by connecting to hundreds of data sources. To highlight performance, enter your marketing analytics data, such as Google Analytics and Google Search Console, and customize your own views, including tables and charts.

Using BigQuery, you can avoid the problem of ‘data silos’

Businesses that handle a lot of data know this nightmare all too well. Individual teams have their own data marts (structures used to recover client-facing data). Data analysis across teams can cause friction and lead to problems with data version control. Integrating with Google Cloud Identity and Access Management, BQ allows teams to collaborate while assigning specific data sets to specific teams. Most commonly we find our clients look to tie the offline with online data where even 1st Party data such as CRM data can be unified in your analysis, reporting, and visualizations.

Start Your GA4 BigQuery Export Project Today!

One important factor in ensuring your website does well is tracking it through Google Analytics. Using BigQuery can add transformative value to your business.

However, we can help ensure that you’re on the right track. Our team at Emerge Digital helps you to ensure a smooth transition by developing and implementing a metrics system, setting up parallel tracking correctly, and integrating Google Analytics 4 with Google BigQuery.

Getting the most out of your digital footprint requires effective data practices and measurement, and we can help.

Learn more about our Data & Analytics Measurement services and how our solutions can benefit your business.