GA4 Five Years Later: The Current State Of Marketing Analytics

As a marketing specialist who has gone through the transition from Universal Analytics to Google Analytics 4 on countless projects, I can confidently say that no platform migration has divided the marketing community quite like GA4.

Five years after the initial launch of GA4 in October 2020, and more than a year since the complete Universal Analytics shutdown, it’s time for an honest review of where we stand with Google’s flagship analytics platform.

The Great Migration: A Bumpy Road To The Future

When Google announced in March 2022 that Universal Analytics would stop processing data by July 2023, the marketing world was in shock. The short window between the announcement and the sunset date caught many marketers off guard, causing mild panic among companies and website owners.

What followed was one of the most contentious platform migrations in digital marketing history.

Starting July 1, 2023, standard Universal Analytics properties stopped processing hits, with Universal Analytics 360 properties receiving a one-time processing extension ending on July 1, 2024.

For many of us who had spent over a decade mastering Universal Analytics, this wasn’t just a platform change; it was the end of an era.

The fundamental shift from UA’s session-based model to GA4’s event-based architecture represented more than a technical upgrade. It was a complete reimagining of how we measure and understand user behavior.

While Google positioned this as future-proofing for a privacy-first, cross-device world, the reality on the ground was far more challenging.

The Promise Vs. The Reality

Google’s marketing pitch for GA4 was compelling: enhanced user journey tracking, privacy-compliant measurement, advanced machine learning, and more intuitive reporting.

As someone who eagerly adopted GA4 early, I was excited about these possibilities. However, the execution has been a mixed bag at best.

The User Experience Crisis

Perhaps the most important criticism of GA4 has been its user interface, with widespread negative feedback from the marketing community.

The interface complaints aren’t just about aesthetics; they’re about productivity. Tasks that took two clicks in Universal Analytics now require six or more steps in GA4. Filtering for a single page, something marketers do dozens of times daily, has become an exercise in frustration.

Data Reliability Concerns

Beyond usability issues, GA4 has struggled with data reliability problems that strike at the heart of marketing decision-making.

According to Piwik PRO’s analysis, conversion tracking discrepancies, inaccurate traffic reports, integration problems with Google Ads, and discrepancies between GA4 data and BigQuery exports have been persistent issues since launch.

These aren’t minor technical glitches; they’re fundamental problems that affect how we measure campaign performance and allocate marketing budgets.

The shift from UA’s goal-based conversion tracking to GA4’s event-based system has created confusion around what we’re actually measuring, particularly when comparing year-over-year performance.

Signs Of Progress: Recent Improvements

To Google’s credit, it hasn’t ignored the criticism. The past year has seen several meaningful updates that address some of the most pressing concerns.

Google Analytics has introduced a Generated Insights feature that summarizes trends and changes in data, helping users make quicker decisions. These insights are displayed at the top of detail reports and include action buttons for report modifications. This AI-powered analysis is genuinely helpful for identifying patterns that might otherwise be missed.

The addition of Anomaly Detection in detail reports automatically flags any unexpected spikes or dips in your data, represented as circles on your charts. For busy marketers juggling multiple campaigns, this proactive approach to data monitoring is a welcome improvement.

Perhaps most significantly for agencies and enterprises, as of March 2025, GA4 finally supports the ability to copy reports and explorations from one property to another. If you’ve ever had to manually rebuild the same custom reports across multiple client accounts, you’ll appreciate how much time this saves.

The Broader Impact On Marketing Analytics

The GA4 transition has forced the entire marketing analytics landscape to evolve. Current data shows that over 15 million websites use GA4, making it the de facto standard for web analytics regardless of individual opinions about the platform.

Screenshot from trends.builtwith.com, August 2025

Looking into historical Universal adoption, more than 21 million websites used Universal Analytics, which leaves a gap to be filled. So, despite GA4 leading the analytics industry, it still has a long way to reach the former adoption rate, which creates some sort of vacuum.

This shift has had several unintended consequences. Many organizations have diversified their analytics stack, supplementing GA4 with specialized tools that fill specific gaps. There is an increased interest in alternatives like Matomo for privacy-focused measurement and more sophisticated attribution modeling platforms for enterprise users.

