Per-URL Demographics in GA4: What’s possible, What’s Not

Google Analytics 4 (GA4) has introduced a more flexible and event-driven model for digital analytics, replacing Universal Analytics as the go-to choice for marketers, analysts, and business owners. With GA4, users are offered significantly more control over how data is collected and reported. However, questions often arise regarding the practical limitations of demographic data – especially when broken down *per URL*. In this article, we’ll explore what’s possible and what’s not when attempting to access per-URL demographic information in GA4.

Understanding Demographics in GA4

Demographic data in GA4 refers to information such as:

• Age
• Gender
• Interests (Affinity and In-Market segments)
• Location (Country, Region, City)
• Language
• Device type and platform

This information is made available through Google Signals – an optional feature in GA4 that sources data from users who are signed into their Google accounts and have enabled Ad Personalization.

What’s Possible With Per-URL Demographics in GA4

GA4 is structured around an event schema rather than the session-based model of Universal Analytics. This makes granular tracking more flexible, but that flexibility comes with a set of trade-offs when it comes to demographics per individual page or URL. That being said, certain functionality is available:

1. Built-In Demographics Reporting

GA4 includes a default “User” section under Reports with demographic insights. You can see information like age, gender, and interests, but this is presented at the *user level*, not per specific page view. However, you can create explorations or apply secondary dimensions to dig deeper.

2. Custom Explorations

GA4’s Explore tool allows you to build custom reports that combine demographic dimensions with content-specific metrics. By adding Page path + query string or Page title as dimensions, you can create reports that simulate per-URL demographic insights. For example:

• Create a Free Form or Pivot Table exploration
• Add Page path as a row dimension
• Add Age, Gender, or Interest category as columns or filters
• Use Users or Event count as a metric

This approach will not be 100% accurate—due to data thresholds and sampling—but it does offer a way to view *approximate* demographic trends per URL.

3. Using BigQuery Export

Perhaps the most powerful option is exporting GA4 data to BigQuery. Here, demographic dimensions are available in the exported data when Google Signals is enabled and thresholds are not triggered. Once data is in BigQuery, you can write SQL queries that cross-reference URL paths with demographic data to analyze patterns with much finer control.

Example Query Concept:

SELECT
  user_pseudo_id,
  event_name,
  platform,
  geo.country,
  device.category,
  traffic_source.name,
  event_params.key AS page_path
FROM `your_project_id.analytics_xxxxxxxx.events_*`
WHERE event_name = 'page_view'
AND event_params.key = 'page_path'

This allows you to group demographic data by any number of custom or pre-defined parameters associated with page URLs.

What’s Not Possible – Limitations You Should Know

Despite these options, GA4 has strict limitations when it comes to per-URL demographic breakdowns. These include:

1. Data Thresholding

One of the biggest challenges in GA4 is thresholding, which hides data in standard and custom reports when a demographic dimension is added and the user count is low. This is to protect user privacy.

Thresholding can result in metrics returning as “(not set)” or even omitting entire rows. When looking at pages with minimal traffic, this is especially problematic. This makes it practically impossible to get high-fidelity demographic data for low-traffic URLs.

2. No Out-of-the-Box Per-URL Demographic Report

GA4 does not offer a direct report where you get demographic insights for every single URL your site hosts. Any such capability must be manually constructed through explorations or BigQuery exports.

3. Not All Visitors Are Included in Demographic Data

Only a subset of users are included in the demographics reports, specifically those for whom Google Signals data is available. If a significant portion of your visitors is using browsers or settings that block data collection, or if you’re targeting regions like the EU where consent models are required, your demographic data pool may be small or imprecise.

4. Time Lag in Data Availability

Demographic data is not available in real-time. It can take 24 to 48 hours before it appears in reports. This adds latency, which complicates time-sensitive analysis.

Moreover, demographic data is not retroactive: if Google Signals was disabled during user activity, you won’t be able to recover attributes for those users later on.

Workarounds and Best Practices

Despite these limitations, there are best practices and workarounds that can help approximate per-URL demographics:

• Focus on high-traffic pages: Thresholding is less likely for URLs with larger user samples.
• Use BigQuery for scalability: Export your data and query it for customized insights.
• Build segments carefully: Create demographic-based segments and apply them across content reports to analyze user behavior based on target groups.
• Avoid over-segmentation: The more granular your report, the more thresholding you may encounter.

Segmenting your data smartly can provide directional insights even if fine-grained clarity is blocked by data thresholds or privacy restrictions.

Conclusion

GA4 offers improved flexibility and richer analytics capabilities, but when it comes to per-URL demographics, the picture is nuanced. Here’s a summary of what you can and can’t do:

Ultimately, while GA4 does not deliver per-URL demographic reporting in a straightforward way, the building blocks to engineer such insights do exist. Analysts need to understand the structural limitations and be willing to leverage tools like BigQuery, segment-based reporting, and exploratory analysis to draw meaningful conclusions. Recognizing both the power and the limitations of GA4 is key to making informed, data-driven marketing and product decisions in 2024 and beyond.