GA4 is enhanced with an AI assistant for better data analysis

GA4 is moving into a more practical era for businesses that want to quickly understand what's happening in their channels without getting lost in dozens of reports. According to Semrush's analysis, Google is adding 4 features to Google Analytics that revolve around two big needs: smarter support via AI assistant and a clearer view of channel performance through improved channel performance reporting. For an e-commerce owner, this isn't just another tool update. It's a change in the way decisions are made about budget, campaigns, conversion tracking, attribution modeling and customer journey.

Until recently, GA4 was considered by many professionals to be more demanding than Universal Analytics. The logic of events, the different report structure, the emphasis on data-driven attribution and the need for proper setup made many online store owners view analytics more as a technical obligation than a development tool. Google's new direction shows that GA4 wants to become more «conversational,» more direct and more useful for marketing teams that don't always have a data analyst available. The critical thing, though, is to understand that AI doesn't fix a bad setup. If events, conversions, consent mode v2 and acquisition channels are not set up correctly, the AI assistant will provide faster answers, but not necessarily correct business answers.

What changes in GA4 with AI assistant and channel performance

The basic idea behind the GA4 AI assistant is that the user can get to answers faster that previously required manual navigation through reports, comparing segments and extracting data. Google had already built in analytics intelligence and custom insights features, but the new approach makes the experience more natural: instead of looking at where a particular metric is, you can ask more business-oriented questions, such as why paid search purchases dropped, which channel brings in higher value users, or if there's a change in the purchase funnel after a campaign.

The second important point is the channel performance report. For e-shops, channel analysis is often the most critical area of digital marketing analytics, because it's where the budget is decided: Google Ads, organic search, email, affiliates, social media, direct or referral. A cleaner channel performance report helps the business owner see not just sessions or traffic, but conversions, revenue, engagement and each channel's potential contribution to the end market. This is especially important because the last click rarely tells the whole truth. A user may learn about a brand from TikTok, return through organic search, sign up for a newsletter and ultimately buy after remarketing. GA4, when set up correctly, can give a more complete picture of this journey.

Google's move is not happening in a vacuum. The use of AI in business is growing rapidly. McKinsey reported that 72% of organizations reported AI adoption in 2024, while 65% reported regular use of generative AI, percentages that explain why tools like GA4 are moving towards smarter and more automated workflows. As shown in the graph below, AI has now moved from the experimental stage to everyday operational use.

Why the change directly affects e-commerce brands

For an online store, GA4 is not just a reference tool. It's the basis for decisions that directly affect profit margins, inventory, promotions, remarketing, remarketing, SEO strategy, product feeds and customer retention. If an e-shop sees an increase in traffic but a decrease in conversion rate, it needs to quickly identify whether the problem lies in channel quality, price, checkout, product availability or overall user experience. This is where the combination of AI assistant and channel performance report can significantly reduce diagnosis time.

The importance becomes even greater if we consider the size of the market. Global retail e-commerce sales are estimated by Statista to grow from around $6.3 trillion in 2024 to around $8.0 trillion in 2027. This means that the competition will not only be decided on who has a better product, but on who makes better use of their data. A brand that sees early enough that organic search brings higher average order value, that email has a better repeat purchase rate or that a paid channel is «burning» budget on low-intent users can adapt faster than the competition.

The chart below shows the projected growth of global retail e-commerce sales, an environment where quality analytics is becoming a strategic advantage.

In practice, GA4 can help an e-commerce owner answer questions that have direct commercial value. Which campaign is bringing in new customers and which is simply bringing back existing ones? Which channel has high traffic but a low add-to-cart rate? Are there products that attract visits but don't result in checkout? What percentage of users abandon between begin_checkout and purchase? The difference now is that the AI assistant can act as a question accelerator, while channel reports can act as a checkpoint for final decisions.

How to use GA4 step-by-step

Proper utilization of GA4 starts before the first question to the AI assistant. The entrepreneur should treat the analytics setup as he would treat his warehouse or ERP: if the data entry is wrong, any report will have limited value. The following plan is designed for e-commerce teams that want practical application, not theory.

1. Clear the measurement setup before trusting the answers

The first step is to check the basic events. In an e-shop, events such as view_item, add_to_cart, begin_checkout, add_payment_info and purchase must be recorded consistently. The events must contain correct parameters such as item_id, item_name, item_category, price, quantity, currency and transaction_id. If the purchase event is double counted or if revenue is missing, GA4 will give a misleading picture of ROAS, conversion value and channel performance. It is equally important to check conversion tracking in Google Ads, Meta or other platforms so that differences between ad platforms and GA4 are interpreted correctly and not as a system «error».