The emphasis on first-party data collection has also intensified. With the end of third-party cookies and stricter consent rules, website data coverage will decrease, limiting your leverage.

First-party data will become even more important than ever. This has pushed marketing teams to become more strategic about data collection and customer relationship building.

Practical Recommendations For Marketing Teams

After five years of working with GA4, here’s my advice for marketing teams struggling with the transition:

Invest In Education

The learning curve has been steep, but unavoidable.

As former Google Analytics team member Krista Seiden wisely noted:

“The only way to learn a new tool is to dive in and actually get your feet wet.” Budget time and resources for proper training.

Focus On Trends, Not Absolutes

When comparing year-over-year performance, focus on trends and seasonality rather than absolute numbers. GA4’s different measurement methodology means exact numerical comparisons with UA data are largely meaningless.

Supplement Strategically

Don’t try to make GA4 do everything. Identify specific gaps in your analytics needs and fill them with specialized tools.

Many successful marketing teams now use GA4 as their foundation while leveraging additional platforms for detailed attribution, customer journey mapping, or real-time optimization.

Embrace The Event-Based Model

Rather than fighting GA4’s event-based structure, lean into it. Google recommends implementing new logic that makes sense in the event-based context rather than simply copying over existing event logic from UA. This approach will yield better insights in the long run.

Looking Forward

Cookie deprecation and enhanced privacy regulations mean that features like enhanced conversions, consent mode V2, and offline conversion tracking are now necessary rather than nice-to-haves. GA4, despite its flaws, is better positioned for this privacy-first future than Universal Analytics ever was.

The platform will undoubtedly continue improving. Google has shown responsiveness to user feedback, and the recent updates demonstrate a commitment to addressing the most pressing usability concerns. However, marketers should expect GA4 to remain more complex and technical than its predecessor.

The Bottom Line

Five years after its launch, GA4 represents both the promise and peril of modern marketing analytics. It offers capabilities that Universal Analytics couldn’t match: cross-platform tracking, privacy compliance, and AI-powered insights. Yet, it also demands a level of technical sophistication that many marketing teams struggle to achieve.

The forced migration was undoubtedly painful, and the criticism of GA4’s usability is largely justified. However, the platform is here to stay, and fighting that reality serves no one. The organizations that will thrive are those that invest in proper GA4 implementation, supplement it strategically with other tools, and adapt their processes to work with rather than against its event-based philosophy.

As marketers, we’ve weathered platform changes before, and we’ll weather this one, too. The key is approaching GA4 not as a replacement for Universal Analytics, but as a fundamentally different tool for a fundamentally different digital landscape. Once we make that mental shift, GA4 becomes less frustrating and more powerful.

The future of marketing analytics is privacy-first, cross-platform, and AI-enhanced. GA4, for all its current limitations, is our best free gateway to that future. It’s time to stop mourning Universal Analytics and start mastering what comes next.

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Featured Image: kenchiro168/Shutterstock

When Direct Means We Don’t Know: CMOs Need To Rethink Attribution In AI Search via @sejournal, @gregjarboe

I was asked recently to take a closer look at the data for a website in Google Analytics 4 (GA4).

This was for “Measurement Queen” Katie Delahaye Paine, a pioneer with over 30 years of experience in communications research and measurement, who now feels like she is flying blind.

From looking at her data in GA4, it turns out that 86% of the new users who visited her website over the last 28 days came from the “direct” channel.

That means the author of Measure What Matters can’t identify the sources of the vast majority of her website’s traffic.

So, I compared user acquisition for the last 28 days with the same period last year (matching the day of week). The good news was that total new users were up 29% year-over-year (YoY).

But here’s the bad news: Direct traffic to her site was up 126% YoY, while referral traffic was down 90%, organic social traffic was down 33%, and organic search traffic was down 28%.

This means that more than six out of seven users are now arriving on Paine’s site without a traceable referrer.

This includes situations where a user types her website address directly into their browser, uses a bookmark to access her site, or arrives from a source that doesn’t pass referrer information.

Digging Deeper Into What’s Behind The Traffic Surge

So, I asked the Measurement Queen a couple of standard questions:

She replied, “I don’t even have a TikTok account and haven’t used WhatsApp in years!”

I’d call that a big no. But it also indicated that I should use GA4’s search function to discover “top landing page by users for first user default channel group of direct traffic.”

