TF-IDF
Also called: term frequency inverse document frequency, term frequency-inverse document frequency, TF*IDF, tf idf
TF-IDF (term frequency-inverse document frequency) is a statistic that scores how important a word is to one document relative to a larger collection of documents. It multiplies how often a term appears in a document by how rare that term is across the whole corpus.
TF-IDF comes from information retrieval, not from Google specifically. The math is simple. Term frequency (tf) counts how often a word shows up in one document. Inverse document frequency (idf) is usually log(N / df), where N is the number of documents in the collection and df is how many of them contain the word. Multiply the two and you get the weight: tf-idf = tf × idf.
The idf half exists to discount words that appear everywhere. “The” and “and” show up on almost every page, so their idf sits near zero and they carry little signal. A term that appears often in one document but rarely across the corpus gets the highest weight, because it actually describes what that document is about.
Does Google use TF-IDF?
Not as a standalone ranking factor. Google’s John Mueller called it “a fairly old metric” and said “things have evolved quite a bit over the years,” pointing to newer techniques. Modern retrieval and language models use TF-IDF-style ideas as one input among many, not as a dial you tune directly.
That is why SEO tools that hand you a “TF-IDF score” to hit can mislead. Writing to a target number pushes you toward keyword stuffing, which is the opposite of what the concept rewards. The useful read is directional: cover the terms a topic genuinely needs, and do not pad with words that add nothing.
How it affects your traffic
On-page SEO is where this concept earns its keep. If your page about a topic is missing the words and subtopics that genuinely belong to it, both classic retrieval and modern language models have a harder time matching it to relevant queries, so it ranks for fewer terms and pulls less traffic. Our On-Page SEO work maps the vocabulary a topic actually needs and works it in naturally, without stuffing to a fake target score. The result is pages that read like a human wrote them and still cover the ground search engines expect.
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