Data Migration

Navigating the Future of Analytics: Blending Data Between Universal Analytics and Google Analytics 4, with BigQuery

Discover the future of analytics by blending data between Universal Analytics, Google Analytics 4, and BigQuery. Explore the power of advanced analytics and stay ahead in the competitive landscape.

May 31, 2024

Navigating the Future of Analytics: Blending Data Between Universal Analytics and Google Analytics 4, with BigQuery| Cover Image
Navigating the Future of Analytics: Blending Data Between Universal Analytics and Google Analytics 4, with BigQuery| Cover Image

In the ever-evolving landscape of digital analytics, the ability to extract, integrate, and analyze data from multiple platforms is crucial for staying ahead of the curve. The combination of BigQuery, Google Analytics 4 (GA4), and Universal Analytics (UA) offers a singular chance to gain deeper insights and promote better decision-making. This comprehensive guide aims to demystify the complexities of blending data between these platforms, offering practical insights and innovative strategies to enhance your analytics capabilities.

Understanding Universal Analytics (UA), Google Analytics 4 (GA4), and BigQuery

Before diving into the integration process, it's essential to understand the unique features and benefits of each platform:

Universal Analytics(UA): 

Universal Analytics (UA) is a popular version of Google Analytics, used by businesses to track and analyze website traffic and user behavior. Introduced in 2012, it uses a cookie-based tracking system and session-based data model, tracking pageviews, events, transactions, and other user interactions. 


  • Pageview-Centric approach: UA uses a pageview-centric tracking approach, which is easy to use for companies that are mostly concerned with knowing how visitors to their website interact with individual pages. Because of its simplicity, customers who do not need Google Analytics 4’s more sophisticated capabilities can utilize it.

  • Common Reporting Metrics: UA provides pageviews, sessions, bounce rate, and other common reporting metrics. These indicators provide a basic overview of user engagement and website performance, which may be enough for companies with less complex analytics requirements.

  • Session-Based Tracking: By classifying user interactions into sessions, UA facilitates understanding user involvement over a given period. This session-based method is consistent with web analytics best practices.

  • Customization via Goals and Events: UA still permits customization by creating goals and events, but it is less adaptable than GA4’s event-centric tracking. Companies can define specific behaviors and interactions that are crucial to achieving their goals using these customization videos.

Navigating the Future of Analytics: Blending Data Between Universal Analytics and Google Analytics 4, with BigQuery| Understanding Universal Analytics (UA), Google Analytics 4 (GA4), and BigQuery

Google Analytics 4 (GA4)

Google Analytics 4 is a significant analytics upgrade that focuses on customer lifecycle and user engagement across platforms. It introduces new features like event-based tracking and machine learning-driven insights and integrates with other Google products for a comprehensive view of marketing performance and customer behavior.


  • Event-Centric Tracking: GA4 uses an event-centric tracking paradigm, enabling precise and adaptable measurement of user activities. This approach allows businesses to monitor various events, from bespoke interactions like downloads and video views to standard pageviews.

  • Cross-Platform Tracking: GA4 easily combines app and web tracking into one cohesive property. This gives a complete picture of the customer experience as consumers switch between websites and mobile apps by giving a consolidated view of user interactions across various digital touchpoints.

  • User Lifecycle reporting: GA4 presents a user-centric reporting paradigm that centers on the full customer lifecycle, encompassing acquisition, retention, and beyond. Companies may customize user experiences and marketing tactics by gaining information about the behavior of users at different stages.

  • Machine Learning Insights: GA4 uses machine learning to generate insights automatically. With the help of this functionality, users may see possible problems, trends, and opportunities without having to perform manual analysis. The platform gives users useful information by making inferences from data patterns.

Navigating the Future of Analytics: Blending Data Between Universal Analytics and Google Analytics 4, with BigQuery| Understanding Universal Analytics (UA), Google Analytics 4 (GA4), and BigQuery


BigQuery is a fully managed corporate data warehouse with integrated capabilities like business intelligence, machine learning, and geographic analysis, ideal for large-scale analytics projects. Its serverless design allows for SQL queries without infrastructure administration, and its architecture consists of a computing and storage layer.


  • Data Security and Encryption: BigQuery offers robust data security measures, including fine-grained access controls, default data encryption, and centralized access management through Google Cloud Identity and Access Management.

  • Integrations: BigQuery's seamless integration with Google Cloud Platform services, including Dataflow, Dataprep, Google Cloud Storage, and Data Studio, enables users to create end-to-end data pipelines and carry out complex analytics and visualization operations.

  • BigQuery Data Transfer Service: This service makes it easier to ingest data into BigQuery from a variety of sources. With its pre-built connections for well-known SaaS programs like YouTube, Google Analytics, and Salesforce, customers can simply plan and automate data transfers.

  • Reasonable Pricing Structure: BigQuery offers a flexible, cost-effective pricing structure based on on-demand queries and storage usage, with predictable flat-rate pricing options for businesses to suit their budgets and usage habits.

Navigating the Future of Analytics: Blending Data Between Universal Analytics and Google Analytics 4, with BigQuery| Understanding Universal Analytics (UA), Google Analytics 4 (GA4), and BigQuery

Why The Urgency to Transition From Universal Analytics (UA) to Google Analytics 4 (GA4)?

The urgency to transition from Universal Analytics (UA) to Google Analytics 4 (GA4) is driven by a significant shift in the digital analytics landscape, marked by Google's announcement to sunset UA. 

This transition is not merely a technical upgrade but a strategic necessity for businesses aiming to stay ahead in an increasingly digital world. GA4 introduces a more integrated, user-centric analytics platform, offering cross-platform tracking and advanced AI-powered insights, which are crucial for understanding complex user behaviors across web and app ecosystems. 