The site’s homepage was the top landing page for direct traffic, but only 18.37% of users landed there. In second place was her blog, The Measurement Advisor, which got 13.96% of the site’s direct traffic.

When I shared this data with Paine, she revealed, “I’ve been blogging more frequently.”

I’d call that a big yes. So, I just asked Google about the titles of her recent blog posts in Google’s search box.

Here’s what I saw when I Googled [Sorry boss, I never got the Memo. How to know if you’re reaching the unreachables?].

Screenshot from search for [Sorry boss, I never got the Memo. How to know if you’re reaching the unreachables?], Google, June 2025

It’s worth noting that even when her content appears in a Google AI Overview, the link to her blog post doesn’t pass referrer data to GA4, and the link to her LinkedIn article on the same topic isn’t tracked by her GA4 account.

Then, I just asked Google, [Is Paine Publishing an authoritative site?].

Here’s what I saw:

Screenshot from search for [Is Paine Publishing an authoritative site?], Google, June 2025

So, even some of the direct traffic to her homepage may have come from links in AI Overviews that don’t pass referrer data to GA4.

Why haven’t similar insights been reported to more CMOs?

Reporting Squirrels, who primarily focus on generating reports without necessarily providing deep insights or actionable recommendations, are reluctant to highlight this type of anomaly, especially when “direct” means “We don’t know.”

So, CMOs need to rethink attribution in AI search. They need to independently verify and interpret GA4 event-based data.

And they also need to hire “Analysis Ninjas,” who excel at analyzing data to uncover hidden patterns, generate insights, and provide recommendations for business improvement.

Rethinking Attribution In AI Search

CMOs need to rethink their fundamental assumptions about attribution.

How should they attribute credit to key user actions throughout the customer’s journey toward making a purchase or completing other important actions on their sites?

They should avoid the old discussions that narrowly focused on data-driven attribution versus paid and organic last-click attribution.

Those touchpoints seem less meaningful when AI search is obscuring the sources of six out of seven of their website’s visitors.

Instead, CMOs (and important members of their team) should read “It’s Time for Marketers to Move Beyond the Linear Funnel,”

The article by the Boston Consulting Group cites force-fitting the complex array of touchpoints into a linear, funnel model doesn’t align with actual customer journeys.

This linear, funnel model can lead to missed opportunities due to poorly allocated resources or ineffective communication.

BCG says, “Marketers should instead adopt a more adaptable framework that more accurately reflects the real paths consumers take.”

BCG recommends shifting from the linear funnel to “influence maps.” But, before CMOs fly into that fog bank, they should re-examine the “expanding network of touchpoints – new streaming services, online shopping experiences, GenAI, and social platforms.”

Recognizing The Attribution Gaps That Existed Before AI

If CMOs blow up the funnel model and examine what’s in the awareness stage, they’ll see it includes radio, TV ads, magazines/newspapers, in-store announcements, word of mouth, packaging, and billboards. None of these were ever tracked in GA4.

And, if they analyze what’s in the consideration stage, they’ll see it includes video, brand sites, social media, search, sponsored content, retail media, in-app, and email. These were tracked by GA4 – until AI search started clouding over the sources of this traffic to websites.

In other words, GA4 didn’t track the awareness stage of this “multi-touchpoint landscape” even before the advent of Google AI Overviews.

And now that AI search is obscuring the sources of most of the touchpoints in the consideration stage, CMOs need to rapidly reconsider, review, revise, reassess, reconceptualize, and reimagine their assumptions about data-driven attribution.

These old assumptions may still be valid for Performance Max campaigns in Google Ads, which leverage Google’s AI to maximize performance across all of Google’s advertising channels, including Search, Display, YouTube, Discover, Gmail, and Maps.

And when an organization connects its Google Analytics property to a Google Ads account, it makes it possible to align GA4 and Google Ads conversions using the organization’s most important events.

But, according to a 2024 zero-click search study, paid search accounts for only 1% of clicks.

So, how do CMOs assign credit to SEO, content marketing, social media marketing, and communications for the 40.5% of other Google searches that produce clicks, or the 58.5% of zero-click searches?

Until Google provides a new version of Analytics that measures what matters for professionals across the entire marketing mix, CMOs will need to independently verify and interpret GA4’s event-based data.

