[2026 Latest] Four Ways to Analyze GA4 Data with AI

Introduction: The New Era of GA4 × AI Analysis

The bottom line: GA4 (Google Analytics 4) data analysis has evolved into an era of "no expertise required, fast, and highly accurate" through the use of AI (LLMs).

This article is written for marketers who want to avoid complex GA4 operations and business leaders who need to make rapid decisions.

Here are the four key takeaways from this article:

1. Field-driven analysis using an AI agent (Squadbase)
2. Easy analysis through file uploads
3. Advanced integrated analysis with BigQuery × Gemini
4. Visualization-focused BI tool integration

A comparison table by use case is also included later in the article to help you find the best method for your organization.

Why Is AI (LLM) Necessary for GA4 Analysis?

Challenges with GA4

GA4 is a powerful analytics tool, but many marketers face the following challenges.

It's not uncommon to hear complaints like "Exploration reports are hard to use" or "There are too many metrics to identify the real issues." Traditional GA4 analysis required specialized knowledge and significant time for everything from report design to data extraction and interpretation.

The AI Breakthrough

The emergence of AI has dramatically changed this situation. Simply by asking questions in natural language like "Why did CVR drop?", AI instantly generates data-driven hypotheses and explanations. Complex report design and SQL queries are no longer necessary.

Expected Benefits

AI-powered GA4 analysis can deliver the following benefits:

• Up to 80% reduction in analysis time: No more manual report creation
• Elimination of person-dependent analysis: Anyone can perform the same quality of analysis
• Improved predictive accuracy: AI pattern recognition forecasts future trends

Method 1: Next-Generation Analysis with the AI Agent "Squadbase"

Overview: Decision Support That Starts by Simply Entering Connection Details

Squadbase is an AI agent that connects directly to databases and performs data analysis through natural language conversations. It enables advanced analysis in a chat format without requiring any SQL knowledge.

Implementation Steps

Step 1: Connect Your Data Connect your GA4 data stored in BigQuery to Squadbase. The connection setup can be completed in just a few clicks from the admin panel.

Step 2: Ask Your Questions Simply enter questions like "Visualize yesterday's ad campaign performance" through Slack or the dedicated UI, and the AI will generate the appropriate queries and return the results.

Pros

Squadbase's greatest strength is "democratization of analytics." Even non-engineers can freely work with database data. It creates an environment where marketing staff can explore data in real-time with their own hands.

Another major advantage is the ability to create flexible dashboards using natural language. With simple instructions like "Create a graph showing weekly CVR trends by channel," you can instantly generate the visualizations you need.

Cons

As a new platform launched in 2025, it is still in its early stages of establishment. Compared to legacy tools like Tableau and Looker Studio, the community is smaller, and there is still room for growth in terms of knowledge accumulation and information sharing.

Method 2: File Upload to ChatGPT / Gemini (Beginner Level)

Overview: Quick Analysis Completed in One Minute

This is the easiest way to start AI analysis. Simply upload data files exported from GA4 to ChatGPT or Gemini to begin your analysis.

Implementation Steps

Step 1: Export Data from GA4 Download data in CSV or Excel format from GA4's standard reports or exploration reports.

Step 2: Upload Files to an LLM Upload the files to a compatible LLM such as ChatGPT Plus, Gemini Advanced, or Claude.

Step 3: Enter Your Prompt Enter prompts like "Analyze the factors behind month-over-month changes" or "Tell me the characteristics of channels with high conversion rates," and the AI will immediately return the analysis results.

Pros

The appeal of this method is, above all, its simplicity. You get results immediately, and you can start for free or at low cost. No coding is required at all. This is the ideal introductory method for those who want to "try AI analysis first."

Cons

There are data size limitations. Due to LLM token limits, large datasets need to be split for analysis. Also, since data export is manual, real-time capability is lacking. For regular analysis, the export work required each time should also be considered.

Method 3: BI Tool Integration (Looker Studio × Gemini)

Overview: Seamless Fusion of Dashboards and AI Insights

The integration of Looker Studio and Gemini creates an environment where you can simultaneously leverage visual dashboards and AI-generated explanations.

Implementation Steps

Step 1: Connect GA4 to Looker Studio Use Looker Studio's standard connector to connect your GA4 data source. If you're already using Looker Studio, this setup should already be complete.

Step 2: Enable Gemini Features Enabling Gemini features in Looker Studio makes automatic chart summary generation and anomaly explanation functions available.

Pros

You can simultaneously create reports that integrate visual elements (charts) and logical elements (AI explanations). This is ideal for executive reports and team sharing. Since AI explains "why this metric changed" while you're looking at the chart, data interpretation becomes smoother.

Easy team sharing is also a significant advantage. Simply by sharing a URL, the entire team can view the same dashboard and AI insights.

Cons

It's not suited for deep-dive analysis or ad-hoc exploration. Since analysis centers on pre-designed dashboards, it's difficult to accommodate spontaneous analysis needs like "I want to look at it from this angle."

Method 4: BigQuery × Gemini Integrated Analysis (Intermediate to Advanced)

Overview: An Enterprise Approach Leveraging the Full Google Ecosystem

The integration of BigQuery and Gemini is the most powerful option for enterprises handling large-scale data. Even datasets with hundreds of millions of records can be analyzed using natural language.

Implementation Steps

Step 1: Set Up Data Export from GA4 to BigQuery Enable data export to BigQuery from the GA4 admin panel. Once configured, data will automatically accumulate going forward.

Step 2: Use Gemini Within BigQuery Studio Access BigQuery Studio and enable the Gemini feature.

Step 3: Generate and Execute SQL with Natural Language When you ask questions in natural language (with support for multiple languages) like "Tell me the landing pages with the highest conversion rates over the past 30 days," Gemini automatically generates and executes the SQL, returning the results.

Pros

The greatest strength is scalability that can handle data processing at the scale of hundreds of millions of records. If you accumulate all GA4 data in BigQuery, you can freely perform detailed analysis going back in time.

The ability to perform real-time analysis of the latest data is also important. By setting up streaming export, you can build a near real-time data analysis environment.

Additionally, since it operates on Google Cloud's security infrastructure, it can meet enterprise-level security requirements.

Cons

Knowledge of Google Cloud configuration is required. A certain level of technical understanding is needed, including basic BigQuery concepts and IAM (access management) settings. When implementing, we recommend considering support from in-house engineers or Google Cloud partners.

It should also be noted that visualization is limited to Looker-based tools. If you wish to integrate with other BI tools, separate consideration will be required.

This is fundamentally a solution designed for internal operations, and sharing reports with external clients or collaborating with external partners requires additional configuration and access management.

[In-Depth Comparison] Which Method Should You Choose?

The optimal analysis method varies depending on your use case and organizational situation. Use the comparison table below to select the method that best fits your organization.

Tips for Choosing

For those who "want to try it first" We recommend starting with the file upload method. It costs nothing and you can start today.

For those who "want to utilize it across the entire team" Squadbase or Looker integration is suitable. They're easy to use even for non-engineers and can achieve democratization of analytics.

For those who "want to build a full-scale data infrastructure" Consider BigQuery × Gemini integration. While there are initial setup hurdles, it's the most flexible and scalable option in the long term.