Incorporating advanced machine learning, GA4 promises several exciting new AI-powered features. Individual user-level predictions of purchase and churn probability offer a chance to make more targeted outreach decisions, while expected revenue predictions aid forward planning. The new search bar allows users to get instant answers to questions like “how many customers joined this month vs this time last year?”, and audience templates help uncover customers with similar demographics and purchasing patterns. The attribution section is also upgraded, with easy options for comparing different attribution models, including the new data-driven attribution model which uses machine learning to reduce the reliance on potentially inaccurate platform data.

One of the main changes that has left marketers frustrated is the move from a session-based to an events-based data model. In UA, metrics were based on sessions – the set of interactions a customer made on a site during a single period of use. UA then presented users with aggregate numbers which gave an overview of typical session behaviour. In contrast, GA4 looks at individual events, such as clicking in to a new page, or adding a product to cart. A set-up process, event tagging, has to be carried out for each event before it can be registered. This has left marketers feeling they now have to do more work to get useful outputs from GA4, with available metrics now varying from the previous heavy focus on page-views as the most important figure.

Another major adjustment is the way channel data is collected in GA4, as shown by the differences in figures across GA4 and UA. Google claims that their new data-driven attribution models are more accurate, relying less on the assumptions in UA’s predictive analytics. However, as the new models take a future-proof approach to data privacy in light of increasing regulation on trackers and cookies, this means less user-specific information is available. The result is that metrics like revenue and visit numbers are now more accurate for Google-owned channels such as Google Ads, but less accurate for other channels.

Many marketers also used UA to create effective and informative reports on key metrics and the customer journey. In GA4, the built-in reports options are slimmed down, leaving many feeling it is no longer enough to use it as a stand-alone platform. While users can now create custom reports in the ‘Explorations’ tab in GA4, this is time-consuming and users have found it difficult to build clear visualizations to share with non-marketing-oriented stakeholders.

To minimize any disruption during the crossover to GA4, business owners should check that their events tagging is set up correctly for external channels and investigate their data sampling settings to prevent any unintentional data loss. Revisiting measurement plans and building a more holistic approach to data analysis is likely to be necessary during the adjustment period after moving from UA. Given that limited historical data is available, measuring success may need to focus more on real-time, forward-looking metrics rather than relying on comparisons to past trends, at least in the short term.

While the forced migration to GA4 might inevitably cause difficulties for users accustomed to UA, the time is ripe to reassess how marketing success is tracked and evaluated as the purchase journey, website capabilities and access to customer information continue to evolve. Whether this means investing time and resources to understand GA4 better, or realizing it no longer functions as a stand-alone source of truth and investigating new solutions, the transition may provide the opportunity to check if your marketing strategy is still effective and aligned with the broader direction of your business.