How to Integrate GA4 with Data Warehouse and Business Intelligence Tools

The process has finally begun, and Google has brought down the curtains on Universal Analytics. Since July 1, 2023, Google has stopped processing hits, including standard properties in Universal Analytics. The search engine giant had started automatically creating GA4 properties for existing users who hadn’t created GA4 properties by themselves. 

Millions of website owners and marketers now must undergo forced migration to Google Analytics 4, or GA4. The historical UA data will be available until July 2024. In this write-up, we shall cover the basics of GA4 and discuss how to integrate GA with data warehouse and BI tools. 

Google Analytics 4 (GA4) - Overview of GA4 and Its Key Features

Google Analytics 4 is a completely new version of analytics. It isn’t an incremental change over Universal Analytics but a complete overhaul. The focus has shifted from tracking traffic to engagement. Unlike its predecessor, it isn't a web-only tracking tool but introduces app tracking to align itself with the realities of a cross-platform world. It has been long awaited in the marketing industry as marketers had to rely on UA and other app tracking tools. 

With this new analytics tool, you can track customers' complete journey and interaction with your web and app platforms on a single dashboard. You gain deeper insights that will influence your campaigns and optimize your digital presence across all the platforms. GA4 leverages AI (artificial intelligence) and ML (machine learning) and lets you gain detailed insights into how customers interact with your website and apps. It is no longer about where the users come from and where they go but what they do during those engagements. 

Key Features of Google Analytics 4

Insightful Dashboard

The updated dashboard is the first thing one notices when using GA4. It is more insightful than the Universal Analytics dashboard. On this new, cleaner dashboard provided by GA4, creating and maintaining audiences from web and mobile apps is simple.

Event-Based Tracking

This feature has received the most attention. While in UA, the emphasis was on page views, sessions, and users, GA4 gives you the complete picture of an event. You may feel anxious about this functionality, but it enables us to get more precise data for campaign optimization.

Privacy Controls

Privacy is a major issue in the digital ecosystem with recent legislations such as the EU’s General Data Protection Regulation and California Consumer Privacy Act. GA4 focuses on privacy and provides sophisticated capabilities that enable you to maintain compliance with GDPR while controlling the user data you collect and how you use it in your campaigns.

Customization

GA4 gives more power to you. You can customize your dashboard and view the reports that matter. You may build unique visualizations based on the event data on your web and app properties if you currently use Google Data Studio.

Importance of Data Warehousing, Business Intelligence (BI) Tools

We have already covered the basics of Google Analytics 4, and now let us focus on the most important aspect of this write-up. Data Warehousing collects, organizes, and stores large volumes of data from various sources in a centralized repository. It can involve transforming raw data into a structured format for querying and analysis. BI Tools are software applications that let you retrieve, analyze, and visualize the data stored in the warehouse to gain insights. 

Key Components of a Data Warehouse 

Raw Data Storage

It is the first component of your data warehouse. It is where the data warehouse stores the raw, unprocessed data collected from GA4. It can include event data, user interactions, events, associated parameters, and user and session information.

Data Integration

The data warehouse integrates data from multiple sources, including GA4, to create a unified data view. It allows data consolidation from various platforms, such as websites, apps, and other digital properties, into a single location.

Data Transformation

The raw data is transformed into a structured format suitable for analysis and reporting. Your raw data undergoes cleaning and normalizing before it is organized to ensure consistency and accuracy across different data sources.

Data Modeling

In this process, data models define the structure, relationships, and hierarchies within the data warehouse. The GA4 data model can align with the event-driven data structure, allowing for flexibility in capturing custom events and parameters.

Data Aggregation

Aggregating data allows consolidation of the raw data to create meaningful metrics and dimensions. It enables faster querying and analysis by pre-calculating common calculations, such as totals, averages, and counts.

Data Security and Governance

Data security covers access controls, encryption, and other security measures to protect sensitive data. Data governance ensures data quality, compliance with regulations, and adherence to data management policies.

The Integration Process: Connecting GA4 with a Data Warehouse

Integrating GA4 with a data warehouse is a crucial step in unlocking the full potential of your data and maximizing its value. By connecting these two powerful tools, you can gain deeper insights into your website's performance and make informed business decisions. In this section, we will discuss the process of integrating GA4 with a data warehouse and emphasize the importance of data synchronization and consistency.

