Use this free keyword density tool for SEO keyword analysis. Analyze your content for keyword frequency, multi-word phrases, and target keyword prominence — 100% private, nothing is sent to any server.
How Keyword Density Is Calculated
Keyword density measures how frequently a keyword or phrase appears in your text relative to the total word count. The formula is: keyword density (%) = (keyword occurrences ÷ total words) × 100. If "content marketing" appears 10 times in a 500-word article, its density is 2% (10 ÷ 500 × 100).
This tool displays keyword density across three levels:
- Single keywords: the top 20 most frequent individual words, after filtering out common stop words (the, a, and, of, to, etc.)
- Bigrams: the top 20 most frequent two-word phrases
- Trigrams: the top 20 most frequent three-word phrases
What is the ideal keyword density for SEO? Most SEO practitioners recommend keeping your primary keyword at 0.5–2% density. Below 0.5%, the topic may appear undercovered to search engines. Above 3%, the repetition becomes noticeable to readers and may trigger Google's quality filters for keyword stuffing. The safest approach is natural writing — if a keyword appears at a density that feels forced to a human reader, it is almost certainly too high.
TF-IDF vs. keyword density: Modern search engines use TF-IDF (Term Frequency–Inverse Document Frequency) rather than raw density. TF-IDF weighs how often a term appears in your document against how often it appears across all documents on the web. A word that appears frequently in your text but rarely across the wider web carries more topical signal than a common word with similar frequency. Keyword density is a simplified proxy for this relationship; TF-IDF is the underlying mechanism search engines actually use.
Keyword placement matters more than density. A keyword appearing in your H1, opening paragraph, and subheadings signals relevance more strongly than the same keyword clustered in one section mid-article. This tool shows whether your target keyword appears in the first 100 words, which is the most important placement signal for on-page SEO.
Example: Fixing Keyword Distribution in an SEO Article
An SEO writer is optimizing a page targeting the phrase "best running shoes for flat feet." She pastes her 900-word draft and enters the target phrase in the keyword input. The analysis shows:
- "running shoes" (bigram): appears 11 times — density 2.44%
- "flat feet" (bigram): appears 6 times — density 1.33%
- "best running shoes" (trigram): appears 4 times — density 0.89%
The bigram "running shoes" at 11 occurrences in 900 words means it appears roughly every 82 words — noticeable to a reader. More concerning, checking the distribution shows 7 of the 11 occurrences in the first 300 words. The back half of the article uses the phrase only 4 times, making the keyword placement pattern look unnatural.
She redistributes the keyword usage and introduces natural synonyms: "trail runners," "athletic footwear," "stability shoes," and "motion control sneakers." After revision, "running shoes" appears 7 times with even distribution across the article. She then checks the trigram results and notices "motion control shoes" and "arch support insoles" appearing as new topical clusters — subtopics she had not fully addressed. She expands those sections by 200 words, strengthening the topical authority of the page without forcing any single phrase higher.
Key Factors in Keyword Analysis
Stop words are filtered out. Common words like "the," "a," "and," "of," "to," and "in" are excluded from keyword analysis because they appear in virtually every piece of content and carry no SEO signal. The analysis surfaces only meaningful content terms, making it easier to spot genuine topical patterns in your writing.
Keyword variations count separately. "Running shoe," "running shoes," and "best running shoes" are three distinct phrases in this analysis. Google's natural language processing understands them as related, but tracking them independently lets you see exactly how each variant is distributed — which is more actionable than aggregating them into a single number.
LSI keywords (Latent Semantic Indexing terms) are topically related words Google expects to see in content on a given subject. A page about "coffee brewing" should naturally include "beans," "grind," "extraction," "temperature," and "flavor." The keyword density results can reveal LSI gaps: if expected related terms are absent from your top keywords, your content may appear thin on a subtopic that competitors cover more thoroughly.
Keyword stuffing is penalized. Google's guidelines explicitly address keyword stuffing — unnaturally repeating keywords to manipulate rankings — as a quality violation. A density above 5–7% for any single term is a reliable indicator that the text has been optimized for search engines rather than readers, and Google's quality systems treat it accordingly. The 0.5–2% range stays comfortably within natural writing patterns.