How AI Is Reworking Key phrase Research

Practically everyone with an online presence knows the importance of having a solid content strategy. But let me ask you a question: How much time do you spend on your keyword research process? And here’s another one for you: How sound is your keyword research game plan?

We are all familiar with Google’s algorithm updates. While we may not know exactly how they work, what we do know is that this search giant is leaning heavily toward offering its users helpful information. Why do I mention this? Because it’s all linked to the rise in semantic keyword analysis.

And for me, there’s no better way to save time and strengthen my keyword strategy than with the help of artificial intelligence (AI) tools. So, without further ado, let me present my case below.

Related: 5 Common Research Mistakes and How to Avoid Them

Understanding semantic keyword analysis

Let’s rewind the SEO clock to a few years ago. Back then, SEO tools were used to determine high-search-volume keywords. This was good and well, but these keywords would then be unashamedly “stuffed” into content multiple times, sometimes sounding illogical and even spammy.

This was based on the assumption that the more time the seed keyword appeared in a text, the more Google would grasp the lexical meaning and rank your content on its search engine results pages (SERPs).

Fast-forward to the present day. With a lot of technological advancements underway, we’re seeing a rise in use not only in lexical keywords but also in semantic keywords as Google targets search intent and helpful content.

This is where semantic keyword analysis makes its entry. It is an important strategy in improving content relevance and content targeting because it goes beyond traditional keyword matching to better understand context and user intent. In simple English, it means that as Google’s algorithms evolve to understand the semantics behind a search query, so too must we SEOs adapt to these changes.

AI and natural language processing

So, how do we adapt? How do we improve our semantic keyword research? How do we speed up the process while producing quality research outputs and content? Personally, I’m a strong advocate of relying on AI to help us achieve efficiency.

And some AI technologies, based on natural language processing (NLP), are the perfect application for semantic keyword analysis. Why? Because through NLP and machine learning, computers learn how to comprehend and interpret human language.

The right AI tools can help interpret important linguistic nuances that identify semantic relationships between words. This means that NLP can enhance our semantic keyword analysis at a fraction of the cost and for less time than it usually takes to complete a thorough research process.

Related: How to Leverage AI to Boost Your SEO Efforts and Stay Ahead of the Competition

Benefits of semantic keyword analysis with AI

Every SEO specialist, myself included, knows the value of thorough keyword analysis. It is the foundation for producing quality content, optimizing it and outperforming competitors with finesse. That’s why AI-driven semantic analysis really takes center stage in our efforts.

In particular, a few key areas where certain AI tools can help include:

• Improving content targeting accuracy

• Understanding the user search intent

• Improving content optimization efforts

In turn, once these elements are implemented, you can start seeing improvements in your SERP rankings and enjoy higher organic traffic. However, the double whammy comes through greater conversions and improved user engagement with your content.

Implementation strategies

Are you convinced of the power of NLP-powered semantic keyword analysis yet? If so, now is the right moment for me to share some key implementation strategies and practical tips to get started effectively.

• Choose the right AI tool: First things first, you need to choose the right AI tool. This may sound obvious, but you should consider your business needs and budget. Look for tools that offer comprehensive keyword analysis that includes search volume, user intent and content gaps.

• Identify your target keywords: Take your primary keyword and enter it in the AI keyword tool. The results you should get are a list of related keywords. These should be accompanied by search volume, competition and a relevance score. It’s time to put on your thinking cap and analyze the list. You have to choose the most relevant, high-traffic keywords for your content while aiming for low to moderate competition.

• Analyze user intent: Your AI-powered tool should also provide you with insights into the user intent behind search queries. This information can be used to drive your content piece’s outline and content creation process. When you cater to users’ needs through content, you can enjoy better online visibility and engagement.

• Optimize your content: You’ve created a content outline, and you’ve narrowed down the keywords to be used in the article or piece of content based on factual data from your AI tool. Now, it’s time to optimize it. If you’re creating a blog article, your primary keyword should appear in the post’s title, some of your headings and subheadings, as well as in your meta title and/or meta description. Primary keyword variations and semantic keywords should also appear in your content. However, be sure to write with a natural linguistic flow. Important note: Avoid keyword stuffing like you would avoid any disease.

• Monitor, tweak, and refine: Your job isn’t over after you hit the “Publish” button. This is where the real work begins. You need to use your AI tool to monitor metrics such as organic traffic, bounce rate, time on page, conversion rates and others. With solid data at your fingertips, you can easily make the necessary tweaks and refine your content further for optimal performance.

And if you still think this sounds too good to be true, consider the case with my very own blog — InBound Blogging. In the space of just six months, our keyword growth increased from a low of 232 to a whopping high of 3,894 ranked keywords. All this with the help of AI tools such as HARPA AI, NeuronWriter, AgilityWriter and others.

Related: Here’s the SEO Combination You Need to Win Google’s Algorithm

Future trends

As I wrap up, I’d like to leave you with a few expectations I have in terms of semantic keyword analysis using AI.

Firstly, voice search. I’m anticipating that SEO experts will increasingly implement conversational and long-tail keywords in content pieces, capturing the rise in smartphone and voice assistant usage.

Secondly, latent semantic indexing (LSI) keywords are going to be the rising star in SEO because they help search engines like Google better index content and produce more accurate and relevant search results tailored to user queries.

All in all, AI tools have the power to shape our semantic keyword analysis approaches, speed up our processes, and save us valuable time and money while producing excellent results for our readers and users.