Web Analytics: How an ID-Graph Can Power Google Analytics 4

Web analytics has come a long way from its humble beginnings. Today, it is a powerful tool for businesses and marketers, providing insights that help drive decision-making and strategy. 

Web analytics tools like Google Analytics and Matomo are at the forefront of this evolution, offering sophisticated features that enable comprehensive tracking and analysis. These analytics tools provide specific metrics and capabilities, such as tracking returning visitors and new visitors, which are essential for understanding website performance.

But, to truly unlock the potential of GA4, integrating it with an ID-Graph can be transformative. In this guide, we’ll explore how an ID-Graph can supercharge GA4, providing you with practical advice on setting up and leveraging web analytics to its fullest.


The essential in brief:

  • Enhancing Google Analytics 4 with an ID-Graph provides a comprehensive view of user behaviour across devices and sessions, leading to more accurate data analysis and better business decisions.

  • The use of UMIDs allows businesses to track users across multiple devices seamlessly, reducing data silos and improving user experience.

  • GA4, combined with ID-Graph, enables detailed audience segmentation and activation, improving marketing effectiveness and personalisation.



Evolution of Web Analytics Software

Stage 1: Basic Reporting and Exploration

Web analytics started with simple log file analysis, giving basic metrics like page views, visits, and unique visitors. Early tools focused on understanding general traffic patterns and user engagement, providing insights into visit duration, bounce rates, and referrers.


Stage 2: Enhanced Reporting and User Behaviour Analysis

As technology evolved, so did web analytics, with a growing emphasis on analysing data to gain insights and make informed decisions. Platforms began tracking user behaviour more deeply, including event tracking, goal conversions, and funnel analysis. This stage saw the rise of mobile analytics, capturing app downloads, in-app purchases, and user retention metrics, offering a richer understanding of user interactions beyond simple page views.


Stage 3: Activation and Attribution

The next leap integrated web analytics with other data sources, like CRM and transaction data, providing a holistic view of the customer journey through effective customer relationship management. However, challenges such as partial records and data ageing due to data silos from multiple systems can hinder this process.

Advanced audience segmentation became possible, allowing for targeted marketing based on behaviour, demographics, and acquisition channels. Single and multitouch attribution models emerged, improving the understanding of marketing channel impacts on conversions.

Stage 4: Automation and AI

Today, web analytics platforms incorporate automation and AI, enabling businesses to activate audiences and insights directly within marketing and CRM systems. Predictive analytics, like forecasting trends, predictive audiences, and anomaly detection, are now standard features, allowing for more proactive decision-making.


Identity Resolution in Web Analytics

Traditionally, web analytics relied on cookies to track user behaviour. However, with the decline of third-party cookies and the rise of multi-device usage, deduplication and unifying profiles have become crucial. Tracking users across desktop PCs, smartphones, and tablets ensures a seamless experience across multiple devices. 

This is where ID-Graphs come in, enhancing web analytics by providing a comprehensive view of users across devices and sessions. Identifying the same user across multiple devices helps in defining unique pageviews and avoiding duplication.


Adding a Customer Identifier to Google Analytics

GA4 offers two basic ID concepts: the Client ID and the User ID. The Client ID is an internal ID that identifies a specific browser-device combination, while the User ID is an external identifier, often recommended as the login ID. However, using an ID-Graph and a Universal Marketing ID (UMID) has distinct advantages:

  • Always Assigned: UMIDs can be assigned even when users are not logged in.

  • Broader Coverage: UMIDs cover customers and prospects not captured in CRM systems.

  • Simplified Compliance: UMIDs can be anonymised, aiding in compliance with privacy regulations.

  • Unique Identifier: UMIDs act as a unique identifier, distinguishing and connecting customer behaviours and preferences across offline and online interactions.


Cross Domain Analytics for Multiple Devices

For a seamless cross-domain setup, it is crucial to install a tracking code on all web domains and apps under one Google property. This ensures that user IDs are not duplicated across different domains, providing a unified view of user interactions. While this might require restructuring existing setups, it significantly enhances data accuracy and user insights.

If restructuring is impractical, you can still set up a cross-domain ID-Graph and multiple GA4 instances to unify data outside of GA4.


Social Login Integration

Using Google as an identity provider for your website or app allows matching your identifiers with Google accounts without third-party cookies. This requires users to enable Ads Personalisation and the company to turn on data sharing with Google Ads. This integration supports cross-device remarketing and exporting key events to Google Ads, enhancing targeting and personalisation efforts.


Profile Merges Using a Unique Identifier

Over time, user identification leads to more accurate ID-Graphs and profile merges. This ensures that historical data remains accurate, allowing for consistent analysis, reporting, and audience building.

For retrospective deduplication, consider using the GA User Deletion API and re-ingesting transactions associated with deleted UMIDs via the Measurement Protocol.


Adding Your Data to GA4

Enriching GA4 with offline data based on UMIDs can significantly enhance your analytics by analysing the data generated from various user interactions, such as traffic sources, page visits, conversion rates, and customer click-through behaviour. This includes collecting and utilising various data points like customer data from channels, devices, online identifiers, and offline identifiers:

  • Customer Data: Add demographic details like gender and age.

  • Offline Events: Include offline conversions or returns for better attribution.

  • Segmentation Data: Import derived data from data warehouses for refined segmentation.

