[SEO GUIDE][2024] - Search Intent, Keywords, Anchors and More

TomBelfort · Dec 13, 2024

Stagged said:
ill let GPT answer for me then, since that is what we are doing;
View attachment 403135

I am not sure if you are trolling me.

STRINGS from GOOGLES DEFINITION COMES FROM, IN THE MOST SIMPLEST FORM;

1 page title is: Best PBN in the world

Search query: best PBN in world

4 words MATCH; This is indeed what the search query is about.

Google stopped doing strings, and uses ENTITIES, rather just understanding a sequence of characters = STRINGS.

You are confusing yourself.
Also, Ferrari F40 is a thing in Knowledge Graph. There are many Ferrari F40, but there is only one type of Ferrari F40. You can check for yourself.

https://cloud.google.com/natural-language?hl=en

Ferrari = Organization
F40 = Consumer good (product).
View attachment 403137

Here are some articles;

https://inlinks.com/help/entity-based-seo/
part 2
https://inlinks.com/insight/improve-entity-indexing-in-google/

You're still confused here.

You stated originally that Google is using entity relations, ie, the knowledge graph and not AI or a concept of secondary intents.

As evidence of this, you've shown screenshots of Google's natural language machine learning model.

YOU ARE CONFUSING TERMS!

This is NOT Google's knowledge graph.

This is an entity extraction natural language model. It's completely different. It has nothing to do with search.

Look:-

This is for building YOUR OWN CUSTOM KNOWLEDGE GRAPH!

It's not a search interface for their knowledge graph.

You are using THE WRONG API.

Here is the right one - https://developers.google.com/knowledge-graph

Let's use it with my API key to search for "f40"

Now let's search for ferrari.

I'll admit, they DO actually have an entry for "car". I was 100% wrong about that. I admit that.

But that's irrelevant. That knowledge graph entry for 'car' has nothing to do with why if you search for 'best vehicles' you get car pages. It's because they understand the intent of someone searching that is to find cars.

And it's a @Type: Thing. None of the machine learning models need to look up "car" to understand what a car is. They know what a car is.

And of course you completely ignore your ridiculous statement that "relevance" is the same as "useful".

Going back to your original statement you said

Stagged said:
Its not because of some incredible AI that doesnt use keywords, its because since 2012 they have based their algorithm entity based to be able to use their US algorithm in for example German too, where they just translate entity of words.

Then

Stagged said:
Its not because of secondary intent. Its because its within the same "entity". Its not because of a secondary search intent, its because the words is within the same knowledge graph for Google. It either means the same thing, or is closely related - which then google understand a user might be looking for that too.[/B]

You're saying they aren't using AI. They're just using "entity translation".

You're saying there's no such thing as secondary intent, that it's because words are connected.

If this were the case you could just spam connected words and rank.

You're literally saying one of the world's most advanced AI companies isn't using AI on their main product, that it's just entity relations in a graph db.

You dismiss my entire article and just simply claim we're in an SEO age where the most advanced search engine in the world just uses related entities, like "car" and "vehicle".

Google "how could i make it easier to extract salmon from the river"

Top result:

I suppose they got that through entities and not determining from that long weird sentence what my primary intent is?

How about if we scroll down :-

"What is the best bait for salmon in the river" <--- SECONDARY INTENT.

How do you think they know that? From entities?

Because bait is close to what, "extract salmon"?

That's AI. Period.

Google is not operating on keywords or entities for search results. They use the knowledge graph to classify and connect entities which are used by the AI and so they have more of a "real world" understanding of things in the world.

The knowledge graph COMPLIMENTS the machine learning.

From the page you linked:

https://inlinks.com/help/entity-based-seo/

What is an entity?

In general, an entity (or named entity to be more precise) designates a single, well-defined thing or concept which can be linked to a knowledge graph.

And inlinks is a dated piece of software.

It was created years ago when we started to move away from pure keywords to more "topical" based SEO, or "entities" if you want to call it that.

The goal of it was to add in entities related to your "primary topic".

Years ago, if you wanted to rank for "best toaster", you would just spam the keyword "best toaster".

