What a Keyword Clustering Example Actually Shows
Most keyword clustering explainers show you a diagram: a circle in the middle, a few lines branching out to related terms. That's fine for understanding the concept, but it doesn't answer the question people actually have, which is "what does this look like with my keywords, on my site, before I commit a writing day to it?"
This article skips the diagram and shows the work instead. Below are seven before-and-after examples: a real messy keyword list on one side, the finished cluster and pillar-and-spoke plan on the other. Each comes from a different niche (ecommerce, SaaS, local service, content site) so you can find the pattern closest to your own list and copy the logic directly.
If you want to run this on your own list right now rather than read seven examples first, Murkuz's keyword clustering tool does the grouping for free, in your browser, no login required. Come back to these examples afterward to sanity-check what the tool gave you.
Example 1: Ecommerce Product Page (Running Shoes)
Before (raw list, 9 keywords):
best running shoes for flat feet
running shoes for overpronation
best shoes for flat feet running
running shoes flat feet reviews
best running shoes 2026
top running shoes this year
running shoe reviews
best shoes for marathon training
marathon training shoe guide
After clustering:
| Cluster | Keywords | Page |
|---|---|---|
| Flat feet / overpronation | best running shoes for flat feet, running shoes for overpronation, best shoes for flat feet running, running shoes flat feet reviews | Pillar: "Best Running Shoes for Flat Feet" |
| General best-of, current year | best running shoes 2026, top running shoes this year, running shoe reviews | Pillar: "Best Running Shoes 2026" |
| Marathon training | best shoes for marathon training, marathon training shoe guide | Spoke, links to the 2026 pillar |
Why the split matters: "best running shoes for flat feet" and "best running shoes 2026" look like they could share a page, they're both "best running shoes" searches, but the searcher intent is different. One person has a foot condition and needs a specific recommendation; the other wants a general current-year roundup. Merging them produces a page that answers neither question well, which is the single most common clustering mistake on ecommerce sites.
Example 2: SaaS Blog (Project Management Software)
Before (raw list, 8 keywords):
project management software for small teams
best pm tool for small business
project management tool small team
pm software for startups
project management software comparison
best project management software 2026
gantt chart software
free gantt chart tool
After clustering:
| Cluster | Keywords | Page |
|---|---|---|
| Small team / startup PM software | project management software for small teams, best pm tool for small business, project management tool small team, pm software for startups | Pillar: "Best Project Management Software for Small Teams" |
| General comparison, current year | project management software comparison, best project management software 2026 | Separate pillar (broader, more competitive) |
| Gantt charts | gantt chart software, free gantt chart tool | Spoke, feature-specific, links to whichever pillar fits the site's positioning |
Why the split matters: "small teams," "small business," and "startups" all cluster into one group because they describe the same buyer with different words, that's a textbook case for lexical or semantic clustering to catch automatically. But "gantt chart software" is a feature search, not a buyer-segment search, and forcing it into the small-team cluster would dilute both. Feature keywords usually deserve their own spoke, even when the volume is smaller.
Example 3: Local Service Business (HVAC Company)
Before (raw list, 7 keywords):
ac repair near me
emergency ac repair
air conditioning repair cost
how much does ac repair cost
furnace repair
furnace not working
hvac maintenance plan
After clustering:
| Cluster | Keywords | Page |
|---|---|---|
| AC repair (service + urgency) | ac repair near me, emergency ac repair | Pillar: "AC Repair" (service page) |
| AC repair cost (informational) | air conditioning repair cost, how much does ac repair cost | Spoke: "How Much Does AC Repair Cost?" blog post, links to the service page |
| Furnace issues | furnace repair, furnace not working | Separate pillar: "Furnace Repair" (different appliance, different page) |
| Maintenance | hvac maintenance plan | Own page, different intent (recurring plan vs one-time repair) |
Why the split matters: this is the clearest case of intent splitting inside a single-word overlap. Every keyword here contains "repair" or "hvac," so a purely lexical tool run without a human review pass would happily jam all seven into one cluster. But "ac repair near me" is transactional (call now), while "how much does ac repair cost" is informational (research before calling), and they need different pages: a service page optimized for conversion, and a blog post optimized for the cost question that links to the service page. Never accept a cluster on word overlap alone; check that the intent actually matches.