Importance of Data Synchronization and Ensuring Data Consistency

Data synchronization is essential for maintaining accuracy and consistency across your analytics and business intelligence systems. It involves regularly transferring data from GA4 to your data warehouse, ensuring that both datasets are aligned and up to date. By synchronizing data, you can avoid discrepancies and make confident decisions based on reliable information.

Overview of the Steps Involved in Connecting and Syncing Data

Connecting GA4 with a data warehouse involves several steps to enable seamless data transfer between the two systems. Here is an overview of the integration process:

1. Step 1: Assess your data warehouse capabilities and compatibility with GA4. Ensure that your data warehouse has the necessary infrastructure and tools to handle GA4 data.
2. Step 2: Set up appropriate access permissions and credentials in both GA4 and the data warehouse to establish a secure connection.
3. Step 3: Determine the data elements you want to transfer and synchronize between GA4 and the data warehouse. Define the specific metrics, dimensions, and events that align with your analytical goals.
4. Step 4: Configure GA4 data exports to send the selected data elements to the data warehouse. This may involve creating custom data streams or using predefined connectors provided by your data warehouse platform.
5. Step 5: Map the data fields between GA4 and the data warehouse to ensure proper alignment and consistency. Establish clear mappings for dimensions, metrics, and events to avoid data mismatch.
6. Step 6: Test the integration and data synchronization process to verify the accuracy and completeness of the transferred data. Monitor the data flows and troubleshoot any issues that may arise.
7. Step 7: Implement a regular data synchronization schedule to keep your data warehouse up to date with the latest GA4 insights. Automate the process wherever possible to minimize manual efforts and ensure timely data availability.

By following these steps, you can establish a robust integration between GA4 and your data warehouse, enabling seamless data transfer and synchronization. This integration opens up endless possibilities for advanced analysis and strategic decision-making using powerful business intelligence tools.

Benefits of Integrating GA4 With a Data Warehouse and BI Tools

Integrating Google Analytics 4 with a data warehouse and business intelligence (BI) tools enhances your organization's data analytics capabilities. Let us take you through some of the biggest incentives of this integration. 

Thorough Data Analysis

You can combine data from multiple sources and gain a holistic view of your organization's performance. You can analyze GA4 data alongside internal and external datasets, allowing thorough data analysis to gain deeper insights.

Advanced Analytics

A data warehouse provides a robust and scalable infrastructure for storing and processing large volumes of data. With GA4 data in the warehouse, you can leverage advanced analytics techniques, such as predictive modeling, machine learning, and data mining, to uncover valuable patterns, trends, and correlations in your data. It creates an environment for accurate predictions and actionable insights.

Tailored Reporting and Dashboards

BI tools offer powerful visualization capabilities that allow you to create customized reports and dashboards. By integrating GA4 with BI tools, you can design visualizations that align with your business needs, making tracking key performance indicators (KPIs), monitoring trends, and sharing insights across your organization easier. 

Enhanced Data Governance and Security

Integrating GA4 with a data warehouse allows you to maintain a single source of truth, improve data quality, and ensure compliance with data privacy regulations. A data warehouse provides a centralized and controlled environment for managing data. It enables you to implement data governance policies, enforce security measures, and ensure data integrity. 

Higher Scalability and Performance

GA4 generates a vast amount of event-based data, which can impact the performance of your reporting and analysis if not handled efficiently. Integration with a data warehouse lets you offload the data processing and storage workload to a scalable infrastructure for handling large datasets. 

Long-Term Data Retention

GA4 has data retention policies, which may limit the availability of historical data. By integrating GA4 with a data warehouse, you can store and retain historical data for as long as you need, enabling you to perform long-term analysis and comparisons. It is important for your organization if you need access to historical data for strategic decision-making or regulatory compliance. 

Setting Up GA4 Property and Data Streams

Before we discuss the steps of setting up data streams, let's start with the fundamentals. Data streams are a new component in the GA4 structure. They are similar to Views in Universal Analytics. Whereas in Universal Analytics, you had unique properties for different sources of data, i.e., websites and apps, GA4 has abandoned that process. In the present structure, you have one Analytics property with data streams. You can integrate data from different sources - the website, iOS app, and Android app in a single GA4 property. You can apply filters directly to your report to track data from each stream.

Configuring Data Collection in GA4

Configuring data collection in GA4 is easy. In three steps, you can set up and use your data streams. If you're setting up your data streams for the first time, take the following actions – 

Step 1 – Navigate to the “Admin” section of the GA4 account. You will find it in the bottom left.

