Keyword research for AEO: A guide for winning answer engine traffic in 2026

Keyword research for AEO can feel overwhelming because audiences are searching for almost everything in AI search, and queries are nuanced and personalized.

The data isn’t as clear as it used to be. There are no accurate search volumes for AEO search prompts. Yet, it’s critical that search specialists, such as SEO and GEO/AEO professionals, know how to gain visibility in these tools.

The good news? There’s an overlap between traditional keyword research and answer engine optimization keyword research.

This guide covers the core differences between SEO and AEO keyword research, the principles that underpin an effective AEO keyword strategy, the tools that support AEO workflows, and how to apply these approaches in practice.

Table of Contents

• How is keyword research for AEO different from SEO?
• Core Principles for AEO Keyword Research
• Keyword Research for Answer Engine Optimization: Step by Step
• Keyword Research Tools for AEO
• Frequently Asked Questions About Keyword Research for AEO
• Use AEO keyword research and win visibility

How is keyword research for AEO different from SEO?

Traditional keyword research underpins organic visibility, but it’s no longer enough to grab a list of keywords and drop them into content.

Here’s why:

Searchers are no longer typing one-word to five-word keywords into Google. Search is elaborate, nuanced, and personalized. One search can span multiple sentences — even a paragraph or three — with unprecedented detail.

Ofcom’s qualitative generative AI search study supports the idea that people use AI search for longer, more detailed searches. They found that AI search tools are most valued when users ask highly specific, detail-rich questions; the kind of answers that would require multiple queries and significant manual research in traditional search.

In traditional SEO, keyword research has focused on quantitative data like:

• Search volume
• Competitiveness
• Keyword difficulty

Then, users sifted through blue-link listings until they found their answer on a website page. SEO specialists measured success by position on the search engine results pages (SERPs), impressions, and clicks.

In AI keyword research, the focus is mostly on qualitative data like:

• Relevance
• Audience intent
• Problems and solutions

Users expect answers from a range of sources presented within the SERP. Consequently, users don’t click through to a website, so SEO and content pros don’t have the same visibility into how a page ranks. Instead of relying on search volume or clicks as a measure of success, GEO experts consider visibility a metric, qualitative data, such as clicks from AI sources, and, importantly, conversions.

Pro tip: I’m not going into great detail about the reporting side of things in this article, but if you’re interested in that, read this article on SEO reporting. It includes what to put in to demonstrate AI search success.

The table below compares AEO keyword research with traditional SEO keyword research:

HubSpot’s SEO tools within Marketing Hub help bridge this gap by surfacing optimization recommendations based on real content performance, not just keyword targets. This makes it easier to refine pages for clarity, structure, and intent — all critical for improving visibility in AI-generated answers.

Core Principles for AEO Keyword Research

What’s largely different about AEO keyword strategy is that websites don’t always earn visibility in AI tools by ranking the highest in traditional search. When websites create content that is relevant, easily parsed by AI crawlers, and easily synthesized, they earn visibility in AI search. Core principles include intent-first content, entity mapping, cross-engine, answerability, and conversational phrasing.

Intent-First (Including Search and Audience Intent)

Keyword research for AEO starts by understanding why someone is searching, not just what they type. In AI-driven search environments, answer engines prioritize content that clearly and completely resolves intent, especially when questions are complex, nuanced, or contextual (and we know from Ofcom’s research that this is where AI search shines).

Intent-first means that AEO marketers:

• Know their target audience and what they’re looking for. Effective AEO research begins with a deep understanding of an audience’s needs, challenges, and goals. This includes the language they use, the problems they’re trying to solve, and the level of detail they expect in an answer.
• Understand user intent in context. Go beyond static keyword intent labels, such as “Transactional,” “Informational,” or “Commercial.” Consider what prompted the question, what the user likely already knows, and what follow-up questions may come next in the same session. Content that anticipates and addresses this progression is more likely to be selected and synthesized by answer engines.
• Resolve specific problems. AI systems favor content that solves real-world scenarios, not generic definitions. Consider different user contexts and edge cases. If users are searching for a nuanced problem and a brand can explain or resolve it better than anyone else, that site has the best chance of earning visibility.

