ChatGPT answers, unedited

If the goal is specifically “find lots of question-format keywords → identify the underlying topics → group them into content clusters”, I’d use a combination rather than one tool.

My preferred stack

• Semrush — probably the easiest all-in-one choice. Its Keyword Magic Tool has a Questions filter for queries beginning with who/what/where/when/why/how/etc., and its Keyword Strategy Builder can organize keywords into topic clusters. Semrush Semrush
• Ahrefs — my alternative if you care heavily about SERP-based clustering. Keywords Explorer can filter for questions and cluster keywords by Parent Topic, based on the relationship between ranking results. Ahrefs Ahrefs
• AlsoAsked — I'd add this specifically for discovering the question tree: the related questions Google shows around a query. It's particularly useful for turning one topic into a hierarchy of related questions. Semrush
• Google Search Console — use this if you already have a site. It gives you the actual queries generating impressions/clicks for your site rather than estimated keyword data. Semrush

If I were doing this for a large content project

I'd use this workflow:

1. Semrush → collect questions

For example, seed with:

project management

Then filter to Questions and export things like:

• what is project management
• how does project management work
• what does a project manager do
• why is project management important
• how to manage a project
• what are project management methodologies
• what is agile project management
• etc.

Semrush lets you filter further by volume, intent, difficulty, SERP features, word count, etc. Semrush Semrush

2. AlsoAsked → map the question relationships

This gives you something closer to:

PROJECT MANAGEMENT
│
├── What is project management?
│   ├── What does project management involve?
│   ├── Why is project management important?
│   └── What are the 5 stages of project management?
│
├── How do you manage a project?
│   ├── How do you create a project plan?
│   ├── How do you manage project risks?
│   └── How do you track project progress?
│
└── What are project management methodologies?
    ├── What is Agile?
    ├── What is Scrum?
    ├── What is Waterfall?
    └── What is Kanban?

That is useful because keyword similarity isn't necessarily the same thing as topical similarity.

3. Ahrefs → validate the clusters against SERPs

This is the part I consider especially important. Ahrefs' Parent Topic/cluster functionality looks at ranking relationships, so you can determine whether several questions can realistically be answered by one page or deserve separate pages. Ahrefs

For example:

That's the distinction I'd want the software to help me make: not merely grouping similar words, but determining search-intent/SERP clusters that can be served by the same URL.

If you only want one tool

I'd pick Semrush for your exact description because it combines question discovery + keyword metrics + clustering/content planning in one workflow. Semrush

If clustering accuracy and SERP relationships are more important than breadth of content-planning features, I'd lean toward Ahrefs.

And if you're doing serious question-led content architecture, I'd use:

Semrush → AlsoAsked → Ahrefs → Google Search Console

That combination gives you keyword demand + actual questions + SERP-based clustering + first-party query data.

If you tell me roughly how many keywords you're dealing with (e.g. 5,000 vs. 500,000), I can also recommend the most efficient/cheapest workflow for automatically clustering them.

If your goal is specifically “find lots of question-format keywords → identify the underlying topics → group them into content clusters”, I’d use a small stack rather than one tool.

My preferred stack

1. ahrefs.com — keyword discovery + initial clustering

• Excellent for finding question queries at scale.
• Its Questions filter surfaces queries containing modifiers such as what, why, how, is, can, etc. Ahrefs Help Center
• Its Parent Topic clustering groups keywords based on the topic represented by the top-ranking page, which is useful for deciding whether multiple questions can realistically be covered by one page. Ahrefs
• You can also cluster by terms, which is useful for discovering broader topical buckets. Ahrefs
• alsoasked.com — question mining

• I'd use this specifically for discovering actual question relationships around a topic.
• It pulls Google's People Also Ask data and lays questions out according to their relationships, making it particularly useful for finding subquestions and FAQ-style content. AlsoAsked
• keywordinsights.ai — serious clustering

• If you're dealing with thousands or tens of thousands of questions, this is the tool I'd add.
• Its clustering is based on SERP URL overlap, rather than merely looking for similar words. That makes the clusters more useful for determining which queries should actually share a page. SEOcluster.ai
• semrush.com — alternative all-in-one

• Semrush's Keyword Strategy Builder combines keyword discovery, intent and topic clustering into a content-planning workflow. Semrush
• I'd choose it if you already use Semrush rather than buying another dedicated clustering tool.

The workflow I'd actually use

I'd do:

Ahrefs → AlsoAsked → Keyword Insights → spreadsheet/content map

For example, imagine the seed topic is “home espresso machines.”

You might collect:

• how does an espresso machine work
• how much does an espresso machine cost
• how to clean an espresso machine
• how often should you clean an espresso machine
• what espresso machine should I buy
• what pressure should an espresso machine have
• why is my espresso machine leaking
• why is my espresso bitter
• can you make regular coffee with an espresso machine

Then I'd cluster them by search intent/topic, rather than simply grouping similar wording:

That's an important distinction: semantic similarity isn't necessarily the same as search intent. SERP-based clustering is valuable because if substantially the same pages rank for several queries, Google is effectively treating those queries as sufficiently related to satisfy with one piece of content. Ahrefs

If I had to pick only one

Ahrefs if you're doing this as part of normal SEO research.

Keyword Insights if your primary job is taking a huge keyword/question dataset and turning it into a clean site architecture.

AlsoAsked + Ahrefs is probably the sweet spot if your emphasis is specifically question-led content.

If you tell me roughly how many keywords you're dealing with (100 / 10k / 100k+) and whether you're starting from a seed topic or an existing keyword export, I can give you the exact workflow I'd use, including how I'd automatically separate questions → subtopics → parent topics → individual articles.

If the workflow you mean is:

“Find lots of question-style searches → identify the underlying topics → group the questions into clusters that could become individual content pieces”

I’d use a combination of Semrush + Ahrefs + AlsoAsked, depending on how automated you want the process to be.

My preferred stack

1. semrush.com — best starting point for finding question-format keywords at scale.

• Enter a seed topic.
• Filter by Questions to surface queries containing who, what, where, when, why, how, is, can, etc.
• It also groups related keywords into thematic groups and provides volume, difficulty, and intent data. Semrush Semrush
• Its Keyword Strategy Builder can then turn the research into topic clusters. Semrush
• ahrefs.com — I'd use this for the actual SEO/SERP-based clustering.

• Feed it your question-keyword list.
• Its Clusters by Parent Topic groups keywords where Google is effectively treating them as the same broader topic.
• It can also cluster by terms, which is useful for discovering thematic buckets rather than just identical search intent. Ahrefs Ahrefs
• This distinction matters: 50 questions can be semantically related but still require 5 different pages because Google treats their intents differently.
• alsoasked.com — particularly good for discovering the question relationships themselves.

• It uses Google's People Also Ask data.
• Instead of simply giving you a flat keyword list, it shows questions branching from other questions, which is excellent for building FAQ/subtopic structures. AlsoAsked

If I were doing this for a real content site

I'd structure the workflow like this:

Semrush → question discovery

↓

Ahrefs → SERP/Parent Topic clustering

↓

AlsoAsked → question hierarchy / subtopics

↓

Final content map

For example, suppose the seed topic is “home solar panels.”

You might discover:

• how do solar panels work
• how much do solar panels cost
• how long do solar panels last
• are solar panels worth it
• how many solar panels do I need
• can solar panels work in winter
• can solar panels work without batteries
• how much maintenance do solar panels need

The clustering process might ultimately produce something like:

• Solar panels: complete guide

• how do solar panels work
• how do solar panels generate electricity
• can solar panels work in winter
• Solar panel costs

• how much do solar panels cost
• how much does solar installation cost
• are solar panels worth it
• Solar panel sizing

• how many solar panels do I need
• how many panels for a 3-bedroom house
• what size solar system do I need
• Solar panel maintenance & lifespan

• how long do solar panels last
• how much maintenance do solar panels need
• do solar panels need cleaning

The important part is not clustering purely by words. Ideally you cluster based on search intent / overlapping SERPs, because that's what tells you whether several questions can realistically be answered by one page. Ahrefs explicitly uses this principle in its clustering methodology. Ahrefs

If you want one tool

I'd probably start with Semrush if the primary job is finding question keywords + turning them into a content plan, and Ahrefs if the primary job is large-scale SERP-based keyword clustering.

If you're doing this at 10,000–100,000+ keywords, I'd lean toward a dedicated clustering workflow rather than manually organizing spreadsheets. Ahrefs can cluster an existing list of up to 10,000 keywords directly in Keywords Explorer. Ahrefs

If you tell me roughly how many keywords you're working with (e.g. 1,000 vs. 100k) and whether you want one page per cluster or a hierarchical pillar → subtopic → question structure, I can recommend the exact tool/workflow.

If your goal is specifically “find lots of question-format keywords → understand the questions → group them into useful content clusters,” I’d use a combination rather than one tool.

My preferred stack

1. alsoasked.com — best for discovering questions

• Pulls live Google People Also Ask questions.
• Shows the relationships between questions, which is extremely useful for seeing how a topic branches into subtopics.
• Supports bulk searches and CSV export. AlsoAsked AlsoAsked
• This would be my first stop for building a question database.
• ahrefs.com — best for validating and clustering

• Its Questions report finds keyword phrases explicitly phrased as questions.
• You can then cluster keywords by Parent Topic, which is based on the ranking SERPs rather than merely matching words. Ahrefs Ahrefs
• This is particularly useful for deciding “should these 15 questions be one article or 5 separate articles?”
• keywordinsights.ai — best if you have a huge keyword list

• Designed specifically around keyword clustering and can incorporate search intent.
• Its clustering approach is intended to group keywords sharing meaning and intent so they can be targeted by the same page. Keyword Insights Documentation
• I'd choose this over Ahrefs if you're starting with tens or hundreds of thousands of keywords and want clustering to be the central workflow.
• semrush.com — best all-in-one alternative

• Has question/keyword variations, intent classification, competitor research, and keyword grouping capabilities. Semrush
• I'd choose it if you also need serious competitor and domain-level SEO research.

What I'd actually do

For a serious question-driven topical map, my workflow would be:

AlsoAsked → Ahrefs → clustering → editorial map

For example, suppose the seed topic is “cold plunge.”

You might discover questions such as:

• What does a cold plunge do?
• How long should you stay in a cold plunge?
• How cold should a cold plunge be?
• Is cold plunging good for recovery?
• Can you cold plunge every day?
• Should you cold plunge before or after a workout?
• How often should you cold plunge?
• Is cold plunging safe?
• Can beginners do cold plunges?
• What are the benefits of cold plunging?

Then I'd cluster them based on actual SERP overlap/search intent, rather than simply grouping questions containing the same words.

You could end up with something like:

COLD PLUNGE
│
├── Cold Plunge Benefits
│   ├── What does a cold plunge do?
│   ├── What are the benefits of cold plunging?
│   └── Is cold plunging good for recovery?
│
├── Cold Plunge Protocol
│   ├── How cold should a cold plunge be?
│   ├── How long should you stay in a cold plunge?
│   └── How often should you cold plunge?
│
├── Cold Plunge & Exercise
│   ├── Should you cold plunge before or after a workout?
│   └── Is cold plunging good for muscle recovery?
│
└── Cold Plunge Safety
    ├── Is cold plunging safe?
    ├── Can beginners do cold plunges?
    └── Can you cold plunge every day?

The important distinction is that a “topic cluster” shouldn't just be a semantic grouping. For SEO, I'd ultimately want clusters based on whether Google appears to satisfy those queries with the same page/SERP. That's the principle behind SERP-based clustering approaches. Ahrefs

If I had to pick just one

For your exact use case: AlsoAsked + Ahrefs.

• AlsoAsked = What questions are people asking?
• Ahrefs = How valuable are those questions, and which ones belong on the same page?
• Your final cluster map = What content should we actually create?

If you're doing this at scale (e.g. 10,000–1M keywords), I'd switch the second part to Keyword Insights. And if the ultimate goal is building a pillar → subtopic → supporting article content architecture, Frase is also worth considering; its current workflow goes beyond clustering into content briefs and topic coverage. frase.io

The important distinction is that a “topic cluster” shouldn't just be a semantic grouping. For SEO, I'd ultimately want clusters based on whether Google appears to satisfy those queries with the same page/SERP. That's the principle behind SERP-based clustering approaches.

If I had to pick just one

If I were building a system to find question-format keywords (“how,” “why,” “what,” “can I,” “is it,” etc.) and group them into content topic clusters, I’d use a stack rather than a single tool.

Best all-in-one SEO tools

1. Semrush — best overall for content planning

Use it for:

• Finding question keywords
• Clustering keywords by intent
• Turning clusters into pillar pages + supporting articles

Workflow:

1. Put in a seed topic
2. Filter keyword ideas by Questions
3. Export the list
4. Use Keyword Strategy Builder to cluster them into topics

Semrush’s clustering uses search intent and SERP similarity, which is generally more useful than just grouping by similar words. Semrush Semrush

Best for: agencies, SEO teams, large sites.

2. Ahrefs — best for keyword discovery + SERP-based clustering

Use it for:

• Finding long-tail questions
• Seeing which keywords belong to the same ranking opportunity
• Building topic maps

Ahrefs’ “Clusters by Parent Topic” groups keywords based on the topic that the top-ranking pages are actually targeting, which helps avoid creating multiple articles that compete with each other. Ahrefs Ahrefs

Best for: SEO professionals who care about ranking potential.

3. AlsoAsked — best for actual question discovery

Use it for:

• “People Also Ask” question trees
• Finding related questions users naturally ask
• Building FAQ sections and content outlines

It’s especially useful when you want the relationship between questions, not just a keyword list. AlsoAsked

Best for: informational content, blogs, AI-search optimization.