Step 2 – In the “Admin,” you will find “Data Streams” under “Property.” If you don’t have an existing property, start by creating one. 

Step 3 – In this step, you need to add a new data stream (website, iOS App or Android App). If you have already added them, you can configure them.   

GA4 Data Schema and Event Tracking

GA4 has introduced a new data schema and event-tracking model. The new schema provides more flexibility and support for complex data analysis. Compared to Universal Analytics, it takes tracking to a whole new level. Here are a few highlights of GA4 data schema and event tracking - 

• Event-Driven Model: GA4 comes with an event-driven model, where events are the core unit of measurement. Events represent user interactions with your website or app, and you can categorize them into predefined event types (page_view, purchase, sign_up) or custom events you define.
• Event Parameters: In GA4, each event can have associated parameters that provide additional information about the event. For example, a purchase event might include parameters like transaction_id, item_name, and item_category. Thus, you can capture and analyze more specific details about user interactions.
• Flexible Event Structure: Universal Analytics had a major downside with its fixed hit-based model. With GA4, you can define custom events and parameters based on your specific tracking requirements. This flexibility enables you to track user actions beyond the standard predefined events.
• Enhanced Measurement: The enhanced measurement features automatically capture certain user interactions without an explicit event tracking code. For example, GA4 can automatically track video engagement, file downloads, outbound link clicks, and more.
• User-Centric Data Model: GA4 focuses on a user-centric data model, where individual user interactions are linked using an anonymous User ID or device-specific identifiers. This feature allows you to analyze user behavior across multiple sessions and devices.
• BigQuery Integration: GA4 directly integrates with BigQuery, Google's data warehouse solution. It allows you to export your raw GA4 data to BigQuery for advanced analysis and custom reporting.

Key Metrics and Dimensions for Integration In GA4 

In GA4, you can integrate and utilize several key metrics and dimensions to gain insights into your website or app performance. Let us dig into some of the most important metrics and dimensions in GA4:

Key Metrics in GA4

These are quantitative measurements of your data and let you gain deep insights into the performance of your website or app. 

• Active Users: It is one of the most important metrics and measures the number of unique users who had at least one session within the selected time.
• Sessions: With this metric, you can track the number of sessions (user interactions) within the selected period.
• Engaged Sessions: The measure of engagement on your website or app leads to a certain activity, such as form filling or a download. 
• Engagement Rate: This is the average duration of sessions in seconds and gives you insights into the quality of content on your page. 
• Conversion Rate:  It is the percentage of users who completed a specific goal or conversion event.
• Revenue: Perhaps the most important metric, especially for ecommerce sites, is the total revenue generated from transactions or other monetization events.
• Retention: Here, you can measure the percentage of users who return to your app or website over a specific period.

Key Dimensions In GA4

GA4 dimensions are an attribute of your data. When you track the dimensions, you gain a bird’s eye view of the performance of your property. 

• User ID: It is an anonymous identifier for analyzing user behavior across multiple sessions and devices.
• Device Type: As the name suggests, it helps you identify the type of device the user uses while browsing your platform. 
• Traffic Source: These let you track the source or medium from which the user arrived at your site/app, such as organic search, direct traffic, or referrals. 
• Location: The geographic location of the user, including country, city, and region.
• Landing Page: It is the first page or screen users interacted with when they entered your site/app.
• Campaign: The name or ID of the marketing campaign that brought users to your site/app.

You can use the metrics and dimensions individually or combine them to gain insights into user behavior, engagement, conversion, and more. Analyzing and comparing these metrics with different dimensions allows you to identify trends, optimize your marketing efforts, and improve the user experience.

Integrating GA4 with Data Warehouse

Integrating Google Analytics 4 with a data warehouse allows you to centralize and consolidate your data for advanced analytics, reporting, and data-driven decision-making. Here’s a step-by-step guide to integrating GA4 with data warehouses - 

• Choose a Data Warehouse – You can choose data warehouse options such as Google BigQuery, Snowflake, Amazon Redshift, or others based on your specific requirements, budget, and scalability needs. Once you choose a data warehouse, evaluate factors like pricing, performance, ease of use, and integration capabilities with GA4.
• Establish Data Pipeline: In this step, create a pipeline to extract GA4 data and load it into your chosen data warehouse. In GA4, you will have different options for exporting data, such as BigQuery export, Data Import API, Firebase Analytics export, etc. Configure the necessary settings within GA4 or use relevant APIs to stream or export the data into your data warehouse.
• Transform and Cleanse Data: It is important to transform and cleanse the data to ensure GA4 data aligns with your data warehouse schema and requirements. At this stage, you need to use tools or scripting languages like SQL, Python, or ETL (Extract, Transform, Load) tools specific to your data warehouse for these tasks.
• Load GA4 Data into the Data Warehouse: Map the extracted GA4 data to your data warehouse's appropriate tables or schemas. Design the data loading process to load and update your warehouse efficiently. 