Independently Verifying And Interpreting GA4 Event-Based Data

How do CMOs discover the critical data and strategic insights they need to successfully navigate through the fog bank surrounding the awareness stage of customer journeys?

They should conduct more old-school market research. Ironically, many brands cut their budgets for market research after Google started offering free brand lift studies to advertisers for their YouTube campaigns in March 2013.

But, CMOs don’t need to limit independent brand lift studies to asking questions about ad recall. They can ask questions about brand awareness, consideration, and purchase intent to understand the value of their entire marketing mix.

In 2019, my digital marketing agency helped the Rutgers School of Management and Labor Relations (SMLR) launch a new online master’s degree program.

We won the U.S. Search Award for Best Use of PR in a Search Campaign and were a finalist in the Best Integrated Campaign category.

We conducted pre- and post-launch surveys six months apart to show:

• The percentage of respondents who said they were “familiar with” Rutgers SMLR had increased from 13.8% pre-launch to 18.5% post-launch.
• The percentage of respondents who said they were “very likely” to recommend Rutgers SMLR to a friend or colleague had increased from 16.7% pre-launch to 19.0% post-launch.

Next, CMOs can successfully navigate through the low clouds now obscuring the touchpoints in the consideration stage by putting someone in charge of audience research as well as market research.

There are several excellent audience research tools, each with unique strengths, to help understand their needs, behaviors, preferences, and motivations.

For online behavior and digital footprints, SparkToro and Similarweb are highly effective.

For psychographic and cultural affinity analysis, Audiense and BuzzSumo are great choices.

For social listening and brand monitoring, Sprout Social and Keyhole are powerful options.

Taking Control When Analytics Falls Short

Next, CMOs should challenge their SEO, content marketing, social media marketing, and communications teams to create their own audiences in GA4 just like the ones that the paid media team is already using for remarketing campaigns.

For example, a PR audience could include users who:

• Scroll to 90% of a blog post or article.
• Download a whitepaper.
• Play at least 50% of a product video.
• Complete a tutorial.

The communications team can share their PR audiences with their colleagues in paid media, who can use Google Ads to remarket to these groups of users.

• If users scroll to 90% or more of your blog post or download a whitepaper, then they can use ads to invite them to subscribe to your newsletter.
• If users play at least 50% of a product video or complete a tutorial, then they can use ads to invite them to attend one or more in-person or virtual events.

CMOs should also ask their digital analytics teams if they have used “Explorations” this month. This is a set of advanced tools in GA4 designed to go beyond basic reports, allowing them to gain deeper insights into their customers’ behavior.

There’s no way to predict what different digital analytics teams will discover, but CMOs who feel they’re flying blind will want to know what their team saw when they used:

• User Exploration to dig into data about individual users or groups within your segments to analyze detailed user journeys.
• Cohort Exploration to study user groups with shared traits to understand behavior trends and performance over time.
• Segment Overlap to compare how user segments intersect to uncover hidden audiences that meet specific conditions.
• Funnel Exploration to track the steps users follow to complete key actions, helping you optimize conversion paths and spot performance issues.
• Path Exploration to visualize the actual navigation paths users take through your website or app.
• User Lifetime to assess long-term user behavior and value from the first visit through their customer lifecycle.

Hire Analysis Ninjas Who Excel At Analyzing Data

Finally, CMOs need to ask themselves: How did the attribution problem manage to fly under the radar for so long?

They could blame GA4’s Analytics Intelligence. Automated insights are supposed to detect unusual changes or emerging trends in their website’s data and notify their digital analytics team automatically, on the Insights dashboard, within the Analytics platform.

If the so-called Reporting Squirrels were reluctant to highlight this type of anomaly, especially when “direct” means “We don’t know,” then who is really to blame?

That’s why CMOs also need to ask themselves: How do I turn at least one of my Reporting Squirrels into an Analysis Ninja?

To encourage a Reporting Squirrel to evolve into an Analysis Ninja, CMOs must shift from asking for data to encouraging someone on their digital analytics team to actively interpret it and recommend solutions.

This also involves encouraging them to develop skills in statistical analysis, understand business context, and communicate findings effectively.

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Featured Image: Viktoriia Hnatiuk/Shutterstock