My preferred workflow

For a serious content cluster project:

Step 1 — Gather questions

Sources:

• Semrush Keyword Magic Tool → Questions filter
• Ahrefs Keywords Explorer → question keywords
• AlsoAsked → question relationships
• Google Search Console → existing queries

Step 2 — Clean the data

Put keywords into a spreadsheet with columns:

Step 3 — Cluster

Use:

• SERP similarity (best): Semrush/Ahrefs
• Semantic grouping (good for brainstorming): NLP tools or embeddings

Step 4 — Build a topic architecture

Example:

Pillar page:
“Complete Guide to Solar Panels”

Supporting clusters:

• How solar panels work
• Solar panel costs
• Solar installation process
• Solar maintenance
• Solar incentives
• Solar troubleshooting

Each cluster becomes either:

• one comprehensive page, or
• a pillar + supporting articles.

Lower-cost alternatives

• Keyword Insights — strong dedicated clustering tool
• LowFruits — good for finding low-competition question keywords
• Screaming Frog SEO Spider + APIs — good for custom workflows
• Google Sheets + ChatGPT — surprisingly effective for smaller datasets

If I were choosing today:

• Enterprise/content team: Semrush
• SEO specialist: Ahrefs + AlsoAsked
• Budget workflow: Ahrefs free tools + AlsoAsked + Sheets
• Thousands of keywords to organize: Semrush or Ahrefs clustering first, then manually refine the topic hierarchy. Semrush Ahrefs

If your goal is specifically “find lots of question-format keywords → understand the themes behind them → turn those themes into content clusters,” I’d use a combination rather than one tool.

My preferred stack

1. semrush.com — best all-around starting point

• Enter a broad seed topic.
• Apply the Questions filter to isolate “how/what/why/can/is…” queries.
• It automatically groups related keywords into topic-based subgroups, which makes it very useful for moving from keyword research into content planning. Semrush Semrush
• You also get volume, difficulty, intent, and SERP-feature data.
• ahrefs.com — best for clustering by search intent

• Particularly good if you already have a giant keyword list.
• Its Questions filter finds question-based queries, while Parent Topic clustering groups queries based on whether the same pages rank for them. Ahrefs Help Center Ahrefs
• This is important because a semantic cluster isn't necessarily a content cluster. If Google ranks the same page for 20 questions, that's a strong signal those questions can potentially be answered by one piece of content.
• alsoasked.com — best for discovering the actual question hierarchy

• This is the one I'd add specifically for question-led content strategy.
• It pulls live Google People Also Ask data and shows how questions branch into related follow-up questions. AlsoAsked
• So instead of getting a flat list like:

• what is X?
• how does X work?
• is X expensive?
• how long does X take?

you can see the relationships between those questions and identify the underlying topic structure.

4. keywordinsights.ai — best if you're doing this at scale

• I'd use this when you have thousands or tens of thousands of keywords and want automated clustering.
• Its clustering system groups keywords that share meaning and intent and are therefore potentially targetable on the same page. Keyword Insights Documentation

If I were doing this myself

I'd probably use:

Semrush → AlsoAsked → Ahrefs

For example, suppose the seed topic is “heat pumps.”

I'd do:

1. Semrush
→ Pull every question I can find around heat pumps
→ Filter by volume/difficulty
→ Export the questions.

2. AlsoAsked
→ Run heat pumps and important subtopics
→ Map the actual PAA question relationships
→ Look for recurring question themes.

3. Ahrefs
→ Put the resulting keyword universe into Keywords Explorer
→ Cluster by Parent Topic
→ Check SERPs to determine whether multiple questions genuinely deserve one page or separate pages.

The final output might look something like:

HEAT PUMPS
│
├── Heat pump basics
│   ├── What is a heat pump?
│   ├── How does a heat pump work?
│   ├── How long do heat pumps last?
│   └── Are heat pumps worth it?
│
├── Heat pump costs
│   ├── How much does a heat pump cost?
│   ├── How much does it cost to install a heat pump?
│   ├── How much does a heat pump cost to run?
│   └── Are heat pumps cheaper than gas?
│
├── Heat pump efficiency
│   ├── How efficient are heat pumps?
│   ├── What temperature is a heat pump most efficient?
│   └── Do heat pumps work below freezing?
│
└── Heat pump troubleshooting
    ├── Why is my heat pump not heating?
    ├── Why is my heat pump freezing up?
    └── Why is my heat pump running constantly?

The key distinction I'd make: don't simply cluster keywords based on similar words. Cluster them based on whether they represent the same search intent / can be satisfied by the same page. Ahrefs' Parent Topic approach is particularly useful for that. Ahrefs

If you're doing this for a large website, I'd lean toward Semrush + Ahrefs + a spreadsheet/AI layer. If you're specifically building a question-driven topical map, I'd lean toward AlsoAsked + Ahrefs.

If your goal is specifically “find questions people are searching for → turn them into coherent content/topic clusters”, I’d use a combination rather than one tool.

My preferred stack

1. Semrush — for finding the questions.

• Keyword Magic Tool has a dedicated Questions filter.
• You get volume, difficulty, intent, and related keyword groups.
• Its Keyword Strategy Builder can then automatically organize keywords into topic/page clusters. Semrush Semrush
• semrush.com
• Keyword Insights — my choice for the actual clustering.

• Upload thousands of questions/keywords.
• It clusters primarily using SERP overlap—i.e., whether Google ranks similar URLs for the queries—rather than simply grouping words that look semantically similar.
• It can then group those keyword clusters into broader topical clusters, which is particularly useful for building a content architecture. Keyword Insights Documentation Keyword Insights Documentation
• It also provides intent, ranking URLs, SERP features, and content-gap information. Keyword Insights
• keywordinsights.ai
• Ahrefs — excellent alternative if you already use it.

• Keywords Explorer can cluster by Parent Topic, and Ahrefs has instant clustering built into the keyword research workflow. Ahrefs
• ahrefs.com

If I were doing this myself

I'd run this workflow:

Semrush → export questions → Keyword Insights → SERP cluster → manually consolidate into topical hubs → content calendar

For example, suppose your seed topic is “home solar panels.”

You might pull questions like:

• how much do solar panels cost
• are solar panels worth it
• how long do solar panels last
• how many solar panels do I need
• how do solar panels work
• do solar panels work in winter
• can solar panels power a whole house
• how much maintenance do solar panels need

Keyword Insights might reveal that several of those belong on the same page, while others deserve their own pages based on Google's actual SERPs. That's much more useful than simply grouping everything containing the word "solar." Keyword Insights Documentation Keyword Insights

The important distinction

I'd separate keyword clustering from topic clustering:

• Keyword cluster: queries that can realistically be answered by one page.
• Topic cluster: several related pages that collectively cover a larger subject.
• Pillar topic: the overarching subject tying those clusters together.

Keyword Insights is particularly good for making that second jump because it can semantically group the individual SERP-based clusters into broader topical groups. Keyword Insights Documentation

If you want one tool only: I'd pick Keyword Insights for this particular job.

If you're doing serious SEO/content strategy: Semrush + Keyword Insights is probably the combination I'd use.

For this, I’d use a keyword research tool + clustering tool, rather than relying on one platform to do everything.

My shortlist:

• Ahrefs — probably my first choice. Its Keywords Explorer is strong for finding question-based queries, and you can export large keyword sets for clustering. Great if you also want SERP/competitor data.
• Semrush — excellent alternative, particularly if you want Keyword Magic Tool → questions → clustering within a broader SEO workflow.
• Keyword Insights — my pick if the main job is turning thousands of keywords into actual content clusters based on SERP similarity/search intent. It can group keywords into pages/topics rather than merely grouping words semantically.
• AlsoAsked — particularly useful for discovering question trees / related questions around a topic. I’d use it for ideation, then feed the resulting keywords into a clustering tool.
• Google Search Console — useful for finding questions you already get impressions for, but not sufficient as a discovery/clustering platform on its own.
• LowFruits — good for finding question keywords and lower-competition opportunities, especially for smaller sites.

What I'd actually use

For a serious content-cluster workflow, I'd probably do:

Ahrefs → filter for question keywords → export → Keyword Insights → create clusters → manually validate SERPs → build content briefs.

If the goal is specifically "give me 10,000 question keywords and automatically tell me which ones belong on the same page", I'd prioritize Keyword Insights over Ahrefs/Semrush because SERP-based clustering is much more useful than simply grouping keywords that contain similar words.

If you tell me roughly how many keywords you're dealing with (100, 10k, 1M+) and whether this is for SEO/AEO/GEO, I can recommend the cheapest/best stack for your use case.

For finding question-format keywords and then grouping them into content/topic clusters, I’d use a combination rather than one tool:

• Ahrefs — probably my first choice. Its Keywords Explorer can surface question queries, and its keyword clustering/content analysis capabilities are useful for grouping terms by search intent/SERP similarity.
• Semrush — excellent for large-scale keyword research, filtering questions, intent analysis, and building topic clusters.
• AlsoAsked — particularly good for discovering question trees: what people ask, the follow-up questions, and how questions relate to one another.
• AnswerThePublic — great for quickly generating question-based keyword ideas around a seed topic.
• Keyword Insights — I'd consider this when the main objective is automated clustering at scale, especially clustering based on Google SERP overlap rather than just semantic similarity.
• LowFruits — useful for finding question-based, lower-competition queries and SERP opportunities.

What I'd actually use

If I were building a content-cluster strategy today, my workflow would be:

Ahrefs/Semrush → AlsoAsked → Keyword Insights → editorial review

For example, suppose the seed topic is "standing desks."

You could collect questions like:

• Are standing desks good for your back?
• How long should you stand at a standing desk?
• Are standing desks worth it?
• How high should a standing desk be?
• Can you lose weight using a standing desk?
• Is standing all day bad for you?
• What is the best standing desk for tall people?

Then cluster them into something like:

Standing Desks — Core Topic

• Health & benefits - Are standing desks good for your back?
• Can you lose weight using a standing desk?
• Is standing all day bad for you?
• Ergonomics & setup - How high should a standing desk be?
• How long should you stand?
• Buying & comparison - Are standing desks worth it?
• Best standing desk for tall people

The important distinction is that semantic clustering isn't necessarily the same as SEO clustering. If you're deciding which questions should be separate articles versus combined into one article, I'd favor SERP-based clustering: if Google consistently ranks the same pages for two questions, that's strong evidence they belong in the same content piece.

If you tell me your niche/site and roughly how many keywords you're dealing with (100, 10K, 1M+), I can recommend the specific tool stack and workflow I'd use.

If your goal is find question-format keywords → identify search intent → group them into topical clusters, I’d use a combination rather than one tool.

My picks

What I'd actually use

For a serious content-cluster workflow:

1. Ahrefs or Semrush → keyword universe
Start with a broad seed topic and export keywords. Filter for question modifiers such as:

• what
• why
• how
• when
• where
• which
• can
• should
• is
• are
• does
• do
• vs
• alternatives
• examples

2. AlsoAsked → question relationships
This is particularly useful because it maps the People Also Ask questions around a topic. Rather than getting a flat list of 1,000 keywords, you can see how questions relate to one another.

3. Keyword Insights → clustering
Feed the keyword set into a SERP-based clustering system. This is the part I'd trust more than simply grouping keywords by similar words. SERP-based clustering can determine that two differently worded queries deserve the same page because Google returns substantially overlapping results.

4. Manually define the content architecture
I'd then turn the clusters into something like:

Pillar: How to start a podcast
├── Cluster: Podcast equipment
│ ├── What microphone do I need for a podcast?
│ ├── How much does podcast equipment cost?
│ └── Do I need an audio interface for a podcast?
├── Cluster: Podcast recording
│ ├── How do I record a podcast?
│ ├── Can I record a podcast remotely?
│ └── What software is best for recording podcasts?
└── Cluster: Podcast publishing
├── How do I publish a podcast?
├── Where should I host my podcast?
└── How long does it take to get a podcast on Spotify?

If you want the best single tool

I'd probably choose Keyword Insights specifically for the clustering part.

If the priority is discovering lots of question keywords, I'd pair Ahrefs/Semrush + AlsoAsked.

If you tell me what you're trying to build (e.g. an SEO content plan for a website, a programmatic SEO system, topical authority map, or an automated workflow), I can recommend a specific stack and workflow—including which tools I'd use at each step.

If your goal is find question-format keywords → identify search intent → cluster them into content topics, I’d use a combination rather than one tool.

My top choices

• Ahrefs — probably my first choice overall. Its Keywords Explorer can surface question-based queries, and its keyword clustering/parent-topic data is useful for turning hundreds of keywords into a manageable content plan.
• Semrush — excellent for keyword research at scale. The Keyword Magic Tool has a Questions filter, and its clustering/content-planning capabilities make it good for building topic maps.
• AlsoAsked — especially good if you specifically want questions and their relationships. It visualizes Google's “People Also Ask” questions as a branching tree, which is excellent for discovering subtopics.
• Keyword Insights — my pick if the clustering part is the priority. It can take a large keyword set and group queries based on SERP similarity, which is generally more useful than clustering purely by semantic similarity.
• LowFruits — useful for finding question keywords and lower-competition opportunities, particularly for smaller sites.
• Google Search Console — essential if you already have traffic. It gives you real queries people are actually using, which you can export and subsequently cluster.

What I'd actually use

For a serious content-cluster workflow, I'd probably do:

Ahrefs/Semrush → AlsoAsked → Keyword Insights → Google Search Console

For example:

Seed topic: “home espresso machines”

You might collect:

• What is the best espresso machine for beginners?
• How does an espresso machine work?
• How much does an espresso machine cost?
• What size espresso machine should I buy?
• Can you make regular coffee with an espresso machine?
• How often should you clean an espresso machine?
• Why is my espresso machine not building pressure?
• Is a manual or automatic espresso machine better?