Example 4: Content Site (Personal Finance)
Before (raw list, 10 keywords):
how to build an emergency fund
emergency fund calculator
how much should be in an emergency fund
emergency savings account
best high yield savings account
high yield savings account rates
compound interest calculator
how does compound interest work
budgeting apps
best budgeting app 2026
After clustering:
| Cluster | Keywords | Page |
|---|---|---|
| Emergency fund | how to build an emergency fund, emergency fund calculator, how much should be in an emergency fund, emergency savings account | Pillar: "How to Build an Emergency Fund" |
| High-yield savings | best high yield savings account, high yield savings account rates | Spoke: comparison-style, commercial intent |
| Compound interest | compound interest calculator, how does compound interest work | Separate spoke, purely educational |
| Budgeting apps | budgeting apps, best budgeting app 2026 | Separate spoke, product-comparison intent |
Why the split matters: this is the classic "big topic, four sub-intents" pattern. A newer site is tempted to write one giant "personal finance basics" page covering all ten keywords, but each cluster has a distinct intent (build a habit, compare a product, understand a concept, evaluate software). Four focused pages, linked from a personal-finance-basics hub, beat one page trying to rank for all four.
Example 5: B2B SaaS (HR Software) with Semantic Overlap
Before (raw list, 8 keywords):
employee onboarding software
new hire onboarding tool
staff onboarding platform
onboarding checklist template
employee offboarding process
offboarding checklist
hr software for startups
best hris for small business
After clustering:
| Cluster | Keywords | Page |
|---|---|---|
| Onboarding software (semantic match) | employee onboarding software, new hire onboarding tool, staff onboarding platform | Pillar: "Employee Onboarding Software" |
| Onboarding templates | onboarding checklist template | Spoke, different content format (template, not software review) |
| Offboarding | employee offboarding process, offboarding checklist | Separate cluster entirely, opposite lifecycle stage |
| HR software (broad) | hr software for startups, best hris for small business | Separate pillar, broader category page |
Why the split matters: "employee onboarding software," "new hire onboarding tool," and "staff onboarding platform" share almost no words beyond "onboarding," exactly the case a lexical tool can miss and a semantic tool catches by meaning instead of matching text. A purely word-based pass might scatter these three into separate clusters when they should be one page.
Example 6: Recipe / Food Blog (Keyword Cannibalization Case)
Before (raw list, and the site ALREADY has two live pages):
Existing page A: "easy banana bread recipe" (ranks position 14)
Existing page B: "moist banana bread recipe" (ranks position 22)
Keyword list: easy banana bread recipe, simple banana bread recipe,
moist banana bread recipe, best banana bread recipe, banana bread recipe
After clustering:
| Finding | Detail |
|---|---|
| Cluster | All five keywords land in one cluster; they describe the same dish with minor adjective variation |
| Problem | Two live pages both target this one cluster: classic keyword cannibalization |
| Fix | Consolidate into one page targeting "banana bread recipe," 301-redirect the weaker page (position 22) into the stronger one, and let the merged page's authority combine |
Why this example is different: the first five examples show clustering BEFORE you publish. This one shows why clustering still matters AFTER you've published, when two existing pages are quietly splitting the same ranking. It's the single most common thing a clustering pass finds on a site that's been publishing for more than a year: nobody planned for two pages to compete, it just happened gradually as different writers picked slightly different phrasing for the same search intent.
