Keyword Density Checker

What is the Keyword Density Checker?

The Keyword Density Checker is a free tool that counts how often a keyword appears in your text and what share of all words it makes up. It also ranks the single words, two word phrases and three word phrases you repeat most, which shows what a page is about to a reader, a search engine or an AI model.

How to use the Keyword Density Checker

1. Paste the body copy of the page or draft. Leave out the menu, footer and sidebar, which are not what the page is about.
2. Type your target keyword, if you have one, to get its exact count, density and whether it appears in the first paragraph.
3. Switch between 1, 2 and 3 word phrases to see what the text repeats.
4. If the top phrases are not the topic you meant, the page reads as being about something else.

What is a good keyword density?

There is no ideal density, and Google has never used one. Pages that rank usually land somewhere around 0.5% to 2% for their main keyword because that is what natural writing on a topic produces, not because anyone aimed for it. Above about 3% a single keyword usually reads as repetitive, and this tool marks it amber.

A better check is the phrase list. If your three most repeated two word phrases are the topic and its close variants, the page is on topic. To see whether answer engines agree, run the AI Brand Visibility Checker. If they are your brand name and "click here", it is not.

How is keyword density calculated?

Keyword density is the number of times the keyword appears, divided by the total number of words, times 100. A keyword used 6 times in 600 words has a density of 1%. Matches are whole words and ignore case, so "SEO" matches "seo" but not "seoul".

Some tools multiply the count by the number of words in the phrase. This one does not, so a two word phrase used 6 times in 600 words is 1% here, which is the more common convention.

Does keyword density matter for AI search?

Not as a number. Answer engines retrieve passages by meaning, using embeddings, so a passage that answers the question in different words can still be retrieved. What still matters is that the exact entity or term appears where the answer is: a model asked about "AI visibility tools" is more likely to quote a passage that names them than one that says "these platforms".

What is a keyword density checker?

A keyword density checker is a tool that counts how often a word or phrase appears in a text and shows that count as a share of all the words. This one also shows the total and unique words, whether your keyword appears in the first paragraph, and the 15 single words, two word phrases and three word phrases you repeat most. A two or three word phrase is listed only once it appears at least twice, so one-off combinations do not crowd the table.

It runs in your browser on the text you paste, so it works on a draft before it is published, on a competitor's article, or on a page you cannot edit. Nothing is uploaded. Because you choose exactly what goes in, you can also check one section at a time, such as the introduction, to see whether the topic is clear from the start.

Read the single word list first. Your topic should sit at or near the top. If a generic word such as "service" or "solution" outranks it, the copy is talking around the subject rather than about it.

How do I check keyword density in Google Docs or Word?

Neither has a density tool, but both give you the two numbers you need. In Google Docs, press Ctrl+F (Cmd+F on Mac) and type the keyword: the box shows how many matches it found. Then press Ctrl+Shift+C for the word count. In Word, the search box in the Navigation pane shows the number of results, and the word count sits in the status bar.

Divide the matches by the word count and multiply by 100. Both apps find partial matches by default, so a search for "rank" also counts "ranking" and "frank", which inflates the figure. Pasting the text here does the whole-word matching and the sum for you, and adds the phrase list, which neither app can produce.

On a live page, Ctrl+F in Chrome or any other browser also shows a match count, but it counts the menu, the footer and any sidebar too, so the figure runs high on short articles. Copy just the article text when the number matters.

What is keyword stuffing?

Keyword stuffing is filling a page with a keyword, or with lists of keywords or numbers, in an attempt to manipulate rankings. Google's spam policies give examples: blocks of text listing the cities a page is trying to rank for, lists of phone numbers that add nothing, and the same words repeated so often that the text sounds unnatural. A page can break that policy without a high density at all, for example with a footer that lists every town the business serves.

Density is the quickest way to spot the last kind in your own copy. If one term sits well above 3%, or the same two word phrase opens several paragraphs in a row, read the text aloud. Wherever the keyword has replaced a pronoun or a more natural word, swap it back. The page will read better and lose nothing a search engine needs, since it already knows the topic from the title, the headings and the first paragraph.

How do I compare keyword use with the pages that rank?

Paste the body text of two or three pages that rank for your keyword, one at a time, and note their top two and three word phrases. Then paste your own. You are looking for subtopics they all repeat that your page never mentions, not for a density figure to copy.

If every ranking page repeats "monthly cost" and "free trial" and yours names neither, that is a section you are missing. Matching their percentage for the main keyword does nothing on its own. To see where a term appears on a live page, in the title, headings, alt text and links as well as the body, use Find Keyword on Page.

Run the check again after you rewrite. Your phrase list should now include the subtopics you added, and the main keyword should still appear in the first paragraph. If the new sections pushed the keyword density down, that is expected: a longer page that covers more of the topic uses the exact phrase less often.