Keyword Density Checker: How It Works
Keyword density counts how often a term appears relative to total words. It stopped being a ranking factor many years ago, but the tool remains useful for the opposite purpose: spotting pages that have drifted into over-optimisation, or that never mention their own subject clearly.
The calculation
Density % = (occurrences of the term ÷ total words) × 100
A term appearing 12 times in an 800-word page has a density of 1.5%.
Why the old targets are obsolete
Early search engines ranked largely on term frequency, so 'aim for 2–3% density' became standard advice — and immediately became a target to game. Modern systems evaluate meaning rather than counting: they identify related concepts, recognise synonyms and word forms, and assess whether a page comprehensively addresses a topic. A page can rank strongly for a phrase it never uses verbatim, and a page repeating a phrase 40 times will rank worse for it, not better.
| Density | Usually indicates |
|---|---|
| 0% | The topic may not be addressed explicitly at all |
| 0.5% – 2% | Natural usage — what well-written content produces on its own |
| 2% – 4% | Possibly deliberate; worth reading aloud to check |
| Above 4% | Almost always reads badly and risks being treated as spam |
The right way to use these figures is as a diagnostic, not a target. Write the page naturally, then check the density: if it landed above 3%, you probably repeated yourself; if the main term is absent entirely, the page may be unclear about its own subject.
What to look at instead
- Topical coverage. Does the page address the subtopics a reader would expect? A page about mortgage rates that never mentions fixed versus variable is incomplete regardless of density.
- Entity coverage. Are the relevant people, products, standards and places named?
- Query intent. Does the format match what the query wants — a definition, a comparison, a procedure, a tool?
- Uniqueness. Does it contain anything the top ten results do not?
Where placement still matters
Frequency counts for little; position counts for something. A term in the title, the H1, the opening paragraph, a subheading and the URL signals the subject clearly — and that is five occurrences rather than fifty. Beyond that, natural variation serves you better than repetition: singular and plural, related phrasings, the question form someone would actually type.
Reading the report usefully
Look at the whole distribution rather than one term. A useful page shows a spread of related terms at low densities — the vocabulary of the topic. A thin or over-optimised page shows one term spiking above everything else, with little supporting language around it. That shape, more than any single percentage, is what the tool is actually good for.
Two-word and three-word phrases
Multi-word phrase analysis is often more revealing than single words. It exposes repeated stock phrasing — the same construction opening six paragraphs — which reads as formulaic to humans long before any algorithm notices. Fixing that improves the page for readers, which is the version of optimisation that still works.