I’d like to share a real-world example that shows how intent-first AEO and understanding target audiences are key. I searched “Accounting tools for lawyers” in private browsing on Google.

Here are the results:

In the top organic spots, big accounting businesses are present: Xero and Clio. Naturally, the AI Overview also features these brands.

What’s magic for small businesses is that relevancy in AI pays off. Brands such as CosmoLex, PC LawSoft, and LawPay are also featured.

These brands gain visibility through their targeting and relevance. CosmoLex ranked on page two; LawSoft and LawPay weren’t even in the top five organic search results for the search term.

The takeaway: SEO or GEO/AEO specialists must not be deterred by traditional SEO when trying to rank in AEO. If they focus on relevancy, their site can still get visibility, even if it’s not ranking well in traditional SERPs.

Entity Mapping

Entity mapping helps answer engines (and traditional search engines) understand what the content is about and how it relates to the broader knowledge graph.

Here’s an example of how entities are included in content, using this article. When optimizing for “keyword research for AEO,” an entity-based approach doesn’t stop at keywords alone. It connects that topic to related concepts such as:

• AI search
• Large language models (LLMs)
• User intent
• AI visibility measurement
• And more

These are distinct entities that, together, form comprehensive topical knowledge that search engines use to understand, evaluate, and trust content.

The entities associated with the article go beyond the on-page topics listed above. HubSpot itself is a significant entity in the broader landscape of search and AI search. Writing articles like this ties HubSpot (the brand) and its products to the AEO keyword research entity. Later, in the tools section, the article specifically mentions HubSpot’s XFunnel as a keyword research tool for AEO and LLMs.

Pro tip: Entity SEO has been around a long time. To some, it may feel like the new buzzword, but I think it’s important to not get too lost in entity SEO. Most good search and content marketers will naturally weave in the right entities, because common sense goes a long way. For a sophisticated approach to entities, read about structured data and schema markup.

Here are some tips for entity mapping:

• Map core and related entities. Start by identifying the primary topic entity for the content, then expand outward to include related tools, technologies, organizations, roles, and concepts. For example, a topic like “AEO keyword research” naturally connects to entities such as AI search, LLMs, content optimization, or a related product or service.
• Strengthen contextual understanding. Strong entity coverage helps answer engines understand relationships between concepts, not just keyword proximity. When entities are clearly defined and consistently referenced, AI systems are better able to interpret meaning, relevance, and authority.

Cross-Engine

Generally, traditional SEO has had one primary focus: Google. SEO focused on Google because it held the largest search market share worldwide (over 88%). Traditionally, there was Google and a couple of other leaders, Bing or DuckDuckGo, with minimal share compared to Google.

However, in 2026 and beyond, search is changing, and it’s becoming more fragmented. There are Google and traditional SEO, AI Overviews, and multiple AI platforms like ChatGPT, Claude, and Perplexity that are gaining recognition and users.

FirstPageSage reports a growing number of ChatGPT users, with significant growth in Q2 and Q3 of 2025.

And that’s just one search platform.

Here’s the challenge: SEO teams like SEO, AEO, or GEO experts can’t conduct keyword research for every search tool, yet they need to write and optimize content to help it rank across search engines.

Users discover information across a fragmented ecosystem that includes:

• Traditional search
• AI-powered SERP features
• AI search tools like ChatGPT or Perplexity
• Social media

A cross-engine approach ensures the keyword and entity strategy holds up wherever discovery happens.

Search specialists must:

• Research beyond Google alone. While Google still matters significantly, relying solely on Google keyword data creates blind spots. Different answer engines surface different questions, follow-ups, and interpretations of intent. Cross-engine research seeks patterns that appear consistently across AI tools, not just in a single interface.
• Validate visibility across multiple systems. AEO teams can’t measure success in AEO by a single ranking. Recurring mentions, citations, and visibility across multiple answer engines validate it. This makes cross-engine testing and monitoring a core part of the keyword research process, not a downstream activity.
• Account for different algorithms. Some engines, like ChatGPT, summarize information without citations, while others, like AI Overviews, commonly cite sources. Others, like Sigma AI, guide users through follow-up questions.

