

How can GA4 Data be supercharged with Generative AI in BigQuery?
Unlock deeper insights from GA4 with Generative AI in BigQuery. Learn how to analyze multilingual feedback, summarize page performance, and apply NLP tasks using SQL—no external tools required.

Shreya Banker
Data Scientist
Data Analyst enthusiast. More than 7 years of exposure in Data Analysis and Software programming. I am a highly motivated, versatile IT professional with experience in Data Analysis, Visualization and Database Management. I look for the hardest problem to solve and where I can learn and develop the most. I love a challenge and never run from a difficult task. I'm determined to succeed, and I look forward to what life has to offer.
GA4 has revolutionized digital analytics with its event-based data model, flexible schema, and seamless integration with BigQuery. But for many teams, one major challenge remains:
How do you analyze unstructured data like customer feedback, search terms, or multilingual inputs at scale?
Traditionally, this required:
Exporting data from BigQuery
Using external NLP libraries or APIs (like Google Cloud Translation or sentiment scoring)
Writing complex Python scripts or custom pipelines
That’s no longer necessary.
With BigQuery’s built-in support for Vertex AI models via AI.GENERATE()
, we can now perform tasks like:
Translation
Topic classification
Sentiment analysis
Summarization
All directly within SQL, and apply it to raw GA4 data.
Read More —>> Ready for What? AI Readiness in Marketing Today
Use Case: Analyzing Multilingual Feedback from GA4
Let’s say you’ve implemented a GA4 custom event called nps_feedback
, which captures open-text user feedback via an event parameter named feedback
.
The challenges:
Text is unstructured and subjective
Responses come in multiple languages
You want structured outputs for dashboards: translation, topic, sentiment
Step 1: Export GA4 to BigQuery, which includes feedback in different languages (Prerequisite)
Read More —>> How to Use BigQuery to Get Powerful Marketing Insights from Google Marketing Products and First-Party Data
Step 2: Store Feedback

Step 3: Use AI.GENERATE() to Enrich the Feedback
Now comes the magic: using Gemini inside BigQuery to get structured results, no external APIs, no extra tools.
WITH ai_function_feedback AS (
SELECT
feedback AS original_feedback,
AI.GENERATE(
CONCAT(
'Translate this feedback to English if not already in English. ',
'Identify if it is about customer service and provide a positivity score. Text: ', feedback
),
connection_id => 'us-central1.vertex_ai_conn',
endpoint => 'gemini-2.0-flash',
model_params => JSON '{"temperature": 0}',
output_schema => 'englishreview STRING, is_about_service BOOL, positivity_score FLOAT64'
) AS Englishreview
FROM `your_project.your_dataset.feedback_table`
)
SELECT
original_feedback,
Englishreview.englishreview,
Englishreview.is_about_service,
Englishreview.positivity_score
FROM ai_function_feedback;
Step 4: Output Example

Now you can use these columns to:
Filter sessions with low positivity
Report on customer-service-related feedback
Visualize feedback trends in dashboards
Segment users by their experience
How This Helps in GA4 Analysis
GA4 tells you what users are doing — page views, clicks, sessions, but without context, behavior is just numbers. By integrating Generative AI, you can transform unstructured inputs into meaningful, structured insights.
Challenge | How Generative AI Helps |
Feedback is unstructured and multilingual | Translate and normalize directly within SQL |
Sentiment is subjective and inconsistent | Generate a standardized |
Feedback is hard to segment or categorize | Automatically classify using an |
Manual labeling is slow and labor-intensive | Get structured outputs instantly with Gemini in BigQuery |
Generative AI bridges the gap between raw behavioral data and rich contextual understanding, all within your existing GA4 pipeline.
Use Case: AI-Summarized Page Performance in GA4
In this use case, we utilized AI.GENERATE()
to automatically summarize user engagement for each GA4 page_path
. By combining quantitative metrics like pageviews and average session duration, the model generates concise, human-readable descriptions of page performance. Additionally, it assigns a performance label (e.g., High, Low) to help quickly identify content that requires attention or optimization
This approach enables:
Faster content performance audits
Scalable page-level insights
Smarter dashboard annotations - all with SQL and no external processing.

All generated with just SQL — no scripts needed.
Read More —>> Google Ads and Analytics AI Agents: Smarter Marketing or Risky Move?
Conclusion
Generative AI in BigQuery transforms GA4 from raw data into rich, human-readable insights: all with SQL. Whether you're analyzing multilingual feedback or summarizing page performance, AI.GENERATE() removes the need for external tools, speeds up analysis, and unlocks smarter decision-making directly within your data pipeline.
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How can GA4 Data be supercharged with Generative AI in BigQuery?
Unlock deeper insights from GA4 with Generative AI in BigQuery. Learn how to analyze multilingual feedback, summarize page performance, and apply NLP tasks using SQL—no external tools required.

