Google Analytics 4 (GA4) problems: The state of GA4 4 months after UA sunset

After numerous delays, the complete shutdown of Universal Analytics finally took place on July 1st, 2024, forcing users to swiftly transition to Google Analytics 4 to maintain data access and measurement capabilities.

However, Google Analytics 4 (GA4) employs an entirely different measurement model than Universal Analytics. Although GA4 offers new features and approaches, a range of discrepancies are proving baffling to marketers.

The implementation and use of Google Analytics presents a raft of challenges that may hinder accurate data collection and analysis. This article discusses the state of Google Analytics 4 months after its predecessor’s sunset and the ongoing significant post-migration problems with GA4. We will also provide you with an alternative if you are looking for a familiar platform with robust analytical capabilities that will empower you to respect user privacy every step of the way.

Major current problems with Google Analytics 4

Google Analytics 4 (GA4) is still grappling with significant issues that impact its user-friendliness and the reliability of its data. Here are the key challenges it’s currently dealing with:

1. Conversion tracking problems

Data models in UA and GA4 differ fundamentally. UA was session-based, while GA4 is event-based. This means that while both platforms allow you to track a similar number of details, they measure them differently. In UA, goals were counted once per session, while in GA4, you can count them either once per event or per session. For instance, if a user completes the same goal 5 times in a single session, UA would display one conversion, while GA4 will potentially display 1 or 5 key events. This shift in the data model has significant implications for data collection and analysis in GA4, and understanding them is crucial for your reporting.

As Google explains, there are three main reasons for conversion differences in GA4 vs UA:

Setting up conversion tracking in GA4’s Admin section within the Events or Conversions category is much different – it’s quite unlike UA, which had a strict method for defining events, including four event-specific dimensions to organize user actions. You need to create a separate Event for each conversion you want to monitor.

What do conversion tracking problems mean to you?

Shifting from UA to GA4 involved and, in many cases, still involves proactive updates to tracking code and event tagging. It can typically require reevaluating the measurement framework on the website, as misconfigurations can lead to inaccurate reporting of key performance indicators (KPIs). Before generating your report, double-check that you have chosen the right Event type and linked it to, for example, the appropriate “thank you” page to avoid inaccurate reporting of KPIs.

2. Inaccurate traffic reports

In GA4, Traffic Acquisition reports, a typical first step in identifying traffic sources, only track the first traffic source. Thus, if a visitor arrives at your site via search and then via social, only the search is attributed to their visit.

Moreover, GA4 tends to over-report Google paid traffic. For example, in the new User Acquisition report, the entire session is attributed to Google Paid Search, even if a user clicks a paid ad and immediately after clicks an organic search result. Another challenge is the variable categorization of traffic sources in GA4. The classification may be retroactively applied for 12 days after collecting initial data. This variation introduces an additional element of doubt, making real-time analysis and the capability to promptly react to marketing performance more intricate.

Inaccurate traffic reports and over-reporting of paid traffic are caused by different attribution models. Moreover, session fragmentation is the root cause of most attribution issues in GA4. For example, if a user clicks through to your website from a social link today, then revisits it in four months and subsequently converts, Universal Analytics would assign conversion credit to the social click. Google Analytics 4 gives conversion credit to direct. This can lead to data inconsistencies and post-migration issues.

Google reassures that they have improved the accuracy of their model, but differences between session counts still occur.

What do inaccurate traffic reports mean to you?

Out-of-the-box GA4 reports will always lack some source information. You can set up custom reports to navigate the issue, but testing and troubleshooting take time and effort to ensure accuracy.

3. GA4 and Google Ads issues

here are ongoing challenges with integrating Google Ads and Google Analytics 4 (GA4), primarily due to data discrepancies and differences in data measurement. You may have run into the following issues since migrating to GA4 if you didn’t correctly link GA4 to Google Ads:

• Google Ads conversion tracking is not working
• Google Ads do not display site engagement metrics
• There are no audiences available in Google Ads

For example, your conversions from Google Ads campaigns are being attributed to a direct source in GA4 reporting. However, in the Google Ads dashboard, the conversions are attributed to a specific campaign that matches the URL tracking data. This means your GA4 is not properly set up, which is why GA4 is given as the direct source.

What do GA4 and Google Ads issues mean to you?

Marketers and analysts are still facing hurdles with the integration of Google Ads and GA4, dealing with disparities in conversion data, varied attribution methods, and technical setup challenges. Problems with linking Google Ads and Analytics accounts can result in cost-per-click (CPC) metrics not being collected. This typically requires checking auto-tagging settings within Google Ads.

4. Discrepancies between BigQuery results vs Google Analytics reports

Do the numbers not add up when you query the GA export data in BigQuery? Google Analytics 4 processes data differently than BigQuery. And even with the recent updates that support additional traffic source fields, discrepancies between Google Analytics 4 (GA4) and BigQuery still exist. GA4 and BigQuery might not show the same numbers for various reasons:

• Definitions: The raw data found in the BigQuery export schema results from data collection and processing by Google Analytics. Users accustomed to GA reports will soon realize that a number of metrics are not included. To address this issue, you have to calculate the metrics using the definitions provided by Google. Unfortunately, the documentation is frequently inconsistent or unavailable. The solution is to create your own definitions.
• Sampling: To ensure an accurate comparison between your BigQuery export data and standard reports, Data API reports, or Exploration reports, make sure they are not using sampled data.
• Scopes: Dealing with data on different levels can be challenging. In GA4, user, session, event, and item scope data is available. Therefore, you must understand how these scopes relate to each order to generate meaningful results. Remember: BigQuery will only tell you if your results are correct on condition that your query is valid.