Pro tip: Although meeting algorithm expectations is important, don’t lose the human you’re writing for in favor of the machine.

Answerability Over Volume

In AEO keyword research, the ability to answer a question that the ideal client is asking matters more than how often the audience searches for the question.

Why?

Because it’s more important to reach the audience, solve their problems, answer their questions, and convert them, rather than chasing vanity metrics like visibility alone. Plus, AEO focuses on answerability: how easily an answer engine can extract, understand, and trust the content.

A simple way to evaluate answerability is through an answerability score, based on three core factors:

• Clarity. Is the answer direct, unambiguous, and easy to understand without additional context? Write a clear, concise explanation as succinctly as possible; elaborate later if needed.
• Extractability. Can the answer be easily pulled from the page? Content structured with clear headings, short paragraphs, lists, and FAQs is far easier for answer engines to extract and reuse.
• Entity coverage. Does the content clearly define and connect the key entities related to the question? Strong entity coverage helps AI systems validate accuracy and relevance against other trusted sources.

Equally important is identifying the questions people actually ask, which takes us almost full circle back to intent and to knowing what audiences search for.

Tools like HubSpot’s AEO Grader can help validate this by analyzing how well content aligns with answer engine expectations. It provides a practical way to assess clarity, structure, and overall AEO readiness.

Conversational Phrasing

Conversational phrasing mirrors how users interact with AI systems. People don’t prompt AI tools with fragments; they use full sentences, comparisons, examples, and scenario-based prompts. Optimizing for this conversational behavior increases the likelihood that content aligns with how answer engines interpret and respond to queries.

HubSpot’s Content Hub supports this by providing real-time SEO suggestions as marketers write, helping teams naturally incorporate conversational phrasing and structure. This makes it easier to create content that aligns with how users actually interact with AI tools.

Keyword Research for Answer Engine Optimization: Step by Step

Keyword research still plays an important role in AEO, but it’s a starting point.

Here are two things to be mindful of:

1. Traditional keyword tools have never been accurate. Search volumes are based on historical data and are rarely accurate. We know this because SEO keyword research tools can show zero clicks, yet in reality, the keywords receive clicks and even conversions.
2. A keyword was always the starting point. An SEO strategy built on keywords alone, without strategy, content clustering, business objectives, or topical depth, was always destined to fail.

AI-driven search has significantly widened the gap between keywords and actual search. As search becomes more conversational, personalized, and context-rich, no single tool can fully capture every phrase or question, or how answer engines interpret them.

That doesn’t mean keyword research is obsolete. It means it needs to expand if AEO is the focus. The next section provides some ways search specialists do keyword research for AEO.

1. Find conversational queries with autocomplete.

Autocomplete features remain one of the most reliable ways to understand how users naturally phrase questions. While volume data isn’t available, autocomplete surfaces real language patterns driven by actual searches.

Here’s how to do AEO keyword research using Google, but know that this method applies to other tools, particularly social media search.

Enter a seed keyword into a search engine, AI tool, or social media search.

I typed in “SEO keyword research for…”

Autocomplete opened as I typed and displayed a list of commonly searched queries.

These queries can all inspire content or audiences.

Use this information to:

• Discover full-sentence suggestions, comparisons, and scenario-based phrasing.
• Capture follow-up-style prompts that suggest deeper or adjacent intent (Sigma AI is good for this).
• Discover audiences that marketing should target.

Here’s what the follow-up section in Sigma AI looks like:

Autocomplete is especially useful for AEO because it reflects how users move beyond short keywords toward long-tail.

In practice, autocomplete provides strong directional insight, but it doesn’t capture the full picture. Speaking with customers helps uncover nuance, context, and problem framing that keyword tools alone can’t reveal.

Pro tip: For autocomplete AEO research, work in incognito so search history doesn’t influence what shows up.

