Google doesn't read your content the way you think.

[MadOnGrind]

It converts it into a vector a mathematical representation and measures distance from the query vector.

Ranking is, essentially, a proximity calculation. Most SEOs don't optimise for this at all.

What's actually happening:
Every page on the web gets embedded into a high-dimensional vector space. So do queries. When someone searches, Google retrieves the pages whose vectors sit closest to the query vector and then re-ranks them. Keywords are a coarse input to that calculation. Semantic neighbourhood is the real signal.

Why your page is ranking #15:
Not because the keyword is missing. Because the vector is too far from the cluster the query lives in. The usual reasons:
→ Topic coverage too thin (vector under-developed)
→ Related entities missing from the copy
→ Sub-topics that the top 10 share aren't in your piece
→ The words are technically right but the meaning sits off-cluster

How to fix the distance:
→ Pull the top 10 ranking pages for your target query
→ Extract the named entities each page shares (free NLP tools work fine for this spaCy, Google Cloud NLP free tier)
→ Build the shared entity set these define the semantic cluster
→ Compare your page against the set. The gaps are the distance.
→ Add what's missing, in genuine context (not as keyword stuffing)

Real example I ran last month:
Query: "best CRM for startups"
Top 10 pages all referenced: pricing tiers, native integrations, scalability paths, onboarding time, free-tier limits, mobile UX.
Client page covered: pricing tiers and integrations. That's it.

We added the 4 missing entity clusters. Page moved from position 19 to position 6 in 4 weeks.

The fix wasn't more words. It was less distance.

Stop optimising for keywords. Start optimising for proximity.

 

[tazarbm]

yeah, this truth keeps getting revealed more and more each day (which is cool).

I knew that google (well, their spiders) doesn't read the text on a page (only humans do this), but I thought that they read the HTML of the page (which I'm pretty sure that they still do...)

But the vectorial stuff is new to me, only learned about it these days when a few other members mentioned it. And it makes sense since google - at their core, and in their 1998 beginnings - has been a math-based algorithm created by 2 students in their dorm... or at least that's what the official story goes, we don't know who actually created this algo and how

But yeah, I do believe that the vectorial and proximity craps are real, that much I can believe...

Semantic neighbourhood is the real signal.

yep! That's why optimizing your content with LSI terms is still a huge ranking factor, it helps the idiot googlebots "read" the math behind the algorithm, if this makes any sense (if not, apologies for not knowing how to explain better, I've never been good at math)

 

[alonelywriter]

I agree. I personally ranked a blog post for a moderately competitive keyword simply by creating the kind of content I would want to read myself. Covering the topic properly and fulfilling user intent worked better than focusing only on keywords.

 

[BradyX]

there's definitely some truth to this. focusing only on exact-match keywords feels outdated now. i've seen good ranking improvements just by covering related entities, answering adjacent questions, and matching the overall topic depth of top-ranking pages. that said, proximity alone isn't everything. authority, links, user signals, and search intent still play major roles.
the real takeaway is that comprehensive, context-rich content usually performs better than keyword-focused content.

 

[nielspostma]

I like this framing a lot, it matches what I’ve been seeing too pages don’t just miss keywords they miss entire parts of the topic

 

[isadejong]

It converts it into a vector a mathematical representation and measures distance from the query vector.

Ranking is, essentially, a proximity calculation. Most SEOs don't optimise for this at all.

What's actually happening:
Every page on the web gets embedded into a high-dimensional vector space. So do queries. When someone searches, Google retrieves the pages whose vectors sit closest to the query vector and then re-ranks them. Keywords are a coarse input to that calculation. Semantic neighbourhood is the real signal.

Why your page is ranking #15:
Not because the keyword is missing. Because the vector is too far from the cluster the query lives in. The usual reasons:
→ Topic coverage too thin (vector under-developed)
→ Related entities missing from the copy
→ Sub-topics that the top 10 share aren't in your piece
→ The words are technically right but the meaning sits off-cluster

How to fix the distance:
→ Pull the top 10 ranking pages for your target query
→ Extract the named entities each page shares (free NLP tools work fine for this spaCy, Google Cloud NLP free tier)
→ Build the shared entity set these define the semantic cluster
→ Compare your page against the set. The gaps are the distance.
→ Add what's missing, in genuine context (not as keyword stuffing)

Real example I ran last month:
Query: "best CRM for startups"
Top 10 pages all referenced: pricing tiers, native integrations, scalability paths, onboarding time, free-tier limits, mobile UX.
Client page covered: pricing tiers and integrations. That's it.

We added the 4 missing entity clusters. Page moved from position 19 to position 6 in 4 weeks.

The fix wasn't more words. It was less distance.

Stop optimising for keywords. Start optimising for proximity.

The idea of semantic distance makes sense, especially when you compare pages that rank vs ones stuck on page 2 with similar keyword usage

 

[Take Action]

Yeah, that makes sense, I agree. But I think people over-focus on the vector part and forget the rest of the stack. Semantic proximity helps, but if the page misses intent, has weak internal linking, or no authority, it can still sit nowhere even if the wording is on-topic.

In practice it feels more like the topic match gets you in the room, then the other signals decide where you land.

 

[Ali Khatri]

I agree with the general idea. Many pages don't rank poorly because they're missing an exact keyword; they rank poorly because they don't fully satisfy the topic and search intent.

That said, I wouldn't reduce rankings to just vector proximity. Google still uses hundreds of signals, including backlinks, authority, user signals, freshness, and content quality.

In practice, I've seen rankings improve when content covers the entities, subtopics, and questions that users expect to see. Comprehensive topic coverage often moves the needle more than simply adding keywords.

 

[MadOnGrind]

I agree with the general idea. Many pages don't rank poorly because they're missing an exact keyword; they rank poorly because they don't fully satisfy the topic and search intent.

That said, I wouldn't reduce rankings to just vector proximity. Google still uses hundreds of signals, including backlinks, authority, user signals, freshness, and content quality.

In practice, I've seen rankings improve when content covers the entities, subtopics, and questions that users expect to see. Comprehensive topic coverage often moves the needle more than simply adding keywords.

Search performance is rarely about a single factor like keywords or vectors. The pages that win tend to combine strong intent coverage with authority, relevance, and overall content quality.

 

[MadOnGrind]

Yeah, that makes sense, I agree. But I think people over-focus on the vector part and forget the rest of the stack. Semantic proximity helps, but if the page misses intent, has weak internal linking, or no authority, it can still sit nowhere even if the wording is on-topic.

In practice it feels more like the topic match gets you in the room, then the other signals decide where you land.

Semantic relevance gets you considered, but rankings still depend on authority, structure, and overall SEO signals. It’s only one part of the system, not the whole thing.

 

[Akinyande ayo]

This was the idea of long-form contents to enable writers cover the topic in-depth.

However, titling such a page can be an issue sometimes, and Google still judges the page content by the title, and factor it in ranking.

For example, a page that covers treatment methods of a particular disease may likely rank better if a search is about treatment, than a page that covers the disease as a whole, and a subheading on treatments