Believe it or not, there was a time when you could not ask a question as a question in Google.
If you wanted to search for “How to make delicious stir fried tofu?” you would have to strip it down to “stir fried tofu” for meaningful results.
Search engines were limited in their understanding of the natural flow of human language. For them, every website was a bunch of code with a bunch of information and keywords. When a user would type in a search query, search engines would simply check for the web pages with that word and produce a list of search results for information.
This limitation of search engines made it necessary for internet users to adopt a keyword-based searching method. It was not so natural.
But now, search engines are catching up. Equipped with the advanced capabilities of NLP machine learning algorithms, search engines can very well understand our natural language.
You simply need to say, “Ok Google, how to make delicious stir fried tofu?”, and the virtual assistant will instantly show you some amazing recipes. This is called natural language search.
In this blog post, we'll talk about the idea of natural language search and how it affects SEO in general.
What is natural language search?
If you have had the pleasure of searching for something on Google in the early 2000s, you know how far search has come since then.
This is how a Google search on April Fool’s Day looked in 2000.
It didn't give any direct answers to the search query. There were no images, featured snippets, or ‘People Also Ask’ links.
It did not even have the most common source of keywords in the form of the beloved ‘Related searches’ section. In fact, the SERP linked to competitor websites for alternative search results.
It was just an average search engine that literally matched search queries with thousands of pages without considering whether they helped the user. Users had to use technical tricks like Boolean logic to refine their search and get relevant results.
Back then, search engines were not a big deal. The internet did not have enough users. In fact, there were just 413 million internet users in 2000.
When there were fewer people online, there were fewer websites, which meant there was less information for search engines to index, which led to low-quality search results.
But fast forward to 2022, and things have changed vastly. With the proliferation of smartphones, smart speakers, smart cars, and smart homes, search engines have become the go-to source for any information.
Today, there are more than 5 billion internet users, and companies of all sizes are developing websites and putting out content to reach the huge number of people who use the internet. This is making it necessary for search engines like Google to use advanced technologies like AI, NLP, and machine learning to produce better search results.
This is what a search for ‘April Fool's Day’ looks like today.
Rich snippets are sprinkled all across the SERP. The date of April Fool's Day is displayed in a neatly highlighted box. There is a brief Wikipedia description of April Fool’s Day on the right, with images on every search result website and a special section for videos.
And, of course, the ‘Related Searches’ section, a favorite of all SEO professionals.
Thanks to NLP, users don’t have to adapt their questions and convert them into keywords to look for specific information. They simply have to type or talk the way they naturally would, and the search engine produces the most relevant results.
From an internet user’s perspective, natural language search refers to the ability to search for information online using search phrases and sentences that come naturally to you.
From a marketer's perspective, it refers to the rise of new types of long-tail keywords or phrases that people use to search for very specific information they want. And with the rise of voice search, this is a growing trend that every marketer should know about.
Natural language search vs keyword search
Natural language search differs from keyword search because it does not require the user to do anything except tell the search engine what they want to know. No breaking down of complex ideas into smaller, search engine-friendly phrases.
If you want to know who is the shortest person on earth, you can just type or say ‘who is the shortest person on earth’ in Google. It will give you the exact information right on the SERPs without having to visit websites. That is natural language search in action.
In contrast, a keyword-based search means that instead of a complete sentence, you would search for ‘shortest person.’
Natural language searches consist of long phrases or complete sentences instead of short keywords. It resembles how a person would ask another person for the same information. Internet users love it when search engines understand exactly what they want, and natural language search does that.
Example of natural language search
Let’s take a look at some examples of natural language search as compared to keyword-based search for more clarity.
- “How to execute a content marketing campaign?” vs “content marketing campaign”
- “What is the best Christmas gift for daughters?” vs “Best Christmas Gift”
- “When was the BBC founded?” vs “BBC Founded”
- “What is organic digital marketing?” vs “organic digital marketing”
As you can see, natural searches are longer and more descriptive than keywords. Instead of searching for information in broad terms, they specify exactly what the user is looking for. Here is a helpful Scalenut blog on 12 real-world examples of Natural Language Processing (NLP).
Evolution of natural language searches
To understand the impact and importance of natural language search, it is important that you have some background knowledge of the topic. Natural language search began as early as the 90s with askjeeves.com. It was a simple search engine that let users ask questions and get answers.
Then came Google, the master of the search universe. Google changed how we search, with continuous improvement in the quality of search results. One major breakthrough in the crusade to “organize the world’s information” was the release of BERT API in 2018.
This free-to-use API was a powerhouse of linguistic analysis, which was later permanently included in the Google search algorithm with the 2019 BERT Update. Since then, Bing has also started using NLP search algorithms to provide better results.
That’s not all—thousands of business software worldwide are using NLP to process human language and understand the underlying meaning behind written or spoken words. Virtual assistants like Google Home, Alexa, and Siri run on NLP programs that help them understand exactly what users are saying to them.
How does natural language work
Natural Language Search relies on advanced computing technology called natural language processing. It runs machine learning and statistical algorithms to dissect a search phrase and understand the meaning and search intent behind a user’s query.
If you ask Siri to find you the closest restaurants, it searches through the internet for restaurants closest to your current location. This is done by first parsing your sentence as smaller parts and understanding the contextual meaning of ‘restaurants’ and ‘near me.’
There was a time when this would take a lot of computing power. But NLP reduces the computational power required through syntax analysis, sentiment analysis, and salience scores.
Through syntax analysis, it understands that you want to find restaurants; through sentiment analysis, it gauges the search intent as navigational; and through salience scores of words and phrases like ‘near me,’ it knows that you want to find restaurants near your location.