2. Talk to customers and find specific problems your product or service can solve.

Some of the most valuable AEO keyword insights don’t come from tools at all; they come directly from customers. Customer interactions can refine a B2B SEO strategy, especially in niche B2B. Real conversations surface nuance that search data can’t fully capture.

Taking the autocomplete search from above. There are a few audiences there: beginners, YouTubers, and online advertisers.

As an SEO, if I wanted to help these audiences, I’d find customers or focus groups who fit these categories and ask them what they want from me.

This means:

• Reviewing sales calls, support tickets, and onboarding questions to identify recurring problems and language patterns.
• Listening for repeated phrasing, objections, and edge cases that don’t show up in keyword tools.
• Documenting how customers describe their problems in their own words, not how marketers label them.
• Noting the context behind questions, such as budget constraints, experience level, or technical limitations.
• Identifying follow-up questions customers ask after an initial answer, which often map to multi-turn AI search behavior.
• Spotting gaps between what customers ask and what existing content addresses, revealing high-value AEO opportunities.

These insights help transform keyword research from abstract search data into real, answerable problems — the exact type of content AI systems are designed to surface and cite. It’s only when marketing understands audiences and their problems that it can serve them.

Questions to Ask Your Audience (for AEO keyword research):

Understanding the problem

• What problem were you trying to solve when you started looking for a solution?
• What made this problem urgent or important for you?
• What have you already tried, and why didn’t it work?
• What would success look like if this problem were solved?

How they search and ask questions

• How would you describe this problem in your own words?
• What was the first question you asked when you started researching?
• What follow-up questions did you have after getting an initial answer?
• What confused you or felt unclear while searching?

Language and phrasing

• What terms or phrases felt natural to you when searching?
• Were there any words or explanations that felt too technical or unclear?
• How would you ask this question out loud to a colleague or an AI tool?
• Did you search using full questions, comparisons, or examples?

Evaluating existing answers

• What answers did you find helpful, and why?
• What answers felt incomplete or generic?
• What information did you still need after reading existing content?
• Was there anything you wished someone had explained more clearly?

Decision-making and trust

• What made you trust one source over another?
• Did brand reputation influence which answers you believed?
• What proof or detail helped you feel confident in the answer?
• What would have made an answer more useful or actionable?

Context and constraints

• What constraints were you working within (budget, time, tools, experience)?
• Did your role or level of experience affect how you searched?
• How did your needs change as you learned more about the topic?

3. Use LLM query fan-outs to expand ideas.

A query fan-out is the process of taking a single question and expanding it into related follow-up questions, refinements, and edge cases. It mirrors how real users explore a topic in AI-powered search. Large language models (LLMs) are particularly effective at this because they simulate conversational discovery rather than linear keyword expansion.

Query fan outs help marketers understand the conversation space around a topic, not just the initial query.

Instead of focusing on one phrasing, query fan-outs reveal how a question evolves as users seek clarity, comparisons, and context. The system generates multiple smaller searches in parallel — follow-ups, clarifications, and comparisons — then synthesizes the results into one comprehensive answer. This covers not just what the user explicitly asked, but the implicit needs and related aspects behind the original query

This means the AI answer is richer, more complete, and better aligned with what users really want to know, not just the single sentence they typed.

This technique is useful for marketers to try, too.

It means:

• Entering a core question into an LLM.
• Asking it to generate follow-up questions, clarifications, and edge cases.
• Identifying patterns in how problems are reframed or refined.

In practice, LLM fan-outs often reveal intent layers that traditional keyword tools miss, especially comparisons, constraints, and “what if” scenarios. These insights become powerful inputs for AEO-focused content that anticipates how conversations unfold.

4. Map entities and semantic variants.

Mapping entities and semantic search variants helps ensure the content builds contextual understanding that goes beyond the words that appear on the page.

This means:

• Identifying the primary topic entity that the content covers, for example, answer engine optimization, keyword research, or AI search.
• Expanding to related entities, such as concepts, tools, roles, industries, and use cases that naturally connect to the primary topic.
• Mapping semantic variants, including synonyms, alternate phrasing, and commonly used industry terms that describe the same ideas in different ways.
• Defining relationships between entities, rather than listing them in isolation.