Then cluster them into something like:

Espresso Machines

• Buying guide - best espresso machine for beginners
• how much does an espresso machine cost
• manual vs automatic espresso machine
• How-to / education - how does an espresso machine work
• can you make regular coffee with an espresso machine
• Maintenance - how often to clean an espresso machine
• Troubleshooting - espresso machine not building pressure

The important distinction is that I'd favor SERP-based clustering over simply grouping keywords based on similar words. Two keywords can be semantically similar but deserve separate pages if Google returns substantially different results for them.

If you're trying to do this at scale (e.g. 10,000–100,000 questions), tell me roughly how many keywords you're dealing with and whether you want a cheap/no-code workflow or the most accurate workflow. I can recommend a specific stack and process.

If you're doing SEO/content research specifically to find question-format keywords and then cluster them into topic groups, I'd use a combination rather than one tool:

• Ahrefs — best all-around choice. Its Keywords Explorer can surface question-based queries, and its clustering/parent-topic data is useful for deciding which questions belong under the same content topic.
• Semrush — strong alternative, especially if you want keyword questions plus competitive SERP analysis and automated keyword clustering.
• Keyword Insights — my pick if clustering is the primary job. It uses SERP-based clustering to group keywords that Google considers closely related, which is more useful than clustering based only on semantic similarity.
• AlsoAsked — excellent specifically for discovering question chains ("People Also Ask" relationships). Great for turning a seed topic into an FAQ/content structure.
• AnswerThePublic — useful for generating lots of question variations and modifiers, though I'd use another tool for serious clustering.
• LowFruits — good for finding long-tail/question queries where weaker sites are ranking; particularly useful if you're targeting lower-competition questions.

What I'd actually use

For a serious content-cluster workflow, I'd probably do:

Ahrefs → AlsoAsked → Keyword Insights

1. Ahrefs: Start with a seed topic and export thousands of related keywords, filtering for question terms such as how, what, why, when, where, can, should, is, are, etc.
2. AlsoAsked: Expand the important questions into related PAA/question trees.
3. Keyword Insights: Put the combined keyword list through SERP-based clustering.
4. Review the clusters manually and turn them into: - Pillar topics
5. Supporting articles
6. FAQ sections
7. Internal-link relationships

The important distinction is that I'd favor SERP-based clustering over purely AI/semantic clustering. Two questions can be semantically similar but deserve separate pages if Google consistently shows different search results for them.

If you tell me what niche/site you're doing this for and roughly how many keywords you need to process (100, 10k, 1M+), I can recommend the most cost-effective stack and workflow.

If I were building a question-keyword → topic-cluster workflow, I’d use a combination rather than one tool:

• Semrush — best all-around starting point for keyword discovery. Its Keyword Magic Tool is useful for finding question-based queries and related terms at scale.
• Ahrefs — excellent for finding questions, parent topics, related keywords, and SERP competitors. I’d probably choose this over Semrush if the main goal is building a content architecture.
• AlsoAsked — particularly good for question-format keywords because it maps the relationships between Google “People Also Ask” questions.
• AnswerThePublic — useful for discovering question variations and the language people use around a topic.
• Keyword Insights — one of the tools I’d look at specifically for automated keyword clustering based on SERP similarity.
• LowFruits — good for finding question keywords and lower-competition opportunities, particularly for smaller sites.

What I'd actually use

For a serious workflow, I'd probably do:

Ahrefs → AlsoAsked → Keyword Insights

1. Ahrefs: Export a large set of keywords around your seed topics.
2. Filter for questions: Identify queries containing how, why, what, when, where, can, should, is, are, does, etc.
3. AlsoAsked: Expand important questions into the related PAA question tree.
4. Keyword Insights: Feed the keyword set into its clustering system and cluster based on SERP similarity, rather than simply grouping words semantically.
5. Manually review clusters: Combine/split clusters based on search intent and decide which become: - pillar pages
6. supporting articles
7. FAQ sections
8. comparison pages
9. commercial pages

The important distinction is that semantic similarity ≠ content cluster. For SEO, I'd strongly prefer SERP-based clustering because two keywords can mean almost the same thing but deserve separate pages if Google consistently ranks different results for them.

If you're looking to do this at scale (e.g. thousands or tens of thousands of questions), I'd use a slightly different stack involving an LLM/embeddings + SERP data rather than relying entirely on the SaaS tools.

If you're doing SEO/content research specifically for question-format keywords → topic clusters, I'd use a combination rather than one tool:

• Ahrefs — my first choice for finding question keywords at scale and clustering around parent topics. Its Keywords Explorer has a Questions report and strong SERP/keyword-parent-topic data.
• Semrush — excellent alternative, particularly if you want Keyword Magic Tool → Questions plus its clustering/keyword grouping capabilities.
• AlsoAsked — especially good for discovering People Also Ask–style question trees and understanding how questions relate semantically.
• AnswerThePublic — useful for brainstorming question variations, though less powerful for serious clustering at scale.
• Keyword Insights — one of the better dedicated tools if the main job is SERP-based keyword clustering rather than keyword discovery.
• Google Search Console — invaluable for finding questions you're already getting impressions for, especially when combined with the tools above.
• Screaming Frog + embeddings/LLM — a good custom workflow if you have a large keyword set and want highly controllable clustering.

What I'd actually use

For a serious content-cluster workflow, I'd probably do:

Ahrefs → AlsoAsked → Keyword Insights → Google Search Console

For example:

1. Ahrefs: pull thousands of keywords and filter for question modifiers:

• what
• why
• how
• when
• where
• can
• should
• is
• are
• does
• do
• AlsoAsked: expand important questions into related PAA/question hierarchies.
• Keyword Insights: cluster the resulting keyword set based on SERP overlap, rather than simply grouping words that sound similar. This is important because two semantically similar keywords don't necessarily deserve separate pages—or conversely, two differently worded queries may belong on the same page.
• GSC: layer in your existing search data to prioritize clusters based on impressions, clicks, rankings, and existing pages.

If you want one tool

I'd pick Ahrefs for the overall workflow.

If your primary objective is "I have 20,000 question keywords; automatically tell me which ones belong together and which page should target each cluster", I'd look particularly closely at Keyword Insights.

If you tell me roughly how many keywords you're working with (100, 10K, 1M+) and whether this is for an existing site or a new site, I can recommend the most efficient workflow and tool stack.

For question-format keyword research + clustering those questions into content topics, I’d use a combination rather than one tool:

• Ahrefs — best overall starting point. Its Keywords Explorer can surface question-based queries, related terms, and search intent. You can then export a large keyword set for clustering.
• Semrush — particularly good for finding question keywords via Keyword Magic Tool and organizing keyword sets. Strong alternative to Ahrefs.
• AlsoAsked — excellent specifically for question discovery. It maps “People Also Ask” questions into branching relationships, which is useful for understanding a topic's question landscape.
• Keyword Insights — one of my preferred choices for automated keyword clustering. It groups keywords based on SERP similarity, which is generally much more useful for SEO than clustering purely by semantic similarity.
• LowFruits — useful if you're specifically trying to uncover question keywords and lower-competition opportunities.
• InLinks — useful when you want to go beyond keyword clusters into entities/topics and build a broader topical map.

What I'd actually use

For a serious content-clustering workflow, I'd probably do:

Ahrefs → AlsoAsked → Keyword Insights → editorial review

1. Ahrefs: Pull thousands of keywords around your seed topics.
2. Filter for questions: who, what, when, where, why, how, can, should, is, are, etc.
3. AlsoAsked: Expand the important questions and see how Google relates them hierarchically.
4. Keyword Insights: Upload the keyword set and cluster based on SERP overlap.
5. Manually review clusters: Combine/split clusters where the SERPs indicate that Google considers the queries the same or different intent.
6. Turn the resulting clusters into a pillar → subtopic → individual question content architecture.

The key distinction is that I wouldn't rely on an AI semantic-clustering tool alone. For SEO content planning, clustering by actual SERP similarity is usually more defensible: if “how much does X cost?” and “what does X cost?” produce essentially the same search results, they probably belong in one article even if a semantic model treats them as separate topics.

If you tell me your industry/site and roughly how many keywords you're dealing with (e.g. 500, 10k, 100k), I can recommend the most efficient tool stack and workflow for it.

If the goal is finding question-format keywords (“how,” “what,” “why,” “can,” “is…”) and clustering them into SEO content topics, I’d use a combination rather than one tool.

My preferred stack

• Semrush — Best all-around starting point. Its Keyword Magic Tool can surface question keywords and related terms, while Keyword Strategy Builder can help turn keyword sets into topic clusters.
• Ahrefs — Excellent for discovering question-based keywords via Keywords Explorer and grouping keywords by parent topic/intent. I particularly like it for competitive SERP analysis.
• AlsoAsked — Probably the tool I'd specifically choose for question discovery. It maps relationships between questions based on Google's “People Also Ask” results, which makes it useful for building FAQ/content structures.
• Keyword Insights — My pick if clustering is the main problem. It uses SERP-based clustering to determine which keywords should be targeted by the same page versus separate pages.
• LowFruits — Useful for finding question keywords and lower-competition opportunities, particularly for smaller sites.
• Google Search Console — Essential for finding the questions people are already using to discover your site. I’d feed those queries back into the clustering process.

If I had to pick just one

Semrush for an integrated workflow.

But if your exact requirement is:

“Give me thousands of questions, then automatically organize them into meaningful content clusters.”

I'd lean toward:

Semrush/Ahrefs → AlsoAsked → Keyword Insights

The workflow would be:

1. Seed topics → Semrush or Ahrefs
2. Expand into questions → AlsoAsked + question filters in Semrush/Ahrefs
3. Clean/deduplicate → spreadsheet or script
4. Cluster by SERP similarity → Keyword Insights
5. Assign cluster intent → informational / commercial / navigational
6. Build content architecture → pillar page → subtopics → individual articles/FAQs

One important distinction: semantic clustering isn't necessarily the same as SEO clustering. For content planning, I strongly prefer SERP-based clustering—if Google ranks substantially the same pages for two keywords, they're usually candidates for the same URL. That avoids creating 20 thin articles targeting subtly different versions of the same question.

If you tell me roughly how many keywords you're dealing with (e.g. 5k, 50k, 1M) and whether you want cheap/free vs. enterprise, I can recommend the best specific setup.

If the goal is specifically finding question-format keywords and then clustering them into content topics, I’d use a combination rather than one tool:

• Ahrefs — my first choice for keyword discovery. Its Keywords Explorer is strong for question-based queries, modifiers, search volume, and SERP analysis. You can filter for terms containing how, what, why, when, can, should, etc.
• Semrush — excellent alternative, particularly if you want its Keyword Magic Tool plus broader competitive/keyword-gap data.
• AlsoAsked — particularly good for question discovery and grouping questions by “People Also Ask” relationships. Great for turning one seed query into a question hierarchy.
• AnswerThePublic — useful for generating large sets of question-style queries organized around what/why/how/where/can/etc. It’s more of a discovery tool than a sophisticated clustering engine.
• Keyword Insights — one of the better choices if your priority is automated keyword clustering based on SERP overlap. This is closer to “give me 5,000 keywords and organize them into pages/topics.”
• LowFruits — useful for finding long-tail, low-competition question keywords, especially for smaller sites.

What I would actually use

For a serious content-cluster workflow, I'd probably do:

Ahrefs → AlsoAsked → Keyword Insights

1. Ahrefs: Build the initial keyword universe.
2. AlsoAsked: Expand the important seeds into related questions and subquestions.
3. Keyword Insights: Cluster the resulting keywords based on SERP similarity, rather than simply grouping words that look semantically similar.
4. Manual review: Turn the clusters into a content architecture: pillar page → supporting articles → FAQs.

The important distinction is that semantic clustering isn't necessarily the same as SEO clustering. For SEO, I generally want to know whether Google is ranking the same pages for two queries. If it is, those queries often belong on the same page; if the SERPs are substantially different, they may deserve separate content even when the keywords sound closely related.

If you tell me what kind of site/niche you're doing this for and roughly how many keywords you have (100, 10k, 1M+), I can recommend the most efficient stack and workflow.

If I were building a question-keyword → topic-cluster workflow, I’d use a combination rather than one tool:

• Ahrefs — best overall for finding question-format keywords from Google data, filtering by question terms, and analyzing parent topics/keyword relationships.
• Semrush — strong alternative, especially for its Keyword Magic Tool and topic/keyword grouping capabilities.
• AlsoAsked — excellent specifically for question discovery and mapping related questions around a topic. Particularly useful for “People Also Ask” relationships.
• AnswerThePublic — good for quickly discovering the question landscape around a seed keyword, though I’d use it more for ideation than rigorous clustering.
• Keyword Insights — my pick if the primary goal is automated keyword clustering based on Google SERP similarity. It can take a large keyword list and group keywords into content opportunities.
• LowFruits — useful for finding long-tail, question-based queries and lower-competition opportunities, particularly for smaller sites.

My preferred stack

For serious SEO/content research, I'd probably do:

Ahrefs → question extraction → Keyword Insights → SERP-based clustering → content briefs

For example, suppose you're targeting “home coffee brewing.”

You could collect questions like:

• How do you make coffee without a coffee maker?
• What is the best coffee-to-water ratio?
• How long should coffee steep?
• Why does my coffee taste bitter?
• How fine should coffee beans be ground?
• What temperature should water be for coffee?

