What is vectorization—without the math?
Think of every text (a sentence, a paragraph, a whole page) as a dot on an invisible map. Texts with similar meaning land near each other; unrelated texts land far apart.
Vectorization is simply the process of turning your text into the coordinates of that dot, so computers can measure how close two texts are.
• “How to tune a road bike” and “Beginner guide to cycling setup” land close together.
• “How to tune a road bike” and “Sourdough starter tips” land far apart.
This is how Google and AI tools like ChatGPT understand content today: by measuring semantic closeness, not just shared keywords.
Why does this matter for SEO?
1) Topic alignment beats keyword matching
If your article thoroughly covers the topic (definitions, steps, examples, entities), it will sit close to the user’s intent on the “semantic map,”
even if you don’t repeat the exact query words everywhere.
2) Link building becomes topical-fit engineering
The value of a link depends on how topically related the linking page is to your page. Using vectorization, you can check that relatedness with a similarity score.
A link from a page that’s close to you in meaning often carries more value than several off-topic links.
3) Smarter internal linking
Link from each article to the closest related hub and a couple of nearest sibling pages. This creates tight topical clusters that search engines understand and reward.
A quick, practical example
You’re publishing a guest post that links to your “Industrial Safety Checklist” page. Before sending it live, you:
1) Vectorize the guest post paragraph that includes your link.
2) Vectorize the target page you’re linking to.
3) Compare the two with a similarity score (0.0–1.0).
| Score | Action |
| Below 0.20 | Rewrite the content |
| 0.2 to 0.50 | Relevant – improve if there is time |
| Above 0.50 | Strong topical fit – Keep |
• If the score is below 0.20 → the paragraph is off-topic: rewrite it.
• 0.20–0.50 → somewhat relevant: improve if you have time.
• above 0.50 → strong topical fit: publish as is.
Result: your link sits in text that truly matches your page’s topic, which is exactly what search engines look for.
Paragraph-level polishing (the secret SEO win)
Most pages are a mix of strong and weak paragraphs. Vectorization lets you spot weak paragraphs holding back the page’s overall “topic closeness,”
rewrite those paragraphs to add the missing concepts/entities, and re-measure to confirm you’re above your chosen threshold (e.g., 0.50).
Over time, this turns your site into tightly connected, clearly focused topic clusters.
How this shows up in AI Search (SGE, Copilot, Perplexity)
AI assistants often retrieve and summarize the most semantically relevant snippets. If your paragraphs are close to the query in the semantic space,
they’re more likely to be retrieved, quoted, and linked.
Tips:
• Use short, citable sections with clear claim → evidence → takeaway.
• Include key entities and definitions so your text is easy to “match.”
• Keep reading level appropriate—clarity increases your chance of being cited.
What we use at INCREV: QueryMatch
Our tool Query Match vectorizes text (using Sentence-BERT) at paragraph level and gives each block a similarity score.
It can also rewrite weak paragraphs (or full pages) to raise the score above a threshold you choose (e.g., 0.20 → 0.50).
Beyond similarity, it checks readability so the rewrite stays clear for your audience.
In this table, you can see an example where we use QueryMatch to analyze the top 5 websites on the keyword “TrustRank” on Google.
As you can see, they all have high Similarity Score. When the competition on similarity score is hard with almost the same numbers, other factor like general authority, site speed and user interaction takes over to impact rankings.
Top 5 websites similarity score on the keyword “TrustRank”
On interesting example here is that the website increv.co ranks higher with its article on Google TrustRank than Ahrefs article Does. Even if Ahrefs.com is a way more authorative websites than IncRev.co, the Similarity score of 99% on IncRevs article impacts search rankings and puts them higher in the results than the stronger website (Ahrefs) with the lower score of 72%.
Many other factors also impact search rankings so its not only about maximizing similarity score. But if you study a lot of serps, you can see that most of the time, there is a big correlation with search rankings and similarity score. The one with highest similarity score outranks competitors with lower score and thus less relevant content.
| URL | Ranking position | Similarity score to “TrustRank” |
| TrustRank.org | 1 | 97% |
| Wikipedia.com | 2 | 82% |
| IncRev.co | 3 | 99% (!!) |
| Ahrefs.com | 4 | 72% |
| Positional.com | 5 | 94% |
If you want the full mathematical details behind vectorization (including why this works), see our companion article in IncRev SEO Research Community on Zenodo.org:
“Mathematical Foundations of Text Vectorization and the Sentence-BERT Algorithm.”
FAQs
• Is this just keyword density with a fancy name?
No. Vectorization looks at meaning. Two texts can have different words and still land near each other if they mean the same thing.
• Do I need code to try this?
Tools like Query Match do the heavy lifting. If you prefer DIY, you can use open-source libraries (e.g., Sentence-BERT) to embed and compare texts.
SEO implementation checklist (copy/paste)
• Create a short entity checklist per topic (people, standards, tools, steps).
• Measure paragraph similarity to your target intent; rewrite low scorers.
• Use 0.20 / 0.50 thresholds to triage backlinks and guest posts.
• Build internal links to the nearest hub and nearest siblings.
• Structure answers in citable blocks for AI Search.
• Monitor monthly—topics and queries drift.