Then came related keywords after hummingbird in 2013 and you would then start using what people called "LSI keywords". Related ones like "toaster reviews", "top toasters".

Later, came the concept of "topics" or "entities" where you would add in knowledge graph entities related to your core topics and topical ideas related to your main topic.

This was the glory days when you could create massive info sites easily. You had huge deep articles just blabbering on about every topic under the sun and they'd rank.

Then came BERT and Google started to understand the actual USER INTENT behind the searches and it would look for pages that match that intent beyond keywords, entities or topics, but rather pages that answered the primary intent.

This is why AI PAA spam dominated. It was pure user intent.

Later, they got more advanced and started to understand secondary and tertiary intents and were better able to match pages to search queries.

Then they started using engagement metrics to combat the AI and also to better understand which pages really satisfied the intents of the users and undersatnding that search queries have more than 1 intent, hence why I say secondary/tertiary. Every person who searches for "cars" does not have the same intent.

Stagged · Dec 13, 2024

splishsplash said:
splishsplash said:

You stated originally that Google is using entity relations, ie, the knowledge graph and not AI or a concept of secondary intents.

Click to expand...

You are correct. That was wrong from me.

Let me change that, Google uses entities to learn about what a page is about - and also uses AI.

splishsplash said:
And of course you completely ignore your ridiculous statement that "relevance" is the same as "useful".

I agree with the things above this, and its not important to elobarate even further. By this, i did not ignore it, i just felt it was stupid to repeat myself. If I am writing a article, and i am linking something - if its useful for a reader - its probably also relevant for the reader. Therefore, links have to be relevant? That makes sense for me, and also makes sense for click data

This is my last post, because I hate discussing in english. Saying you only need a little bit context, is over simplifying this and simplifies it.

splishsplash said:
You're saying they aren't using AI. They're just using "entity translation".

You're saying there's no such thing as secondary intent, that it's because words are connected.

If this were the case you could just spam connected words and rank.

You're literally saying one of the world's most advanced AI companies isn't using AI on their main product, that it's just entity relations in a graph db.

You dismiss my entire article and just simply claim we're in an SEO age where the most advanced search engine in the world just uses related entities, like "car" and "vehicle".

Google "how could i make it easier to extract salmon from the river"

I am not saying they dont use AI, i am saying they moved to entities from strings and words because they needed to translate their algorithm.

Other than that, I agree here. It is faulty how I am stating it. I am trying to simplify it, and by doing that I dismiss a lot of aspects. Of course Google uses AI, and my explanation is way too simple to tell how Google search engine works. There are a gazillion factors, i am just stating why they are ranking for "MAP".

Google is not understanding your examples by secondary intent, it is understand it because they are closely related by using entities. "How could I make it easier to extract salmon form the river"
Man... Again, same example. It understands extract, you can change any verb.

Without entities, they could not understand this query this well. Its not because of AI is mapping this in real time, its because of;

I can use pull out, remove or extract because they essantially mean the same thing, and the search results wont differ. (Thats why "Intent mapping" also will show. ITS CLOSELY RELATED."

Entities & and their index.
How else could they determine what a search intent is?

But its faulty to say a AI is just creating these overviews or search results. From your example, about search intent - its because of entities in the whole article, and those entities help understand your query. If you really want to understand how those search intents work, you need to understand how Google is using knowledge graph.

splishsplash said:
I suppose they got that through entities and not determining from that long weird sentence what my primary intent is?

splishsplash said:
They use the knowledge graph to classify and connect entities which are used by the AI and so they have more of a "real world" understanding of things in the world.

The knowledge graph COMPLIMENTS the machine learning.

From the page you linked:

1st. Yes, thats how they can also determine your search intent man..

2. Yes, you are correct.

Royal me · Dec 13, 2024

Thanks for taking the time.

TomBelfort · Dec 14, 2024

Stagged said:
Entities & and their index.
How else could they determine what a search intent is?

But its faulty to say a AI is just creating these overviews or search results. From your example, about search intent - its because of entities in the whole article, and those entities help understand your query. If you really want to understand how those search intents work, you need to understand how Google is using knowledge graph.

