How should you read the result?
Twenty measurements return sixty subquery occurrences. One measurement can return several phrases. This is not total public search demand.
AI Visibility
What question does it answer?
Inspect returned subqueries associated with measured questions and the observable search intents behind them. Stored provider results must contain subquery data. Coverage varies. The feature does not invent missing subqueries or expose the complete internal reasoning of an AI system.
Sample project
Explore this feature with sample data
Northstar · illustrative project · comparable 30-day periods. These examples explain how to use and interpret the measurements.
Measurements returning subqueries
20Subquery occurrences
60Shown phrases
4Figures shown are illustrative sample data.
Sixty occurrences overall. Review measurement counts and results returning subquery data alongside the trend.
Figures shown are illustrative sample data.
- Select a prompt, period, and available providers
- Review recurring subqueries and measurement counts
- Identify pricing, reference, and comparison intents
- Use these signals in related URL investigations
| Metric / scope | Sample value | Interpretation |
|---|---|---|
| Main prompt | Choosing an SEO agency | Observation starting point |
| Prices | 24 occurrences | Recurring search direction |
| References | 18 occurrences | Trust-related sub-intent |
| All occurrences | 60 | Not sixty unique prompts or users |
Figures shown are illustrative sample data.
Which decision does it support?
Recurring pricing and reference phrases help focus review of the corresponding service page.
What should you check first?
More subqueries do not automatically mean better performance. Retrieval counts and provider coverage affect the chart.
What do you see?
- Stored subqueries and occurrences by prompt
- Provider, period, and topic breakdowns
- Recurring phrases and subquery context also used in URL analysis
How do you use it to decide?
- Select a prompt, period, and available providers
- Review recurring subqueries and measurement counts
- Identify pricing, reference, and comparison intents
- Use these signals in related URL investigations
An illustrative use case
A supplier-selection prompt returns subqueries about prices, references, and services. These observable signals help investigate whether your page addresses the relevant sub-intents.
An illustrative scenario, not a customer result or a promise of results.
What does measurement require?
Stored provider results must contain subquery data. Coverage varies. The feature does not invent missing subqueries or expose the complete internal reasoning of an AI system.
Frequently asked questions
Is fan-out the same as prompt suggestions?
No. Suggestions help select new questions to measure; fan-out describes subqueries returned for questions already measured.
Are all subqueries visible for every provider?
No. Only available returned data is shown. Missing data does not prove that the provider performed no search.
How does URL analysis use fan-out?
It collects stored subqueries for the selected prompt or topic context to help compare the page with the observed search intents.
What should I check before comparing periods?
Check the selected question or URL, filters, sample size, source coverage, and data freshness. Different scopes can produce differences unrelated to a real change.
Does missing data mean zero performance?
No. A source connection, retrieval, or processing step may be missing. Check availability before interpreting an empty block.
Can I use this to guarantee an AI recommendation?
The feature supports evidence-based investigation. It does not control provider answers or guarantee recommendations or citations.
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Request a quote for your project
Tell us which brand and website you want to measure and what question you need to answer. We will discuss the subscription and data connections your project requires.