How to Cluster Keywords in Google Sheets (Free, Step by Step)

You can cluster keywords in Google Sheets for free using nothing but helper columns and three formulas — SEARCH, REGEXMATCH, and COUNTIF — to tag each keyword with a “primary term” and sort the chaos into groups. It works, it costs nothing, and it’ll get you surprisingly far on a few hundred keywords. It also breaks down the moment your list outgrows your patience, and we’ll be honest about exactly where that line is.

But first, the part that actually helps: a method you can follow right now.

The idea behind term-based clustering in Sheets

Clustering means grouping keywords that share the same intent so one page can target the whole group instead of splitting your authority across five competing pages. Sheets can’t read meaning, but it can match strings. So we cheat: we assume keywords sharing a head term usually share intent.

“keyword research tool,” “best keyword research tool,” and “keyword research software” all contain tool-ish signals and all mean “I want a tool.” Match the shared term, and you’ve approximated the cluster. It’s a blunt instrument, but a useful one.

The plan: build a list of candidate terms, flag which keywords contain each term, assign every keyword a single primary term, then sort. Four formulas, four columns, done.

Step 1: Get your keywords into column A

Paste your keyword list into column A, one per row, with a header in A1. If you’re starting from a bulk keyword export, drop the keywords in column A and (ideally) their monthly search volume in column B — we’ll want volume later, and you’ll be glad it’s already there.

A                              B
keyword                        volume
keyword research tool          12100
best keyword research tool     2400
how to do keyword research     1900
keyword research software      880
keyword research for youtube   720
free keyword research tool     1600
keyword research tutorial      390

Step 2: Build a list of candidate terms

Off to the side — say column E — list the head terms you want to cluster around. This is the one judgment call you can’t formula your way out of. Skim your keywords and write down the recurring nouns: the things people are actually after.

E
tool
software
tutorial
how to
youtube
free

Order matters here, and not alphabetically. Put your most specific, most intent-defining terms at the top and the generic ones below. “youtube” should rank above “free,” because keyword research for youtube is a YouTube cluster first and a free-tool cluster never. We’ll use that ordering in Step 4.

Step 3: Flag which keywords contain which terms

In C1, label the column matches and drop in a formula that checks whether the keyword contains any of your candidate terms. REGEXMATCH is built for this — it takes a pipe-delimited pattern (tool|software|tutorial) and returns TRUE on the first hit:

=REGEXMATCH(LOWER(A2), "tool|software|tutorial|how to|youtube|free")

Lowercasing both sides keeps “Tool” and “tool” from being treated as strangers. Fill it down. Now every keyword reads TRUE (we have a home for it) or FALSE (it matched nothing — note it; you may need another term).

Want to count how big a term’s group is before committing? COUNTIF with wildcards tallies every keyword containing a term:

=COUNTIF(A:A, "*"&E2&"*")

Drop that beside your term list and you’ve got an instant size estimate per cluster — handy for spotting which groups are worth a page and which are a rounding error.

Step 4: Assign each keyword ONE primary term

A keyword can contain several terms — “free keyword research tool” hits both free and tool. Clustering needs one home per keyword, so we pick the first candidate term that matches, reading top-down through your ordered Step 2 list. That’s why you put the intent-defining terms up top.

In D1, label it cluster, and in D2:

=IFERROR(
  INDEX($E$2:$E$7, MATCH(TRUE, ISNUMBER(SEARCH($E$2:$E$7, LOWER(A2))), 0)),
  "unclustered"
)

What it does, plain-English: SEARCH looks for each term inside the keyword and returns a number when found; ISNUMBER turns that into TRUE/FALSE; MATCH(TRUE, …, 0) grabs the position of the first TRUE; INDEX returns the term sitting at that position. Anything that matches nothing falls to "unclustered" instead of erroring out.

Fill D2 down the whole column. If your term list isn’t exactly E2:E7, adjust both ranges to match.

If SEARCH throws errors or refuses to spill down, you’re likely on an older Sheets setup — wrap the whole thing in ARRAYFORMULA(...) and enter it once in D2; it’ll populate the column on its own.

Step 5: Sort or pivot into clusters

Now the column finally pays off. Two ways to see your groups:

Sort — select your data, Data → Sort range, sort by column D (cluster), then by column B (volume, descending). Your keywords settle into blocks, each block a cluster, highest-volume keyword on top. Skim down column D and there’s your content plan: one block, one page.

Pivot — Insert → Pivot table, drag cluster into Rows and volume into Values as SUM. You get one row per cluster with its total monthly search volume — the single most useful number for deciding what to write first. Sort that pivot by volume and you’ve ranked your entire content calendar by demand.

That’s a real, working clustering pass, built for free, in about ten minutes. For small lists, genuinely use it.

Where the Sheets method quietly falls apart

Here’s the honest part, because string-matching is not the same as understanding, and the gap shows up fast:

• It clusters words, not meaning. “apple keyword research” lands wherever your terms send it, blind to whether someone means the fruit or the company. Synonyms scatter — “cheap,” “affordable,” and “budget” are three clusters to Sheets and one intent to a human. This is the difference between term-matching and semantic clustering, and it’s the whole ballgame.
• You hand-build the term list. Your clusters are only as good as the terms you thought to type in Step 2. Miss one and a whole topic hides in “unclustered.”
• First-match is a coin flip on overlap. Order the term list wrong and “free keyword research tool” files under free when it wanted tool. You’ll be re-sorting by hand.
• It dies past a few hundred keywords. On 200 keywords, curating terms and skimming results is a coffee’s work. On 5,000-plus from a real bulk research run, maintaining the term list, fixing misfires, and eyeballing the output becomes the afternoon you were trying to avoid. The formulas scale; your attention doesn’t.

None of this makes the method worthless. It makes it a small-list method — which is exactly what most people need it to admit.

The one-click version (with volume already attached)

When the list is big enough that babysitting formulas stops being fun, the job is identical — group by shared meaning and intent, attach the demand — but it’s not a spreadsheet’s job anymore.

KeywordOrbit does this clustering pass in one click: it groups thousands of keywords by intent and shows the total monthly search volume per cluster automatically — no helper columns, no curated term list, no first-match guesswork. The pivot table you’d build by hand in Step 5, already done and ranked by demand. See how it works →

Because KeywordOrbit pulls every keyword straight from Google Autocomplete with search volume attached, the clusters arrive demand-ranked the second they appear — no separate export, no VLOOKUP to bolt volume back on. If you’d rather understand the machinery before reaching for a button, the keyword cluster tool guide walks through exactly what automated clustering does under the hood.

The short version

For a few hundred keywords, Google Sheets clusters them for free: list your terms, flag matches with REGEXMATCH, assign a primary term with INDEX/MATCH/SEARCH, then sort or pivot by volume. It’s a genuinely good trick and you should keep it in your back pocket.

Just know what you’re holding. It groups strings, not intent, and it taps out somewhere north of a few hundred rows. Past that, one-click clustering does the same job without the manual labor — and hands you the search volume that tells you which cluster to write first.