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Virtually everyone with an online presence knows the importance of a solid content strategy. But let me ask you a question: How much time do you spend on keyword research? And here’s another question for you: How solid is your keyword research plan?
We all know about Google’s algorithm updates. While we may not know exactly how they work, we do know that this search giant is heavily focused on offering helpful information to its users. Why do I mention this? Because everything is related to the increase in semantic keyword analysis.
And for me, there is 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 turn the SEO clock back to a few years ago. Back then, SEO tools were used to identify keywords with high search volume. That was all well and good, but these keywords were then shamelessly “crammed” into the content multiple times, which sometimes sounded illogical and even spammy.
This was based on the assumption that the longer the seed keyword appears in a text, the better Google would capture lexical meaning and rank your content in its search engine results pages (SERPs).
Fast forward to the present. With many technological advances underway, we are seeing an increase in usage of not only lexical keywords but also semantic keywords as Google targets search intent and helpful content.
This is where semantic keyword analysis comes in. It’s an important strategy for improving content relevance and content targeting because it goes beyond traditional keyword mapping to better understand context and user intent. In plain English, this means that as Google’s algorithms evolve to understand the semantics behind a search query, we SEOs must also adapt to these changes.
AI and natural language processing
So how do we adapt? How do we improve our semantic keyword research? How can we speed up the process while producing high quality research and content? Personally, I am a strong supporter of relying on AI to increase our 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 to understand 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 improve our semantic keyword analysis at a fraction of the cost and in less time than it would normally take to complete a thorough research process.
Related: How to Use AI to Boost Your SEO Efforts and Stay Ahead of the Competition
Benefits of semantic keyword analysis with AI
Every SEO specialist, including me, knows the value of thorough keyword analysis. It is the foundation for producing high-quality content, optimizing it and outperforming the competition with finesse. That’s why AI-powered semantic analysis is at the heart of our efforts.
In particular, key areas where certain AI tools can be helpful include:
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Improve content targeting accuracy
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Understand user search intent
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Improving content optimization efforts
Once these elements are implemented, you can in turn see improvements in your SERP rankings and enjoy increased organic traffic. However, the double effect comes from higher conversions and improved user engagement with your content.
Implementation strategies
Are you already convinced of the power of NLP-based semantic keyword analysis? If so, now is the time for me to share some key implementation strategies and practical tips for getting started effectively.
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Choose the right AI tool: The first thing you need to do is 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 takes into account search volume, user intent, and content gaps.
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Identify your target keywords: Take your primary keyword and enter it into the AI Keyword Tool. The result you should get is 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 need to choose the most relevant keywords with the highest traffic for your content while aiming for low to moderate competition.
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Analyze user intent: Your AI-powered tool should also provide you with insights into user intent behind search queries. This information can be used to drive your content’s outline and content creation process. Addressing user needs through content can help you achieve better online visibility and engagement.
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Optimize your content: You’ve created a content map and narrowed down the keywords to use in the article or content based on factual data from your AI tool. Now it’s time to optimize it. When you create a blog article, your primary keyword should appear in the post title, some of your headings and subheadings, and your meta title and/or meta description. Primary keyword variations and semantic keywords should also appear in your content. However, make sure to write with a natural flow. Important note: Avoid keyword stuffing like you would avoid any disease.
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Monitor, optimize and refine: Your work isn’t finished after you click the Publish button. This is where the real work begins. You need to use your AI tool to monitor metrics like organic traffic, bounce rate, time on page, conversion rates, and others. With solid data at your disposal, you can easily make the necessary optimizations and further refine your content for optimal performance.
And if you still think that sounds too good to be true, consider my own blog – InBound Blogging. In just six months, our keyword growth went from a low of 232 to a whopping high of 3,894 ranked keywords. All this with the help of AI tools like HARPA AI, NeuronWriter, AgilityWriter and others.
Related: Here is the SEO combination you need to win the Google algorithm
Future trends
Finally, I would like to share with you some expectations that I have regarding semantic keyword analysis using AI.
First, voice search. I expect SEO professionals to increasingly implement conversational and long-tail keywords in content posts to accommodate the increasing use of smartphones and voice assistants.
Second, latent semantic indexing (LSI) keywords will be the rising star in search engine optimization as they help search engines like Google better index content and deliver more accurate and relevant search results tailored to user queries.
All in all, AI tools have the ability to shape our semantic keyword analysis approaches, speed up our processes, save us valuable time and money, while delivering excellent results for our readers and users.