At this stage consent mode v2 should also be checked, especially for companies operating in the European Union. Consent affects data availability, conversion modelling and the ability of platforms to deliver correct results. GA4 can work with modeled data, but the quality of the setup and clarity in the consent banner affect how reliable the conclusions will be. For larger e-shops, it's also worth enabling BigQuery export to access raw event-level data for deeper analysis, cohort reports, LTV models and linking to CRM or ERP.

2. Link channel performance to real business goals

The channel performance report should not be read based on traffic alone. A channel with many sessions but a low conversion rate can be useful for awareness, but it should not be evaluated by the same criteria as a performance channel. Similarly, email may have lower visit volume but higher profitability because it targets users who are already aware of the brand. GA4 allows metrics such as engaged sessions, key events, revenue, average purchase revenue, new users and returning users to be examined so that each channel can be evaluated based on its role in the customer journey.

A practical evaluation model is to divide the channels into three groups. First, demand channels, such as paid search and shopping campaigns, which often target users with a direct purchase intent. Second, discovery channels, such as organic social, display and content marketing, which may not directly close the sale but feed the funnel. Third, reactivation channels, such as email, remarketing and direct, which often influence the final decision. With this logic, attribution modeling becomes fairer and closer to reality.

The need for a deeper analysis is clearly shown by the basket abandonment data. The Baymard Institute calculates the average cart abandonment rate at 70.19%, while capturing specific reasons that drive users to abandon the cart. As shown in the graph, extra charges remain the most important reason for abandonment, which can be captured in GA4 if you properly track checkout steps.

3. Turn AI assistant into a control tool, not an autopilot

The AI assistant in GA4 can be very useful when you use it with specific questions. Instead of general queries like «what's happening in my sales?», go for questions that link channel, time period and business outcome. For example: «Which channels had the biggest drop in revenue last week compared to the previous week?», «Which channel brought in the most new shoppers last month?», «Is there a change in conversion rate from mobile devices since the last checkout change?» or «Which product categories have high view_item but low add_to_cart?».

The crucial thing is to use the AI assistant's answers as a starting point and not as a final decision. If GA4 detects a drop in a channel, check if there has been a change in budget, bidding strategy, landing page, stock availability, pricing, tracking or consent rate. If it detects an increase in conversions, consider whether it's a real commercial improvement or a tracking anomaly. In an e-commerce environment, small technical issues can skew big decisions. A duplicate purchase event can inflate revenue, while a wrong cross-domain setup can show payments as referral traffic from payment providers.

A mature workflow for a marketing team can work as follows. Every Monday a channel performance check is done for the previous week. The three biggest positive and negative deviations in revenue, conversion rate and engaged sessions are recorded. The AI assistant is then used to identify potential causes or segments that need analysis. Then, the team reviews the data in exploration reports, compares it to Google Ads or CRM and decides on actions: increase budget, change landing page, correct tracking, adjust offer or improve checkout. In this way, GA4 becomes a functional management tool rather than just a dashboard.

Particular attention needs to be paid to predictive metrics. GA4 can, under conditions of data volume and quality, provide predictions such as purchase probability or churn probability. These metrics are useful for audiences, remarketing and segmentation, but should not replace trade judgment. A customer with a high purchase probability may have a low margin, while a customer with a lower probability may be in a high lifetime value category. The best use of predictive metrics is in conjunction with first-party data, CRM, margins and purchase history.

For TWO DOTS and any team that supports e-commerce brands, the biggest value of this development is not that GA4 became «smarter». It's that the distance between data and decision is reduced. When an owner can ask questions faster, see channels more clearly and identify where revenue is being lost, the conversation shifts from «what do the reports show?» to «what do we do now?» This is the essence of modern analytics: less inertia, more accountability and a better connection between marketing, technology and commercial outcomes.

The practical recommendation is clear. Don't expect the AI assistant to solve problems that you haven't mapped. Start with an audit in GA4, confirm the ecommerce analytics setup, check channels, set key events correctly and create a weekly evaluation process. Then use the channel performance report and Analytics Intelligence to accelerate the analysis. The cleaner your data, the more useful the answers will be. And the more useful the answers are, the faster you can improve budget allocation, conversion rate, customer journey and ultimately the profitability of your e-shop.

Frequently Asked Questions

Sources

Semrush: GA4 Adds AI Assistant, Channel Performance Report, and More

Google Analytics Help Center

Google Analytics 4 Events

Google Analytics 4 Attribution

McKinsey: The State of AI

Statista: Retail e-commerce sales worldwide

Baymard Institute: Cart Abandonment Rate Statistics