When entity mapping is done well, content stops competing on phrasing alone and starts competing on understanding, which is exactly what answer engines are designed to reward.

This entity mapping will also help with traditional SEO. The more a website demonstrates depth of knowledge about what a business does, who it serves, and how it serves them, the better the chance of ranking.

With HubSpot’s Content Hub, marketers can build and optimize content with SEO recommendations baked in, helping ensure strong entity coverage and semantic depth. This supports content that’s easier for answer engines to interpret and trust.

5. Refer to Google Search Console for zero-search insights.

Google Search Console (GSC) is a powerful source for AEO keyword discovery, especially for surfacing niche, intent-rich queries that don’t show up reliably in keyword research tools.

Because GSC reflects real queries that already triggered content, it’s uniquely valuable for identifying how users phrase questions, explore nuance, and search beyond obvious keywords.

This means:

• Analyzing the queries a site already appears for, not just the ones SEO intentionally targeted.
• Identifying long-tail and conversational queries with impressions but limited coverage.
• Spotting niche questions that indicate specific use cases, constraints, or audience segments.

These queries often represent AEO opportunities because they show interest, intent, and real language.

Finding opportunities like this is simple. Use the performance report and review ranking keywords. Tools that identify long-tail keywords lead to specific problems or audiences. For example, “[product] for [problem].”

Combining GSC with Search Analytics for Sheets makes reviewing keywords even easier.

Here’s how I use it:

Open Google Sheets > Open the extension in the menu > Extensions > Search Analytics for Sheets > Open Sidebar.

Once the sidebar is open, customize the request by adding filters and dimensions.

Once done, scroll down and click “Request Data.”

In this example, I filtered the keywords to those containing “SEO.” This is what the output looks like in Google Sheets:

From here, I rely on formulas and conditional formatting to help me work.

Content strategists can pair these insights with HubSpot’s SEO tools to analyze performance and uncover optimization opportunities directly within content workflows. This helps teams turn long-tail, intent-rich queries into structured, answerable content that’s more likely to be surfaced by answer engines.

Pro tip: For niche queries or specific problems, try highlighting keywords containing words like “for,” “with,” “without,” “versus,” or “best.”

Keyword Research Tools for AEO

XFunnel

Source

HubSpot’s XFunnel measures LLM visibility and AI search performance. XFunnel helps marketers understand how brands and content appear in AI-generated answers, not just whether pages rank in traditional search results.

It’s purpose-built for AEO and GEO and shows whether and how AI systems reference and cite a brand. XFunnel’s Research functionality is particularly valuable for shaping AEO keyword strategy.

How XFunnel helps AEO:

• Explore which prompts and questions trigger AI responses on a topic.
• Identify the brands, entities, and sources that LLMs already trust.
• Compare how different queries surface different responses across answer engines.
• Identify surface gaps and areas where entity coverage is thin, topic depth is lacking, or competitors are cited instead.

These insights can improve the keyword research process by guiding decisions on which questions to target, which entities to prioritize, and how to structure content to be more likely to be selected and synthesized by AI.

Semrush

Source

Semrush is a comprehensive SEO platform that has AEO features.

How Semrush helps AEO:

• Seed keyword and topic discovery help marketers identify topics.
• Semrush AIO helps marketers track visibility in AI engines.

Starting price: $199/month, AI features are an extra $99.

What I like: Semrush has been in the SEO space for a long time and has been quick to integrate AI features. I’ve used the AI Visibility Plans, and the recommendations the tool provided were very good.

AlsoAsked

Source

AlsoAsked is a question-based search tool that visualizes how people ask follow-up questions around a topic.

How AlsoAsked helps AEO keyword research:

• Surface real question chains and follow-ups, which mirror how users interact with AI search and multi-turn conversations.
• Helps marketers understand question depth and progression, rather than isolated keywords.

Starting price: Free, limited usage; then $12/month.

What I like: AlsoAsked is excellent for uncovering how questions naturally evolve. It’s easy to use and can inspire content strategy.

