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Word Vector SEO Guide – WordGraph

Chapter 3:

How Word Vector Impact SEO?

Google has reached a level of data size and processing capability, at which they’re able to make almost daily updates to their algorithm(s). Machine learning will only accelerate that development.

Machine learning is only used in small, specific use cases for organic search but its usage seems to be growing quickly. Our hypothesis is that it will open many doors for Google and transform search in general.

If you’re attentive to the SEO industry, you’ll notice that many SEO specialists and marketers have sparked opinions that Word Vectors may determine the ranking of a website.

Over the past 5 years, Google has introduced two algorithm updates that put a clear focus on content quality and language comprehensiveness. Let’s take a step back and talk about the updates itself.

In 2013, Hummingbird gave search engines the capability of semantic analysis. By utilizing and incorporating semantics theory in their algorithms, they opened a new path to the world of search.

This new path leads to Google understanding user intent much more precisely

And in 2015, Google announced Rankbrain, which marked the beginning of Google’s Artificial Intelligence (AI) first strategy.

Simply put, Rankbrain is an algorithm that helps Google break down and understand complex search queries. You can say that Rankbrain translates search queries from “human” language into language that can be easily be processed by Google.

This also means that Google has began using multiple AI-driven techniques to rank search results.

The thing is, why was Rankbrain created in the first place?

There are 3 issues the algorithm is designed to address:

1. Enhance Accuracy

Moving from keyword matching to context understanding allows higher accuracy in handling variants of the same keyword (eg. singular vs plural abbreviations, misspellings etc.)

2. Handling Complex Searches

Everyday there seem to be tons of long and complex new searches that isn’t discovered by Google before (15% or 450 million unseen searches!). Lacking previous history on handling these new searches, there has to be a way to relate them to user’s previous searches to deliver the most accurate results

3. Handling Ambiguity

Many keywords have a level of vagueness that can mean anything based on different context. Having a self-learning AI to process existing search histories allows Google to effectively grasp what a keyword actually means

This time, Rankbrain is paying close attention to user’s satisfaction with the results presented for their query

The concept is simple, your site rank increases or drops by observing:

As you can see, these updates has caused SEO to shift focus.

Keyword research is still important, but the role has changed. Simply put, AI systems can now understand way beyond individual keywords. Much like humans do, new systems like Word2Vec can understand relationships between topics and develop a contextual interpretation.

So, it’s safe to say, without implementing the correct word vector, you are less likely going to rank on the first page of SEO.

Jeff Dean, the main man of Google AI, mentioned in one of his videos that he sees exponential growth about deep learning (a subset of machine learning) starting in 2014.

Keep in mind that this graph is only until 2016. Imagine how much further Google integrated machine learning from there

With this piece of information, we’re basically at a tipping point at which it’s important to understand what’s coming at us so we can adjust and reposition ourselves in the right manner.

To stay on top of SEO and still be successful in 2020, make sure to get the user intent right, measure mobile-first, and have a close eye on your industry. We hope it’s clear to you now on how Word Vector can impact SEO in the long run.

*In the link below, the team from SEOPressor has featured Word Vector in their blog, if you’d like to know more on this.

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