How semantic search helps the algorithm make your business findable
Semantic search is a technology that revolves around understanding the meaning and intent behind what someone is searching for. This means that search engines such as Google are getting better and better at interpreting what you really mean, even if your search query is not perfectly phrased. This process takes into account not only the literal words you use, but also the relationships between those words, the context of your query and even your previous search behaviour.
We are happy to take you through what semantic search involves and how this technology influences the results of both search engines and large language models (LLMs). We look at the benefits of semantic search for SEO and why businesses need to pay attention to it.
How semantic search works
Say someone searches for “best laptops for graphic design students.” A search engine that uses semantic search understands that this concerns a specific group of people who belong to a targeted audience. The search results will then be more likely to show laptops with powerful graphics cards and enough RAM, because that is the type of laptop graphic designers count on. In this way, semantic search improves the relevance of search results.
Traditional search engines worked mainly by searching for the exact words that appear in the search query. As a result, the search results did not always match the user’s actual intent. Semantic search changes this by using natural language processing (NLP) and machine learning to interpret the context behind a search query.
With semantic search, a search engine can determine what you actually mean, even if your keywords don’t perfectly match the content of a web page. For example, if you search for “restaurants with good pizza nearby,” semantic search takes into account your location, synonyms such as “places to eat,” and suggestions for similar restaurants, even though you don’t use exactly those words.
The following factors play a major role in semantic search:
- The intent of the search query: What is the user really trying to find?
- Synonyms and context: Words with similar meanings are also recognised.
- Location and search history: Search engines use your data to show more relevant results.
Semantic search and Google
Google has been using semantic search technology for a number of years. One of the most important innovations in this area is the RankBrain algorithm. RankBrain uses machine learning to interpret the intent behind search queries, even if the keywords don’t match the exact content of web pages.
Google’s Hummingbird update, introduced as early as 2013, laid the foundation for semantic search by shifting the focus from individual keywords to understanding the full context of a search query. Since then, Google has taken steps to become ever smarter at delivering the right results, especially through the use of natural language processing and AI models.
This is an important point for SEO, because it means that pages that focus only on using exact keywords will no longer achieve the highest rankings. Websites that do take search intent and broader context into account, on the other hand, have a better chance of ranking high in search engines.
The influence of semantic search on large language models (LLMs)
Like search engines, large language models (LLMs) such as ChatGPT also use semantic search. These AI models are trained to understand human language and can interpret the intent of a question, which makes them very effective at generating relevant and useful answers. When an LLM receives a question, it doesn’t simply look for exactly the same words in its dataset. Instead, it tries to understand the meaning behind the question and formulate an answer on that basis. This makes LLMs very handy for applications such as chatbots, virtual assistants and even advanced search functions on websites.
The way LLMs apply semantic search is very similar to how Google does it: by picking up contextual hints, using synonyms and taking the user’s broader intent into account. This means that businesses that want to optimise content for both search engines and AI models need to shift their focus from simply filling pages with keywords to delivering content that truly responds to what users want to know.
Why is semantic search important for SEO?
Semantic search means that SEO strategies have to evolve. It is no longer enough to simply put a keyword on a page a few times and hope for a high ranking. Instead, you need to optimise your content with users’ search intent in mind.
Here are a few ways you can optimise your website for semantic search:
- Use natural language: Write the way you would speak. Semantic search understands sentences such as “what time does the supermarket open” just as well as “supermarket opening hours.”
- Synonyms and related terms: Variation matters. Don’t just use the same keyword over and over, but add synonyms and variations too.
- Contextual relevance: Consider where, when and why someone would search for something. Make sure your content matches the user’s specific situation.
- Structure and readability: Use clear headings, bullet lists and a good paragraph layout. This makes it easier for search engines to understand your content and makes for a better user experience.
By embracing semantic search and adapting your SEO strategy to the user’s intent, you can significantly improve the relevance and visibility of your website.
What does semantic search mean for your business?
Semantic search is changing how search results are generated and how businesses need to optimise content. Google and LLMs such as ChatGPT use this technology to better understand the intent of search queries, which leads to more relevant and useful results for users.
For your business, this means you need to adjust your SEO strategies. Want to know how your website performs in terms of semantic search and SEO? Request a BE Digital scan now and find out how you can improve your online visibility.
