In this playbook, we'll build a GA4 Analytics Agent that queries cost-effective BigQuery aggregations of Google Analytics data and uses AI to translate natural language into precise SQL, giving your team direct access to insights.
Trying to get clear insights from Google Analytics 4 can be a real headache. GA4 does track a ton of data, but its complicated event setup means you need to know SQL to make sense of it. Most marketers and analysts don’t have the time (or desire) to dive into BigQuery whenever they want to answer a simple question like, 'Which traffic sources are converting?
This playbook shows you how to make GA4 data way more accessible by combining the power of BigQuery with the simplicity of an intelligent agent. You’ll learn how to build an automated pipeline that transforms raw GA4 data into clean, structured tables—and create an agent-powered interface that lets anyone on your team uncover insights without touching SQL.
The setup has three main parts:
A data exploration script analyzes your GA4 data structure to understand what information is available
A daily aggregation process transforms raw GA4 events into summarized tables organized by traffic source, device type, country, and other key dimensions
An AI agent converts questions like "show me conversion rates by device" into optimized SQL queries against your aggregated data
This approach is different because it bridges the technical gap while protecting you from exploding BigQuery costs. Since GA4 data in BigQuery uses complex nested structures, inexperienced users often write inefficient queries that scan terabytes unnecessarily. Our solution pre-aggregates the data into flat, efficient tables and includes dry-run checks to prevent costly mistakes.
