Tracking·Research Stage·
How do I find out what prompts buyers actually use?
THE SHORT ANSWER
The queries in your GEO plan were probably invented in a meeting, not spoken by an actual buyer — and that is often why the plan is not moving your numbers. Most brands optimize for questions they assume people ask, not questions people actually ask. No AI engine publishes a log of what its users type, but you can approximate it: mine sales calls, support tickets, and forums for the exact phrasing buyers use, treating your own guesses as a hypothesis to replace.
WHO IS ASKING THIS
A marketer or GEO practitioner building a query-testing programme or content plan, who now realizes every "buyer question" on the current list came from a brainstorm rather than a real conversation, and who needs a defensible way to source the real thing before spending budget optimizing for questions nobody outside the company actually types.
THE BREAKDOWN
Why invented queries quietly sink a GEO programme
A team writing "queries buyers ask" in a workshop reliably produces questions that sound right to people who already know the category deeply — using internal terminology, feature names, and framing the company itself uses. Real buyers, especially early in a search, use vaguer, problem-first language: "AI tool that tells me when ChatGPT mentions my company" rather than "share-of-model tracking platform." Optimizing content and measurement around the internal version means testing against queries nobody outside the building actually types, and getting misleadingly clean-looking results that do not reflect real exposure to real buyers.
Where real buyer language actually lives
Sales call transcripts and discovery notes capture how prospects describe their problem before they have learned your terminology. Support tickets and onboarding questions capture post-purchase language. Community forums and category subreddits capture how people phrase the problem when no vendor is in the room. Review platforms like G2 and Capterra capture the language reviewers use to explain why they chose or rejected a tool. Each source reflects a different stage of the buyer journey — forums skew early research, sales calls skew active evaluation, support tickets skew after the decision is already made — so pulling from only one source biases the picture toward that stage.
A workable process, not a perfect one
Pull 20 to 30 real customer-facing conversations — calls, tickets, chat transcripts — and extract every instance of a customer describing the problem or comparing options in their own words. Cross-reference that against forum and review language in the category. Build a list of 15 to 30 candidate real-world phrasings, then test that list against AI engines and refine it based on which phrasings surface useful, category-relevant answers versus off-topic noise. This is closer to qualitative research than a clean data pull, and it will never be complete — the goal is a defensible, evidence-based approximation, not a finished census of buyer intent.
Signals that your query list is actually representative
Two practical checks help confirm the list is working before you build a whole measurement programme on top of it. First, run each candidate query and check whether the AI response reads as genuinely relevant to your category — if a phrasing consistently returns off-topic or generic answers, it is probably not close to how real buyers actually ask. Second, watch for diminishing returns: once new customer-facing conversations mostly repeat phrasings already on the list rather than introducing new ones, the list has reached a reasonable level of coverage for now. Neither check proves completeness, but both are better evidence than confidence based on how the list feels in a meeting. Revisit the list on a fixed schedule rather than treating it as a one-time exercise, since the language buyers use tends to drift as a category matures and as your own published content starts shaping how people describe the problem.
What no amount of process will get you
No engine will hand you its actual query logs, and there is no way to fully verify you have captured the real distribution of how buyers phrase questions to AI assistants specifically, as opposed to how they phrase them in reviews, forums, or sales calls with a human. Treat every prompt list — including the output of this process — as a working hypothesis to keep testing and revising, not a finished, authoritative dataset. Anyone claiming a definitive list of "the queries buyers use" is overstating what is actually knowable.
THE VERDICT
Build your query list from real conversations, not a meeting, and revisit it quarterly — buyer language shifts as the category matures, and your own content starts training people to describe the problem the way you do.
SHARE-OF-MODEL SNAPSHOT
Illustrative share-of-model snapshot for real buyer-sourced queries versus assumed queries.
Illustrative pattern based on category monitoring, not a live reading.
Inclusion is not endorsement.
PEOPLE ALSO ASK
Can I just ask ChatGPT what questions people ask it about my category?
You can, and it is a reasonable input, but treat it as a hypothesis, not ground truth. The model is generating plausible-sounding questions based on patterns in its training data, not reporting actual user query logs, and it has no way to know the real current distribution of what people ask.
How many real queries do I need before I can trust a pattern?
Aim for a working list of 15 to 30 distinct real-world phrasings per major use case before drawing conclusions, and expect to keep expanding it. Fewer than that and you risk overfitting your content and measurement to a handful of idiosyncratic examples that do not generalize.
Do buyer questions to AI assistants differ from what they type into Google?
Often yes — AI assistant queries tend to be longer, more conversational, and more likely to include qualifying context, such as team size or an integration requirement, than a Google search, which favors short keyword fragments. Pulling only from Search Console keyword data will miss this longer, more specific phrasing pattern.
Should I test the same question phrased multiple ways?
Yes — this is one of the more reliable findings from real query research: small phrasing changes, such as a feature name versus a plain-language description of the same feature, can change whether and how a brand appears, especially on training-path engines. Testing three to five phrasings of the same underlying question is more informative than testing one phrasing five times.
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