Example 7: Informational Blog (How-To Cluster With a Clear Pillar)
Before (raw list, 9 keywords):
how to clean a cast iron skillet
cast iron skillet cleaning
how to season cast iron
seasoning cast iron pan
cast iron rust removal
how to remove rust from cast iron
best oil for cast iron
cast iron care guide
cast iron cooking tips
After clustering:
| Cluster | Keywords | Page |
|---|---|---|
| Cleaning | how to clean a cast iron skillet, cast iron skillet cleaning | Spoke |
| Seasoning | how to season cast iron, seasoning cast iron pan, best oil for cast iron | Spoke |
| Rust removal | cast iron rust removal, how to remove rust from cast iron | Spoke |
| Overall care (pillar) | cast iron care guide, cast iron cooking tips | Pillar, links down to all three spokes |
Why the split matters: this is the cleanest possible cluster structure, one pillar and three tightly scoped spokes, because the keywords describe genuinely different tasks even though they share one umbrella topic. If your cluster naturally separates into "the overview" plus a few "how do I do this one specific sub-task" groups, you've found a real pillar-and-spoke structure, not just a pile of similar-sounding keywords.
The Pattern Across All Seven Examples
Look back across these seven and the same three checks show up every time:
- Word overlap is a starting point, not the answer. Every "before" list above has keywords that share words but split into different clusters anyway (flat feet vs. 2026 roundup, ac repair vs. ac repair cost, onboarding vs. offboarding).
- Semantic-only overlaps are the ones lexical tools miss. Example 5 is the case to watch for: keywords describing the same thing in different words. If your cluster feels "off" even though the words don't obviously match, that's usually why.
- Clustering isn't only a pre-publish exercise. Example 6 shows the same logic applied to two pages that already exist and are already competing. Run the same check periodically on your published content, not just on new keyword lists.
If you want to try this against your own keyword export, paste it into the keyword clustering tool: it groups lexically in your browser (free, no login, nothing sent to a server), then hands you back a pillar-and-spoke plan you can review with the intent checks above.
Frequently Asked Questions
What is an example of keyword clustering?
A simple example: "best running shoes for flat feet," "running shoes for overpronation," and "best shoes for flat feet running" all cluster into one group, because they describe the same search (someone with flat feet looking for a shoe recommendation) using different phrasing. They become one page instead of three.
How do you know if two keywords belong in the same cluster?
Check whether the same searcher would be satisfied by the same page. If the words overlap but the underlying need is different (a repair versus a repair cost question, a product review versus a buying guide), split them into separate clusters even though they look similar on paper.
Can keyword clustering fix pages that are already competing with each other?
Yes. Running a clustering pass on your existing published URLs, not just a fresh keyword list, is how you catch cannibalization that has already happened. Example 6 above walks through exactly this case: two live pages targeting the same cluster, both underperforming as a result.
Do I need a paid tool to see results like these?
No. Every cluster in this article is the kind of grouping a free lexical tool produces in seconds. The judgment calls (splitting by intent, catching semantic-only overlaps) are things you review by eye afterward, which costs time, not money.
How many keywords make a good cluster?
There's no fixed number; several of the examples above use as few as two keywords per cluster and still justify a standalone page, because the volume and intent were specific enough to earn one. Prioritize intent match over hitting a target keyword count.
Turn Your Own List Into a Cluster
Reading seven examples is useful for pattern-matching, but the real test is your own keyword list. Pull your keywords from Search Console or a research tool, paste them into Murkuz's free keyword clustering tool, and run the same intent check against each group these examples walked through.
The clustering itself is a one-afternoon job. What actually compounds over time is what happens after: watching your published pages so a cluster that made sense on day one doesn't quietly split into two competing pages eighteen months later, the same drift Example 6 showed above. That ongoing detection, not just the initial grouping, is what Murkuz's workflow is built to handle. If you're comparing plans, the pricing page lays out what's included at each tier.
I'm Junaid Khalid, founder of Ertiqah, the team behind Murkuz. I've run SEO as the first growth channel across every SaaS product I've built, and keyword cannibalization from unreviewed clusters is one of the most common, least-noticed problems I've had to clean up on mature sites. These examples are the exact checks I use before I trust any cluster, tool-generated or not.
