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Migrate and thrive: everything you need to know about Google Analytics 4

This page covers all angles of the Universal Analytics to Google Analytics 4 migration, from transferring historical data to ensuring dashboard reliability.

Trusted by data-driven brands

Google Analytics 4 (GA4) is the new generation of Google Analytics, that will permanently replace Universal Analytics (UA) on July 1, 2023. GA4 introduces a new data structure and data collection logic, replaces a session-based with event-based data modeling, and brings some of the Google Tag Manager and GA360 functionalities to the general audience.

Key GA4-UA differences changing the approach to marketing analytics

Event-based modeling

GA4 tracks all user interactions separately, out of the box, and as events, which enables:

  • More granular data analysis
  • In-depth insights into user behavior
  • Analyze customer journeys, not URL performance

It also means more complicated audience migrations, difficulties with historical data analysis and dashboard and reports maintenance.

Cross-platform tracking

GA4 can track user interactions across websites, apps, and other digital platforms. Marketers now:

  • Take on less fragmented approach
  • Save time merging data from multiple tracking systems

Custom reporting

GA4 emphasizes custom reporting and introduces Explorations, a combination of advanced reporting techniques. New features:

  • Introduce capabilities for advanced data analysis & data visualization
  • Allow to dive into your data in great detail
  • Require to overcome a learning curve

How to transfer historical data from UA to GA4

Setting up a new tracking system is just the first step — the most critical process is historical data migration. This step is vital for the continuous support of your marketing dashboards and reports, historical benchmarking, and revenue attribution. Due to the difference in data schema, terminology, and metrics calculations, marketers can't simply merge Universal and Google Analytics 4 data.

Four ways to migrate historical data

Method 1

Manual data extraction

It's a time-consuming and error-prone method, which suits only small businesses since there's a limit on the number of data rows exported. Other options available to SMBs are Google Sheets Add-on and UA Query Explorer.

Method 2

Aggregate data in Google BigQuery

GA4 and GA360 support Google BigQuery integration, enabling data export. This method still requires users to harmonize the data to match the GA4 data schema. Alternatively, UA users can extract their data to third-party storage via API and push it to BigQuery. Either way, the process requires enormous IT and analytics resources.

Method 3

Using in-house managed storage

If your marketing department already utilizes a data warehouse, your team can extract data from UA and GA4 and start from there, normalizing and transforming data to bring it to a unified format.

Method 4

Using third-party solutions managed storage

Advanced marketing analytics provide pre-built data pipelines to transfer data from UA and GA4 to a database or data warehouse. Solutions with a robust data transformation engine can also run data mapping and harmonization to ensure you can still feed your data to a BI tool and track metrics QoQ and YoY.

How to analyze paid ads performance with new data from GA4

Ad platforms integration with UA and GA4 differs in several ways, including conversion tracking, audience creation and sharing, and revenue attribution. GA4 no longer supports first-click, linear, time-decay, position-based, and last non-direct click attribution models. One mistake in merging UA and GA4 data and marketing teams analyze performance based on inconsistent attribution, leading to skewed interpretations of what drives conversions and revenue, over- or under-investment in certain channels, and disruption of the budget allocation process.

Use Improvado's Paid Ads × Google Analytics 4 dashboard recipe to ensure data consistency and precise ROI calculations.

  • See key metrics and KPIs in one dashboard.
  • Tap into granular performance data on website traffic related to ad campaigns beyond the destination URL.
  • Analyze ad spend returns across multiple regions, geos, and devices, and how it correlates to particular campaigns, ad groups, and engagement-level analytics.
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