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- March 24, 2025
Search Engine Optimisation (SEO) has always been about understanding how search engines interpret and rank content. As Google’s algorithms become more sophisticated, leveraging Natural Language Processing (NLP) techniques like embeddings is no longer just for data scientists, it’s becoming a powerful tool for SEOs looking to stay ahead.
Embeddings allow us to move beyond traditional keyword matching, helping search engines and AI-driven models understand the deeper relationships between words, topics, and intent. In this article, we’ll break down what embeddings are, how they’re applied in SEO, and practical use cases where they can give you an edge.
Embeddings are vector representations of words, phrases, or entire documents, capturing their semantic meaning in a way that enables machines to process language more like humans. Instead of treating words as isolated tokens, embeddings map them in a multi-dimensional space based on context and relationships.
For SEO, this is useful because Google no longer relies solely on exact-match keywords. Instead, it evaluates the relationships between words and concepts. If you’re still optimising pages with outdated keyword-stuffing techniques, you’re already behind.
Google’s use of embeddings has evolved significantly, with major updates incorporating NLP models such as:
In practical terms, these updates mean Google now ranks content based on meaning and relevance rather than just keyword density.
Image credit: STAT
Here’s where embeddings become actionable. SEOs can use them for various tasks, from content optimisation to technical SEO and search intent analysis.
Traditional keyword research tools rely on search volume and competition scores, but embeddings allow us to go deeper by:
For example, instead of simply targeting “cheap sofas,” embeddings can help surface variations like:
This aligns with how search engines interpret user intent rather than just matching text.
Google evaluates whether your content is topically authoritative rather than just containing the right keywords. Embeddings can:
If you’re working with embeddings, you can analyse how well your content aligns with a topic cluster rather than just hitting keyword targets.
Not all keywords carry the same intent, and Google is increasingly sensitive to this. Embeddings allow SEOs to:
For example, embeddings can help distinguish:
Aligning content to the right intent is crucial for ranking well.
Google’s Knowledge Graph and entity-based ranking systems rely on embeddings to understand connections between:
SEOs working on E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) can leverage embeddings to:
Embeddings allow us to go beyond traditional silo structures and build intelligent internal linking systems that mimic how Google sees relationships between topics. Some applications include:
This approach helps distribute link equity more effectively while reinforcing topic authority.
Google’s AI search features prioritise content that’s well-structured and easy to summarise. Embeddings help SEOs:
With AI-driven search expanding, optimising for summarisation-based rankings is becoming front and centre.
If you want to integrate embeddings into your SEO workflow, here are some tools and techniques to consider:
If you’re running Python scripts or working with AI-driven SEO tools, embeddings can significantly refine your research, content strategies, and ranking models.
Image Credit: Bernard Marr
As search engines rely more on AI and NLP models, embeddings will play an even greater role in ranking algorithms, search personalisation, and AI-generated search results. Future developments may include:
For SEOs willing to embrace AI-driven strategies, understanding and applying embeddings is a competitive advantage. Those who don’t will fall behind as search shifts further towards machine-learning-driven relevance.
SEO has always been about keeping up with Google’s evolving algorithms, and embeddings represent one of the biggest shifts in how search engines understand content. By leveraging embeddings in keyword research, content optimisation, internal linking, and entity recognition, SEOs can stay ahead of the curve.
But this isn’t just theoretical. I’ve built Python scripts that apply embeddings and NLP techniques for tasks such as detecting duplicate content, identifying off-topic pages, and improving semantic relevance in search. If you’re interested in practical applications, I’ve detailed one of these scripts in my forum post:
Detecting Duplicate Content and Off-Topic Pages Using Python, AI & NLP
This script uses sentence embeddings, similarity scoring, and NLP techniques to compare page content and flag instances where articles are too similar, off-topic, or potentially lacking relevance. In a world where Google’s algorithms penalise thin and duplicate content, this kind of approach is becoming essential for technical SEOs.
The key takeaway? Stop thinking in terms of just keywords. Start optimising for meaning. If you’re not using embeddings and AI-driven analysis in your SEO strategy yet, now is the time to start.
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