Conversational Keyword Research: How to Find the Questions People Actually Ask AI

Traditional keyword research finds the terms people type into Google. Conversational keyword research finds the full questions people ask AI tools like ChatGPT, Perplexity, and Claude.

The difference is fundamental.

When someone searches Google for “B2B content strategy,” they expect a list of resources. When they ask ChatGPT “How do I create a content strategy for a B2B SaaS company with a team of three?” they expect a specific, actionable answer built for their situation.

That shift, from keywords to questions, changes everything about how you do Answer Engine Optimization. You can’t just know what people want to know. You have to know exactly how they ask for it.

I learned this the hard way. My carefully researched posts targeting “demand generation tactics” got decent Google traffic and zero citations from AI tools. Meanwhile, a throwaway post answering “What’s the difference between marketing automation and demand gen for small teams?” got cited constantly.

The second one was an accident. The first one was strategy. That tells you something.

Why AI Search Changes Everything About Keywords

From keyword fragments to complete questions

Traditional search taught us to think in fragments. People typed “email marketing B2B” because every extra word cost time and might hurt results.

AI search flips this. When people talk to Claude or ChatGPT, they ask complete questions with full context. They say “What’s the best email marketing strategy for a B2B SaaS company selling to technical buyers with a six-month sales cycle?” They include the details because the details improve the answer.

I pulled transcripts from 50 sales calls and found prospects asking variations of that exact question. Not one keyword tool flagged “email marketing strategy for technical buyers” as a research opportunity. Traditional tools miss the conversational layer entirely.

Context matters more than volume

Search volume becomes close to meaningless when AI gives a personalized answer. A question asked by 100 people a month with specific context clues can be worth more than a generic query with 10,000 searches.

AI engines don’t show ten blue links. They give one answer, often citing multiple sources. Getting included in that synthesis matters more than ranking #1 for a phrase.

And the money isn’t in the highest-volume questions anyway. It’s in the specific, contextual queries that signal someone is close to deciding.

The 5-Step Process for Conversational Keyword Research

Step 1: Mine your sales calls

Start with recorded sales conversations. Prospects ask your sales team the exact questions they later ask AI tools. These calls reveal the natural language your keyword tools never surface.

I use Claude to analyze transcripts with this prompt:

“Extract all questions the prospect asked during this call. List them exactly as spoken, then identify the underlying information need for each question.”

The results surprise me every time. Prospects don’t ask “What’s your pricing model?” They ask “How much would this cost for a company our size with about 50 users, and can we start smaller and scale up?”

Document these in a spreadsheet with three columns: the exact question, the underlying need, and the funnel stage where it shows up.

Step 2: Use AI tools as research tools

Once you have a base set of questions from calls, use AI to find related queries. The key is prompting for variations, not just similar topics.

My go-to prompt for ChatGPT:

“I’m researching how people ask about [topic]. Here are 5 questions I know people ask: [list]. What are 10 other ways people might ask about this same topic? Focus on different phrasing, contexts, and levels of specificity.”

This reveals the question clusters around your core topics. People might ask “How do I measure content ROI?” or “What metrics should I track for our blog?” or “How do I prove content is driving pipeline?” Same need, three phrasings.

AI engines understand semantic relationships, so optimizing for question clusters beats optimizing for individual keywords.

Step 3: Map questions to funnel stages

Organize your queries by where they live in the buyer journey. Early-stage questions sound different than evaluation-stage ones.

• Top of funnel: “What’s the difference between content marketing and content strategy?”
• Middle of funnel: “How do I build a content team at a Series A company?”
• Bottom of funnel: “What should I look for in a content marketing consultant?”

This mapping determines format and depth. Answer-first writing works best for specific, late-stage questions. Broader educational content serves early-stage queries.

Step 4: Test query variations

Small changes in phrasing dramatically alter AI responses. Test your target questions directly in ChatGPT, Claude, and Perplexity to see what they cite.

Ask “How do I improve our B2B content strategy?” Then ask “What’s wrong with our B2B content strategy?” Same need, different framing, often completely different sources cited.

Build a testing spreadsheet: the question, the tool you tested, the sources cited, the quality of the answer. This shows you which questions to target and which formats AI prefers.

Step 5: Track what gets cited

Monitor which of your pieces get referenced by AI tools. The basic process is simple: regularly test your target questions and note when your content shows up.

Content cited consistently reveals successful targeting. Content that never gets mentioned is aimed at the wrong queries or written in the wrong format.

I track this weekly for my top 20 questions. The pattern is clear: specific, actionable content formatted as a direct answer gets cited. Generic thought leadership doesn’t.

Tools and Techniques That Actually Work

Free methods using AI tools

The best conversational keyword research tool is often the AI platform itself. Build a dedicated research assistant with a custom GPT or a Claude project.

My research GPT runs on this prompt:

“You are a keyword research assistant specializing in conversational queries. When I give you a topic, provide 10 questions people might ask AI tools about that topic. Focus on natural language, specific contexts, and different expertise levels.”

Tools like AnswerThePublic and AlsoAsked work better when filtered for question-based queries. Export their suggestions, then test the most natural-sounding ones in AI tools to see which produce useful responses.

Traditional tools, adapted

Existing keyword tools give you raw material. Your job is to transform it into natural questions.

• “Content marketing metrics” becomes “What metrics should I track for our content marketing?”
• “B2B lead generation” becomes “How do I generate more qualified leads for our B2B business?”

You’re not abandoning traditional research. You’re adapting it for conversational search.

Common Mistakes to Avoid

Assuming volume predicts conversational value. It doesn’t. A high-intent question with low volume often beats a generic phrase with huge volume.

Optimizing without understanding context. “How do I use AI for marketing?” could come from a CMO or a solo founder. Same words, completely different information needs.

Not testing your target questions in AI tools. You might think you’re targeting the right query, but if AI never surfaces your content when someone asks it, your optimization failed. Test, don’t assume.

Conversational keyword research isn’t a separate discipline you bolt on. It’s the input layer for an AEO content system that turns real buyer questions into content that gets cited. If you want help building that system, book a call.

Related reading: What Does AEO Stand For? A B2B Guide to Answer Engine Optimization · score yourself with the matching audit · start with an audit