GA4 Demographics for Specific Pages: Explorations with BigQuery

As digital analytics evolves, marketers and analysts are seeking increasingly granular insights to better understand their audience and measure performance. Google Analytics 4 (GA4) marks a significant shift from the familiar Universal Analytics paradigm, particularly in how data is collected and reported. One area where GA4 has introduced both opportunities and complexities is in analyzing demographic data at the page level. Using BigQuery—with its powerful querying capabilities—offers a path to dissecting GA4 demographics by specific pages, affording valuable insights for content optimization, personalization strategies, and broader marketing objectives.

Understanding GA4’s Demographics Framework

GA4 offers demographic dimensions such as age, gender, interest categories, and geo-location. These are available in standard reports and can also be accessed through Explorations. However, their availability varies by user consent and data sampling thresholds. Importantly, GA4 does not provide demographic dimensions directly associated with specific pageviews in the out-of-the-box interface.

This is where BigQuery integration becomes essential. When GA4 is linked to BigQuery, user and session data is exported in raw format, offering a granular layer of analysis not feasible in the standard UI.

Why BigQuery is Essential for Page-Level Demographics

One of the key limitations with GA4’s reporting interface is the inability to report demographics by page path or screen name. For example, if you want to determine the age and gender breakdown of users visiting a specific blog post or landing page, standard GA4 reports won’t allow such granularity. Instead, BigQuery lets analysts write SQL queries against exported GA4 data to uncover this information.

With raw event-level data stored in BigQuery, each event (including page_view events) is accompanied by user-level properties like demographic traits, assuming the data is present and not restricted due to user consent settings or data thresholds.

Preparing Your GA4 BigQuery Dataset

Before beginning your analysis, ensure the following:

• GA4 is connected to BigQuery: This can be done via the GA4 admin panel under “BigQuery Linking”. Once linked, data exports will begin populating your BigQuery dataset daily.
• Demographics are enabled in GA4: In the GA4 property settings under “Data Settings > Data Collection”, toggle on Google signals, which is a prerequisite for demographic data.
• Understand the schema: Demographic information can be found in the user_properties record field. Key values include user_age_bracket and user_gender.

Querying Demographics by Page in BigQuery

Let’s explore how to write a query to segment page-specific traffic by demographics. The core strategy is to:

1. Filter for event_name = 'page_view'.
2. Extract page path from event_params.
3. Extract demographics from user_properties.

Here’s a basic sample SQL query:

SELECT
  ep.value.string_value AS page_path,
  up_age.value.string_value AS age,
  up_gender.value.string_value AS gender,
  COUNT(*) AS page_views
FROM
  `your_project_id.your_dataset_id.events_*`,
  UNNEST(event_params) AS ep,
  UNNEST(user_properties) AS up_age,
  UNNEST(user_properties) AS up_gender
WHERE
  _TABLE_SUFFIX BETWEEN '20240101' AND '20240131'
  AND event_name = 'page_view'
  AND ep.key = 'page_path'
  AND up_age.key = 'user_age_bracket'
  AND up_gender.key = 'user_gender'
GROUP BY
  page_path, age, gender
ORDER BY
  page_views DESC

This query returns the number of pageviews for each page path, segmented by age and gender brackets. You can further filter for specific pages, timeframes, or include additional demographic dimensions like interests.

Analyzing and Interpreting Results

After running the query, you may export the results to a visualization tool such as Looker Studio or Tableau, where trends become easier to digest. Some questions you can explore include:

• Which age groups frequent your high-converting landing pages?
• Are certain blogs more popular among female visitors?
• Do interest categories align with the themes or tone of a given page?

These insights can guide content strategy, digital ad segmentation, and on-page optimization efforts. For instance, if your long-form content consistently attracts users in the 25–34 age segment interested in technology, you might double down on similar topics or adjust your ad copy accordingly.

Cautions and Considerations

There are several important limitations and ethical considerations when dealing with demographic data:

• Data thresholds: GA4 may apply thresholds that suppress demographic data when user counts are low, to protect privacy.
• Consent: Not all users have demographics — especially if consent is not given or GA signals are off.
• Sampling: If working outside of BigQuery and using Explorations, sampled data may skew results, whereas BigQuery yields unsampled data.
• Respect privacy: Use demographics for aggregate analysis only — avoid targeting subsets that could lead to discriminatory or intrusive practices.

Advanced Techniques

Once you are comfortable with basic queries, consider expanding your analysis with these techniques:

• Join with geo data: Combine demographic insights with user location for deeper segmentation.
• Session-based grouping: Use session IDs to avoid double-counting across multiple pages/views during a single visit.
• Machine learning: Integrate demographic predictions into user clustering or page recommendation models within BigQuery ML.

For example, a retailer might train a model to predict high-converting demographics for a category page and dynamically personalize content accordingly. This is part of a broader movement toward data-driven UX personalization.

Conclusion

GA4’s integration with BigQuery opens powerful doors for demographic-level insights at a specificity that GA4’s front-end alone cannot achieve. By leveraging SQL to distill page-level demographic patterns, organizations can make more informed decisions about their audience, content, and marketing strategy. However, analysts must remain mindful of the limitations related to data availability and user privacy.

For teams serious about analytics maturity, combining GA4’s robust data collection methods with BigQuery’s querying power is essential. Whether you are optimizing conversions, tailoring user journeys, or building data models, understanding who visits which page—segmented by demographic—is a foundational step toward intelligent decision-making in the digital domain.