There's not much more I can say other than I am an myself an AI engineer. How you are describing doesn't make sense from an engineering standpoint.

You cannot understand intent from the knowledge graph. This is not possible. There's no way to do it. It's just a graph database. There is no understanding.

We have had graph databases since the 80's. If what you're saying was true, we would have had systems to understand the meaning behind language since then.

Understanding the meaning/search intent IS natural language understanding which is literally AI.

Here, since you've clarified your argument as this

Stagged said:
But its faulty to say a AI is just creating these overviews or search results. From your example, about search intent - its because of entities in the whole article, and those entities help understand your query. If you really want to understand how those search intents work, you need to understand how Google is using knowledge graph.

You are saying that Google does not use machine learning to understand search intent, but instead they use entities from the knoweldge graph.

Quite simply "it's faulty to say an AI is creating these overviews or search results".

From Google.com:

https://developers.google.com/search/docs/appearance/ranking-systems-guide

"BERT
Bidirectional Encoder Representations from Transformers (BERT) is an AI system Google uses that allows us to understand how combinations of words express different meanings and intent."

They don't hide this. They literally tell you that they use transformer models to understand the search queries' meaning and intent.

https://blog.google/products/search/search-language-understanding-bert/

"This breakthrough was the result of Google research on transformers: models that process words in relation to all the other words in a sentence, rather than one-by-one in order. BERT models can therefore consider the full context of a word by looking at the words that come before and after it—particularly useful for understanding the intent behind search queries.

"

How can you still say my entire guide is wrong, and that Google are using the knowledge graph and entities to understand search queries?

This is just like made up stuff. You may as well say "Google uses monkeys to determine search intent".

It doesn't work from an engineering standpoint, and the only evidence you're citing for this is an article about entity SEO from 2021 from a site selling you an entity tool.

The funny thing is in the article you posted, if you'd actually read it, you'd see he says (https://inlinks.com/help/entity-based-seo/)

The knowledge graph is not a ranking tool. It never was a ranking tool. It's a knowledge presentation tool. It's literally just a knowledge repository that Google uses to enhance the serps when it detects a knowledge graph entity searched for. It has nothing to do with determine search meaning.

Then he goes on to say

But yet you say this is wrong, that AI isn't used and Google is just matching entities in a graph database and looking for those entities like they used to look for keywords on a page, then ranking those pages. This is WRONG. I will say nothing else on the matter as I've already presented more than enough evidence, least of which is the fact that this is impossible from an engineering standpoint. You NEED a transformer model to understand meaning, and they could not do it before they had transformers.

Stagged · Dec 14, 2024

Jeez, AI engineer or not, you’re an absolute pro at twisting words or picking bits and pieces.

I’m not saying your entire article is wrong—the part about intent is correct—but I’m saying your example is wrong, and I’m explaining why it shows in SERP. I just simplified it a bit so it could be easier to understand.

I have also never talked about ranking factors. You’re the one rambling about that.

Clear language might get you a snippet, but that’s not a ranking factor either. So stick to what I’m actually saying.

I’m using your example about search intent and explaining why Backlinko’s guide also ranks for intent mapping in 14th place. I’m not talking about understanding intent.

The reason these super long articles rank at all for the keyword search intent is because the intention behind the keyword “search intent” is diffuse.

Even a human can’t always be certain about what you actually mean when you search for it. Do you want more information? Are you looking to hire someone? Do you just want a definition? Do you want to analyze search intent? Are you looking for a tool?

So Google shows long articles that cover multiple intents. And yes, it’s the BERT model behind understanding this.

But why intent mapping appears is not just because of BERT. It’s largely due to entities and how Google understands words next to each other.

Search intent can be linked to entities like user intent, intent, user, analysis, while intent mapping can be linked to analysis, intent. These connections allow Google to understand that they are about similar things.

BERT understands the context behind the intent of a query. But there’s no clear intent behind your examples, so they rank more because of entities and the semantic understanding of the words.

If you don’t get that, I don’t know what else to say.

TomBelfort · Dec 14, 2024

Stagged said:
But why intent mapping appears is not just because of BERT. It’s largely due to entities and how Google understands words next to each other.