Then cluster them based on search intent and SERP overlap, rather than simply grouping semantically similar words:

Cluster: Coffee brewing ratios

• what is the best coffee-to-water ratio
• how much coffee per cup
• how many grams of coffee per 100ml water
• coffee-to-water ratio for French press

Cluster: Bitter coffee

• why does my coffee taste bitter
• how to fix bitter coffee
• why is my French press coffee bitter
• why does my coffee maker make bitter coffee

That distinction matters because semantic similarity ≠ separate content opportunities. SERP overlap is generally a much better signal for deciding whether several questions should be answered on one page or deserve separate pages.

If you tell me what kind of content/site you're doing this for (affiliate, SaaS, ecommerce, local, informational, etc.) and roughly how many keywords you're dealing with, I can recommend the cheapest/most effective stack and workflow.

For finding question-format keywords and then turning them into content topic clusters, I’d use a combination rather than one tool:

• Ahrefs — probably my first choice. Its Keywords Explorer is strong for question-based keyword discovery, SERP analysis, and parent-topic relationships. Great for building clusters around search intent.
• Semrush — excellent for large-scale keyword research and its Keyword Strategy Builder, which can automatically organize keywords into topic clusters/subtopics. Particularly useful if you want a more automated workflow.
• AlsoAsked — best specifically for question discovery. It visualizes “People Also Ask” relationships, making it useful for finding related questions and deciding which should become supporting content.
• AnswerThePublic — useful for quickly harvesting questions and other query variations around a seed topic.
• Keyword Insights — one of the better choices if your main goal is automated clustering. It uses SERP similarity to group keywords based on whether Google considers them to have the same search intent.
• LowFruits — useful if you're targeting easier/less competitive question keywords, especially for smaller sites.
• Google Search Console — essential once you have existing traffic. It reveals the actual queries your site is appearing for, which you can feed into a clustering process.

What I'd actually use

If I were building an SEO content strategy today, my stack would be:

Ahrefs/Semrush → AlsoAsked → Keyword Insights → Google Search Console

The workflow would look like:

1. Seed topics: Start with your main products/services/topics.
2. Question harvesting: Pull questions from Ahrefs/Semrush + AlsoAsked.
3. Filter: Keep questions with meaningful search demand and/or strong relevance, rather than blindly targeting every variation.
4. SERP clustering: Run the keywords through Keyword Insights (or cluster them yourself using SERP overlap).
5. Assign intent: Informational, commercial investigation, transactional, navigational.
6. Build the hierarchy: - Pillar: “How to choose a home espresso machine”
7. Cluster: “What size espresso machine do I need?”
8. Cluster: “Are expensive espresso machines worth it?”
9. Cluster: “What espresso machine is easiest to clean?”
10. Cluster: “How long do espresso machines last?”
11. Map questions to pages: Avoid creating five articles when Google is effectively treating those five questions as one topic.
12. Use GSC later: Find additional questions/queries that your pages are already getting impressions for and fold those into updates.

If you want the most automated solution: Semrush + Keyword Insights.

If you want the best question research: Ahrefs + AlsoAsked.

If you're doing this at serious scale: Ahrefs/Semrush + a SERP-based clustering tool + your own spreadsheet/database.

If you tell me what kind of site/content you're building (affiliate, SaaS, local business, ecommerce, informational, etc.), I can recommend the exact tool stack and workflow I'd use.

If I were building a workflow specifically for finding question-format keywords and clustering them into content topics, I’d use a combination rather than one tool:

• Semrush — Best all-around starting point. Its Keyword Magic Tool is good for finding question queries and related keyword variations, and its clustering/content tools can help organize them.
• Ahrefs — Excellent for discovering question-based queries through Keywords Explorer, especially when you want to analyze parent topics, SERPs, and search intent.
• AlsoAsked — Particularly useful if your goal is questions. It maps Google "People Also Ask" questions into branching question relationships, which can translate naturally into content clusters.
• Keyword Insights — One of my top choices for the actual clustering step. It groups keywords based largely on SERP overlap, which is more useful for SEO than simply clustering by linguistic similarity.
• LowFruits — Useful for finding question-style long-tail queries and lower-competition opportunities.
• Google Search Console — Essential if you're clustering topics for an existing site. Your own queries reveal questions Google is already associating with your pages.

What I'd actually use

For a serious content-cluster workflow, I'd probably do:

Semrush/Ahrefs → AlsoAsked → Keyword Insights → Google Search Console

For example:

Seed topic: running shoes

Questions discovered:

• What are the best running shoes for beginners?
• How often should you replace running shoes?
• Are running shoes good for walking?
• How should running shoes fit?
• What running shoes are best for flat feet?
• Can you run in walking shoes?
• How many miles do running shoes last?

Then cluster them by search intent + SERP overlap, rather than merely putting semantically similar phrases together:

Pillar: Running Shoes

• Choosing running shoes - best running shoes for beginners
• how should running shoes fit
• how to choose running shoes
• Running shoe lifespan - how often to replace running shoes
• how many miles do running shoes last
• signs you need new running shoes
• Running vs. walking shoes - are running shoes good for walking
• can you run in walking shoes
• Running shoes for specific foot types - best running shoes for flat feet
• best running shoes for wide feet

If you want the best single tool: I'd pick Keyword Insights for clustering, paired with AlsoAsked for question discovery.

If you tell me whether you're doing this for SEO content, a topical-authority site, affiliate content, or an AI/LLM content strategy, I can recommend a much more specific stack and workflow.

If your goal is specifically “find lots of question-format keywords → group them into meaningful content topics/clusters → turn those clusters into articles”, I’d use a combination rather than one tool.

My top picks

1. semrush.com — best all-in-one

• Keyword Magic Tool has a dedicated Questions filter, so you can pull queries such as “how do…,” “what is…,” “why…,” etc. Semrush Semrush
• It automatically organizes keywords into topic-specific groups and subgroups.
• You can then move keywords into Keyword Strategy Builder, which clusters them into topics/pages for content planning. Semrush Semrush
• This would be my choice if you want one subscription to do most of the job.
• ahrefs.com — best for SERP-based clustering

• Keywords Explorer can cluster thousands of keywords by Parent Topic.
• Its clustering is based on the pages ranking for the keywords, which is useful because it answers the practical question: “Could these keywords realistically be covered by the same page?” Ahrefs Ahrefs
• You can paste in up to 10,000 keywords and cluster them quickly. Ahrefs
• I particularly like this for deciding whether 15 related questions should be one comprehensive article or several separate articles.
• alsoasked.com — best specifically for question discovery

• It pulls Google's People Also Ask questions and presents them as interconnected question trees.
• This is excellent for finding the subquestions surrounding a topic, rather than just keyword variations.
• It also supports bulk searches and CSV uploads. AlsoAsked

What I'd actually use

For a serious content-cluster workflow, I'd do:

Semrush → AlsoAsked → Ahrefs

For example, suppose your subject is “home solar panels.”

Semrush
→ find thousands of questions:

• how much do solar panels cost
• are solar panels worth it
• how long do solar panels last
• how many solar panels do I need
• can solar panels work in winter
• do solar panels work on cloudy days
• etc.

AlsoAsked
→ expand the questions into the questions Google associates with them.

Ahrefs
→ throw the resulting keyword set into Clusters by Parent Topic and determine which questions belong on the same page based on actual SERP overlap. Ahrefs

You might end up with:

SOLAR PANELS
│
├── Solar panel cost
│   ├── How much do solar panels cost?
│   ├── How much does solar installation cost?
│   └── Are solar panels worth the cost?
│
├── Solar panel sizing
│   ├── How many solar panels do I need?
│   ├── What size solar system do I need?
│   └── How much power does one solar panel produce?
│
├── Solar panel performance
│   ├── Do solar panels work on cloudy days?
│   ├── Do solar panels work in winter?
│   └── How much energy do solar panels produce?
│
└── Solar panel lifespan
    ├── How long do solar panels last?
    ├── How often do solar panels need replacing?
    └── What happens when solar panels get old?

If I had to pick just one

Semrush for the overall workflow.

Ahrefs if your priority is accurate SEO page clustering based on SERP overlap.

AlsoAsked if your priority is discovering huge numbers of genuine question-based content ideas.

And if you're doing this at large scale (thousands/tens of thousands of questions), there are more specialized clustering tools I'd consider too.

If the goal is specifically “find lots of question-format keywords → group them into useful content/topic clusters → decide what pages to create,” I’d use one of these:

My top choices

1. semrush.com — best all-in-one

• Keyword Magic Tool has a dedicated Questions filter.
• It automatically organizes keywords into topical subgroups.
• Its Keyword Strategy Builder can turn keyword lists into topic clusters, pillar pages, and subpages. Semrush Semrush
• This would be my choice if you want one platform for keyword discovery, questions, clustering, competitor research, and content planning.
• ahrefs.com — best for SERP-based clustering

• Keywords Explorer has a Questions filter.
• You can cluster keywords by Parent Topic, which is useful for determining which queries can realistically be covered by the same page.
• It can also cluster by terms, which is useful for finding broader topical groupings. Ahrefs Ahrefs Help Center
• I'd favor Ahrefs if your primary concern is SEO-driven content architecture, rather than just collecting questions.
• keywordinsights.ai — best dedicated clustering tool

• Particularly interesting if you already have a huge keyword export.
• It clusters based on SERP similarity—i.e., whether the same URLs rank for different keywords—rather than simply grouping words that look semantically similar.
• It supports clustering up to 200,000 keywords at once and provides search intent, ranking URLs, traffic opportunities, and other metrics alongside clusters. Keyword Insights

What I'd actually do

For a serious content-cluster project, I'd probably use:

Semrush → export question keywords → Keyword Insights → validate clusters in Google/SERPs.

The important distinction is that semantic similarity isn't necessarily the same as search intent.

For example, suppose you have:

• "how does email marketing work"
• "what is email marketing"
• "why is email marketing important"
• "how to create an email marketing campaign"
• "how often should you send marketing emails"

A basic NLP clustering tool might put all five together because they're semantically related.

But SERP-based clustering might tell you that the first three belong on an “Email Marketing Explained” page, while the latter two deserve separate pages. That's much more useful for actually building a site.

If I had to pick just one

Semrush for the easiest end-to-end workflow.

Keyword Insights if you already have a giant keyword/question dataset and the quality of the clustering itself is the main concern.

Ahrefs if you're already an Ahrefs user and want clustering integrated with SERP, backlink, difficulty, traffic-potential, and competitor research. Ahrefs

If you tell me what kind of site/topic you're doing this for and roughly how many keywords you have, I can also recommend a specific workflow (including which tool I'd use to generate the questions and which I'd use to cluster them).

If your goal is specifically “find lots of question-format keywords → organize them into useful content/topic clusters → turn those clusters into a content plan,” I’d use Semrush as the primary tool.

My picks

1. semrush.com — best all-in-one

• Keyword Magic Tool has a dedicated Questions filter, so you can pull queries containing how, what, why, can, is, etc. Semrush Semrush
• Keyword Strategy Builder can then cluster those keywords and organize them into topics, pillar pages, and subpages. Semrush
• Its clustering is SERP-based, meaning it looks at actual ranking-page overlap rather than merely deciding that words sound semantically similar. That's a big advantage for deciding whether several questions should be answered on one page. Semrush

Workflow I'd use:
Seed topics → Questions filter → export/save keywords → Keyword Strategy Builder → clusters → pillar/subtopic structure

2. ahrefs.com — excellent alternative

• Keywords Explorer can cluster keywords by Parent Topic.
• Ahrefs determines Parent Topic based on the query sending the most traffic to the #1-ranking page, which makes it useful for figuring out which questions can be covered by a broader page. Ahrefs Help Center Ahrefs
• Particularly good if you're already doing competitor/content-gap research in Ahrefs.
• keywordinsights.ai — worth considering for large-scale clustering

• I'd consider this if you already have thousands/tens of thousands of keywords and the primary job is clustering rather than keyword discovery.
• It's more of a specialist clustering/content-planning workflow than an all-in-one SEO suite.

What I'd personally choose

If I were doing this for a content strategy today:

Semrush → export question keywords → cluster by SERP intent → manually review clusters → build pillar/subtopic hierarchy.

The important distinction is that keyword clusters ≠ topic clusters. A keyword cluster answers “which queries should one page target?” A topic cluster answers “which collection of pages should we build around this broader subject?” Semrush explicitly distinguishes these two levels. Semrush

For example:

Broad topic: Home espresso
→ Pillar: How to make espresso at home
→ Cluster: espresso grind size questions
→ Questions: “What grind size for espresso?”, “How fine should espresso be?”, “Why is my espresso grinding too fine?”
→ Cluster: espresso extraction questions
→ Questions: “How long should espresso take?”, “Why is my espresso bitter?”, “Why does espresso run too fast?”

If you're doing this at scale, I'd actually combine a keyword database tool + clustering tool rather than expecting one tool to perfectly generate the final editorial architecture.

If you tell me roughly how many keywords you're dealing with (100, 10K, 100K+) and whether this is for SEO/blog content, programmatic SEO, or an AI-search/content strategy, I can recommend the exact stack and workflow I'd use.

If your goal is specifically “find lots of question-format keywords → group them into sensible content/topic clusters → turn those clusters into articles”, I’d use this stack:

My top choice: Semrush

semrush.com is probably the closest all-in-one fit.