To know more about how NLP works and how it impacts the world of SEO, you may want to refer to this in-depth Scalenut blog, ‘NLP SEO: What Is It And How To Use It For Content Optimization.’
How you can improve your website content for natural language searches
As marketers and strategists trying to make a brand more visible online, you must adjust your content strategy for natural language searches. This does not mean that conventional keywords are not important anymore. They still play a major role in search.
But with a little bit of natural language, you can be sure that your content ranks higher on search engines. It might even get picked to be a featured snippet, or a website in the ‘People also ask’ section.
Here are a few ways to improve your website's content for natural language searches:
Make your content more readable
If this was just an industry norm for you before, it should be a vital aspect of your content strategy now. As much as 20% of all the searches done on the Google app are done through voice search. That just means that as much as 20% of all users are using natural language search.
Considering that Google is the biggest search engine with the largest user base (desktop and mobile), this is a pretty big number. For Google to use your content in voice search results, you must ensure it is easy to read and understand. Break up big ideas into smaller sentences, and make sure your writing flows well from start to finish.
Use long tail keywords in your content
If we were to describe natural language search queries as another term, it would be long-tail keywords. Just like long-tail keywords, natural searches are usually three words or more and have a very specific search intent. Users know what they want when they ask Google’s voice search, ‘Who is the current PM of the UK?’
Therefore, it makes sense to enhance your integration of long-tail keywords. As a marketer, you already use some long-tail keywords, but it’s time to focus on this goldmine of organic traffic even more.
You can use long-tail natural search keywords in many ways. For example, if you are creating an infographic about the step-by-step guide for something, be sure to use long-tail keywords in the meta tags of your image.
Or you can add the long-tail natural search keywords in the structured data of your web pages. For example, a recipe for stir fried tofu should have something like “how to cook stir fried tofu” or “how to make delicious stir fried tofu?” in the schema of that web page.
Answer your customer queries via content
Most of the natural searches are nothing but direct questions. Whether it is voice or text, if a person asks a direct question, chances are very high that they want a direct answer.
Search engines like Google understand this and are trying to answer questions right there on the SERP with rich snippets like knowledge panels, featured content, and ‘People also ask.’
If you're a marketer planning your next piece of content, try to figure out what your audience wants to know most and the words they use to frame their questions.
Answer them in an easy-to-understand way with simple sentences. Doing this will help search engines like Google and Bing better index and understand your content. So the next time somebody asks a question you have covered, your web page may be among the top results.
Build content around user intent
User intent is the underlying reason behind an internet user’s search. Some may be looking for basic information, while others may be looking for a comparative analysis of the best products.
If you show up on a search that does not match the search intent you are targeting, you will not get relevant traffic. In fact, people may just leave your website, which tells search engines that your content is not the right answer to the type of search.
While creating content, it is important that you understand the search intent of your keywords. A person searching with ‘McDonald’s’ may just want to learn about its history. So, if you show them your review of a McDonald’s outlet, it won’t be helpful.
Search intent has become the most important part of content marketing since Google's BERT update. Google is continuously improving its understanding of the search intent behind queries, and you too should have a better understanding of the search intent behind your target keywords.
At Scalenut, we are also search intent buffs like Google. You can understand the search intent of users with the help of our SERP analyzer and SEO hub features.
The SERP analyzer helps you get instant content ideas based on the most successful content for a keyword. The SEO Hub gives you a comprehensive analysis of the top-ranking pages for a keyword with detailed outlines, a list of NLP terms, statistical sources used by them, a content grade, and the most popular questions asked for a keyword in a location of your choice.
With all this information from the top-ranking pages, you can identify users’ search intent when they use your target keyword. If most of the top-ranking pages explain a concept, the search intent is informational. If they are loaded with CTAs, the search intent is transactional, and if there are many web pages of the same website, chances are the search intent behind the keyword is navigational.
Based on the competitive analysis you get from Scalenut’s SEO Hub and SERP analyzers, you can build content around user intent and attract your target audience.
Include NLP terms in your content
If you want to rank for natural language searches, it is important that you include NLP terms in your content. There are many ways it might help.
For instance, one of the ranking factors of Google’s search algorithm is the TF-IDF method. It is an information retrieval machine learning measure of the importance of a word in amplifying the meaning and usefulness of content for a specific search query. It helps Google’s algorithms to understand the value of a word in a given context.
Methods like TF-IDF and NLP make Google results more effective, independent of keyword focus. Instead, they help Google rank web pages based on the meaning of the entire text.
If you include high TF-IDF words and NLP terms in your content, Google will see your website as a good source of information. Further, if somebody uses the same terms, Google will know immediately that your website is a good candidate for the search results.
Use content optimization platforms like Scalenut
Another great way of optimizing your content for natural language searches is to use an NLP-powered platform like Scalenut.
From brainstorming ideas through topic clusters to creating better content than your competitors, platforms based on AI technologies like NLP and GPT-3 are ideal for thorough research and optimized content creation.
For example, we help content creators publish the best content by using a content grading system that looks at the presence of NLP terms in the content.
Is your content ready for natural language searches?
If you are a marketing professional in today’s day and age, you understand the importance of good rankings. You also know that there is fierce competition among websites for every valuable keyword.
In such a scenario, natural language search helps you carve a niche for your content marketing campaigns. If you understand this concept, you will not only be able to make better content, but you will also be able to make sure that your efforts bring the right people to your website.
And if you need some help, Scalenut is always ready.
We'll help you make sure you use the best NLP terms for a target keyword, which will solidify your position as an expert on the topic. Take Scalenut for a spin. Start your free trial and unlock a powerhouse of content creation and NLP search engine optimization today.
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