What do discrepancies between BigQuery and GA4 mean to you?

You must be a highly advanced analytics user to utilize your Google Analytics 4 data in BigQuery. BigQuery helps to combine, calculate, and analyze digital metrics in a new way. And Google Analytics reports are not necessarily a representation of reality. Even if you have the perfect implementation and can measure all behavior on your website in detail, your GA4 reports would still be lacking. Google Analytics is best used as a tool to spot trends. When interpreted correctly, it allows you to catch signals among the noise when dealing with large amounts of behavioral website data.

BigQuery, on the other hand, is the intended starting point for advanced users who want to go beyond standard reports, Explorations, and the Data API, or even do something completely customized. If you’re going to improve the alignment between your GA4 data and what you see in BigQuery, you must put significant time and effort into it. Many hacks must be implemented to match BigQuery and the GA4 UI data. For example, you need to align query logic by ensuring that the SQL queries used in BigQuery align with how GA4 calculates metrics.

5. Bot traffic distortion

In GA4, bot traffic significantly impacts website traffic, session duration, bounce rate, and conversion rate. Thus, artificial traffic generated by bots can skew your engagement metrics and conversion performance. Despite GA4’s built-in bot filtering capabilities, challenges persist.

Additionally, bot traffic can distort audience segmentation and demographic insights, resulting in ineffective marketing strategies and misinterpreting target audiences. Google’s primary focus on Google Ads means they do not prioritize implementing strong bot exclusions or preventing ad fraud.

What does bot traffic distortion mean to you?

Using bot traffic in GA4 analytics can distort key metrics, confuse decision-making, and undermine the integrity of data-driven strategies. Inaccurate data resulting from bot activity can also undermine your efforts to optimize user experience, as you may make assumptions about user preferences and behavior based on inaccurate data. You have to keep an eye on unexpected surges in traffic to detect possible bot activity.

6. Limited flexibility of real-time reports in GA4

Real-time reports in GA4 exhibit several limitations compared to UA, impacting their flexibility and effectiveness for users. For example, GA4’s real-time reports don’t capture all visitors. GA4 filters out traffic from spam and bots, which means the data may not represent the total audience accurately, and the reported numbers may not accurately reflect total engagement levels. Additionally, the report only shows data from the last 30 minutes, restricting users from accessing a broader timeframe for analysis.

Additionally, you don’t have access to detailed metrics. GA4 provides basic metrics like active users and page views but lacks deeper insights into user behavior, such as session duration or navigation paths. This deficiency limits the ability to analyze user journeys effectively. Lack of detailed metrics means you might miss critical insights that could help you better understand your customers. And if you need to customize your real-time reports, GA4’s real-time reports offer limited options for doing so. You can add basic comparisons but cannot tailor reports extensively to fit specific analytical needs.

What do limited GA4 real-time reports mean to you?

GA4 provides a basic framework for real-time reporting, but its limitations in data scope, customization options, and detailed metrics make it less flexible than alternatives. To overcome these challenges, you may need to integrate real-time data with historical analytics or utilize additional tools that provide more robust reporting capabilities.

7. Data freshness and processing time

The processing time for data to appear in your GA4 account is much longer. There is a significant delay in GA4’s new data collection methodology. 

The typical processing time for standard and 360 properties is 12 hours. However, the delays in data processing may range from 24 to 48 hours. This is a notable increase compared to Universal Analytics, which typically had a maximum delay of about four hours for standard reporting. Processing can take even longer during peak traffic times, complicating timely decision-making based on recent data.

What do data processing delays mean to you?

Such delays might cause confusion regarding the results, resulting in squandered investments and unreliable business insights, with your enterprise potentially having to wait as long as 48 hours for accurate data to be available in your account.

What is the alternative to GA4 post-migration issues?

Migrating to a new analytics platform has its challenges. While some significant improvements have been made to GA4, four months after the complete UA sunset there are still numerous issues causing data discrepancies, as well as a steep learning curve and a long adjustment period for those used to Universal Analytics. The process becomes even more complex if you have large volumes of data or many custom configurations.

Your data is essential, and its accuracy, control, and integrity are equally crucial, so it’s good to consider all your options. One of your alternatives is choosing a different yet familiar platform offering robust analytical capabilities with a session- and event-based model to collect more data by default without custom implementations.

On top of that, platforms like Piwik PRO Analytics Suite are built with privacy in mind, aiding you in compliance with regulations such as GDPR or TTDSG/TDDDG. Piwik PRO gives you complete control over the data you collect, flexible hosting to employ analytics in more contexts without violating user privacy, and native integrations with Customer Data Platform, Tag Manager, and Consent Manager.