Semantic Search: What It Is and Why It’s So Efficient for SEO

SEO

As time goes on and we get more and more used to search engines, we expect them to understand us better. We no longer just type in a few keywords and hope for the best – we want search engines to “get” us, to know what we’re looking for, and to give us the most relevant results possible. The need of immediacy and instant gratification means that users want answers fast, and they want them right. Semantic search has emerged as a key tool for businesses to meet these expectations.

Has your car broken down? Google will give you a list of the nearest mechanics. Want to know how to get to the Eiffel Tower? Google will give you turn-by-turn directions. You can’t remember a song’s name? A quick search will give you the lyrics.

This is all made possible by semantic search, which is the ability of a search engine to understand the user’s intent and return results that are not just keyword-matched, but semantically related to the user’s query.

In this post:

What semantic search is and how it works
How semantic search is different from traditional search
A brief history of Google’s algorithm updates
Semantic search in practice: A step-by-step overview
What’s the relevance of semantic search for SEO?
Semantic search best practices for killer SEO
Lots of moving parts, one goal

What semantic search is and how it works

Semantic search is a pattern that uses natural language processing and machine learning to understand the meaning behind users’ search queries and return the most relevant results. It relies on artificial intelligence (AI) to fully understand search intents and provide users with relevant results, rather than just returning matching keywords.

This allows semantic search engines not only to understand what the user wants but also to make more accurate recommendations based on their search history and previous interactions.

Semantic search goes beyond the traditional Boolean model of search, which relies on matching keywords to return results. The semantic search model understands the relationships between concepts and the context in which they are used. That’s why Google has the ability to return correct results even when users input mispelled words or search for something using different terminology.

How semantic search is different from traditional search

When it comes to search engine optimisation, much has changed over the last decade. In the early days of SEO, businesses could use keyword stuffing, fake backlinks, and other black hat techniques to game the system and rank higher in search results. But as semantic search has become more sophisticated, these old tricks no longer work.

For over a decade now, Google has been actively working on semantic search. With semantic search, businesses need to focus on the user experience that they’re providing. That means creating content that is relevant, informative, and engaging – content that will not only rank well in search results but also encourage users to click through and stay on the site.

The main difference between semantic and traditional search is that semantic search focuses on understanding the user’s intent behind their query – that is, the reasons for that search –, while traditional search engines simply match the query with relevant keywords. In other words, search engines have evolved from lexical analysis to semantic analysis. This translates into more relevant results, even if the user’s query is not an exact match for the keywords on the website.

A brief history of Google’s algorithm updates

In order to fully understand semantic search, we need to take a look at the history of Google’s algorithm updates. Over the years, Google has made a number of changes to its algorithms in an effort to provide users with more relevant results.

Panda algorithm (2011)

In February 2011 Google introduced the Panda algorithm as a way of filtering low-quality content from its search results. The main target was “content farms”, which were websites with large amounts of poor quality content.

Hummingbird algorithm (2013)

The Hummingbird algorithm was first introduced in August 2013, and it is said to be the greatest change to the algorithm since 2001. It was named this way because it was designed to be “precise and fast”. It consisted of a rewrite of the way searches were processed.

Hummingbird put more emphasis on natural language queries and went further into the website’s content in order to match the query. This meant that pages could be ranked based on natural writing with a conversational tone rather than just keywords.

Pigeon algorithm (2014)

In July 2014, Google announced the rollout of their Pigeon algorithm in the US and UK. The Pigeon algorithm was designed to improve local search results by better understanding the user’s location. It also put more emphasis on traditional SEO signals, such as backlinking and keyword usage.

RankBrain algorithm (2015)

In October 2015, Google announced the rollout of their RankBrain algorithm. RankBrain is a machine learning system that helps Google interpret queries that it has never seen before. It does this by understanding the relationship between words in a query and matching them with relevant documents.

How does it work? It uses artificial intelligence to implant words into numerical entities that can be better approached by computers. This way, words that are unknown will be converted to similar ones in order to provide meaningful results. That explains why typos don’t make obstacles for Google.

BERT algorithm (2019)

In October 2019, Google announced they were rolling out their BERT algorithm. BERT is a neural network that helps Google understand the context of a query by looking at the relationship between words in a sentence.

How’s BERT different to the RankBrain algorithm? Much is yet to discover about BERT. What we do know is that it’s not a replacement for RankBrain – it’s an addition to it. Its acronym states for Bidirectional Encoder Representations from Transformers, and it enabled Google to process texts as a whole instead of processing words one-by-one in order.

Page experience update and Core Web Vitals (CWV) (2021)

In May 2021, Google announced an update to its algorithm that was going to take into account the “user experience” of a website. A page’s user experience is measured by several factors, such as how long it takes for the page to load, how easy it is to use on mobile devices, and whether or not there are pop-ups that block the content.

These factors are collectively known as “Core Web Vitals” (CWV). The rationale behind this update is that Google wants to provide its users with the best possible experience when they are searching for something. This is why most SEO experts will insist that if your site provides a good user experience, that’s half the battle won to get good rankings.

You can test your website Core Web Vitals here.