You can't map intents with a graph db. It's just keywords and basic relations. BERT or transformer models don't need or use graph databases to understand relations. The whole magic behind transformer models is that they are bi-directional in their understanding of words and how they connect to each other. The transformer model doesn't need to look up an entity relation database to understand that "car" is related to "vehicle". It knows this because of the training, and because of how the architecture of the model works. (I won't get into this, it's far too complex unless you have a background in machine learning and linear algebra)

Stagged said:
Search intent can be linked to entities like user intent, intent, user, analysis, while intent mapping can be linked to analysis, intent. These connections allow Google to understand that they are about similar things.

BERT understands the context behind the intent of a query. But there’s no clear intent behind your examples, so they rank more because of entities and the semantic understanding of the words.

If you don’t get that, I don’t know what else to say.

The knowledge graph does not contain information about words like "intent", "analysis". It does not link "intent mapping" to "analysis". These connections are not what allows Google to understand they are similar things. BERT is. BERT understands that intent mapping is related to intent and analysis. The knowledge graph is used to display static information about things like places, people and events. It's not used, and never has been used for document understanding.

Quite simply, you can't. If we could understand complex intents with a simple entity graph db, then why did we even need AI in the first place?

Why was BERT even invented to understand the meaning of sentences, when we could understand the meaning of sentences with an entity db?

It's because we couldn't.

We couldn't understand meaning. We could only understand keywords and relations. Not meaning.

The "semantic understanding of words" doesn't make sense in this context. We need semantic understanding of sentences, not just words. This is precisely what made BERT so powerful. It could understand meaning in sentences, beyond words. The meaning of words changes depending on their position in a sentence.

We're going round in circles here however.

The bottom line is, you are completely wrong. It's not even subjective or open to interpretation. You cannot understand meaning of a sentence with a graph db. You need a transformer model. Period. End of story.

Look, we can disprove your whole theory instantly, right now.

Let's look up "search intent" in the knowledge graph.

It doesn't exist. There is no "search intent" in the knowledge graph.

So how then could Google understand, using the knowledge graph that "entities" like "search intent" can be mapped to "user intent" ?

Here's the knowledge graph entry for "user intent"

It also doesn't exist.

What you were using was natural language entity extraction model. This is an open model from Google to extract entities. You were not using the knowledge graph api.

The knowledge graph API is here. Try it : https://developers.google.com/knowledge-graph

Consider for a moment..

If your understanding is such that you thought one of Google's open entity extraction models was their knowledge graph, might it be reasonable to conclude that you are very confused about all this? Your entire premise was based on the screenshots you sent me showing "entities" from Google's open entity extraction model. This was what you were explaining to me was Google's knowledge graph.

This here

This is the playground for the cloud API natural language machine learning model. One of them is entity extraction. This is not, and never was the knowledge graph. It's a model which extracts and labels entities from a document.

You can see this used in code here - https://codelabs.developers.google.com/codelabs/cloud-natural-language-python3#4

But what you also have to understand is this is also a transformer model. It extracts entities, because the weights of the model its self contains the understanding of language and meaning, so it's able, like a human to classify entities in a document. It doesn't "use" a graph db, which is what the knowledge graph is.

This simplistic view of search that Google just looks up related entities for words in the search query, and ranks pages that cover lots of entities is wild to me. You could rank so easily by just spamming endless entities in a page and totally disregarding the searcher intent, since no one, including Google, according to you, know what it is.

So if we want to rank for "how to groom a persian cat", we just spam tons of entities about everything related to "persian cat", and boom, we rank. No understanding, no machine learning. Just a brainless word relation machine is what you're saying Google is.

Cannibalization we would never need to worry about. Google has no understanding of user intent or meaning. It's just matching based on related entities. You could have 100's of duplicate pages about cats, with no meaning at all. Just spammed entities all related to cats.

Stagged · Dec 14, 2024

I think you’re a bit confused here. Entities are a database, and they are static. They do NOT change search results—that’s driven by other factors and BERT, as you mentioned.

