Sep 29, 2021

How NLP is affecting the SEO & search visibility?

Suman Samal, Asst. Marketing Manager
Suman Samal
How NLP is affecting the SEO & search visibility?
Suman Samal, Asst. Marketing Manager
Suman Samal
Sep 29, 2021

How NLP is affecting the SEO & search visibility?

Since the BERT update, NLP has revolutionized the SERP results. What is NLP and why does it matter in SEO? Read this article to find out why NLP is key to Search engine rankings.
How NLP is affecting the SEO & search visibility?

Table of contents

Over the years, the Google algorithm has changed with SEOs focusing on various things at a time.

The latest Google algorithm has now made the use of natural languages more valuable and important in the content. NLP is connected to Google's algorithm and plays a significant role in improving the content quality and the intent.

With this, there has been a lot of buzz among the digital marketing industry about NLP and its usage. In this blog post, we have shared a comprehensive guide on NLP and how to use it for SEO.

Importance of NLPĀ in today's world

Many internet users confuse the two NLPs; Natural language processing and neuro-linguistic programming.

While neuro-linguistic programming helps communicate effectively with people around you whereas natural language processing helps interact with machines.

NLP or Natural Language Processing has proved a boon in the pandemic era of economic slowdown to boost sustainability.

Due to the recent COVID19 outbreak, many businesses needed smarter ways to understand changing customer behavior and adapt quickly.

This is where NLP has been phenomenal in helping several businesses in real-world applications such as medical research, search engines, and business intelligence with interpretations of usersā€™ demands.

One of the significant uses of NLP is in voice assistants that have created an entire ecosystem of ā€˜voice commerceā€™.

According to Juniperā€™s research, there will be more than 8 billion voice assistants by 2023, growing at a rate of 25.4%. Most voice assistants are powered by NLP-based technologies and are driving the growth of such smart devices.

Over the years, many enterprises have leveraged NLP-based technologies to create smart products. For example, according to eMarketer, eCommerce giant Amazon has seen massive penetration in the smart assistant market.

However, apart from such intelligent assistants, there are other applications of NLP technologies gaining traction, like automatic content generators that use NLG or Natural Language Generation.

So, letā€™s understand what is NLP and how it works.

What is NLP?

What does NLP stand for? Natural Language Processing (NLP) is a sub-branch of Artificial Intelligence and linguistics dedicated to interpreting human language for machines or computers.

NLP uses algorithms that analyze the language patterns and train models to offer arrangements for machines that use such training as a reference for executing specific tasks.

For example, a chatbot will ask several questions to users regarding their issues, aggregate data, and analyze and process it to offer related solutions.

NLP was first conceived as an experiment for Machine Translation back in the 1940s. One of the most significant leaps in NLP development was ARPA Speech Understanding Research (SUR) in the 1960s.

One example that defines the true path to the evolution of NLP of modern times is ELIZA, a chatbot developed at MIT (Massachusetts Institute of Technology) based on DOCTOR script.

Since then, there have been several projects during the modern era of eCommerce innovations that have exploited NLPā€™s full potential.

How does NLP work?

NLP is, at its core, a language interpretation tool. A language is a set of rules or symbols that help in communication and interpretations.

Symbols are combined to convey information or broadcast data. NLP uses language symbols or patterns for interpretation to keep up with the grammatical structure of the sentence.

With the rise of voice search, there needs to be a greater degree of NLP development in the field. Ā Until then, we see applications being put into learning outputs for bots creating more advanced interactions with users.

What are NLP techniques?

Every NLP model has two major activities: interpretation and text generation. The first part interprets human language, search intent, and syntax to offer data that enables text generation.

NLP can be categorized into two parts:

  • Natural Language Understanding (NLU)
  • Natural Language Generation (NLG)

Natural Language Understanding (NLU)

NLU is a part of NLP that deals with the understanding and comprehension of human language. It interprets the meaning of communication that a user does with a machine.

When you are speaking to another person, both of you share a common knowledge of the language that makes it easy to understand.

However, this is not the case with a machine that only understands ones and zeros or any other form of machine language.