You can:

1. Enter a broad seed topic.
2. Filter specifically for Questions — e.g. what, why, how, can, should, is, where. Semrush Semrush
3. See search volume, difficulty, intent, SERP features, etc.
4. Use its keyword groups/subgroups to organize related questions. Semrush
5. Send the keywords to Keyword Strategy Builder, which can automatically cluster them into topics based on search intent and SERP similarity, with primary and secondary keywords assigned to each content page. Semrush Semrush

So you could end up with something like:

SEED: home espresso

Cluster: Espresso Machines
├── what is the best espresso machine
├── how much is an espresso machine
├── what espresso machine should I buy
└── are expensive espresso machines worth it

Cluster: Espresso Grinding
├── what grind size for espresso
├── how fine should espresso be ground
├── why is my espresso grind too fine
└── can you use regular coffee grounds for espresso

Cluster: Espresso Extraction
├── why is my espresso bitter
├── why is my espresso sour
├── how long should espresso take to pull
└── how much espresso should a shot yield

That is very close to how I'd build a question-led topical content map.

If questions are the priority: AlsoAsked

alsoasked.com is excellent as a second tool.

It pulls Google's People Also Ask questions and organizes them into relationships/branches, so it's particularly good for discovering the questions surrounding a topic, rather than simply finding keyword variations. AlsoAsked

I'd use it to answer:

“What questions do people naturally ask around this subject?”

Then use Semrush to answer:

“Which of those questions have meaningful search volume and ranking potential?”

Another strong option: Ahrefs

ahrefs.com is probably my choice if you already live in Ahrefs.

It has a huge keyword database and can generate keyword ideas and cluster keywords by Parent Topic/related terms. Ahrefs

It's particularly good when you want to combine question discovery with competitor/SERP analysis.

What I'd actually use

For a serious content-cluster project, I'd do:

AlsoAsked → Semrush → Google/SERP validation

• AlsoAsked: discover the question universe.
• Semrush Keyword Magic: quantify those questions — volume, KD, intent, etc.
• Semrush Keyword Strategy Builder: automatically cluster them into potential pages/topics.
• Google/SERP: manually validate that keywords in each cluster actually have the same search intent and can realistically be answered by one page.

If you're doing hundreds or thousands of questions, I'd favor Semrush. If you're trying to uncover the actual question hierarchy around a topic, I'd add AlsoAsked.

If you tell me your niche/topic, I can also show you exactly how I'd go from seed keyword → question keywords → clusters → pillar pages + supporting articles.

If your goal is specifically “find question-format keywords → group them into useful content/topic clusters → decide what pages to create,” I’d use a combination rather than one tool.

My top choices

1. semrush.com — best all-in-one

• Has a dedicated Questions filter that surfaces keywords phrased as questions.
• Groups keywords into related thematic groups.
• Gives volume, intent, difficulty, SERP data, etc.
• Its Keyword Strategy Builder can turn keyword research into topic clusters. Semrush Semrush

I'd probably start here if you're building a large content strategy from scratch.

2. alsoasked.com — best for discovering actual questions

• Pulls live Google People Also Ask data.
• Shows the relationships between questions, rather than simply giving you a flat keyword list.
• Particularly good for discovering the subquestions surrounding a topic. AlsoAsked AlsoAsked

For example, starting with project management software, you might uncover clusters around:

• what is project management software?
• how does project management software work?
• what features should it have?
• how much does it cost?
• is it suitable for small businesses?
• how does it compare with spreadsheets?
• ahrefs.com — best for SEO-driven clustering

• Excellent keyword database and SERP analysis.
• Can cluster keywords by Parent Topic, which is useful for determining which queries can realistically be addressed by one page.
• It can also cluster by terms for broader topical grouping. Ahrefs Ahrefs

The important distinction is that Ahrefs' Parent Topic clustering is based on ranking-page relationships, so it's particularly useful for answering “should these keywords be one page or separate pages?” Ahrefs

4. Keyword Insights — best if clustering itself is the main job

• I'd consider this if you already have a huge keyword export from Semrush/Ahrefs and want sophisticated SERP-based clustering.
• Dedicated clustering tools compare ranking results to determine which keywords have sufficiently similar intent. Ahrefs

The workflow I'd actually use

Semrush → AlsoAsked → clustering → content map

For example:

1. Semrush
Start with your core topic and filter to Questions.

2. Export the questions
Get a few hundred/thousand questions rather than trying to manually pick them.

3. AlsoAsked
Take your major seed topics and expand them through PAA. This gives you the question hierarchy and related questions that Google associates with the topic. AlsoAsked

4. Cluster
Use Ahrefs/Keyword Insights/Semrush to determine which questions belong on the same URL.

5. Turn clusters into a content architecture

For example:

Pillar: Project Management Software

→ Cluster: Project management software basics
→ Cluster: Project management software features
→ Cluster: Project management software pricing
→ Cluster: Project management software for small business
→ Cluster: Project management software comparisons
→ Cluster: Project management software implementation
→ Cluster: Project management software alternatives

Then each cluster can become either a dedicated article or a section of a larger pillar page, depending on SERP overlap.

If I had to pick just one

Semrush for the overall workflow.

AlsoAsked + Ahrefs is the combination I'd choose if I cared more about building a genuinely comprehensive topical map than simply generating a keyword list.

And if you have a specific niche/site in mind, I can also give you a much more concrete workflow for going from 10–20 seed topics → thousands of questions → deduplicated clusters → pillar/cluster content map, including which tool I'd use at each step.

For finding question-format keywords (e.g., “how do I…”, “what is…”, “why does…”) and clustering them into content topic groups, I’d typically combine a keyword research tool + NLP clustering tool. The best choice depends on your budget and workflow.

Best all-in-one SEO tools

Semrush

Great for large-scale keyword discovery and topic planning.

Useful features:

• Keyword Magic Tool → filter question keywords using modifiers like: - how
• what
• why
• when
• where
• can
• should
• Keyword Strategy Builder → automatically groups keywords into topic clusters and pillar pages
• Search intent classification
• SERP analysis

Best for: SEO teams building content hubs.

Ahrefs

Strong for competitor-driven topic clustering.

Useful features:

• Keywords Explorer → filter by question terms
• Matching terms → Questions report
• Parent Topic grouping
• Content Gap analysis
• SERP competitor analysis

Best for: finding topics competitors already rank for.

SE Ranking

A lower-cost alternative with solid clustering capabilities.

Useful features:

• Keyword grouping
• Keyword database
• SERP-based clustering
• Content planning tools

Best for: smaller teams and agencies.

Tools specifically good at topic clustering

Keyword Insights

One of the strongest dedicated keyword clustering platforms.

Workflow:

1. Export thousands of keywords.
2. Upload CSV.
3. It analyzes SERPs and groups keywords based on ranking overlap.
4. Produces clusters and suggested pages.

Best for: programmatic SEO and large content sites.

Surfer SEO

Useful when moving from keyword research into content briefs.

Features:

• Content clusters
• SERP-based content planning
• NLP recommendations

Best for: writers and content teams.

Low-cost / DIY stack

A practical workflow:

1. Find question keywords

• answerthepublic.com
• Google Keyword Planner
• alsoasked.com
• Export keywords into a spreadsheet.
• Cluster using:

• keywordinsights.ai
• Python + embeddings (for custom workflows)
• Google Sheets + manual grouping for smaller lists

My typical stack by use case

For a serious content cluster project, I’d usually do:

Semrush/Ahrefs → export question keywords → Keyword Insights clustering → map clusters to pillar pages + supporting articles.

If your goal is specifically “find question-format keywords → understand their intent → group them into content/topic clusters,” I’d use a combination rather than one tool.

My picks

• semrush.com — best all-in-one

• Keyword Magic Tool has a dedicated Questions filter.
• It can group keywords into related subtopics and its Keyword Strategy Builder can turn keyword research into topic clusters.
• Good choice if you want search volume, KD, intent, questions, competitors, and clustering in one platform. Semrush Semrush
• ahrefs.com — best for SERP-based clustering

• Keywords Explorer can instantly cluster keywords by Parent Topic, which is useful for determining which questions can realistically be answered by the same page.
• It can also cluster by terms, which is useful for building broader topical buckets. Ahrefs Ahrefs
• I'd favor Ahrefs if avoiding keyword cannibalization and deciding “one page vs. multiple pages” is especially important.
• alsoasked.com — best for discovering actual question relationships

• It pulls Google's People Also Ask questions and presents them as branching question relationships.
• This is particularly useful for finding the supporting questions underneath a primary topic, rather than simply generating a flat keyword list. AlsoAsked

What I'd actually use

For a serious content-clustering workflow, I'd do:

Semrush → AlsoAsked → Ahrefs

For example, suppose the seed topic is “home espresso machines.”

1. Semrush

• Pull thousands of related keywords.
• Filter to questions: what, why, how, can, should, which, etc.
• Export questions + volume + KD + intent. Semrush Semrush
• AlsoAsked

• Run searches around the major questions.
• Identify the natural question hierarchy: - What is an espresso machine?
• How does an espresso machine work?
• Which espresso machine is best for beginners?
• How much does an espresso machine cost?
• How do you clean an espresso machine?
• This gives you the question architecture around a topic. AlsoAsked
• Ahrefs

• Run the resulting keyword set through Clusters by Parent Topic.
• Use the SERPs to determine whether multiple questions should live on one page or deserve separate pages. Ahrefs Ahrefs

The important distinction is that “keyword clustering” isn't necessarily the same thing as “content topic clustering.” A good content cluster should account for search intent and SERP overlap, not just words that happen to appear together.

If I had to pick only one: Semrush for the broadest workflow.
If I had to pick two: Semrush + AlsoAsked.
If SEO architecture/cannibalization is the priority: Ahrefs + AlsoAsked.

If you tell me your industry/site type and roughly how many keywords you need to cluster (100, 10K, 100K+), I can recommend the most efficient stack and workflow.

If your goal is specifically “find lots of question-format keywords → organize them into useful content/topic clusters → turn those clusters into an editorial plan,” I’d use one of these workflows:

My top choice: Semrush

Semrush Keyword Magic Tool + Keyword Strategy Builder

This is probably the best all-in-one option for what you're describing.

• Keyword Magic Tool lets you filter specifically for questions (what, why, how, can, is, etc.). www.semrush.com
• It automatically groups related keywords into topical subgroups.
• You can send the questions into Keyword Strategy Builder, which clusters keywords based on SERP similarity/search intent, rather than merely grouping words that sound alike. www.semrush.com
• It can then turn those clusters into a structure of pillar topics → subtopics/pages.

I'd choose this if you're building a serious SEO/content strategy.

Best for discovering actual questions: AlsoAsked

AlsoAsked

AlsoAsked is particularly good if you want to discover the questions Google users ask around a topic, rather than simply generating keyword variations.

It pulls live People Also Ask data and displays questions in a relationship/intent structure. alsoasked.com

For example:

electric cars
→ Are electric cars worth it?
→ How long do electric car batteries last?
→ How much does it cost to replace an EV battery?
→ Are electric cars cheaper to maintain?

That makes it excellent for discovering subtopics and FAQ-style content.

Best alternative: Ahrefs

Ahrefs Keywords Explorer

Ahrefs is particularly strong for the clustering side.

Its Keywords Explorer can group keywords by Parent Topic, which essentially identifies groups of queries that can potentially be addressed by the same page. It also supports clustering by related terms. ahrefs.com

I'd pick Ahrefs if you also care heavily about competitor research, backlinks, traffic potential, and identifying which clusters are realistically worth pursuing.

The workflow I'd actually use

If I were doing this for a new site, I'd combine Semrush + AlsoAsked:

1. Start with broad seed topics

For example:

home solar

2. Semrush → Questions

Export questions such as:

• how does home solar work
• how much does home solar cost
• is home solar worth it
• how long do solar panels last
• how many solar panels do I need
• can solar panels power a house
• what happens to solar panels at night

3. AlsoAsked → expand the questions

Use the strongest questions to uncover the adjacent questions Google associates with them.

4. Semrush Keyword Strategy Builder → cluster

Now group the entire keyword set according to actual SERP overlap/search intent.

You might end up with:

The important distinction is that keyword clusters ≠ topic clusters. A keyword cluster tells you which queries can potentially be targeted by one page; a topic cluster is the larger site-level architecture connecting multiple related pages. Semrush explicitly distinguishes the two. www.semrush.comwww.semrush.com

If I had to pick just one

Semrush.

If I had to pick the best two-tool combination, I'd do:

AlsoAsked → Semrush Keyword Magic Tool → Semrush Keyword Strategy Builder

That gives you real questions → keyword data/volume → SERP-based clusters → content architecture.

If you tell me what niche/site you're doing this for, I can also show you exactly how I'd structure the keyword research and clustering process for it.

If your specific goal is “find question-format keywords → identify the underlying themes → group them into content/topic clusters,” I’d use a combination rather than one tool.

My preferred stack

If I had to pick just one

Semrush is probably the closest match to what you're describing.

You could:

1. Enter a broad seed topic.
2. Filter the Keyword Magic results to Questions.
3. Export/save the relevant questions.
4. Put them into Keyword Strategy Builder.
5. Let it group them into clusters.
6. Turn each cluster into a potential pillar/supporting-content structure.

Semrush explicitly supports question discovery and automated keyword clustering/content mapping. www.semrush.com

But I'd actually use Ahrefs + AlsoAsked

For content strategy, I think this workflow is stronger:

Ahrefs → AlsoAsked → clustering → content map

For example, imagine you're researching “CRM software.”

Ahrefs might give you:

• what is CRM software?
• how does CRM software work?
• what does CRM stand for?
• how much does CRM software cost?
• what is CRM used for?
• how to choose CRM software?
• is CRM software worth it?
• what is the best CRM for small business?

Then Ahrefs' Parent Topic/keyword clustering helps determine which terms can realistically be targeted by the same page versus separate pages. ahrefs.comahrefs.com

Then AlsoAsked gives you the question hierarchy—essentially showing how searchers naturally branch from one question into related questions. alsoasked.com

That distinction is important: keyword similarity isn't necessarily the same thing as a good content cluster.