As UA’s data collection ceases, delaying the migration risks losing access to new data and insights, potentially hindering decision-making processes and competitive edge. Therefore, the push towards GA4 is not just about adapting to a new tool; it's about embracing a future-oriented approach to analytics, ensuring businesses can leverage data more effectively in an evolving digital environment.

Key differences between Universal Analytics (UA) and Google Analytics 4 (GA4) regarding metrics and dimensions 

Businesses looking to get insight into their online presence have long favored Universal Analytics(UA) as their analytics platform of choice.  Nevertheless,  there has been a noticeable advancement in the tracking and analysis of user interactions since the release of Google Analytics 4 (GA4).GA4 is a big step in the direction of a more flexible and comprehensive method of analyzing user activity on many digital platforms. 

In contrast to UA,  which mostly uses session-based monitoring, GA4 enables businesses to measure a wider range of user activities,  such as button clicks, video views,  downloads, and more,  rather than just tracking page views and sessions. With this increased granularity,  organizations may gain deeper insights into user behavior and more effectively improve their online presence. Below stated are the most prominent differences between Universal Analytics (UA) and Google Analytics.

Key Differences:

  1. Total users

    Universal Analytics (UA)- In UA, "Total Users" refers to the total number of unique users who have visited your website within the specified time frame. "New Users" specifically refers to users who are visiting the site for the first time during the specified time frame.

    Google Analytics (GA4)- GA4 uses "Users" instead of "Total Users" to represent unique users who have interacted with a website or app, and "New Users" to refer to users who started sessions attributed to a specific time frame.

  2. Purchases

    Universal Analytics (UA)- In UA, "Purchases" typically refer to e-commerce transactions tracked through the e-commerce tracking feature.

    Google Analytics (GA4)- GA4 enhances e-commerce tracking by focusing on event-based tracking, allowing for a wider range of conversion events, including specific interactions and non-revenue-generating actions.

  3. Page Views

    Universal Analytics (UA)- In UA, "Page Views" represent the total number of times a page on your website has been viewed by users.

    Google Analytics (GA4)- GA4 introduces an event-driven model, recording interactions with content as events, including scrolls, video views, and downloads, in addition to traditional page views.

  4. Sessions

    Universal Analytics (UA)- In UA,  "Sessions" represent a group of interactions that take place within a given time frame on your website or app. Sessions are often used to measure user engagement and behavior

    Google Analytics (GA4)- GA4 shifts from session-based metrics to event-centric models, allowing for a comprehensive understanding of user behavior by tracking interactions as events.

  5. Content Grouping

    Universal Analytics (UA)- Universal Analytics allows content grouping to organize content logically, enabling comparison of metrics by group name, such as aggregated page views for pages like 'Men/Shirts'.

    Google Analytics (GA4)- Google Analytics 4 properties have a predefined event parameter for a content group, which populates data into the "Content Group" dimension, and additional custom dimensions can be implemented separately.

    Innovative Use Cases

    Blending data between GA4 and UA offers a myriad of innovative use cases, enabling marketers, analysts, and decision-makers to gain deeper insights into user behavior, optimize conversion funnels, and deliver more personalized experiences across web and app platforms. From cross-platform attribution and behavioral analysis to segmentation, personalization, and predictive analytics, the possibilities are virtually endless.

    Cross-Platform Attribution:

    • Traditional attribution models often struggle to capture the full customer journey, especially in today's multi-device and multi-platform landscape. By blending data from GA4 and UA, marketers can stitch together interactions across web and app platforms, providing a more comprehensive view of the customer journey.

    • This blended data approach enables marketers to understand how users interact with various touch points before converting, allowing them to allocate marketing budgets more effectively. For example, they can identify which channels and devices contribute most to conversions and adjust their marketing strategies accordingly.

    Segmentation and Personalization:

    • With blended data, marketers can create more refined audience segments based on a combination of web and app interactions. For example, they can segment users who engage heavily with the app but rarely visit the website, or vice versa.

    • These segmented audiences can be leveraged for targeted marketing campaigns and personalized messaging tailored to specific user behaviors and preferences. By delivering more relevant content and offers, businesses can increase engagement and conversion rates across both platforms.

    Conversion Rate Optimization (CRO):

    • Blending data allows CRO specialists to identify conversion bottlenecks and friction points in the user journey, regardless of the platform. By analyzing user interactions and behaviors leading up to conversions, businesses can pinpoint areas for improvement.

    • This data-driven approach to CRO enables businesses to implement targeted optimizations, such as streamlining checkout processes, optimizing app navigation, or refining call-to-action (CTA) messaging. By removing barriers to conversion, businesses can improve conversion rates and overall user experience.

    Campaign Performance Analysis:

    • By blending data from GA4 and UA, marketers can evaluate the effectiveness of marketing campaigns across multiple channels and platforms. They can track campaign performance metrics such as impressions, clicks, app installs, and website conversions.

    • This comprehensive view of campaign performance allows marketers to understand the holistic impact of their marketing efforts and optimize campaigns in real-time. They can allocate budgets to the most effective channels, adjust targeting parameters, and refine messaging to maximize ROI.


    Blending data between Universal Analytics, Google Analytics 4, and BigQuery is a strategic move that can revolutionize your analytics capabilities. By following this guide, you can navigate the complexities of data integration, unlock deeper insights, and future-proof your analytics strategy. As the digital landscape continues to evolve, the ability to blend data across platforms will be key to staying ahead of the competition. Embrace the power of data blending and unlock the full potential of your analytics today

    Make sure that your data-driven decision-making process is not affected by the switch to GA4. Get in touch with Analytics Safe right now to schedule a demo or consultation and see how we can assist you in combining BigQuery, GA4 API, and UA data to make sure your analytics strategies are resilient and perceptive despite changing digital obstacles.