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I’m making the switch from Universal Analytics to Google Analytics 4 (GA4). What key differences and considerations should I be aware of?

Justin Dambach
Written by Justin Dambach  May 16, 2023

If you’re feeling like July 1, 2023 is sneaking up on you, you’re not alone. The day when Google will officially sunset Universal Analytics and replace it with Google Analytics 4 (GA4) is fast approaching, and it’s important to understand the key differences and considerations involved.

Our team’s been helping clients make this transition proactively since last year, and we’ve uncovered quite a few insights about the differences between UA and GA4 that we’ll fill you in on to help your own shift go as smoothly as possible. In this guide, we’ll explore the replacement nature of the transition, the changes in data models and metrics, the implications for historical data, and the importance of strategic planning.

By understanding the nuances between Universal Analytics and GA4, you’ll be better equipped for a successful transition to the new measurement framework.

First things first: this is a replacement, not a migration. 

Your historical data, goals, and conversions in UA will not carry over to GA4, and the metrics and their definitions will change as well.

While some metrics may have similar names, it’s important to note that GA4 introduces a new data model and measurement framework that differs from UA, and the underlying definitions and calculation methods may differ. You will not see apples-to-apples data when comparing UA to GA4. That said, similar trends should still exist inside the two individual platforms. For example, UA highlights Total Users (shown as Users) in most reports, whereas GA4 focuses on Active Users (also shown as Users). So, while the term Users appears the same, the calculation for this metric is different between UA and GA4.

While UA primarily relied on sessions and pageviews, GA4 utilizes an event-driven model that shifts the focus from tracking individual ‘sessions’ to tracking ‘users’ across multiple devices and platforms. Its new metrics and measurements align with its user-centric approach.

When transitioning from UA to GA4, historical data cannot be directly transferred due to the differences in the data models. You can (and we encourage you to!) export data from UA for reference in PDF or spreadsheet format; our team’s go-to solution has been to build a Google Data Studio dashboard with historical Universal Analytics data, which makes it easy to visualize, segment, filter, and manipulate those exports for reporting and comparison purposes.

“But wait, isn’t there an automigration option?” you ask…

Technically, yes, though all analysts agree that you should avoid this option if possible – it’s important to thoroughly plan and prepare for the switch from Universal Analytics to GA4. Auto-migration may result in data discrepancies between UA and GA4. The underlying data models and tracking methodologies are different, which can lead to variations in metrics, dimensions, and attribution. Important considerations include:

  • Auto migration may not capture all the customizations and configurations you had in UA. This includes custom dimensions, goals, segments, filters, and other settings. You will need to review and manually recreate these customizations in GA4 to ensure continuity in tracking and reporting.
  • Goals and event tracking in UA may not be automatically migrated to GA4. You may need to redefine and recreate your goals and event tracking setup in GA4 based on the new event-driven data model.
  • If you have custom reports and dashboards in UA, they will not be automatically transferred to GA4.
  • GA4 has a different user interface and navigation compared to UA. It may take some time for users familiar with UA to adjust to the new interface and locate the desired reports, settings, and features in GA4.

New product = new KPIs. 

In the transition from UA to GA4, certain KPIs will be deprecated or undergo changes due to the differences in data models and measurement frameworks.

Bounce Rate: In GA4, the concept of bounce rate as it was defined in UA (a single-page session) is no longer applicable. GA4 focuses more on engagement and user interactions rather than session-based metrics. Instead of bounce rate, GA4 introduces metrics like engaged sessions, engagement rate, and engagement time to measure user engagement and interaction levels.

Avg. Session Duration: GA4 does not provide a direct equivalent to session duration as it was measured in UA. Instead, GA4 introduces metrics like engagement time, which measures the total time users spend actively engaged with your website or app, considering all their interactions and events.

Goal Conversion Rate: The specific goal conversion rate metric as it was used in UA may not have a direct counterpart in GA4, but the concept of tracking and measuring conversions still exists.