One important SEO caveat

I wouldn't cluster purely based on semantic similarity or AI embeddings.

The strongest signal for deciding whether two keywords belong on the same page is usually SERP overlap: if essentially the same pages rank for both queries, Google is likely treating them as the same search intent/topic. Ahrefs explicitly uses this concept in its clustering/Parent Topic methodology. ahrefs.comahrefs.com

So my ideal workflow would be:

1. Discover questions
→ Ahrefs / Semrush / AlsoAsked

2. Filter for useful questions
→ volume, difficulty, intent, relevance

3. Cluster
→ Parent Topic + SERP similarity

4. Build the topic hierarchy
→ Pillar topic → subtopics → individual questions

5. Decide page boundaries
→ Don't create a page for every question; combine questions with overlapping intent.

If you're doing this at large scale (thousands/tens of thousands of questions), there are also more specialized clustering tools I'd consider—especially if you want to feed in a CSV and automatically produce a “cluster → primary keyword → supporting questions → recommended article” output.

If the goal is specifically “find lots of question-format keywords → understand the intent → group them into content/topic clusters,” I’d use a combination rather than one tool.

My preferred stack

If I had to pick just one: Ahrefs. Its Keywords Explorer has a dedicated Questions report/filter, search-volume and difficulty data, and clustering by Parent Topic. ahrefs.com

If the question aspect is the most important: I'd pair Ahrefs + PeopleAlsoAsked. PeopleAlsoAsked is specifically designed around Google's PAA/PASF/autocomplete question paths and can turn those into topic structures. peoplealsoasked.com

If you're processing 10,000–200,000 keywords: I'd add Keyword Insights. It clusters based on similarity among the actual ranking URLs/SERPs, which is much more useful for deciding whether two keywords should actually be targeted by the same page. It supports up to 200,000 keywords per clustering operation. www.keywordinsights.ai

The workflow I'd actually use

1. Ahrefs → generate the universe

Start with broad seed terms and pull the Questions report. Export questions along with volume, KD, traffic potential, etc. Ahrefs explicitly supports question-oriented keyword discovery. ahrefs.com

2. PeopleAlsoAsked → expand the question universe

Take your important seed topics and run them through PAA. This gives you relationships like:

What is X?
→ How does X work?
→ How much does X cost?
→ Is X worth it?
→ How do you use X?
→ What are alternatives to X?

That's extremely useful for identifying subtopics and the natural hierarchy of questions, rather than simply sorting keywords based on similar words. peoplealsoasked.com

3. Keyword Insights → cluster the exported list

Upload the combined keyword list and use SERP-based clustering. The important distinction is that it asks, essentially, “Do these keywords have sufficiently similar Google results that one page could rank for them?” rather than merely “Do these keywords contain similar words?” www.keywordinsights.ai

4. Manually turn clusters into a content architecture

For example:

Parent topic: Home solar

• Cluster: How does solar power work?
  • how does solar energy work
  • how do solar panels work
  • how does photovoltaic energy work
  • how does solar electricity work

• Cluster: How much do solar panels cost?
  • how much do solar panels cost
  • how much does solar installation cost
  • how much does a solar system cost
  • how expensive are solar panels

• Cluster: Are solar panels worth it?
  • are solar panels worth it
  • is solar power worth it
  • are solar panels worth the investment
  • should I get solar panels

Then you can decide that each cluster = one article/page, while the larger set of clusters forms the topic cluster.

One important distinction

I'd not rely solely on AI/semantic clustering.

For SEO content architecture, SERP similarity is the final arbiter. Two keywords can be semantically almost identical but deserve separate pages if Google consistently returns different results; conversely, seemingly different questions can belong on one page if the SERPs substantially overlap.

That's why my ideal setup is:

Ahrefs → question discovery + metrics
PeopleAlsoAsked → question expansion + relationships
Keyword Insights → SERP-based clustering
Human judgment → final content architecture

Semrush can replace Ahrefs if you're already using it; its Keyword Overview surfaces questions and its Keyword Strategy Builder organizes keywords into clusters. www.semrush.com

If you're doing this for a large website/content program, I can also give you a very specific “10,000 questions → 100 topic clusters → content calendar” workflow, including which exports/filters I'd use in each tool.

If the goal is specifically “find lots of question-format keywords → understand the themes → turn them into content/topic clusters,” I’d use a combination rather than one tool.

My preferred stack

If I had to pick just one

Semrush would probably be my choice for your exact use case.

You can:

1. Enter a broad seed topic.
2. Filter to Questions.
3. Export the question keywords.
4. Cluster them with Keyword Strategy Builder.
5. Organize the resulting clusters into pillar → subtopic → supporting-question content.

Semrush explicitly distinguishes keyword clusters (queries that can generally be targeted by one page) from topic clusters (the broader site-level architecture), which is an important distinction. www.semrush.comwww.semrush.com

But there's an even better workflow

If I were doing this for a serious content strategy, I'd do:

Semrush/Ahrefs → AlsoAsked → clustering → human review

For example, imagine the seed topic is “home solar panels.”

You might pull questions like:

• how do solar panels work
• how much do solar panels cost
• how long do solar panels last
• are solar panels worth it
• do solar panels work in winter
• how many solar panels do I need
• how much electricity does a solar panel produce
• can solar panels power a whole house

Then clustering might produce something like:

Pillar: Solar Panels

→ Solar panel basics

• how do solar panels work
• how are solar panels made
• how much electricity does a solar panel produce

→ Solar panel costs

• how much do solar panels cost
• how much does it cost to install solar panels
• how much are solar panels for a house

→ Solar panel sizing

• how many solar panels do I need
• what size solar system do I need
• how many solar panels to power a house

→ Solar panel performance

• do solar panels work in winter
• do solar panels work on cloudy days
• how long do solar panels last

→ Solar panel economics

• are solar panels worth it
• how much money do solar panels save
• how long until solar panels pay for themselves

AlsoAsked is particularly useful after the initial keyword pull, because it can expose the question relationships Google is actually showing, rather than merely grouping keywords that happen to contain similar words. alsoasked.com

One important distinction

I'd avoid clustering purely by semantic similarity.

For SEO content planning, you ideally want SERP/intent-based clustering: Would Google consider the same page a good answer to these queries? Ahrefs' Parent Topic approach and Semrush's SERP-based clustering are useful precisely because they incorporate ranking results rather than just saying “these words sound similar.” ahrefs.com

My recommendation:

• Solo SEO/content strategist: Semrush + AlsoAsked
• SEO-heavy / sophisticated keyword research: Ahrefs + AlsoAsked
• Huge keyword datasets / automated clustering: Keyword Insights + Ahrefs/Semrush
• Best overall process: Semrush/Ahrefs for volume & competition → AlsoAsked for question expansion → SERP-based clustering → manually turn clusters into a pillar/topic architecture.

If you tell me what kind of site/niche you're doing this for and roughly how many keywords you're dealing with, I can recommend the exact workflow (including which tool I'd use at each step).

If your goal is specifically “find lots of question-format keywords → group them into useful content/topic clusters → turn those clusters into a content plan,” I’d shortlist these:

My pick: Semrush

Semrush Keyword Magic Tool is probably the closest match to what you're describing.

You can:

1. Enter a broad seed topic.
2. Switch to the Questions filter to isolate queries such as how, what, why, where, can, should, etc. www.semrush.comwww.semrush.comwww.semrush.com
3. See related keywords automatically organized into topic-specific groups and subgroups. www.semrush.comwww.semrush.comwww.semrush.com
4. Send the resulting keywords into Keyword Strategy Builder, which can organize them into broader topics, pillar pages, and subpages. www.semrush.com

So, for example, you could start with:

“home coffee”

and end up with something conceptually like:

Coffee → Coffee Brewing → French Press

• how to use a French press
• how long to French press coffee
• what coffee to use in a French press
• why is my French press coffee bitter

Coffee → Coffee Equipment → Grinders

• what is the best coffee grinder
• why use a burr grinder
• how fine should coffee be ground
• what grinder is best for French press

The important distinction is that Semrush's newer clustering workflow can use SERP relationships, rather than simply grouping keywords because they contain the same words. That's much more useful for deciding whether several questions should be answered by one article versus separate articles. www.semrush.com

Ahrefs is my second choice

Ahrefs Keywords Explorer is excellent if you're more SEO-focused.

It has a Questions filter for finding question-based queries, and its clustering can group keywords by Parent Topic or by terms. help.ahrefs.com

The particularly useful feature is Clusters by Parent Topic: Ahrefs groups queries based on whether they can potentially be targeted by the same broader page/topic, rather than merely matching words. ahrefs.com

If clustering is the main job

I'd also look at Keyword Insights. It's more specialized around turning large keyword datasets into clusters and content plans, whereas Semrush/Ahrefs are broader SEO platforms.

What I'd actually use

For a serious content research workflow, I'd probably use:

Semrush → questions → SERP-based clustering → content map

rather than just dumping question keywords into an AI and asking it to cluster them.

The latter tends to produce semantic clusters ("these questions are about the same thing"), whereas what you really want for SEO is closer to:

Which questions have enough overlap in search intent/SERPs that one piece of content could rank for them?

That's the distinction that prevents you from creating 20 thin articles when Google really wants one comprehensive page—or, conversely, stuffing five genuinely different intents into one article.

If you tell me what niche/site you're doing this for, I can also recommend the best tool/workflow specifically for it (including a cheaper/free option if you're trying to avoid a $100+/month SEO subscription).

If your goal is specifically “find question-format keywords → understand search intent → group them into content/topic clusters,” I’d use a combination rather than one tool.

My top picks

What I'd actually use

For a serious content-cluster workflow:

Semrush/Ahrefs → AlsoAsked → Keyword Insights

1. Semrush or Ahrefs
   • Export thousands of keywords around your subject.
   • Filter for question modifiers: how, what, why, when, where, can, should, is, are, does, etc.
   • Look at search volume, difficulty, SERP features, and intent.

2. AlsoAsked
   • Take your important questions and discover the related “People Also Ask” question tree.
   • This is particularly useful for figuring out how Google conceptually connects questions.

3. Keyword Insights
   • Feed it the larger keyword set.
   • Its SERP-based clustering can group keywords that Google considers to have similar search intent.
   • This is the one I'd favor if your goal is turning a huge keyword list into actual content clusters/pages rather than simply finding related keywords.

If you want one tool

I'd probably choose Keyword Insights for the specific workflow you described.

If you're doing broader SEO research as well—competitors, backlinks, keyword gaps, rank tracking, etc.—I'd choose Semrush or Ahrefs, then use Keyword Insights for the clustering step.

If you tell me what kind of site/content you're building and roughly how many keywords you're dealing with (e.g. 500 vs. 100,000), I can recommend the most efficient stack and workflow.

If your goal is specifically “find lots of question-format keywords → understand the underlying themes → turn them into content clusters,” I’d use a combination rather than one tool.

My preferred stack

1. Semrush — best all-in-one

This is probably the closest to what you're describing. Its Keyword Magic Tool has a Questions filter, and Semrush automatically groups keywords into topical groups. Its Keyword Strategy Builder can then turn those groups into content clusters. www.semrush.com

Workflow:

Seed topic → Questions filter → filter volume/KD → topical groups → content clusters

2. Ahrefs — best for SERP-based clustering

I'd use Ahrefs if you care more about whether several keywords can actually be targeted by the same page. Keywords Explorer can cluster keywords by Parent Topic, based on Google's search results, and also offers term-based clustering. ahrefs.com

That's particularly useful because:

“What is X?”
“How does X work?”
“Is X worth it?”
“How much does X cost?”

may look like separate topics but potentially belong to one SERP/content cluster—or they may require separate pages.

3. AlsoAsked — best for discovering the actual question universe

I'd add this to either Semrush or Ahrefs. It pulls live Google People Also Ask questions and, importantly, shows the relationships between questions. That's extremely useful for figuring out the structure of a topic rather than just getting a flat keyword list. www.semrush.comalsoasked.com

For example:

Running shoes
│
├── How should running shoes fit?
│   ├── How much toe room should running shoes have?
│   ├── Should running shoes feel tight?
│   └── How do I know if running shoes fit?
│
├── How often should you replace running shoes?
│   ├── How many miles do running shoes last?
│   ├── Do running shoes expire?
│   └── How do you know when running shoes are worn out?
│
└── What type of running shoe do I need?
    ├── Do I need stability shoes?
    ├── Are cushioned shoes better?
    └── What's the difference between stability and neutral shoes?

That's much closer to a content architecture than conventional keyword research.

If I were doing this professionally

I'd probably use:

Semrush → AlsoAsked → Ahrefs

• Semrush: generate a large database of question keywords + search metrics.
• AlsoAsked: uncover the question relationships and subtopics.
• Ahrefs: validate whether keywords belong on the same page using SERP/Parent Topic clustering.
• Then build the final pillar → cluster → article → supporting questions structure.

If you want one tool only, I'd pick Semrush for this particular job. If you already have a large keyword list and the main challenge is deciding which questions should share a URL, I'd pick Ahrefs.

If your specific goal is “find lots of question-format keywords → group them into useful content/topic clusters”, I’d shortlist these:

1. My default: Semrush

Semrush Keyword Magic Tool

This is probably what I'd use if you're building a content strategy from scratch.

Its Keyword Magic Tool has a dedicated Questions filter and automatically organizes keywords into topic-specific groups and subgroups. www.semrush.com

For example, if your seed is:

project management

You can get questions such as:

• what is project management
• how does project management work
• what are project management methodologies
• how to manage a project
• what is agile project management
• how to create a project plan

Then use the topic groups to start constructing your pillar → cluster → article architecture.

