The percentage of times a keyword appears relative to total word count, largely replaced by NLP-based optimization.
Keyword density is the percentage of times a target keyword appears in content relative to the total word count. Calculated by dividing keyword occurrences by total words and multiplying by 100, this metric was once a cornerstone of SEO optimization. A page with 1,000 words containing a keyword 20 times would have a 2% keyword density.
This metric dominated SEO strategy for over a decade, with practitioners religiously targeting 2-5% density ranges. However, modern search algorithms have evolved far beyond simple keyword counting, making density largely obsolete as a primary optimization signal.
Why It Matters for AI SEO
Google's AI systems like BERT, MUM, and neural matching have fundamentally changed how search engines understand content relevance. Instead of counting keyword repetitions, these models analyze semantic meaning, context, and topical relationships. A page about "digital marketing strategies" might rank for "online advertising techniques" without ever using that exact phrase. Modern AI-powered SEO tools reflect this evolution. Platforms like Clearscope and MarketMuse focus on semantic relevance scores rather than keyword density percentages. They analyze top-ranking pages to identify related terms, entities, and concepts that create comprehensive topical coverage. This shift from keyword counting to meaning understanding represents the most significant change in on-page optimization since search engines began.
How It Works in Practice
Today's content optimization workflow prioritizes natural language over mechanical keyword insertion. Tools like SurferSEO still display keyword density but emphasize their "content score" that weighs semantic relevance more heavily. Writers should focus on covering topics thoroughly rather than hitting specific density targets. Effective modern optimization involves using primary keywords naturally in key positions (title, H1, early paragraphs) while building semantic richness through related terms. Frase's content briefs, for example, suggest topic clusters and related entities rather than specific keyword repetition counts. This approach creates content that satisfies user intent while avoiding the awkward phrasing that high-density optimization often produced.
Common Mistakes
The biggest misconception is that keyword density still drives rankings. Many practitioners continue targeting arbitrary percentage ranges, creating stilted content that feels unnatural to readers. Over-optimization through forced keyword insertion can trigger Google's spam detection systems, particularly the Helpful Content algorithm that specifically targets content created primarily for search engines rather than users. Another common error is ignoring keyword density entirely. While not a ranking factor, zero mention of target keywords signals poor topical focus. The goal isn't hitting a magic percentage but ensuring your primary keywords appear naturally while building comprehensive semantic coverage around your topic.