Google Analytics 4 (GA4) has become the new standard for website and app analytics, offering a future-proof approach to data collection. However, its event-based structure and significant differences from its predecessor, Universal Analytics, have sparked debate. In this article, we explore both the pros and cons of each part of GA4, helping you decide if it’s the right tool for your business.



GA4 now offers much more flexibility when it comes to tracking. Previously, data was collected based on pre-defined hit types, like page views or button clicks. This limited what you could track and analyze.

GA4 now uses an event-based model. Every user interaction is considered an “event.” This could be a page view, a button click, a video play, or any custom interaction you define.

This event-based approach with custom parameters allows for much richer data collection. You can track almost any user interaction and tailor the data to your specific needs. This empowers you to:

  • Analyze user journeys in more detail.
  • Understand what content resonates with users.
  • Measure the effectiveness of marketing campaigns.
  • Personalize the user experience.


Tracking in GA4 can get more complex if you move beyond the basic setup. Out of the box, GA4 offers automatic tracking for some common actions like page views, scrolls, and outbound clicks. This is great for getting started, but for anything more specific, you’ll likely need to dig into some additional features such as enhanced measurement and custom events.

While GA4 offers a good foundation for tracking with its automatic features, for more in-depth tracking you might need to explore custom events and potentially involve a developer.



In the new platform, attribution models are dynamic. This means when you change the model you’re using, all your reports will reflect that change. This is different from Universal Analytics (UA), where you had a dedicated report for each attribution model. Unlike UA, GA4 doesn’t store data specific to each attribution model. Instead, it uses a single pool of data and applies the chosen model to calculate credit for conversions.


There are now only three attribution models to choose from in GA4. Google claims that this is to simplify attribution and encourages the user to use a data-driven approach with its default DDA (data-driven attribution) model.

A major challenge with GA4’s data-driven attribution is the absence of a clear explanation for how it assigns credit to touchpoints along the customer journey. Unlike rule-based models (like last-click), DDA works like a black box. We can’t see exactly how much weight each touchpoint gets in influencing a conversion.

BigQuery Export


Traditionally, Google Analytics offered limited access to the details behind user behaviour. With GA4, you can export all of this raw event data to BigQuery, Google’s cloud data warehouse.

One of the biggest benefits of using BigQuery exports with GA4 is that it bypasses the standard 14-month data retention limit within GA4 itself. Having access to historical data beyond 14 months can be crucial for businesses, it gives you access to trends, performance year-over-year or across different marketing campaigns and supports any compliance needs. This used to be an option for GA360 accounts only.


Unlike accessing data directly within the GA4 interface, exporting to BigQuery requires setting up a project in Google Cloud Platform (GCP). This can involve creating a new GCP project or using an existing one. There might be a learning curve for those unfamiliar with GCP.

BigQuery itself offers a free tier with limitations on storage and queries. If you exceed those limits, you’ll incur charges. These costs can add up depending on the amount of data you collect and how often you query it.

Whilst exports are useful, they have one key drawback: they only capture data from the day you link your GA4 property to BigQuery. This means you won’t have access to any website or app activity that happened before the connection was made.



One of the biggest strengths of GA4 is its customizable reporting. Unlike older analytics platforms, GA4 lets you move beyond pre-built reports and tailor them to your specific business goals.

Every business has its own objectives. An e-commerce store might prioritize tracking sales and product performance, while a lead generation website might focus on form submissions and user engagement. GA4 lets you design reports that highlight the metrics most important to your success.


GA4 offers much more powerful reporting capabilities, however there’s one key difference from UA, which could be accessed with minimal configuration – GA4 requires setup. The older version was more plug-and-play. You added a tracking code to your website, and data came into the platform. Reports were readily available, although customization options were limited.

There is also the challenge of the shift to a new set of metrics and dimensions. Unlike UA, which had a familiar set of pre-defined reports, GA4 offers more flexibility in building custom reports. This flexibility comes with a downside, ensuring everyone on your team is using the same metrics and dimensions for consistent data analysis.

New Features/Settings


A lot of the new settings within GA4 tackle data hurdles with features like consent mode, cross-device tracking, and data modelling. Here’s how:

  • Consent Mode: Traditionally, website analytics relied on cookies to track user behaviour. With increased privacy concerns and regulations, users are able to opt out of cookies. GA4’s consent mode allows you to gather data even when cookies are disabled, providing a more complete picture of your audience.

  • Cross-Device Tracking: People often use multiple devices (phones, laptops, tablets) to browse the web. GA4 can track user journeys across these devices, giving you a more holistic understanding of how users interact with your website or app. This is achieved using techniques like Google Signals (for signed-in Google users who opt in) and probabilistic modelling.

  • Data Modelling: Data gaps are inevitable. Users might not always log in, or cookies might be cleared. GA4 uses machine learning to model this missing data. This means you get a more complete picture of user behaviour, even with incomplete information.

GA4 now has a simplified approach to conversion tracking. In UA, you had to set up goals to track conversions. This could get complicated if you wanted a conversion to only count once per session, even if the user triggered the event multiple times.

In UA, achieving “once per session” conversion tracking often required workarounds, either through Google Tag Manager (GTM) or custom coding by developers. This could be time-consuming and prone to errors.

Debug Mode – setup or testing tracking this is so useful to see what data is being passed to GA in live stream.


The new features in GA4 give  you much more control and deeper insights than ever before. However, this also introduces a new challenge, these new options can impact your data in unexpected ways. With more features and settings, there’s more to configure and potentially misconfigure. Incorrect settings for things like event tracking or audience definitions can skew your data.

If you’re familiar with the older Universal Analytics (UA), GA4’s new approach requires adapting to a different data model and interface. This can lead to initial difficulties in interpreting the data accurately.

For more support on GA4, our data team will be happy to help set up your tracking and measurement. Get in touch by sending us an email to