AnswerThePublic

Source

AnswerThePublic is a search listening tool that aggregates autocomplete data from search engines, social platforms, and AI tools to reveal how people actually phrase queries. It’s especially useful for AEO because it reflects real, conversational inputs rather than abstract keyword variations.

How AnswerThePublic helps AEO keyword research:

• Surfaces real, conversational queries (most important for AEO). Pulls autocomplete data from platforms like Google, YouTube, and AI tools, giving marketers and SEOs the exact natural-language questions users ask — ideal for optimizing content for AI-generated answers.
• Maps intent through structured question groupings. Organizes queries into categories like questions, comparisons, and prepositions, helping marketers structure content in formats that LLMs can easily parse and synthesize.
• Identifies emerging questions with search listening. Tracks new and evolving queries over time through alerts, helping marketers target fresh topics before they become saturated in search or AI responses.

Starting price: Free (limited searches); paid plans start around $20/month or ~$13/month billed annually.

What I like: AnswerThePublic stands out for its ability to turn raw autocomplete data into structured, intent-driven question sets. It’s one of the fastest ways to translate a single topic into AEO-ready content angles that mirror how users actually interact with AI systems.

Frequently Asked Questions About Keyword Research for AEO

Is there a single keyword tool for AEO?

There isn’t a single keyword tool for AEO, and the available tools don’t work in the same way as SEO keyword research tools. The tools don’t expose consistent volume, rankings, or competitiveness data, so AEO keyword research requires a tool stack and some in-depth manual research to enhance what the tools surface.

How often should I refresh AEO content?

The refresh cadence for AEO content depends on the topic. The key is to keep content fresh, factually accurate, and up to date, especially for competitive or fast-moving topics.

AI answers evolve quickly as new sources are indexed and cited.

Which schema types matter most for AEO?

FAQPage, HowTo, Article, and Product schema matter for AEO because they help define content and provide context. These schema types make it explicit what a page is about, which questions it answers, and how concepts relate to one another. These are all the signals that answer engines use to validate their understanding.

The Product, Person, and Organization schemas are also helpful because they connect entities. These schema types tell answer engines who, what, and which brand the content refers to, or who wrote it.

How do I prove AEO impact to leadership?

The most important metrics that demonstrate AEO’s impact are conversion rate and revenue impact. These can be tracked in Google Analytics by analyzing how many conversions or how much revenue was generated by traffic from AI sources.

Once business impact is established, layer in visibility signals to show how those results are happening. AI mentions, citations, branded references, and presence in answer engines help validate that AEO efforts are influencing discovery, even when users don’t click immediately.

​​HubSpot’s AEO Grader can also support this by giving teams a benchmark for how well their content is optimized for AI visibility. This helps connect optimization efforts to measurable improvements in answer engine performance.

What if LLMs cite competitors instead of us?

Competitors may be cited for content that is clearer, more comprehensive, or better aligned with user intent and entity relationships.

Treat competitor citations as research inputs. Analyze what they’re being cited for, which entities they cover, and how they structure answers. Then improve the content by addressing gaps, expanding depth, and strengthening clarity. Over time, answer engines often adjust citations as higher-quality or more relevant sources emerge.

Use AEO keyword research and win visibility.

Keyword research for AEO isn’t about abandoning SEO fundamentals — it’s about evolving them. As AI-driven search becomes more nuanced, conversational, and fragmented across platforms, effective AEO keyword research shifts focus from volume and rankings to intent, entities, and answerability.

Platforms like HubSpot’s XFunnel bridge that gap by showing how brands and content appear in AI-generated answers, and which entities and questions are driving visibility. Used alongside traditional research methods, this makes AEO keyword strategy more measurable and more actionable.

HubSpot’s SEO tools can support this shift by helping teams continuously optimize content based on performance insights and on-page recommendations. This makes it easier to align content with intent, improve answerability, and increase the likelihood of being surfaced in AI-generated responses.

From my own experience, the teams that succeed with AEO are the ones that stop chasing keywords in isolation and start deeply understanding their audiences and the problems they’re trying to solve. When marketers and SEO specialists focus on relevance, clarity, and intent, earning visibility in answer engines becomes far more achievable.