Best Practices for Integrating GA4 with Data Warehouse

• Document your data warehouse architecture, sources, transformations, and lineage for future reference and collaboration.
• Implement data versioning and backup strategies to safeguard your GA4 data in case of any failures or data loss.
• Maintain a scalable and flexible data model for future growth to integrate new data sources and evolving analytics requirements.
• Review and optimize the data pipeline and warehouse performance to ensure efficient data processing and query performance.

Integrating GA4 with Business Intelligence Tools

Integrating Google Analytics 4 with popular business intelligence (BI) tools such as Tableau, Power BI, and Looker can provide a comprehensive view of your website or app data. It allows you to create powerful visualizations and reports. 

Connecting GA4 with BI Tools

Most BI tools offer native connectors or plugins for GA4, enabling you to establish a connection easily. Here is a step-by-step guide to connecting GA4 to these tools 

• Sign into the Google Analytics 4 property and navigate to the Admin section. 
• Choose the Data Streams setting, find the data stream associated with your property, and note the Measurement ID.
• Open your BI tool (Tableau, Power BI, Looker, etc.) and locate the option to connect to data sources.
• Search for the Google Analytics connector or the specific connector associated with GA4 and click on the connector to initiate the setup process.
• Provide the necessary authentication details to establish a connection between the BI tool and GA4.
• Once connected, you will be prompted to select the GA4 property or data stream you want to work with. Choose the relevant property or data stream. 
• Select the dimensions and metrics you want to include in your analysis.

Create Visualizations and Reports

Once you have established the connection, you can create visualizations and reports using GA4 data within the BI tool. Identify the relevant GA4 data you want to analyze and select the appropriate dimensions (e.g., user, session) and metrics (e.g., page views, conversions) to create meaningful visualizations. You can also utilize the features and capabilities of the BI tool to design interactive dashboards, charts, and graphs that present the GA4 data in a clear and actionable manner.

Advanced Analytics with GA4 and BI Tools

BI tools offer advanced analytics functionalities you can combine with GA4 data for deeper insights. Along with GA4’s analytical capabilities, you will enjoy the following -  

• User Segmentation: Use GA4 data to segment your audience based on various attributes (e.g., demographics, behavior) and analyze their behavior separately.
• Cohort Analysis: Analyze the behavior of specific user cohorts over time to identify trends, patterns, and compare performance.
• Funnel Analysis: Track user interactions through different conversion funnel stages to identify potential bottlenecks or areas for improvement.
• Predictive Analytics: Utilize machine learning algorithms within the BI tool to make predictions based on GA4 data.

Automate Data Updates and Refreshing

BI tools provide options to automate data updates to ensure your visualizations and reports are up-to-date. You can set up a schedule or use event-based triggers within the BI tool to automatically fetch new data from GA4 at regular intervals or when they meet specific conditions. You can configure data refresh settings to determine how often the data should be updated and synchronized with GA4.

Leveraging GA4 Integration for Business Insights

GA4 packs all the bells and whistles for generating business insights. By leveraging extensive insights, you can walk the path of informed and actionable decisions. It improves user engagement and your marketing campaign's performance and increases conversion. Here are some ways in which GA4 integration can offer business insights. 

Analyze User Behavior and Engagement

The new user-centric methodology for data collecting and analysis offers user-centric measurement. Unlike Universal Analytics, which only tracked views, GA4 offers more in-depth insights into user behavior and engagement. It sheds light on how users interact with your website or app individually, across many platforms and devices, and in real-time. 

Track Marketing Campaigns and Conversions

With GA4, you can track marketing campaigns and conversions granularly. You will have information on how users engage with your marketing campaigns and the exact triggers that lead to conversions. 

Monitor And Optimize Website and App Performance

Universal Analytics enabled website optimization, and GA4 helps you do the same for your app. You will have all data related to the performance of your website and apps on a single dashboard. This data enables you to optimize your website and apps based on the changing user aspiration. 

