Measure the questions buyers actually ask AI.
Generate natural buyer questions, classify intent and keep brand-named self-referential prompts out of visibility scoring.
What Buyer Questions helps the team understand.
Generate natural buyer questions, classify intent and keep brand-named self-referential prompts out of visibility scoring.
Natural language mix
Combine short, conversational and situation-rich questions.
Intent categories
Separate research, evaluation, comparison, trust and purchase intent.
Self-reference control
Flag questions that already contain the brand name so they cannot inflate visibility.
Keep every result connected to evidence and action.
The product explains the result, preserves its source context and makes the next review explicit.
The rules buyer questions follows
A feature list is easy to copy; the rules behind the numbers are not. Each of these is enforced in the product, and most of them were added after getting it wrong once.
| Rule | Why it exists |
|---|---|
| Generated questions must not contain your brand name or a competitor's. | This was once broken at the source: the prompt itself asked for questions like "is X a legit brand", and 13 of 15 generated questions carried the brand name. Those questions burn quota and cannot move any metric. |
| Questions are full sentences with a situation in them, not keyword fragments. | Assistants answer situations. A question carrying a budget, a constraint or a use case produces a different set of named brands than the bare category term. |
| Intent is recorded on every question. | Being named in research questions and absent from purchase questions is a different problem, with a different fix, than the reverse. One overall percentage cannot tell them apart. |
| The tracked set stays fixed across runs unless you change it deliberately. | Every percentage has the question set as its denominator. Adding easier questions raises the average while nothing improves. |
Move from summary metrics into the underlying question.
Sample data demonstrates the evidence depth of a formal project.
Visibility evidence
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Questions about Buyer Questions
Does this feature run from the public website?
No. Marketing pages explain the capability. Formal data collection begins only after a user enters the App and confirms the project scope.
Can snapshot results become part of this workflow?
Yes. A valid snapshot can be claimed as an initial sample, while onboarding still confirms markets, competitors and the formal monitored question set.
Are all metrics comparable across AI engines?
Core answer states can be normalized, but citations, search assistance and answer formats still need engine-specific evidence.
How often is the data refreshed?
Measurement runs on the schedule your plan allows, and monitoring quota is shared at the account level. Alerts require two consecutive runs to confirm, so model sampling noise does not become a false alarm.
Can the original AI answer be inspected?
Yes. Every result drills down to the answer that produced it, question by question and model by model, together with the citations found in that answer.
What happens when a model is unavailable?
That run is marked partial rather than being silently filled in. The remaining engines still complete, and the gap stays visible in the result.
Put Buyer Questions into a complete GEO workflow.
Create a project, confirm your market and start measuring the questions that matter.