What People Ask ChatGPT When Looking for Products Like Yours
- 4 days ago
- 8 min read
When someone searches Google for a project management tool, they type something like "best project management software for agencies" and expect the algorithm to interpret what they mean. When that same person opens ChatGPT, they write something closer to: "I run a small design agency with four people, we keep dropping the ball on client deliverables and I've already tried Asana but it felt like overkill, what would actually work for us?" Same person, same problem, completely different behaviour.
This shift is not cosmetic. It changes what buyers reveal about themselves, how they frame their decisions and crucially, what kind of answer they expect in return. Brands that understand this shift can align their content with how buyers actually think. Brands that do not keep optimising for compressed keyword signals while the real conversations happen elsewhere.
Why people think differently inside AI tools
Google trained a generation of users to compress their intent into the fewest possible words. The interface rewarded brevity: a three-word query returned ten blue links, and the user did the rest of the work. Over time, people learned to speak Google's language rather than their own.
AI tools reversed this dynamic. There is no character limit, no need to second-guess what phrasing will return useful results and no penalty for including context. Users describe their actual situation because the tool can handle it, and because a more specific prompt reliably produces a more useful answer. The result is that buyers reveal information in AI queries that they would never include in a Google search: team size, budget constraints, previous tools they have tried, specific frustrations, deadlines and organisational context.The scale of this shift is already measurable. According to an Omnisend survey of 1,200 American consumers conducted in July 2025, 59% already use generative AI tools for shopping tasks, and 57% use it specifically for product research — making it the most common use case, ahead of personalised recommendations and deal-finding. People are not simply experimenting with a new interface, they are restructuring their entire decision-making process around it.
This is commercially significant for two reasons. First, it means the queries that lead to purchase decisions are now richer and more specific than anything keyword research captures. Second, it means that a brand which only exists as a keyword target, optimised for "best [category] software" but not present in the conversations where buyers describe real situations, will be systematically absent from the answers that matter most.
The practice of cataloguing these real conversational queries is called prompt landscape mapping, and it is the foundation of AI visibility work because you cannot optimise for questions you have not identified.
Six types of queries, six states of mind
Buyers do not ask AI tools the same question at every stage of their decision. The type of query someone asks reveals where they are in their thinking, and each type requires a fundamentally different kind of answer. Understanding the psychology behind each one is what separates brands that get cited from brands that get passed over.
Brand queries: "I've heard this name, help me understand it"
Someone asking "what is [brand]?" or "tell me about [company]" has encountered your name somewhere and wants to orient themselves before going further. They are not comparing yet and not deciding yet. They are asking the AI to give them a mental model of what you are.
The buyer has no strong prior and is genuinely open. What they need is a clear, honest description that tells them your category, who you serve and what makes you different from the obvious alternatives. What they do not need is a list of features or a sales pitch, because they have not decided the category is even relevant to them yet.
Brands that answer brand queries well in AI tend to have one thing in common: their homepage and About page describe them the way a knowledgeable colleague would, not the way a marketing team would.
Head-to-head comparisons: "I've narrowed it down, help me decide"
"[Brand A] vs [Brand B] for small teams" or "how does [your brand] compare to [competitor]?" signals a buyer who has done their initial research and is now in the final stage of evaluation. The shortlist exists. The question is which finalist wins.
The buyer wants to make the right call and is worried about making the wrong one. They are looking for a clear differentiation that helps them justify a decision, not a declaration that one option is simply better. Answers that say "choose A if you need X, choose B if you need Y" are cited far more often than answers that declare a winner, because they respect the buyer's specific situation rather than overriding it.
Alternatives requests: "I know what I don't want, show me what else exists"
"What are the best alternatives to [competitor]?" or "what should I use instead of [tool] for [use case]?" comes from a buyer who has already made a category decision and rejected the obvious default. They are not browsing, they are actively replacing something.
This is the highest-intent query type in any category, and it is the one most brands underinvest in. The buyer knows their requirements well enough to have ruled something out, and they are ready to act on a good recommendation. Brands that appear consistently in alternatives answers do so because they exist in the same external conversations as the tool being replaced, not because they have a page titled "alternative to [competitor]." Co-occurrence in trusted third-party sources is what puts you in this answer.
Feature-specific questions: "I know what I need, who does it best"
"Which CRM has the best email automation?" or "what [category] tool handles [specific workflow] well?" comes from a buyer who has moved past category selection entirely and is now evaluating on a specific capability dimension.