Understand Customer Journeys and Funnels

Users are integrating with your brand across devices. With GA4, you have a better understanding of the customer's journey. You can create personalized experiences based on your awareness of your customer's journey. It also helps you improve your sales funnel.

Extract Actionable Insights from GA4 Data Integration

We have discussed this above and reiterate it. With GA4 integration, you will have access to actionable insights on customer journeys, emerging trends in the market, and opportunities for your brand. Leveraging this information, you can stand ahead of the curve, reduce time-to-market, and take advantage of market trends. 

Leveraging Business Intelligence Tools for Advanced Analysis

Business Intelligence (BI) tools are powerful resources that can greatly enhance your data analysis capabilities and provide valuable insights for decision-making. By integrating GA4 with a data warehouse and leveraging BI tools, you can unlock advanced analytics and visualization capabilities that can revolutionize your data-driven strategies.

Introduction to the various features and functions of BI tools.

BI tools offer a wide range of features and functions that enable you to analyze data in-depth. These tools provide interactive dashboards, customizable reports, and data visualization capabilities that allow you to explore and interpret data effortlessly. With BI tools, you can easily navigate through complex datasets and identify patterns, trends, and correlations that might otherwise go unnoticed.

Explanation of how BI tools provide advanced analytics and visualization capabilities.

BI tools enable advanced analytics by leveraging sophisticated algorithms and statistical models. You can perform complex calculations, predictive analytics, and create data models to uncover meaningful insights from your integrated GA4 data. Moreover, these tools offer a variety of visualization options such as charts, graphs, and maps, allowing you to present data in compelling and easily understandable formats.

Showcase of the benefits of using BI tools for data analysis and reporting.

Using BI tools for data analysis and reporting has numerous advantages. Firstly, it enables you to gain a holistic view of your data by bringing together data from various sources, including GA4. This broader perspective can lead to more accurate insights and better decision-making. Additionally, BI tools facilitate faster and more efficient reporting by automating the generation of reports, reducing manual effort, and ensuring data accuracy. This empowers stakeholders to access up-to-date information, enabling faster response times and more proactive strategies.

Using ETL and Data Pipelines for Seamless Integration

When it comes to integrating GA4 with a data warehouse and business intelligence tools, ETL (Extract, Transform, Load) is a crucial component. ETL refers to the process of extracting data from various sources, transforming it into a consistent format, and loading it into a target destination, such as a data warehouse.

The first step in the ETL process is extraction, where data is gathered from different systems and sources. This includes pulling data from GA4, as well as other relevant platforms and databases. Once the data is extracted, it needs to undergo transformations to ensure it is compatible and consistent with the target destination.

Transformations involve cleaning the data, filtering out irrelevant information, and structuring it in a way that aligns with the data warehouse and business intelligence tools. This step is essential for guaranteeing accurate analysis and reporting.

After the data is transformed, it is loaded into the target destination using a data pipeline. Data pipelines act as a seamless conduit for moving and transforming data between different systems and platforms. They provide a structured and automated approach to data integration, ensuring efficiency and reliability.

When integrating GA4 with a data warehouse, ETL and data pipelines play a vital role. They enable the seamless flow of data, allowing businesses to centralize and consolidate their analytics data. This integration enhances data accuracy, reduces manual efforts, and enables better reporting and analysis.

By utilizing ETL and data pipelines, businesses can unlock the full potential of GA4 and leverage its capabilities in conjunction with their data warehouse and business intelligence tools. This integration empowers organizations to make data-driven decisions and gain actionable insights for improved business outcomes.

Real-time Data Integration for Timely Decision-making

In today's fast-paced business environment, timely decision-making is crucial for staying competitive. The ability to access and analyze real-time data is a game-changer, providing organizations with valuable insights to make informed decisions. By integrating GA4 with a data warehouse, businesses can unlock the power of real-time data analytics.

When GA4 is integrated with a data warehouse, organizations gain access to a wealth of real-time data insights. This integration allows businesses to monitor key metrics, track customer behavior, and analyze trends as they happen. The real-time data provides a more accurate and up-to-date understanding of customer preferences and market dynamics.

The benefits of real-time data integration are numerous. Firstly, organizations can make faster and more informed decisions based on the latest data. This agility allows businesses to respond to market changes in a timely manner, giving them a competitive edge. Real-time data also enables organizations to identify and react to emerging trends and opportunities before their competitors.