  • AI/ML Scores: Incorporate scores from external AI/ML models.

  • 2nd/3rd Party Data: Enrich your datasets with additional sources for a comprehensive view.


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Benefits of Adding an ID-Graph to GA4


Reporting and Exploration

Using a UMID as a User ID in GA4 allows for person-level analysis across domains, devices, and apps. This leads to higher deduplication rates and more accurate insights into site visitors' behaviour and preferences.

  • User Explorer Reports: Detailed insights into individual user behaviour, including session timelines and interactions.

  • User Acquisition Reports: Trends in user acquisition across different channels.

  • Active Users Reports: Retention rates and active user trends over time.



Enhanced attribution models become possible when you measure traffic from various sources, enabling effective attribution with cross-device and cross-touchpoint tracking using a UMID. GA4’s machine learning models provide insights into the most effective conversion paths, allowing for more informed bidding strategies and budget allocation.


Audience Segmentation and Activation

A richer dataset enables a seamless customer experience by delivering consistent and relevant messaging across channels. GA4 can create detailed custom audiences based on user behaviour and imported customer data, improving predictive metrics like purchase or churn probability. These audiences can be activated within the Google ecosystem (Ads, DV360, Search Ads 360, Ad Manager) and beyond, ensuring a consistent omnichannel customer experience.



Utilising the GA Data Layer

The GA data layer offers a robust framework for capturing, organising, and using data across various applications, including social media accounts. It ensures standardised, flexible, and accurate data management, enabling businesses to leverage insights across platforms with integrated analytics and reports for social media accounts to make data-driven decisions.

  • Personalisation Engines: Real-time content personalisation on websites and apps.

  • Customer Data Platforms (CDP): Enhanced customer profiles with behavioural data.

  • Advertising Platforms: Improved targeting and retargeting campaigns.

  • Data Warehouse/BI: Comprehensive dashboards and complex analyses for holistic customer views.

By integrating the data layer with UMID, businesses gain standardised data structures, improved data management, and enhanced user experiences.


Summary: 6 Steps to Set Up GA4 with Your ID-Graph

By leveraging an ID-Graph, businesses can gain deeper insights, improve marketing effectiveness, and deliver better user experiences, ultimately driving higher engagement and conversions. Here’s a six-step approach to supercharge your web analytics platform with your own ID-Graph:


Step 1: Integrate with a Tag Management Platform – Integrate your web analytics platform with a tag management system to collect data effectively. Decide on a single- or multi-domain setup based on your business needs.

Step 2: Set Up GA4 with a UMID – Configure GA4 to use a Universal Marketing ID as the declared user ID for both web and app tracking.

Step 3: Add Data to GA4 – Enrich GA4 with events and customer data based on your UMID. This can include offline events, customer demographics, and AI/ML scores.

Step 4: Activate Audiences within Google Ecosystem – Use GA4 audiences for targeted campaigns within the Google ecosystem, including Google Ads, DV360, Search Ads 360, and Ad Manager.

Step 5: Export Audiences Outside Google Ecosystem – Utilise GA4 insights and audiences for activations outside the Google ecosystem, such as on Meta platforms, programmatic advertising, and your own channels.

Step 6: Leverage the Data Layer – Use the GA data layer for real-time requirements and complex trigger events, enhancing personalisation, testing, and optimisation efforts.


By following these steps, you can fully harness the power of GA4 and ID-Graphs, driving superior web analytics and marketing outcomes!


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FAQ: Google Analytics with ID-Graphs

What is an ID-Graph, and how does it enhance Google Analytics 4?
An ID-Graph is a powerful identity resolution tool that helps businesses connect customer data across multiple sources and devices. By using deterministic matching techniques, it creates a unique identifier for each user. This unique identifier allows Google Analytics 4 to analyse data more accurately by recognising the same user across different sessions, devices, and even social media accounts. This leads to deeper insights into user behaviour, enabling businesses to collect data and measure traffic more effectively, ultimately meeting business objectives.
How can an ID-Graph help in reducing data silos within web analytics?
Data silos occur when customer information is isolated within different systems and platforms, preventing a holistic view of user behaviour. An ID-Graph addresses this issue by integrating customer data from various web analytics tools, social media accounts, CRM systems, and offline identifiers. By creating a unified customer profile, businesses can track how users interact across multiple channels, leading to a more seamless customer experience and informed decision-making.
What are the benefits of using an ID-Graph for e-commerce stores?
For e-commerce stores, an ID-Graph provides several benefits, including the ability to identify returning visitors and new visitors more accurately. It helps to track user behaviour across multiple devices and sessions, offering deeper insights into the overall traffic and other metrics such as bounce rate and time spent on landing pages. This comprehensive view enables businesses to optimise their marketing assets and create more personalised customer interactions, driving more customers to the site and enhancing overall traffic.
How does identity resolution in web analytics help in achieving business objectives?
Identity resolution is crucial for understanding the complete customer journey, from initial contact through social media to final purchase on an ecommerce site. By linking data points across various interactions and platforms, businesses can access a more complete picture of user behaviour. This allows for more accurate reporting, better targeting of marketing campaigns, and improved measurement of how marketing efforts drive traffic. Ultimately, this helps businesses create more effective strategies, optimise their web analytics tools, and achieve their business objectives.


About the author

Dirk Rohweder

Dirk Rohweder: COO and Founder | Teavaro