Ten years ago, SEO strategists around the world followed a relatively similar process.
The first step is to conduct keyword research. In the second step, these keywords are randomly written into the text on the page approximately 5 billion times. Step 3-The keyword ranks first.
I don’t want to disturb you, but that’s not the case anymore.
Several algorithm updates such as Hummingbird and RankBrain have brought a new concept: semantic search.
Although this may eliminate the work of black hat keyword fillers, SEO that prioritizes providing a good customer experience can breathe a sigh of relief because Google is now on their side.
Google and other search engines are constantly striving to meet the needs of searchers with the most accurate results-this is where semantic search comes in. In other words, it ties the search intent to the context of your content to provide the most relevant and useful results.
With these updates, how will this affect search traffic? What do SEO need to consider?
This is what I will cover in this article.
What is semantic search?
First, let’s take a deeper look at how semantic search works.
Semantic search is a process used by search engines to try to understand the intent and contextual meaning of a search query in order to provide you with results that match your ideas.
In other words, semantic search aims to know why You are searching for these specific keywords and how you intend to process the information you obtain.
It’s important to note-you don’t want to mistake semantic search for latent semantic indexing (LSI), or what some people might call semantically related keywords. LSI can help provide context about your content (and therefore help match search intent), but semantic search is much more than that.
If we look at semantic search as a whole, the following are the factors that guide the way it works:
1. The user’s search intent.
The term “search intent” refers to the reason you performed the query (or, colloquially: why you searched for something with Google). In most cases, you want to buy, find or learn something.
For example, if I search for “content marketing,” Google will provide results about the definition of content marketing because the intent is quite broad:
However, if I search for “how to start content marketing” instead, Google will Is not Provide a definition of content marketing because my intentions are different:
Takeaway: For all content marketers and SEOs, the important lesson here is that you need to carefully consider search intent when choosing keywords and creating content. Even if your content ranks well, users will leave the page if it doesn’t match the search intent-which of course will not help conversions.
2. The semantics of the search term.
“Semantic search” is created based on semantics, or studies the meaning of words and phrases in a specific context and the relationship between these words. As far as search is concerned, semantics refers to the connection between the search query, the related words, and the content on the website pages.
All these factors combine to help search engines understand the meaning of the search query, rather than literally translate it, so it can display contextually relevant results.
For example, if you search for “wedding dress”, related words might include “wedding”, “cake”, “bride” and “dream”. When searching for “dresses”, related words may be “beautiful”, “knee-to-the-knee”, etc.
Takeaway: When choosing keywords to enter your content, I recommend creating so-called “keyword clusters” or groups of related keywords. These clusters are directly related to semantic search because they ensure that your content covers a wider range of topics. The scope is wider, with multiple keyword rankings per page.
Other factors related to semantic search
Although the above two are the main factors, these factors also affect semantic search:
- Featured snippets: Featured snippets are based on providing searchers with the most direct and useful answers.
- Rich results: These also affect semantic search through images and other content, and you will see how it affects semantic search in the example in the next section.
- Voice search: Voice search queries are usually very straightforward and include natural language, longer phrases, and question words that help search engines process the results.
- RankBrain: The RankBrain algorithm is based on machine learning technology to help Google understand the first set of examples that meet the query conditions and related concepts, phrases, and synonyms.
- Hummingbird: The focus of the Hummingbird algorithm update is to provide better results for voice searches, conversational language, and searches for specific persons.
Semantic search example
To give you a clear understanding of how semantic search works, here are some specific examples.
Here, I searched for “order pizza”, so the results are biased towards local searches:
Here, I searched for “making pizza” on Google, and I saw rich results with recipes:
If I Google just “pizza”, I might still get local search results because more users are looking for command Instead of making it yourself. However, if my search history is full of pizza recipes, my “pizza” results may also be recipes due to the personalization component.
Semantic search basically affects all results received by users.Therefore, the website will provide services as a result of specific keywords only in the following cases content Match on the page Context That search query. The result of “making pizza” will include ingredients, preparation time, etc., while the result of “ordering pizza” will include location, delivery, and price.
Interestingly, current news also affects search results. Before the pandemic, searching for “corona” would mostly return beer brands, but after the spread of COVID-19, you would mostly get results about the virus.
Another example is Jeff Bezos. When you search for his name, you will get a knowledge graph, general information, and then recent news. But if something big happened to Jeff Bezos recently, you will be the first to see the headlines.
How Google uses semantic search
Google’s bottom line is to provide users with the best search experience possible. To this end, they use semantic search to:
- Identify and disqualify low-quality content.
- Better understand user search intent. For example: Did the user search and navigate to a specific page? Or are they seeking more research on a topic?
- Formulate the answer to the question.
- Determine what relevant data to extract from the Semantic Web
- Understand websites and pages based on topics rather than keywords.
- Integrate Google technology, in which semantic search plays a role, such as Knowledge Graph, Hummingbird, RankBrain, BERT.
- Format the data appropriately for inclusion in search results.
- When the search intent is not clear, connect queries with all possible meanings.
How to use the power of semantic search to your advantage
In short, if your content is not semantically related to the search query, it will not appear in the search results. The simple solution to this is to combine the right strategy to match your content to the search term.
In order to stand on the right side of SEO in terms of semantic search, I suggest you try to do the following:
- Focus on the topic, not the keywords.
- Make sure you understand the user’s search intent: Do you want to buy? Reach a specific page of the brand? Learn?
- Build relevance through links (internal and external).
- Use schema markup.
- Use semantic HTML, such as
- Answer all questions related to your topic.
- Based on the answer, structure the sentence to be easy to understand.
Excluding these from your list, you can get a one-stop service of powerful SEO strategies with the support of semantic search.