Real-time data integration also enhances decision-making capabilities by providing a holistic view of the business. By integrating GA4 with a data warehouse, businesses can combine data from various sources, such as sales, marketing, and customer support. This comprehensive view allows decision-makers to have a complete understanding of the business, enabling them to make strategic decisions that align with organizational goals.

Moreover, real-time data integration enables businesses to personalize customer experiences. By analyzing real-time data, organizations can gain valuable insights into individual customer preferences and behaviors. This information can be leveraged to deliver personalized marketing campaigns, tailored product recommendations, and exceptional customer service. The ability to provide a personalized experience gives businesses a competitive advantage and fosters customer loyalty.

In conclusion, real-time data integration plays a pivotal role in enabling timely decision-making in today's fast-paced business environment. Integrating GA4 with a data warehouse provides businesses with real-time data insights that enhance decision-making capabilities. The benefits of real-time data integration are vast and include faster decision-making, agility in responding to market changes, identification of emerging trends, a holistic view of the business, and personalized customer experiences. By embracing real-time data integration, businesses can stay ahead of the competition and make informed decisions that drive success.

Key Considerations for Implementing Integration

• Overview of the key factors to consider when planning and implementing integration of GA4 with a data warehouse and BI tools.
  Integrating GA4 with a data warehouse and BI tools requires careful planning and consideration. Some key factors to keep in mind include:
  • The specific goals and objectives of your integration project.
  • The availability and compatibility of the data warehouse and BI tools with GA4.
  • The scalability and future-proofing of the integration solution.
  • The availability of resources and expertise to support the integration.
  • The potential impact on existing systems and processes.
• By considering these factors, you can ensure a successful integration that meets your business needs and goals.
• Discussion on the importance of proper data mapping, cleansing, and transformation.
  Proper data mapping, cleansing, and transformation are crucial steps in the integration process. It involves:
  • Identifying and mapping the data fields between GA4 and the data warehouse.
  • Ensuring data accuracy and consistency through cleansing and standardization.
  • Transforming the data into a format that can be easily understood and analyzed by BI tools.
• By ensuring proper data mapping, cleansing, and transformation, you can trust the quality and reliability of the integrated data.
• Insight into selecting the right tools and technologies for integration.
  Choosing the right tools and technologies for integration is crucial for a successful implementation. Consider:
  • The compatibility of the tools and technologies with GA4 and your data warehouse.
  • The scalability and performance of the tools to handle the integration workload.
  • The ease of use and availability of support and documentation.
  • The cost and licensing requirements of the tools.
• By selecting the right tools and technologies, you can streamline the integration process and optimize the performance of your data integration solution.

Is The Auto Migration Good Enough?

If your property has undergone forced migration, chances are you may find yourself lost in the new scheme of things. For all the efforts that Google has put into making this migration seamless, it can never meet the unique needs of every Google Analytics user. Here are some of the problems that users have reported with auto-migration - 

• Google Signals: By default, Google is linking and activating Google Signals to your Google Analytics account. However, Google Signals has grey areas and clashes with GDPR and other privacy laws. Automating it for users across all countries might invite trouble. 
• Google Ads: Auto migration allows Google to target and bid for Google Ads. It is again a big concern as you are allowing Google to do the bidding on your behalf and aren’t in complete control over your ad campaign. 
• Nomenclature: When you choose auto migration, the nomenclature of your data sets changes, and it can create problems tracking historical data or identifying old datasets. It is best avoided. 

If your in-house team lacks expertise in UA to GA4 migration, it would be wise to hire GA4 experts. They would look into your unique needs and ensure the process is smooth and doesn’t lead to the loss of old data. 

Final Thoughts

Since the introduction of Universal Analytics, the marketing industry has evolved. Today's marketing takes place in a cross-platform context. Google Analytics 4 is in line with the dynamically changing digital ecosystem. It is more potent than UA and gives you more precise and pertinent information about their clients' online and app usage. Integrating GA4 with your data warehouse gives you a complete cross-channel picture of user activity on web and app platforms. If you aren’t happy with auto-migration or looking to use GA4 for the first time, we can help. At Growth Natives, we have a team of Google-certified experts who can help integrate your data and set up GA4 property for predictive marketing. Call our toll-free number, +1 855-693-4769, or email us at info@growthnatives.com for more.