The buyer has a clear criterion and wants a direct answer. Vague responses about overall quality or general strengths do not satisfy this query, and AI platforms reflect this: they cite content that answers the specific capability question directly, with concrete evidence, not content that gestures at being good at many things. A brand that claims a feature as a core strength but cannot be found in the answers to feature-specific questions about that feature has a credibility problem that no amount of on-page optimisation will fix without external substantiation.
Use-case prompts: "Here is my situation, what fits"
"I'm a solo consultant who needs to track client projects without spending more than an hour a week on admin, what would you recommend?" is a use-case prompt. The buyer is describing their world and asking AI to do the matching work.
The buyer does not want to become an expert in the category, they want a trustworthy recommendation that fits their specific context. What makes a brand appear in these answers is not keyword coverage but the depth of AI's understanding of who the product is actually for. Brands that appear in use-case answers have usually earned that presence through the specificity of their own content and through third-party descriptions that are precise about audience and application, not just about features.
Problem-solution queries: "Something is wrong, help me fix it"
"My team keeps missing deadlines even though we use a task manager, what are we doing wrong?" or "I'm spending three hours a week on client reporting and it's killing me, is there a better way?" These queries arrive before the buyer has decided they need a product at all. They are describing a pain and asking for a diagnosis.
The buyer has not formed a category preference yet and is genuinely receptive to whatever actually addresses the problem. Brands that appear in problem-solution answers do so through content that takes the problem seriously rather than using it as a prompt to pitch a solution. An article that genuinely diagnoses why teams miss deadlines and then mentions relevant tools as part of a broader answer will be cited far more readily than a landing page that opens with "struggling with missed deadlines? Try [product]."
What this means for how you respond
Each query type signals not just what the buyer wants to know but what register they expect the answer to arrive in. Matching that register is what makes content citable.
Problem-solution queries require empathy and diagnosis before any mention of solutions. Head-to-head comparisons require honest acknowledgment of trade-offs rather than advocacy for one side. Alternatives requests require decision criteria that help the buyer evaluate their own situation, not a ranked list that assumes all buyers have the same needs. Feature-specific questions require concrete, evidenced answers, not claims.
The brands that get cited most consistently across all six query types share one characteristic: their content sounds like it was written for the buyer's situation, not for the brand's positioning goals. According to a Microsoft Clarity study analysing over 1,200 websites, visitors arriving from AI platforms convert at significantly higher rates than those from traditional search, which suggests that the buyers AI sends are already well-matched to what they find. That match does not happen by accident: it happens because the content answered the actual question in the register in which it was asked.
The prompt landscape is the tool that lets you audit how well your current content matches across all six states of mind, and identify where the mismatches are before your competitors do.
Questions About How People Search in ChatGPT
Why does the phrasing of an AI query matter more than the topic?
AI platforms respond to the full context of a prompt, not just its subject matter. The same topic phrased as a problem-solution query produces a different answer than the same topic phrased as a head-to-head comparison, because the buyer's state of mind and what they need from the answer is different. A brand can be well-represented in answers to one query type and entirely absent from answers to another on exactly the same topic.
Do all six query types appear in every product category?
Yes, though the distribution varies. Categories with long evaluation cycles and multiple stakeholders tend to generate more head-to-head and feature-specific queries. Categories where buyers are switching away from a dominant incumbent generate disproportionate alternatives traffic. Understanding which types are most common in your specific category helps you prioritise where to build coverage first.
How does buyer behaviour in AI tools differ from behaviour in Google?
In Google, users compress their intent into short phrases and expect the algorithm to interpret what they mean. In AI tools, users describe their full situation because the interface handles context and specificity rewards them with better answers. This means AI queries reveal far more about buyer circumstances, constraints and decision criteria than keyword searches do, and content that addresses those specifics is more likely to be cited than content optimised for broad keyword coverage.
Why do alternatives requests have the highest buying intent?
Because a buyer asking for alternatives has already made two decisions: they have decided they need a solution in this category, and they have decided the obvious default is not right for them. That combination of category commitment and active rejection of the status quo puts them closer to a purchase decision than any other query type. They are not browsing, they are replacing something, and a well-placed recommendation at this moment has direct commercial impact.
What is the most common mismatch between query type and content?
Most brands have reasonable coverage for brand queries because their homepage describes what they do. The most common gap is between problem-solution queries and available content: buyers describing a pain point are met with product pages and feature lists rather than genuine diagnostic content that takes the problem seriously before introducing a solution. This mismatch is why brands with strong products and good SEO can still be absent from the AI answers that reach buyers earliest in their decision process.