Talking about entities doesn’t necessarily mean you’re only referring to knowledge graphs. I’m talking about how natural language processing works and why something appears in a result or doesn’t—and that involves how BERT and other AI models are trained to understand what the text is about. With this understanding, you’ll see that "search intent" and "intent mapping" can be related to each other.

I’ve never said that the knowledge graph can replace BERT or other models—I’m simply explaining how, in practice, a search result can be generated.

Assuming that "search intent" or "intent mapping" has a clear intent is completely incorrect.

Neither you, BERT, nor I can guess what a user is looking for when they search for "search intent."

Therefore, your example is incorrect. The reason it shows up is the understanding that its closely related of the two of the same things—not because of a secondary intent.

___

Last post btw

Wont take away more attention from your thread.

TomBelfort · Dec 14, 2024

Stagged said:
I think you’re a bit confused here. Entities are a database, and they are static. They do NOT change search results—that’s driven by other factors and BERT, as you mentioned.

Talking about entities doesn’t necessarily mean you’re only referring to knowledge graphs. I’m talking about how natural language processing works and why something appears in a result or doesn’t—and that involves how BERT and other AI models are trained to understand what the text is about. With this understanding, you’ll see that "search intent" and "intent mapping" can be related to each other.

I’ve never said that the knowledge graph can replace BERT or other models—I’m simply explaining how, in practice, a search result can be generated.

Assuming that "search intent" or "intent mapping" has a clear intent is completely incorrect.

Neither you, BERT, nor I can guess what a user is looking for when they search for "search intent."

Therefore, your example is incorrect. The reason it shows up is the understanding that its closely related of the two of the same things—not because of a secondary intent.

___

Last post btw Wont take away more attention from your thread.

You're literally changing your statements as we go here.

You posted this a few posts back.

Stagged said:
Entities & and their index.
How else could they determine what a search intent is?

But its faulty to say a AI is just creating these overviews or search results. From your example, about search intent - its because of entities in the whole article, and those entities help understand your query. If you really want to understand how those search intents work, you need to understand how Google is using knowledge graph.

I literally said to you multiple times, the knowledge graph is a graph db. A db is a database.

Now you say to me that I'm confused and that entities are a database and they are static?

Yes, that's what I've been saying to you, over and over.

Now you're saying "talking about entities doesn't only refer to knowledge graphs". You're just completely changing what you've said to try to get around my posts above where I showed it makes no sense.

There's no "knowledge graphs".

There is one knowledge graph, and it is a graph db.

Just to really emphasize the level of distortion here you're creating

Stagged said:
I’ve never said that the knowledge graph can replace BERT or other models—I’m simply explaining how, in practice, a search result can be generated.

Stagged said:
Entities & and their index.
How else could they determine what a search intent is?

But its faulty to say a AI is just creating these overviews or search results. From your example, about search intent - its because of entities in the whole article, and those entities help understand your query. If you really want to understand how those search intents work, you need to understand how Google is using knowledge graph.

You LITERALLY said this very thing.

At this stage you're just dancing around and changing things you've said instead of just admitting that you are wrong here, and that entities have nothing to do with how Google understands intent.

To say that there's endless intents and that no one, including Google understand them is a bizarre claim.

If Google couldn't understand the intent behind search queries, they wouldn't have a search engine.

Stagged said:
The reason these super long articles rank at all for the keyword search intent is because the intention behind the keyword “search intent” is diffuse.

Even a human can’t always be certain about what you actually mean when you search for it. Do you want more information? Are you looking to hire someone? Do you just want a definition? Do you want to analyze search intent? Are you looking for a tool?

It's not about being certain.

Google understands through a combination of transformer models and engagement metrics like what does a user do AFTER they hit the back button, what the intents are. It's retraining transformer models based on new data to better understand intents, of which there are many, but I classify them as primary, secondary and tertiary because that has a specific meaning.

primary/secondary/tertiary intents does not mean "some people have the primary, some people have the secondary and some have the tertiary"

There are multiple primary intents and for each primary intent there are secondary and tertiary intents.

