Start with an audience decision
A keyword can guide research, but a question adds context: who is asking, what they need to understand and which condition matters to their choice.
For KAIRUM, an agency preparing a client diagnosis and a brand investigating its sources may ask different questions. Document the audience without inventing demand volumes.
From a need to a question
An agency preparing a diagnosis for a brand.
What evidence to request before agreeing on a pilot.
What should an AI visibility diagnosis include?
Cover more than one decision stage
Group questions by intent and review which part of the journey each group represents. These categories organize a sample; they do not automatically estimate a market.
| Intent | Example question |
|---|---|
| Discover | What does AI brand visibility measurement mean? |
| Compare | How should I compare GEO and AEO analytics tools? |
| Evaluate | What should a visibility diagnosis include? |
| Research a brand | Which methodology does KAIRUM explain on its website? |
Separate open and brand-specific questions
An open query does not include the brand name. It helps observe which alternatives appear for that wording. A brand-specific query names it and helps review how its offer is described.
Both can be useful. If you mix their results without labels, higher presence may simply reflect writing the name into more questions.
Six questions, with a purpose for each row
For a first editorial review of KAIRUM, we propose the questions below. The first four explore the category without naming the brand; the last two examine how KAIRUM is described. Keep them in separate groups. These are neither real customer queries nor estimates of their frequency.
Their value is that each question changes the decision being investigated. A definition helps someone discover the problem; a comparison criterion helps evaluate alternatives. Repeating the same question with ten synonyms would cover fewer of those decisions.
| Proposed question | Why include it |
|---|---|
| How can I tell whether a brand appears in AI answers? | Discover the problem. Open. |
| What is the difference between a mention and a citation? | Understand a signal. Open. |
| How should I compare brand visibility diagnoses for AI? | Explore criteria. Open. |
| What evidence should I request before buying a diagnosis? | Evaluate a deliverable. Open. |
| What methodology does KAIRUM publish? | Review the brand explanation. Directed. |
| What information about KAIRUM is missing to evaluate a pilot? | Identify information gaps. Directed. |
Choose coverage over repeated variations
Check whether several questions essentially ask the same thing. A long list of paraphrases does not guarantee coverage of different decisions or needs.
Prioritize questions the team can connect to a decision and a possible content review. Without real query data, treat the set as an editorial hypothesis to improve.
Save a version before measuring
Retain exact wording, intent, language and reason for inclusion. Record exclusions and the environment to be observed.
When the set changes, create a new version. Compare periods under compatible conditions and explain added or removed questions.
- Defined objective and audience.
- Wording, language and intent per question.
- Labeled open and brand-specific queries.
- Set version, scope and date.
- Environment, planned repetitions and coverage criteria.
How to improve a question before running it
Read every row aloud. “Why is KAIRUM the best option?” already introduces a conclusion. “What does KAIRUM publish about its method?” lets you investigate something verifiable. A biased question can produce an appealing answer while contributing little to the diagnosis.
If the wording is too broad, add a condition the audience would actually use: language, team type or need. Do not add the brand name to make it appear. Avoid combining price, integrations, methodology and support in one question if you will not be able to connect the finding to a specific need.
Before expanding, ask someone on the team to explain which decision each answer could change. If two rows have the same purpose, choose one for the core and keep the other to explore variation. When you incorporate evidence from real conversations or queries, record its origin and avoid putting private information into test questions.
For a repeat measurement, preserve that core and its conditions. New questions can enter an exploratory block. This lets you learn without presenting a change in sample composition as improved presence.