NLU simplifies the understanding of the human language for computers or machines through text categorizations, analysis of content, and sentiment analysis.

For interpretations, NLU uses the linguistic science concepts of

  • Phonology (sound)
  • Morphology (word formation)
  • Syntax (sentence structure)
  • Pragmatics (understanding)

Natural Language Generation (NLG)

NLG uses NLU to generate output data. It is a part of NLP that leverages the interpretation of human language done by NLU to generate responses or text for users.

Many enterprises are now adopting NLG for applications like auto-text generation, trigger emails, automatic audio responses, etc.

As per a report by Gartner, 25% of enterprises will use natural language technology in some form by 2022. NLG systems are a major driving force in the adoption of NLP by several businesses. For example, NLG can help automate content creation by combining long sentences and sequences and personalizing the experience backed by NLUā€™s data comprehension.

Businesses can leverage such technology to generate automated content for internal communications, product descriptions, marketing purposes, contracts, reports, analytics, and others.

Automating the content generation can reduce the turnaround time, offer standardization across communication channels, and improve accuracy.

There are automatic content generators that use NLG to their advantage, such as:

Now that we have the basic idea of what is NLP letā€™s discuss some of the best NLP tools that you can use for your business.

Top NLP tools for your business

There are two ways to leverage NLP for your business.

The first approach is to build a customized NLP model from scratch. You will have to invest massively in infrastructure, resources, and skilled professionals to develop a bespoke NLP model.

The second and efficient way is to use NLP tools from third-party service providers, which can rapidly integrate into your existing organizational structure at a low cost.

Here are some of the best NLP tools that you can use to improve conversions and increase lead generation through interpretations of usersā€™ search intent.

#1 IBM Watson

IBM Watson is a suite of several AI-based services powered by its cloud storage offerings. One of the most effective AI-based services provided by IBM Watson is NLU.

It has been a phenomenal service that enables recognizing keywords, text categories, emotions, sentence structure, syntax, and others.

The best part about IBM Watson is that it provides a customizable service for different industries like finance, healthcare, manufacturing, etc.

#2 GPT-3

GPT-3 is the third generation iteration of Generative Pre-trained Transformer, a neural network Machine Learning model that enables automatic generation of text.

It is developed by OpenAI and requires little input for automated text generation. GPT-3 is one of the biggest neural networks with more than 175 billion Machine Learning metrics.

#3 Berkeley Neural Parser

It is one of the most accurate parsers that offers AI-based models for more than 11 languages. It is a tool applied in Python and helps break down the syntactic build of sentences into sub phrases for a better understanding of the human language.

This parser uses parsing to split the complex sentences into separate components to find it's meaning.

Berkeley Neural Parser allows you to use the tool easily without much knowledge.

#4 TextBlob

TextBlob works on Natural Language Toolkit or NLTK, a set of libraries and programs that help with human language interpretations using Python.

Considered as one of the finest tools to understand the complexities of NLP, TextBlob enables prototyping of your projects easily. It allows you to use sentiment analysis, translations, extraction of phrases, text classification, and spelling correction.

#5 Gensim

It is another algorithm-based tool that facilitates the interpretation of human language through memory optimization and aggregation of linguistic data.

You can leverage the algorithm by installing Python packages that help in several different linguistic-based analyses.

NLP is a text-based interpretation and linguistic technology that has enabled the automation of content generation. In recent years, content is at the heart of SEO or Search Engine Optimization, and NLP has been empowering SEO-based content generation.

How NLP changed the world of SEO?

SEO relates to Googleā€™s SERP (Search Engine Result Pages), where crawlers index websites based on different parameters.

One of the most significant parameters that Google considers is content that is engaging and related to usersā€™ search intent. Users are now searching more smartly and expect the intended answers. That's when, the BERT update came into existence.

In 2019, Google announced the BERT algorithm or Bidirectional Encoder Representations from Transformers algorithm that uses a transformer architecture.

According to Wikipedia, Bidirectional Encoder Representations from Transformers (BERT) is a transformer-based machine learning technique for natural language processing (NLP) pre-training developed by Google.