Google is able to work out the most common primary intents, and those naturally have secondary and tertiary intents, for example

"How to fish for salmon" - If the primary intent is learning about how to fish for salmon, a secondary intent is "what's the best bait to use when fishing for salmon".

They may not be aware in their mind of that secondary intent, but it is nonetheless a secondary intent for a human searching for that whose primary intent is "how to fish for salmon".

A tertiary intent might be "What's the best way to cook salmon I've caught". I call this tertiary, because it's a little further down the line and less close to the primary, but it will be of interest to some.

All these understandings are done by Google through transformer models. Not through entities. It doesn't match the entity "salmon" to "cooking salmon" and conclude that they're related.

Stagged · Dec 14, 2024

splishsplash said:
I literally said to you multiple times, the knowledge graph is a graph db. A db is a database.

Now you say to me that I'm confused and that entities are a database and they are static?

Yes, that's what I've been saying to you, over and over.

Now you're saying "talking about entities doesn't only refer to knowledge graphs". You're just completely changing what you've said to try to get around my posts above where I showed it makes no sense.

There's no "knowledge graphs".

There is one knowledge graph, and it is a graph db.

Just to really emphasize the level of distortion here you're creating

No, you’re putting words in my mouth. You must understand that I’m talking about how it works, not that it works SOLELY because of entities.

Have I ever said that entities are the sole reason something appears in search results, or am I saying that entities are the reason we’re able to understand what your search is about? Search "Search intent".

splishsplash said:
At this stage you're just dancing around and changing things you've said instead of just admitting that you are wrong here, and that entities have nothing to do with how Google understands intent.

To say that there's endless intents and that no one, including Google understand them is a bizarre claim.

If Google couldn't understand the intent behind search queries, they wouldn't have a search engine.

No, you’re misunderstanding me. And to say that entities have nothing to do with how Google understands intent is completely misleading. Entities provide context for how they interpret intent. Wtf?

I’m saying it’s not because of intent or secondary intent that "intent mapping" is ranked 14th. It’s because these models understand that it’s related to the "search intent" article.

There are endless potential intentions behind a short keyword:

Search intent: This can have multiple possible intentions—I listed a few examples.

Buy Nike shoes: The intention here is much clearer; the user wants to make a purchase.

Again, you’re twisting my words. I’m not saying that Google doesn’t understand intent; I’m saying the intent behind the keyword "search intent" is unclear—and that’s why the long article is displayed.

How do you know what someone searching for the exact keyword "SEO" on Google wants? Do they want a service, a tool, or information? Well, it’s unclear. But Google gives them the super long article that addresses multiple possible intentions. (TO SIMPLIFY IT)

splishsplash said:
It's not about being certain.

Google understands through a combination of transformer models and engagement metrics like what does a user do AFTER they hit the back button, what the intents are. It's retraining transformer models based on new data to better understand intents, of which there are many, but I classify them as primary, secondary and tertiary because that has a specific meaning.

primary/secondary/tertiary intents does not mean "some people have the primary, some people have the secondary and some have the tertiary"

There are multiple primary intents and for each primary intent there are secondary and tertiary intents.

Google is able to work out the most common primary intents, and those naturally have secondary and tertiary intents, for example

"How to fish for salmon" - If the primary intent is learning about how to fish for salmon, a secondary intent is "what's the best bait to use when fishing for salmon".

They may not be aware in their mind of that secondary intent, but it is nonetheless a secondary intent for a human searching for that whose primary intent is "how to fish for salmon".

A tertiary intent might be "What's the best way to cook salmon I've caught". I call this tertiary, because it's a little further down the line and less close to the primary, but it will be of interest to some.

All these understandings are done by Google through transformer models. Not through entities. It doesn't match the entity "salmon" to "cooking salmon" and conclude that they're related.

But this is a completely different example than "search intent" and "intent mapping."

I can agree with what you're saying here—because it's much clearer intention. But I still disagree with your example in your thread.

Google is complex, and the search engine is also complex—but you’re oversimplifying it at some points while overcomplicating it at others.

like, really, last post. I also want to emphasize that your tips in your thread are not wrong. On how to treat SEO or your pages

TomBelfort · Dec 14, 2024

Stagged said:
No, you’re misunderstanding me. And to say that entities have nothing to do with how Google understands intent is completely misleading. Entities provide context for how they interpret intent. Wtf?

