SEO Similarity Score – Understand How Relevant Your Page Is
This free Chrome extension helps you analyze how closely the content on a page matches the keyword or phrase you’re targeting. It highlights each section of a page based on how well it aligns, and provides a total similarity score. It also includes a keyword ideas tool that suggests related terms and shows how semantically similar they are.
Why use this extension?
- Section-level relevance: See how closely each section matches your query
- Visual highlighting: A simple color-coded system shows where your content is strong or off-topic
- Similarity score: Get a quick overview of how relevant the page is overall, based on semantic similarity
- Keyword ideas: Generate up to 100 related keyword ideas with similarity scores, so you can explore supporting terms to include
- Fully local: The extension runs in your browser. You use your own OpenAI API key, and no data is sent to third parties
How it works
- Install the extension: Add the SEO Similarity Score extension to Chrome
- Open a page: Navigate to a page you’d like to analyze and click the extension icon
- Enter a keyword and your API key: Type your target query and use your OpenAI API key for analysis. You can generate an API key on the OpenAI website
- View Highlights: The extension highlights each section of content based on how semantically close it is to your keyword
- Use the Keyword Ideas Tool: Switch to the Ideas tab to generate additional keyword ideas and export them if needed
What are vector embeddings?
Behind the scenes, the extension uses something called vector embeddings. These are mathematical representations of words, phrases, or even entire documents as points (vectors) in a multi-dimensional space. This approach allows the extension to compare the meaning of your target keyword to the content on the page, not just the exact words. The closer the vectors, the higher the similarity score.
Why is this important?
Search engines like Google use vector embeddings and AI models to understand the intent and context behind search queries and web pages. Instead of just matching keywords, Google’s algorithms (including BERT and MUM) compare the semantic meaning of your content to what users are searching for. This means your content can rank for related queries, even if the exact keywords aren’t present.
For example, if your page talks about “cardiovascular fitness” and someone searches for “how to improve heart health,” vector embeddings help search engines recognize the connection, even without an exact keyword match.
How do vector embeddings help with SEO?
- Semantic relevance: Measure how closely your content matches a topic or query, beyond just keywords
- Content gap analysis: Identify missing topics or sections by comparing your content’s vectors to those of top-ranking competitors
- Content clustering & internal linking: Group related articles and find smart internal linking opportunities based on semantic similarity
- Keyword expansion: Generate related keyword ideas that are semantically connected, not just similar in spelling
- Future-proofing: As AI-powered search becomes the norm, optimizing for semantic meaning helps your content stay visible as search evolves
What’s the science behind it?
Each piece of text is converted into a set of numbers (a vector). These vectors are compared using mathematical formulas like cosine similarity. The closer the vectors, the more related the meanings.
Privacy: No tracking, no storage
We don’t collect, store, or share any data. Your API key is stored only in your browser, and all processing happens locally. The extension doesn’t track usage, log what you analyze, or communicate with any external service beyond OpenAI, and only when you request it.
Get the Chrome extension
Add the SEO Similarity Score extension to your browser and start reviewing page relevance with more confidence.
Need help?
If you have questions or run into issues, feel free to get in touch. We’re happy to help or hear feedback.