With this new update rolling out, Google now focuses on the context and the nuance of words in the search strings and then matches them with the results that are closer to user intent.

Similarly, the search engine provides featured snippets to the content that is most relevant to user intent.

Google is now pushing more focus on search relevancy and user intent. NLP methodology has helped with various ranking factors such as a focus on entity recognition for image alt text, long-tail keywords, search intent and keyword stuffing.

So, there is no denying that NLP can help improve the SEO for websites and affect Google search results lately.

Google's NLP API Demo

Google's NLP API is a real-time conversation bot that uses Natural Language Processing algorithm to understand what users are talking about.

The incorporation of natural language API has enabled various data scientists, marketers to plug in and examine the text.

Almost 15% of the search queries or search terms are used for the first time on Google. This means that Google machines and algorithms might not have enough historical data to understand the intent behind these search terms.

The NLP gives a better picture of everything related to your target keyword and compares them with pages in the SERP.

Better insight into keyword research

Since the BERT update, there has been a significant improvement in the pages that are ranking on the SERPs. Keyword relevance with the content is of utmost importance for the search engines to understand your page better.

You can analyze the pages on your site that got hit after the BERT update and work on improving the page user experience for your users. To do that, keep an eye on the top pages that got increased in ranking since the update.

Look for the combination of keywords that these pages are ranking for and what else Google is looking for. Since the update, Google looks for a whole sentence or a paragraph to create meaning out of it rather than focusing on a string of search terms.

Backlinks creation

With NLP, the focus on link structure, the importance of contextual links has increased further. The understanding of anchor text and its relevancy with the linked pages of your site is of utmost importance.

The learning process for the NLP algorithm looks very problematic at first but once you get a hang of how to use them then they become extremely useful in improving lead generation as well as organic rankings.

This means that you can't put irrelevant links on any page and expect it to create value for your page.

Competition analysis

It's clear that no two sites will perform equally well at search engine. With the recent BERT update, the challenge has doubled for everyone; hence to make your page rank better you need to exploit these insights and take advantage of all possible opportunities.

But there must be something that Google likes about the top-ranking pages. Ā Irrespective of the authority or age of your site, it's the on-page content that matters the most.

And, this involves various things and optimization techniques. For example, TF-IDF in one way to optimize your content.

Besides this, Google also analyzes the piece of content by incorporating data sets such as sentiment, entities, category, and salience score.

It is, therefore, crucial to analyze your competitors on the basis of these factors as well. Tools like Surfer SEO, Frase, or SEO Assistants use these factors and other important factors to create NLP SEO-friendly content.

Effects of NLP on SEO

What search engines like Google achieved with NLP is tackling the possibility of getting tricked by conventional forms of SEO.

For example, earlier, keyword stuffing was a significant contributor to SEO success. NLP Journalist Danny Sullivan in 2019 tweeted that there is not much the SEOs can do about NLPs and machine learning in their site.

But, John Muelller cleared the air in his webinar where he said that the best way to optimize for NLP is by writing natural content. The goal is to create the context of words.

But with BERT, Google ensures that the context should be at the core and not just keywords. For example,

Google algorithm now understands the user intent with the help of various prepositions like "of", "in", or interrogative words like "when" Ā and "why" to better understand the context.

Letā€™s see some other significant impacts that NLP has on SEO.

Blackhat Redundancy

Blackhat strategies are, in fact, against search engine guidelines; even so, many marketers achieved success using such strategies for their businesses.

It is a set of practices that help improve the ranks for websites in SERP, but they violate the terms of service defined by search engines.

Search engines can now detect such blackhat SEO practices with NLP-based technologies and even counter them through context-based indexing.

Relative Alt Text

Image size is essential to SEO, but does Alt text make much of a difference? As NLP stresses the context, your images must have alt text in sync with the content. Similarly, if you include a video in the content, it must have content related to the core piece.

For example, if your content is about sports shoes, adding a video on how to leverage shoes in running or other sports can benefit your SEO strategy.