Shreya Banker
Data Scientist
August 13, 2025
Data Analyst enthusiast. More than 7 years of exposure in Data Analysis and Software programming. I am a highly motivated, versatile IT professional with experience in Data Analysis, Visualization and Database Management. I look for the hardest problem to solve and where I can learn and develop the most. I love a challenge and never run from a difficult task. I'm determined to succeed, and I look forward to what life has to offer.
GA4 has revolutionized digital analytics with its event-based data model, flexible schema, and seamless integration with BigQuery. But for many teams, one major challenge remains:
How do you analyze unstructured data like customer feedback, search terms, or multilingual inputs at scale?
Traditionally, this required:
Exporting data from BigQuery
Using external NLP libraries or APIs (like Google Cloud Translation or sentiment scoring)
Writing complex Python scripts or custom pipelines
That’s no longer necessary.
With BigQuery’s built-in support for Vertex AI models via AI.GENERATE()
, we can now perform tasks like:
Translation
Topic classification
Sentiment analysis
Summarization
All directly within SQL, and apply it to raw GA4 data.
Read More —>> Ready for What? AI Readiness in Marketing Today
Use Case: Analyzing Multilingual Feedback from GA4
Let’s say you’ve implemented a GA4 custom event called nps_feedback
, which captures open-text user feedback via an event parameter named feedback
.
The challenges:
Text is unstructured and subjective
Responses come in multiple languages
You want structured outputs for dashboards: translation, topic, sentiment
Step 1: Export GA4 to BigQuery, which includes feedback in different languages (Prerequisite)
Read More —>> How to Use BigQuery to Get Powerful Marketing Insights from Google Marketing Products and First-Party Data
Step 2: Store Feedback

Step 3: Use AI.GENERATE() to Enrich the Feedback
Now comes the magic: using Gemini inside BigQuery to get structured results, no external APIs, no extra tools.
WITH ai_function_feedback AS (
SELECT
feedback AS original_feedback,
AI.GENERATE(
CONCAT(
'Translate this feedback to English if not already in English. ',
'Identify if it is about customer service and provide a positivity score. Text: ', feedback
),
connection_id => 'us-central1.vertex_ai_conn',
endpoint => 'gemini-2.0-flash',
model_params => JSON '{"temperature": 0}',
output_schema => 'englishreview STRING, is_about_service BOOL, positivity_score FLOAT64'
) AS Englishreview
FROM `your_project.your_dataset.feedback_table`
)
SELECT
original_feedback,
Englishreview.englishreview,
Englishreview.is_about_service,
Englishreview.positivity_score
FROM ai_function_feedback;
Step 4: Output Example

Now you can use these columns to:
Filter sessions with low positivity
Report on customer-service-related feedback
Visualize feedback trends in dashboards
Segment users by their experience
How This Helps in GA4 Analysis
GA4 tells you what users are doing — page views, clicks, sessions, but without context, behavior is just numbers. By integrating Generative AI, you can transform unstructured inputs into meaningful, structured insights.
Challenge | How Generative AI Helps |
Feedback is unstructured and multilingual | Translate and normalize directly within SQL |
Sentiment is subjective and inconsistent | Generate a standardized |
Feedback is hard to segment or categorize | Automatically classify using an |
Manual labeling is slow and labor-intensive | Get structured outputs instantly with Gemini in BigQuery |
Generative AI bridges the gap between raw behavioral data and rich contextual understanding, all within your existing GA4 pipeline.
Use Case: AI-Summarized Page Performance in GA4
In this use case, we utilized AI.GENERATE()
to automatically summarize user engagement for each GA4 page_path
. By combining quantitative metrics like pageviews and average session duration, the model generates concise, human-readable descriptions of page performance. Additionally, it assigns a performance label (e.g., High, Low) to help quickly identify content that requires attention or optimization
This approach enables:
Faster content performance audits
Scalable page-level insights
Smarter dashboard annotations - all with SQL and no external processing.

All generated with just SQL — no scripts needed.
Read More —>> Google Ads and Analytics AI Agents: Smarter Marketing or Risky Move?
Conclusion
Generative AI in BigQuery transforms GA4 from raw data into rich, human-readable insights: all with SQL. Whether you're analyzing multilingual feedback or summarizing page performance, AI.GENERATE() removes the need for external tools, speeds up analysis, and unlocks smarter decision-making directly within your data pipeline.
More Insights

How can GA4 Data be supercharged with Generative AI in BigQuery?

Shreya Banker
Data Scientist
Aug 13, 2025
Read More

How Google’s Machine Learning Tools Turn Data Into Predictive Power

Shreya Banker
Data Scientist
Aug 6, 2025
Read More

Your Ultimate Website Relaunch Checklist: Keep GA4 & GTM Tracking Alive (Without Losing Your Mind)

Ketul Dave
Implementation Specialist
Jul 23, 2025
Read More
More Insights
Sign Up For Our Newsletter