Semantic search in practice: A step-by-step overview

As semantic search is still relatively new, its technology is constantly evolving. In the past, semantic search engines relied heavily on structured data, such as metadata and microdata, to understand the context of a query. However, as Google’s algorithms have become more sophisticated, they are now able to understand the meaning behind unstructured data, such as natural language text.

Semantic search engines are now able to understand not only what the user is searching for, but also why they are searching for it. Once a user’s search query is introduced, a number of steps will take place in order to deliver the best (and right) results:

Right after a search query is entered, Google’s algorithm identifies keywords while it analyses the relationship between the different words of the query.
After a keyword is identified, Google identifies the entities behind the keywords, that is, the people, places or things that the keywords are referring to.
Once entities are identified, the algorithm will try to understand the user’s intent behind their query by analysing the context of the query. This includes understanding the user’s location, search history and previous interactions.
After understanding the user’s intent, Google will match the query with the most relevant entities (sources).
Once the algorithm has chosen the most relevant entities, it analyses their contents to find a correlation between the search intent and the website’s content.
Google find the best results and bring them to your SERP.

What’s the relevance of semantic search for SEO?

If you want your website to rank high on Google, it’s important that you understand how semantic search works, so you can optimise your website’s content accordingly.

It’s no longer enough to know which keywords to target – you need to understand the user’s intent behind their query, and create content that satisfies that intent. If someone is searching for “cheap flights to Spain”, they are probably looking for deals on airfare. So writing an informational blog post about why cheap flights to Spain are hard to come by probably won’t cut it.

To really optimise your website for semantic search, you need to make sure that your site’s content is:

Relevant: Target the right keywords and create content that satisfies the user’s intent.
High-quality: Google wants to provide its users with the best possible experience, so it will only rank high-quality content. This means that your content must be well-written, accurate and informative.
Optimised for CWV: As mentioned before, Google’s semantic search algorithms take into account the user experience of a website. So make sure your website is optimised for CWV.
Authoritative: Google wants to provide its users with the most authoritative and reliable results. So make sure your website is seen as a credible source of information by building links from high-quality websites.

Moreover, Google’s semantic search algorithms will consider:

The user’s location: If someone is searching for “restaurants near me” in New York, they will see different results than someone who is searching for the same thing in Los Angeles.
The user’s search history: If someone searches for “pizza” and then clicks on a result for “Domino’s Pizza”, it’s likely that they are looking to order pizza from Domino’s. So the next time they search, “order pizza” is likely to be populated as a suggested search.
The user’s previous interactions: If someone clicks on a result and then immediately goes back to the SERP, it’s likely that they didn’t find what they were looking for. Google will take this into account and try to provide better results the next time the user searches for the same thing.
The language settings: If someone’s language settings are set to Spanish, they will see results in Spanish. Providing users with outstanding content in their language will give you an edge over your competition – and that’s where multilingual SEO comes in.
The user’s device: Google will take into account the type of device the user is searching from. Queries tend to be shorter on mobile devices, and the results will be different than if the same query was made on a desktop computer.

As you can see, semantic search is much more complex than traditional keyword-based search. And it’s only going to become more important in the future as Google continues to develop its algorithms. So if you want your website to stay ahead of the competition, you need to make sure you’re optimising for semantic search.

Semantic search best practices for killer SEO

Here are some semantic search best practices that you can use to make sure your website is optimised for the latest search algorithms:

Use Latent Semantic Indexing (LSI) keywords: LSI keywords are related to your main keyword, and they help Google understand the context of your content. For example, if you’re writing an article about “pizza”, some LSI keywords could be “toppings”, “cheese”, or “crust”.
Use semantic markup: Semantic markup is a code that you can add to your website to help search engines understand your content. For example, if you have an event on your website, you can use semantic markup to tell Google when the event is taking place, what the event is about and who is speaking at the event.
Use questions and answers: Google’s semantic search algorithms are designed to provide users with quick answers to their questions. So if you can answer a user’s question in your content, you’re more likely to rank high in featured snippets – the boxes that appear at the top of the SERP with the answer to a user’s question – and drive traffic to your website.
Run your content through tools like Clearscope: Clearscope is an analysis tool that grades your content based on “content relevance and comprehensiveness” and compares it to the top 10 results for a given keyword. This can help you understand where your content is falling short and how you can improve it to outrank your competition.

Lots of moving parts, one goal

As you can see, semantic search is a complex topic with lots of moving parts. But at its core, semantic search is about understanding the user’s intent and providing them with the most relevant and comprehensive results possible.

If you can do that, you’re well on your way to ranking high in semantic search – and driving more traffic to your website.

Maria Scheibengraf Crisol Translation Services SaaS Translation Services

Author: Maria Scheibengraf

Maria Scheibengraf is an English-to-Spanish marketing and SEO translator specialised in software (SaaS, martech, fintech), and Operations Manager at Crisol Translation Services, which she co-founded in 2016. With a solid background in programming and marketing, Maria has an in-depth understanding of the technical intricacies involved in software programs, websites, and digital platforms. Maria is also the author of The SEO Translation Bible.