Why I'd pick it: you get questions + volume + KD + intent + SERP features + topical grouping in one workflow.

2. Ahrefs if you care more about true topic clustering

Ahrefs Keywords Explorer

Ahrefs is particularly good at taking a large keyword set and clustering it around Parent Topics. Its current Keywords Explorer can instantly cluster keyword ideas and show the keywords within each cluster. ahrefs.com

I'd use this when you already have thousands/tens of thousands of keywords and want to answer:

"Which of these keywords should actually be one page?"

That's a slightly different problem from merely grouping semantically similar words.

3. Keyword Insights for the most rigorous clustering

Keyword Insights

This is the one I'd look at if you're doing large-scale content clustering.

Its clustering is based on SERP similarity: it analyzes the actual ranking URLs and groups keywords when their SERPs overlap. It supports lists of up to 200,000 keywords per clustering operation. www.keywordinsights.ai

That's important because:

semantic similarity ≠ Google considers them the same topic.

For example:

• "how to clean a leather sofa"
• "best way to clean leather couch"
• "how do you clean leather furniture"

are probably one article.

But:

• "leather sofa cleaner"
• "best leather sofa"
• "how to repair leather sofa"

may be semantically related while deserving different pages.

SERP-based clustering catches that distinction much better.

4. AlsoAsked for discovering questions

AlsoAsked

I'd use this alongside one of the tools above rather than as the entire workflow.

It's particularly useful when your objective is to discover the hierarchy of questions around a topic—the sort of things Google surfaces through People Also Ask.

So I'd use it to expand:

"CRM software"

into something like:

What is CRM software?
→ What does CRM software do?
→ Why is CRM important?
→ What features should CRM software have?
→ How much does CRM software cost?

Then feed the resulting keyword universe into a proper clustering tool.

What I'd actually use

For a serious SEO/content project, my stack would be:

Semrush → question discovery → Ahrefs/Keyword Insights → SERP clustering → content map

More specifically:

1. Semrush Keyword Magic Tool
   • Enter 5–20 seed topics.
   • Filter to Questions.
   • Export the keyword universe.
   • Keep volume, KD, intent, SERP features, etc.

2. Keyword Insights
   • Upload the exported keywords.
   • Cluster based on actual SERP overlap.
   • Identify which questions belong on the same URL.

3. Manually create the content hierarchy
   • Pillar topic
   • Core articles
   • Supporting articles
   • FAQs/subquestions

4. Prioritize clusters
   • Search demand
   • Business relevance
   • Ranking difficulty
   • Existing competition
   • Whether you already have a page targeting the topic

If I had to buy only one

Semrush for an all-around content strategist.

Keyword Insights if your main problem is “I have 50,000 keywords and need to turn them into a sane content architecture.”

Ahrefs if you want a broader SEO platform and particularly like its Parent Topic/keyword-clustering workflow.

And if your goal is specifically building an enormous database of question keywords and automatically turning them into content clusters, I can also show you the best workflow/tools for doing that at 10k, 100k, or 1M+ keywords—the answer changes quite a bit at that scale.

If your goal is specifically “find question-format keywords → group them into useful content topic clusters”, I’d use one of these workflows:

My top picks

If I had to pick one: Semrush. Its Keyword Magic Tool has a dedicated Questions filter and automatically organizes keywords into topic-specific groups. www.semrush.comwww.semrush.com

For example, starting with running shoes, you could filter for questions and get things like:

• what running shoes are best for beginners
• how long do running shoes last
• how should running shoes fit
• what running shoes are good for flat feet
• can you run in walking shoes
• how often should you replace running shoes

Then the groups can become content clusters such as:

Running Shoes

• Buying running shoes
  • what running shoes are best for beginners
  • how should running shoes fit
• Running shoe lifespan
  • how long do running shoes last
  • how often should you replace running shoes
• Running shoes for specific needs
  • best running shoes for flat feet
  • best running shoes for wide feet

Semrush also now has a Keyword Strategy Builder specifically designed to turn keyword research into topic clusters/content plans. www.semrush.com

Where Ahrefs is particularly strong

If you already have a big keyword export, I'd lean toward Ahrefs. Its clustering can group keywords by Parent Topic, using the relationship between the keywords and their ranking SERPs rather than merely matching words. That's valuable because two keywords can mean essentially the same search intent even when their wording is quite different. ahrefs.com

The workflow I'd actually use

For serious content research, I'd combine tools rather than expect one tool to do everything:

1. Semrush → collect questions
Use Keyword Magic Tool → seed topic → Questions → export the questions. www.semrush.comwww.semrush.com

2. AlsoAsked → understand question relationships
Use it to uncover the branching questions people ask around the initial query—particularly useful for deciding what belongs inside an article versus what deserves its own article.

3. Ahrefs or Keyword Insights → cluster the full dataset
Cluster by search intent/SERP similarity rather than just shared words. This is the important step for avoiding a content plan with 50 articles that should really be 12.

4. Manually validate the clusters against SERPs
This is crucial. Automated clustering can tell you that keywords are related; you still want to check whether Google is actually serving the same type of page for them.

If you're doing this at scale

I'd probably go:

Semrush → export thousands of question keywords → Keyword Insights → automated SERP-based clustering → manual review → content map.

Keyword Insights is particularly attractive when the bottleneck is clustering a large keyword list, whereas Semrush is stronger as the broader research environment.

If you tell me roughly how many keywords you're dealing with (100, 1,000, 10,000+) and whether this is for one site or multiple clients, I can recommend the cheapest/most efficient stack and workflow.

If your goal is specifically “find lots of question-format keywords → understand the underlying topics → group them into content clusters”, I’d shortlist these:

1. My first choice: Ahrefs

Ahrefs is probably what I'd use if I were building a serious content-cluster strategy.

Its Keywords Explorer lets you find keyword ideas and filter them down, including question-style queries. More importantly, it can cluster keywords by Parent Topic, grouping queries that are likely to be addressed by the same piece of content. ahrefs.comhelp.ahrefs.com

For example, you might start with:

“email marketing”

and end up with clusters such as:

• How to do email marketing
  • how does email marketing work?
  • how to start email marketing?
  • how often should you send marketing emails?
• Email marketing strategy
  • what is an email marketing strategy?
  • what makes a good email marketing campaign?
• Email marketing automation
  • what is email automation?
  • how does email marketing automation work?
• Email marketing metrics
  • what is a good email open rate?
  • how do you calculate email conversion rate?

Ahrefs' clustering is particularly useful because its Parent Topic is based on the search query driving traffic to the top-ranking page, rather than simply grouping keywords because they contain similar words. ahrefs.comhelp.ahrefs.com

2. Best for finding questions: Semrush Keyword Magic Tool

I'd use Semrush if question discovery is the priority.

Its Keyword Magic Tool has an explicit Questions view/filter, along with search volume, intent, difficulty, trends, etc. www.semrush.com

So a good workflow is:

Semrush → find questions → export → cluster

rather than relying entirely on Semrush's own grouping.

3. Best dedicated clustering tool: Keyword Insights

If you've already accumulated a huge spreadsheet of keywords and your primary problem is:

“Which of these 20,000 keywords belong on the same page?”

I'd look seriously at Keyword Insights.

The important distinction is that keyword similarity isn't necessarily topic similarity. Ideally, clustering should consider whether Google returns substantially similar results for the queries. That's much more useful for deciding whether 10 questions deserve one comprehensive article or 10 separate articles.

4. Best for “what questions are people actually asking?”: AlsoAsked

AlsoAsked is particularly useful for uncovering Google's People Also Ask relationships.

I'd use it to build the question architecture around a topic:

Seed topic → primary question → related questions → follow-up questions

That's excellent for developing H2/H3 structures and FAQ sections, although I wouldn't use it as my only keyword-volume/clustering platform.

What I'd actually use

For a professional content strategy, I'd probably use a 2–3 tool stack:

Semrush → AlsoAsked → Ahrefs

1. Semrush — collect a huge universe of question keywords.
2. AlsoAsked — discover the actual question relationships and subquestions.
3. Ahrefs — validate search demand, difficulty, SERPs, and cluster by Parent Topic.
4. Export the resulting clusters into a spreadsheet/content database.
5. Assign each cluster a primary question, supporting questions, search intent, content type, and recommended URL.

The key is that I wouldn't cluster purely by NLP similarity. SERP overlap/Parent Topic is much more actionable for SEO, because the real question is whether Google considers the queries sufficiently related that one page can rank for them. Ahrefs explicitly supports clustering around this concept. ahrefs.com

If you tell me roughly how many keywords you're dealing with (e.g. 500, 10,000, 100,000+) and whether you're doing this for one website or many, I can recommend the cheapest/fastest stack and workflow.

If your specific goal is “find lots of question-format keywords → understand what people are asking → group them into useful content/topic clusters,” I’d use a two-tool workflow, rather than relying on one platform.

My picks

What I'd personally do

1. Start with AlsoAsked

Enter a broad seed such as:

“project management software”

It maps Google's People Also Ask questions into connected branches, rather than just giving you a flat keyword list. That's particularly useful for discovering the questions underneath a topic. alsoasked.com

You might get something conceptually like:

Project management software
│
├── What is project management software?
│   ├── What does project management software do?
│   └── Who needs project management software?
│
├── How does project management software work?
│   ├── How do teams use it?
│   └── What features should it have?
│
├── What is the best project management software?
│   ├── What is best for small business?
│   └── What is best for remote teams?
│
└── How much does project management software cost?
    ├── Is project management software expensive?
    └── What is the cheapest option?

That gives you the question universe.

2. Then run the resulting keyword list through Keyword Insights

This is where I'd do the actual SEO clustering. Keyword Insights groups terms using SERP similarity—i.e., whether Google tends to rank similar URLs for the queries—rather than merely deciding that the words look semantically similar. It can handle lists up to 200,000 keywords. www.keywordinsights.ai

That's important because:

“What is project management software?”
“How does project management software work?”

are semantically related, but they may deserve different pages.

SERP-based clustering gives you a much better signal for “should these keywords actually be targeted by the same page?”

If you want one tool

I'd go with Semrush.

Its Keyword Strategy Builder can take keyword lists and automatically cluster them, showing keywords organized around pages/topics. Its clustering incorporates search intent and SERP relationships. www.semrush.com

So the workflow becomes:

Semrush Keyword Magic Tool → filter questions → Keyword Strategy Builder → clusters → content plan

One important distinction

I'd separate keyword clusters from topic clusters.

For example:

Topic: Project Management Software

Pillar page

• Project Management Software: Complete Guide

Supporting topics

• What is project management software?
• How does project management software work?
• Project management software features
• Project management software for small businesses
• Project management software for remote teams
• Project management software pricing
• Project management software alternatives

Within each supporting topic, you might have 10–50 individual question keywords that belong on the same page.

Semrush itself makes this distinction: keyword clustering is primarily about deciding which keywords belong on a page, whereas topic clustering is about organizing multiple pages around a broader theme. www.semrush.com

If I were building a serious content-research stack

I'd use:

AlsoAsked → Semrush/Ahrefs → Keyword Insights → spreadsheet/LLM

Specifically:

1. AlsoAsked — discover real questions and question relationships.
2. Semrush or Ahrefs — add volume, difficulty, competitors, SERPs, etc.
3. Keyword Insights — cluster the complete keyword set by SERP similarity.
4. LLM/manual review — turn those clusters into actual editorial topics, because automated clustering isn't perfect.

That last step matters. Automated clustering can occasionally combine things that Google considers similar but that you, as an editor, would want to keep separate. www.reddit.com

If your primary objective is question-led content planning rather than traditional SEO keyword research, my #1 combination would be AlsoAsked + Keyword Insights.

If the goal is specifically “find lots of question-format keywords → understand the relationships between them → turn them into content/topic clusters,” I’d use a 2–3 tool stack, rather than relying on one platform.

My preferred stack

If I were doing this myself

I'd probably use:

1. AlsoAsked → find the questions

It pulls live Google People Also Ask data and, importantly, shows the relationships between questions rather than just dumping you a keyword list. alsoasked.com

For example, starting with:

"project management software"

you might discover branches like:

• What is project management software?
• How does project management software work?
• What is the best project management software?
• How much does project management software cost?
• Is project management software worth it?
• What features should project management software have?
• How do you choose project management software?

That's extremely useful for building a content architecture.

2. Ahrefs or Semrush → validate the questions

I'd then pull the larger keyword universe and add:

• search volume
• keyword difficulty
• traffic potential
• search intent
• SERP features
• ranking pages

Ahrefs can group keywords by Parent Topic, using the SERPs to determine which queries can realistically be targeted by the same page. ahrefs.com

Semrush's Keyword Magic Tool has a Questions filter and automatically groups keywords into topic-specific subgroups; its Keyword Strategy Builder can also automatically cluster imported keyword lists. www.semrush.comwww.semrush.com

3. Keyword Insights → if you have a huge dataset

If you're talking about 5,000–100,000+ keywords, I'd consider Keyword Insights specifically for clustering. That's where automated SERP-based clustering becomes much more valuable than manually organizing spreadsheets.

The important distinction

I'd actually separate question discovery from keyword clustering.

For example:

Question discovery

AlsoAsked → PAA questions → related questions → question tree

↓

Keyword expansion

Ahrefs/Semrush → thousands of variants → volume/difficulty/intent

↓

SERP clustering

Keyword Insights/Ahrefs/Semrush → queries that Google treats as the same topic

↓

Human/AI editorial clustering

Primary topic → subtopics → individual articles → supporting questions

That last step matters because SEO keyword clusters aren't necessarily the same thing as good content clusters.

