ChatGPT answers, unedited

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 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.

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.