How to design a buyer-question set for AI visibility
Guide · Continuous Intelligence · 4 min read · last verified 2026-07-25
The question set is the instrument. Everything downstream — your score, your trend line, your competitive read — is only as honest as the questions you chose to ask. Design it well and you measure the market you actually compete in. Design it badly and you measure the wrong market with impressive precision.
Start from real buyer questions, not keywords
A buyer-question set is the list of prompts you track to see whether AI assistants surface your brand. Build it from how buyers actually ask — full, natural questions at each stage of the decision — not from keyword fragments. Keywords describe what people type into a search box; questions describe what they ask an assistant, and assistants answer questions, not strings.
The practical test for any candidate question: would a real buyer, mid-decision, phrase it that way to a colleague or an AI assistant? If it reads like a report title or an internal category name, rewrite it in the buyer's voice before it goes in the set.
Cover the full intent spectrum
A good set spans every stage a buyer moves through, not just the bottom of the funnel. Map questions to intent: category education, comparison, selection, pricing, risk, and the final decision. Gaps in coverage become blind spots you never learn about, because you never asked.
Comparison-intent questions deserve heavy representation. Comparison-style content tends to earn an outsized share of AI citations — so "X vs Y" and "best tool for Z" questions are where a large share of citations are won or lost. Weight the set accordingly.
Balance branded and unbranded questions
Branded and unbranded questions measure different things, and you need both. Branded questions ("is Magrios reliable?", "what does Magrios do?") measure your reputation once a buyer already knows you exist. Unbranded questions ("best continuous market intelligence software") measure discovery — whether AI surfaces you to buyers who have never heard your name.
Growth lives in the unbranded set. It is tempting to over-index on branded queries because you tend to look good there, but that is a comfortable mirror, not a market. Weight unbranded questions heavily if the job is winning new demand.
Weight questions by value, not by volume
Not every question is worth the same. A low-volume question that sits at the point of purchase — a pricing comparison, a shortlist query in your exact category — is worth more than a high-volume, top-of-funnel question that rarely converts. Search-volume instincts mislead here, because AI answers collapse many long-tail phrasings into a handful of buyer intents.
Score each question by proximity to a buying decision and by how directly it maps to your category, then let that ranking decide where you spend attention. The aim is a set weighted toward the answers that move revenue, not the ones that move traffic charts.
How many questions should you track?
Enough to cover intent without diluting your signal. For most B2B categories that means dozens, not thousands — a focused set you can re-scan repeatedly and reason about, rather than a sprawling list that averages your attention to nothing. Depth per intent beats raw breadth: five sharp comparison questions in your category tell you more than five hundred vague ones.
Start smaller than feels comfortable, run it a few times, and expand only where the results reveal a real gap in coverage. A set you actually re-measure on a schedule is worth more than an exhaustive one you scan once and abandon.
Lock the set so the trend means something
The moment you change the set, you break comparability. Add or rephrase questions between scans and a shift in your score may reflect the edit rather than the market. That is measurement drift, and it quietly destroys the value of a trend line.
The discipline is a locked benchmark: fix a core set of questions, keep the platforms and scoring constant, and version any additions separately with their own fresh baseline. Only then does a change in the number mean a change in your position rather than a change in your ruler.
Write questions the way buyers phrase them
Phrasing is not cosmetic — assistants respond to natural language, and a wooden question returns a wooden read on the market. Use the buyer's words and the shapes real questions take.
| Intent stage | Example question | What it measures |
|---|---|---|
| Category education | what is continuous market intelligence | Whether you appear in early, defining answers |
| Comparison | best tools for tracking AI visibility | Presence in shortlist-shaping answers |
| Selection | which AI visibility platform fits a small team | Fit-based inclusion in a narrowed field |
| Pricing | how much does AI visibility monitoring cost | Presence in cost and value questions |
| Risk | is AI visibility monitoring worth it | Whether you counter objections at the decision point |
Operationalizing the question set
A question set is not a one-time deliverable; it is the standing instrument for a measurement loop. Once it is locked, scan it to establish where you appear and where you are absent, route the highest-value gaps into action, then re-scan the same set to see whether the position moved. The set is what makes that loop honest — comparable inputs are the only way a later result can be trusted against an earlier one. Design it once with care, lock it, and let it keep measuring the market you actually sell into.