For example:

Parent topic: Project management software

• What is project management software?
• How does project management software work?
• What are the benefits of project management software?
• How much does project management software cost?
• How do you choose project management software?

could potentially become one comprehensive guide.

But:

Project management software for startups

• Best project management software for startups
• Free project management software for startups
• How much does project management software cost for startups?
• What features do startups need?

could be a distinct commercial cluster.

If you only want one tool

I'd pick Semrush if you want one platform to do almost everything: its question filtering, keyword database, grouping and clustering are all integrated. www.semrush.comwww.semrush.com

If the question relationships themselves are the most important thing, I'd pick AlsoAsked.

If the task is massive-scale keyword → SERP cluster → content map, I'd lean toward Ahrefs + Keyword Insights.

If you tell me what kind of site/niche you're doing this for and roughly how many keywords you want to process, I can recommend the exact stack and workflow (including a cheap/free version).

If you're trying to find question-format keywords (“how,” “why,” “what,” “can,” etc.) and then cluster them into content topics, I'd use a combination of keyword discovery + clustering rather than one tool alone.

My shortlist

What I'd actually use

For a serious content-clustering workflow:

1. Semrush or Ahrefs
Start with a seed topic and export thousands of related keywords. Filter for question modifiers such as:

• what
• why
• how
• when
• where
• which
• can
• should
• does
• is
• are
• do

2. AlsoAsked
I'd use this specifically to uncover the question hierarchy Google associates with a topic. It's particularly useful when you want content that follows the user's actual information journey rather than just a list of semantically similar keywords.

3. Keyword Insights
Then feed the keyword set into a clustering tool. This is the part I'd prioritize if you have 10,000+ keywords and want them grouped into actionable content topics automatically.

The resulting structure might look something like:

TOPIC: Home espresso machines
│
├── Buying
│   ├── what espresso machine should I buy?
│   ├── which espresso machine is best for beginners?
│   └── how much should I spend on an espresso machine?
│
├── Setup
│   ├── how to set up an espresso machine
│   ├── how fine should espresso be ground?
│   └── how much coffee should I put in an espresso machine?
│
├── Troubleshooting
│   ├── why is my espresso machine leaking?
│   ├── why is my espresso coming out too fast?
│   └── why does my espresso taste bitter?
│
└── Maintenance
    ├── how to clean an espresso machine
    ├── how often should you descale an espresso machine?
    └── can you use vinegar to descale an espresso machine?

If I had to pick just one

Keyword Insights if your primary objective is keyword → cluster → content plan.

Semrush if you want a broader SEO platform and clustering is only one part of the workflow.

AlsoAsked if your main objective is discovering questions and building question-led content.

If you're doing this for a large keyword list (e.g. 10k–1M keywords), I'd use a somewhat different stack—I'd combine an SEO database with embeddings/LLM clustering and a search-intent layer rather than relying entirely on the built-in clustering of these tools.

If your goal is finding question-format keywords (e.g. "how do I...", "what is...", "why does...") and then grouping them into topic clusters for SEO or content planning, there isn't one perfect tool. The strongest workflow usually combines keyword research with clustering.

Here's how the leading options compare:

Best overall: Semrush

If you want one platform that handles most of the process:

• Discover thousands of keywords
• Filter for questions
• Automatically group them into clusters
• Build pillar pages and supporting articles
• Estimate search volume and difficulty

This is a solid choice for agencies and larger content teams.

Best for clustering: Keyword Insights

If clustering is the priority, this is one of the strongest specialized tools.

It can:

• Import large keyword lists
• Detect which keywords share search intent
• Group them into article-sized clusters
• Suggest pillar pages
• Generate content briefs

For content production at scale, it's often more accurate than manual clustering.

Best budget workflow

A lower-cost approach is:

1. Use Google Search Console to export your existing queries.
2. Supplement with question keywords from AlsoAsked or AnswerThePublic.
3. Cluster them in Keyword Insights or with AI.

AI workflow (my favorite)

Many content teams now:

• Export 5,000–50,000 keywords from Ahrefs or Semrush.
• Filter to question keywords.
• Use AI (or Keyword Insights) to cluster by search intent rather than just similar wording.
• Create one comprehensive page per cluster instead of one page per keyword.

For example:

Cluster: Starting a Vegetable Garden

• how do I start a vegetable garden
• when should I plant tomatoes
• what vegetables grow in shade
• how often should I water seedlings

Rather than writing four separate articles, you'd create one comprehensive guide covering the cluster.

If you're working with very large datasets (50k+ keywords)

The workflow many SEO teams use is:

• Ahrefs or Semrush for keyword discovery
• Keyword Insights for clustering
• AI (such as ChatGPT) for labeling clusters, identifying content gaps, and generating briefs

This combination scales well while keeping clusters aligned with search intent rather than just keyword similarity.

If I were building a workflow to find question-format keywords (queries like “how do I…”, “what is…”, “why does…”) and turn them into content topic clusters, I’d usually combine SEO keyword tools + clustering software + a spreadsheet/database layer.

Here are the strongest options by use case:

My preferred workflow

1. Collect question keywords

Pull from:

• Ahrefs → Keywords Explorer → filter Questions
• Semrush → Keyword Magic Tool → filter Questions
• AlsoAsked → extract question chains
• Google Search Console → find queries your site already appears for

Example output:

2. Normalize and enrich the list

Put everything into:

• Google Sheets
• Airtable
• Notion

Add columns:

• Primary topic
• Search intent
• Funnel stage
• Suggested page type
• Cluster
• Priority

3. Cluster by search intent/SERP similarity

The best clusters are usually based on what Google ranks together, not just words that look similar.

Example:

Cluster: Solar panel maintenance

• How often should solar panels be cleaned?
• Do solar panels need maintenance?
• How to maintain solar panels
• Solar panel maintenance checklist

→ One pillar page:

“Solar Panel Maintenance Guide”

Supporting articles:

• “How Often Should You Clean Solar Panels?”
• “Solar Panel Maintenance Checklist”
• “Solar Panel Cleaning Costs”

4. Use AI for final grouping

For very large lists (10k+ keywords), I’d export from Ahrefs/Semrush and use:

• Python embeddings (OpenAI embeddings or sentence-transformers)
• ChatGPT with structured prompts
• Airtable AI

A useful prompt:

“Group these keywords into SEO content clusters. Each cluster should represent one search intent and one potential landing page. Return cluster name, primary keyword, supporting keywords, and recommended content type.”

My recommended stack by budget

Solo blogger / small business

• AnswerThePublic
• Ahrefs Lite or Semrush
• Google Sheets
• ChatGPT for clustering

SEO agency

• Semrush or Ahrefs
• Keyword Insights
• Screaming Frog
• Looker Studio reporting

Large content operation

• Ahrefs/Semrush
• Keyword Insights
• embeddings-based clustering pipeline
• enterprise SEO platform

If I had to pick one practical setup today: Ahrefs → export question keywords → Keyword Insights clustering → ChatGPT refinement → content calendar. It gives a good balance of SEO data, automation, and human judgment.

If your goal is finding question-based keywords ("how", "why", "what", "can I", etc.) and organizing them into topic clusters for SEO or content planning, there isn't a single perfect tool. The best choice depends on your budget and how deep you want to go.

Here's how the strongest options compare:

Best overall: Keyword Insights

If content clustering is your primary objective, I'd start here.

It can:

• Import thousands of keywords
• Automatically group them by search intent
• Build pillar pages and supporting articles
• Identify which questions belong together
• Suggest article structures

For example, you might input:

• how to clean leather boots
• can leather boots get wet
• waterproof leather boots
• leather boot care

It would likely cluster these into one comprehensive guide rather than four separate posts.

Best for finding questions: AlsoAsked

This tool is excellent for uncovering how people naturally ask questions.

For example, starting with:

composting

It might branch into:

• How do you start composting?
• What can you compost?
• Why is compost hot?
• Can you compost citrus?
• How long does compost take?

These branches often reveal the structure of an entire content hub.

Best all-around SEO platform: Ahrefs

Ahrefs combines several useful features:

• Question keyword filters
• Keyword difficulty
• Parent topics
• Related questions
• SERP analysis
• Content Gap analysis

You can filter by keywords beginning with:

• who
• what
• when
• where
• why
• how
• can
• should
• does

and export thousands at once.

Budget workflow

If you're trying to keep costs down:

1. Use Google Search Console to export existing queries.
2. Expand ideas with AnswerThePublic.
3. Cluster them using Keyword Insights or even a spreadsheet with AI assistance.

AI-assisted workflow

Many content teams now combine traditional SEO tools with AI:

• Export keywords from Ahrefs or Semrush.
• Feed them into ChatGPT or another LLM.
• Ask it to:
  • group by search intent,
  • identify pillar topics,
  • separate informational from transactional queries,
  • recommend content hubs,
  • generate article outlines.

This approach often produces clusters that are easier for humans to understand than purely algorithmic groupings.

If I were building a content strategy today

For a solo creator or small business:

• AlsoAsked for question discovery
• Ahrefs (or LowFruits if on a tighter budget) for keyword data
• Keyword Insights for clustering
• ChatGPT for refining clusters into editorial calendars and outlines

That combination gives you high-quality question keywords, data-backed clustering, and a practical path from research to publishable content.

If your goal is to find question-format keywords (e.g. "how do I...", "what is...", "why does...") and organize them into content topic clusters, I'd use a combination of keyword research and clustering tools rather than a single platform.

Here's a breakdown by use case:

My preferred workflow

For building a content strategy, I'd typically do something like this:

1. Collect question keywords
   • Ahrefs Questions report
   • AlsoAsked
   • AnswerThePublic
   • Google autocomplete and People Also Ask

2. Combine into one spreadsheet
   • Keyword
   • Monthly search volume
   • Keyword difficulty
   • CPC
   • Search intent
   • Source

3. Cluster the keywords
   • Keyword Insights (automatic SERP-based clustering)
   • Or use AI to cluster semantically into broader topics

Example:

Topic Cluster: Lawn Care

• How often should I water my lawn?
• Why is my grass turning yellow?
• What fertilizer should I use?
• When should I overseed?
• How do I kill crabgrass?
• How do I repair bare patches?

↓

One pillar page:

Complete Homeowner's Guide to Lawn Care

Supporting articles:

• Why Is My Grass Turning Yellow?
• Best Time to Overseed a Lawn
• How to Kill Crabgrass Without Killing Grass
• Lawn Watering Schedule by Season

If you have thousands of keywords

For datasets of 10,000+ keywords, I'd recommend:

• Ahrefs or Semrush for keyword collection
• Keyword Insights for SERP clustering
• OpenAI API (or another LLM) to generate:
  • cluster names
  • pillar page ideas
  • content briefs
  • FAQ sections
  • internal linking suggestions

This approach scales much better than manual grouping.

Lowest-cost stack

If you're on a budget:

• Keywords Everywhere
• AlsoAsked (limited/free tier if available)
• Google Search Console (for existing queries)
• OpenAI API for clustering in batches

This can produce surprisingly strong content clusters without paying for a full enterprise SEO suite.

If you're building programmatic SEO, topical authority, or publishing at scale (hundreds or thousands of articles), a combination of Ahrefs + Keyword Insights + the OpenAI API is one of the strongest workflows for discovering question keywords and turning them into well-organized content clusters.

If your goal is to find question-format keywords (e.g. "how do I...", "what is...", "why does...") and organize them into content clusters, I'd combine a few specialized tools rather than rely on a single platform.

Here's a practical stack depending on your budget and needs:

My favorite workflow

1. Start with AlsoAsked
   • Export all "People Also Ask" questions.
   • This uncovers how users naturally branch from one question to the next.

2. Expand in Ahrefs or Semrush
   • Filter keywords by Questions.
   • Set minimum search volume if needed.
   • Export thousands of question keywords.

3. Cluster in Keyword Insights
   • Upload the exported keywords.
   • It groups keywords by search intent using live SERP overlap rather than just semantic similarity.
   • This often produces clusters that align well with pages you can realistically rank.

4. Build content around each cluster
   • One pillar page.
   • Supporting FAQ sections.
   • Supporting articles if a question deserves its own page.

For example, if your seed keyword is email marketing, you might end up with clusters like:

Getting Started

• What is email marketing?
• How does email marketing work?
• Why is email marketing important?

Software

• What is the best email marketing software?
• How much does email marketing software cost?
• Which platform is easiest for beginners?

Campaigns

• How do you write an email campaign?
• How often should you send marketing emails?
• What makes a good open rate?

Automation

• How do drip campaigns work?
• What is an automated email sequence?
• When should I automate emails?

Each cluster can become either:

• one comprehensive page with FAQs, or
• a pillar page plus supporting articles.

If you want to use AI

A workflow I've found effective is:

• Export 2,000–10,000 question keywords from Ahrefs or Semrush.
• Use an embedding model (such as OpenAI embeddings or similar) to generate vector representations.
• Cluster them with algorithms like HDBSCAN or K-means.
• Have an LLM assign descriptive labels to each cluster and identify:
  • pillar topic
  • supporting questions
  • search intent
  • recommended page type (guide, comparison, FAQ, tutorial)

This tends to produce more coherent editorial groupings than clustering by keyword similarity alone, especially for large datasets.

For agencies or publishers producing hundreds of articles, I'd recommend Ahrefs + Keyword Insights + an LLM. That combination provides comprehensive keyword coverage, search-intent-based clustering, and AI assistance for naming clusters and planning content. For solo creators or small teams, AlsoAsked + LowFruits is a cost-effective way to discover valuable question keywords and organize them into actionable content topics.