Countering BERT with NLP

One of the most significant impacts of NLP is its ability to help marketers counter BERT from Google. Both BERT and NLP have the same approach at their core, where the context of content is crucial. So, organizations can leverage NLP to optimize their content and content BERT algorithms.

Several auto content generators and content idea generation tools use NLG to offer optimizations. For example, Coschedule headline analyzer helps improve the title of a blog, landing page, ebooks, or just about any content.

It uses NLU to understand the intent for each keyword or set of words and offers analytics.

The headline analyzer tool allows users to understand headline optimization by breaking reach words into several categories like emotion words, power words, unique words, common words, and even an SEO score.

Similarly, there are several examples of NLP technology enabling SEO optimizations through different tools. Letā€™s discuss some of these examples.

Typical Examples of NLP for SEO

There are several examples of NLP usage by organizations for SEO optimizations. From retail firms to massive security companies worldwide, NLP has been the go-to technology for SEO-based optimizations.

The Right Step Forward (Rocky Footwear)

Rocky footwear brand was looking to improve its organic traffic and sales. So, they needed a solution that could help improve SEO and create user-friendly content for their website.

So, they leverage an NLP-based tool called BrightEdge recommendations.

Footwear brands used an NLP-based tool to optimize a page title and meta tags, which primarily affects the SEO for improved search engine rankings.

This helped them improve their search-based revenue by 30%, year-over-year revenues by 74%, and traffic of new customers by 13%.

The Media Rumble (Stack Media)

Stack Media is a digital publishing and online media company that delivers content related to athletes looking to meet training goals.

The editorial team was facing the massive challenge of increasing the engagement of users and improving SEO metrics for their media platform.

So, they decided to leverage an NLP-based tool to increase the keyword impact and create more engaging content.

For this, they used Data Cube, which offers keyword insights. Apart from the keywords, the editorial team needed to analyze the search intent of the audiences for improving their content for which they used Intent Signal.

They managed to reduce the bounce rates by 73% and improve the website visits by 61% through NLP-based tools like Data Cube and Intent Signal.

The Secure Success (Fugue)

Fugue is a cloud infrastructure security company that offers compliance and security services to enterprises. Their solutions identify the cloud security risks and compliance issues to ensure preventive measures are in place to avoid catastrophic events.

In addition, it offers baseline drift detection and automatic error detection to eliminate any data breach for enterprises.

The marketing team at Fugue needed to improve their visibility in the Cloud Security Posture Management (CSPM) segment.

CSPM is a category of security tools that includes specific use cases for integration of DevOps, incident response, compliance monitoring, risk visualization, and assessment.

They needed to come up with unique content and yet compete against the market leaders that have created content on CSPM.

So, they chose Frase.IO, an NLP-based tool that offers content ideas, topics, and even entire content, which they leveraged for higher organic traffic, taking their web page from the 10th position on SERP to 1st.

Not just BrightEdge, Data Cube, or Frase.IO, there are several different NLP-based platforms that you can use to have a higher impact on SEO, such as:

  • ContentFusion
  • SEO Assistant
  • HubSpot
  • SEO Scout
  • MarketBrew
  • MarketMuse
  • WordLift


From voice assistants to auto content generators, NLP has powered several smarter solutions for businesses.

With such a tool, you can

  • Understand the exact intent of search by user
  • Identify the pain points of users while using your products or service
  • Implement aggregation of vital user data
  • Check for grammar and optimize for voice search queries.
  • Interpret, analyze, and recommend personalized experiences
  • Improve conversions and elevate lead generation
  • Rank higher through SEO optimization
  • Increase traffic on your content

If you are looking to achieve SEO optimizations for your business through high-end content created through NLP technology, Scalenut is what you need.

Start your journey to a successful business now through smarter content generation with our services.

Suman Samal
Asst. Marketing Manager
ABout the AUTHOR
Suman Samal
Asst. Marketing Manager

Suman Samal is a Asst. Marketing Manager at Scalenut. She is a technology enthusiast with a keen interest in content marketing and SEO. She truly believes that with the right set of tools every organization can improve the ROI of their content marketing campaigns. She spends her time managing content operations at Scalenut and ensuring that everything we publish is of the highest quality.

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