Additionally, there are reports and features that will be depreciated or adjusted in the switch from UA to GA4. The Behavioral Flow Report that shows how users are moving through your website will no longer be available, but you can utilize the exploration templates to follow your user’s journey. Path Exploration is the most similar, but you can also set up Funnel Exploration to show how users are moving along your conversion funnel. All reports are fully customizable (your funnel is not my funnel, after all!), so it’s critical to ensure they’re set up correctly in order to provide value.

Although we’ll be saying goodbye to these KPIs as we’ve known them, GA4 introduces the concept of “Engaged Sessions,” which represents sessions where users had significant interactions. Other metrics like “Engagement Rate,” “Engagement Time,” and “Engagement Depth” provide insights into user engagement levels.

GA4 also provides flexibility for creating custom metrics that align with your specific business goals and tracking requirements. Custom metrics allow you to define and measure metrics that are not available out-of-the-box in GA4, providing more tailored insights for your marketing efforts.

During the transition from UA to GA4, it’s advisable to evaluate your current KPIs and tracking requirements and adjust them accordingly to leverage the new measurement capabilities and metrics offered by GA4.

New product = new reports.

Many familiar report templates and formats will carry over from UA to GA4, although they may feature different metrics or measurement strategies that better map to GA4’s emphasis on helping you glean insights about ‘users’ moreso than ‘sessions.’

Additionally, you will have the opportunity to build more custom reports that align with your business objectives – if these are approached strategically, you’ll end up with more actionable data and insights that are directly relevant to your bottom line, whereas previously you might’ve put a lot of time and energy into translating and comparing proxy metrics.

Custom Reports we already love building in GA4:

  • Conversion flows (funnel exploration in GA4)
  • Detailed eCommerce Activity
  • Conversions from Sources & Pages (engagement pages and screens in GA4)
  • Behavior flow (path exploration in GA4)
  • SEO pages report (#1 outlined in this article)
  • Organic search traffic flow (#2 outlined in this article)

New product = new integrations.

If you’re wondering how the switch to GA4 might impact reporting and analysis for Google Ads, you’re in good company, and we’ll warn you: it’s a tad complicated.

Goals and conversions will need to first be set up in GA4, then imported into Google Ads, retiring your “old” Universal Analytics goals and conversions. It’s likely that you will see discrepancies in what is reported even if you are using the same attribution model (ex: data-driven) for Google Ads and GA4. We recommend importing the new conversions into Google Ads early before changing campaigns to optimize toward them to let the new conversions collect data.

The discrepancy is a result of the different tracking methods between GA4 and Google Ads, and Google offers a few explanations by way of articles (GA4 Import to Google Ads and Comparing Analytics to Google Ads).

When it comes to web conversions, Google Ads will import Google Analytics 4 web conversions on a cross-channel scope, using the attribution model selected for that conversion in Google Ads (including non-last click models).

When conversions are imported on a cross-channel scope, fractional credits may be imported for non-last click attribution models. For example, if data-driven attribution is selected for a conversion action in Google Ads, then the conversions imported will be fractional, data-driven attribution credits from GA4.


  1. When comparing the data between Google Ads and GA4 property, it is always advised to use the “model comparison report” to check the data reporting.
  2. Ensure that the attribution model is set to cross-channel last click. 
  3. The attribution model should be set to cross-channel last click for the following reasons:
    1. GA4 export to Google Ads only includes conversions attributed to Google Ads under the last non-direct click attribution model. Only conversions where Google Ads is the last non-direct click are exported to Ads, even if a non-last click attribution model is selected in Ads.
    2. Cross-channel last click is the only last click model that can be exported to Google Ads. Ads-preferred last click is only available for reporting purposes.

Still have questions?

Whew, we got pretty geeky there for awhile, didn’t we?

Long story short: We’re cautiously optimistic about the new features, metrics, and framework of GA4. The ability to customize your analytics profile to map to your goals is a huge upgrade, but requires a lot of know-how and strategic planning to get right. Our team is here to help, whether that involves handling your data exports, profile setup, and new integrations, or consulting and troubleshooting along the way.

Justin Dambach

Justin Dambach

Justin works to expose insights across campaigns and drive the strategy behind email marketing, automated workflows, and data visualization. He believes that figuring out the "why" behind our metrics is just as important as the "what".