Entities don't provide context. Not at all.

Entities are NOT required. Transformers do not use entities. This is not misleading. This is a hard fact. I understand deeply how transformer models work and it has nothing to do with an external entities db. I don't just understand BERT as an SEO. I understand it as an AI engineer. I could explain in detail how each layer of the transformer works, both the encoder and the decoder. I could explain how it's trained, how it's used and how to fine tune it.

Transformers can in fact extract entities. They don't need an entity db to understand sentences. This is why you are completely wrong on this.

Entities have absolutely nothing to do with mapping search queries to documents.

Stagged said:
Google is complex, and the search engine is also complex—but you’re oversimplifying it at some points while overcomplicating it at others.

This statement here infers that you have a deep understanding of how documents are ranked. We've established above that you are in fact confused about how transformers work and have your terminology mixed up. You have confused the knowledge graph with Google's NLP cloud models for starters.

Logically speaking, based on that, you can't state that I am oversimplifying and overcomplicating in my guide. Your entire theory on how Google ranks documents based on entities is plain wrong and not even close to how it works. Even inlinks doesn't claim what you're claiming.

I don't really like to argue like this and having to take your argument apart, but you have hijacked my latest guide and reduced it to something non-useful. I have to refute your points so people reading this can make up their own mind as to whether my guide is useful, or if I am in fact wrong myself and they should follow your advice. If I cannot backup what I say, then I have no business writing it in the first place, hence why I spend so much time on this.

What I do advise you to do at this stage is to create your own on-page guide. You are very welcome to link to it here for others to read. I give you permission to do that. (Not that you need it within the rules, but you have my blessing)

It would help if you created a full guide explaining, in your opinion how you believe on-page should be done and how google maps keywords to documents, which is ultimately what is happening at the very core of the search engine.

RFT Marketing Agency · Jan 7, 2025

Your article is very accurate and helpful

HifzaKhan99 · Feb 15, 2025

The way you explain is really awesome. Now, it's not just about hitting keywords; it's about meeting various levels of user intent on a single page (and linking strategies to support related topics). Today's ranking requires strong internal linking and comprehensive topic coverage.

DigiVibesX · Feb 15, 2025

Greate . I Really appreciate with you. and well-written guide , thank you

Optimaxx · Feb 21, 2025

Well, after 8 long years, I still optimize the page using keywords and it works 90% of the time.

obusiness701 · Feb 26, 2025

Useful as always...bookmarked to read everything

Emily Willson · Feb 26, 2025

obusiness701 said:
Useful as always...bookmarked to read everything

It’s got some good insights, especially about how search intent plays a bigger role now. Got me wondering—are you working on your own site? Always interesting to hear what niches people are exploring these days.

obusiness701 · Feb 26, 2025

Emily Willson said:
It’s got some good insights, especially about how search intent plays a bigger role now. Got me wondering—are you working on your own site? Always interesting to hear what niches people are exploring these days.

platfom ecommerce selling template powerpoint & google slide
Saas Platform VCC issuing

Emily Willson · Feb 26, 2025

obusiness701 said:
platfom ecommerce selling template powerpoint & google slide

Saas Platform VCC issuing

In competitive niches like yours, general keywords can be tough to rank for. I’ve found that focusing on strong backlinks, paired with smart marketing tactics, usually brings the best results. How’s your experience been so far?

Deleted member 2098368 · Feb 26, 2025

It can be very useful for a start, thank you for your work

Beobeo98 · Mar 12, 2025

Tôi cần email: mật khẩu có mã xác minh 8 facebook chúng ta sẽ chia tiền dropship. Nếu bạn muốn hợp tác với tôi, hãy cho tôi 2 email tôi sẽ kiểm tra xem bạn và tôi có thể hợp tác không
Ai muốn hợp tác cùng nhau thì nhắn tin cho mình nhé

[SEO GUIDE][2024] - Search Intent, Keywords, Anchors and More

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