Natural Language Processing (often seen abbreviated as NLP) comes up frequently in the technology sector in the same way “AI” and “machine learning” do. It sounds new, sexy, and impenetrably technical—and its proponents tell you it can solve some real problems.
While engineers at tech companies are coming up with tons of applications for NLP, it is still relatively new to the SEO space. And underneath the superlatives and the technical jargon about NLP lies a great deal of potential value to SEOs.
But before we get into how, we should talk about what exactly Natural Language Processing is.
What is NLP?
Let’s keep it simple: Natural Language Processing is a broad term for technological solutions that help computers understand human language. NLP creates artificial intelligence that analyzes the syntax of language and the semantics of language.
Syntax analysis looks into what a person says or writes. Understanding this helps an AI compare the content to pre-programmed standards about language. It looks at aspects of the language like sentence structure and word usage. Essentially, it looks at how content is put together.
Semantic analysis looks into the meaning of what a person says or writes. Based on the syntax analysis and the AI’s pre-programmed knowledge about language, semantic analysis helps the AI understand what a person means when they say or write something. It helps answer the question of human intent.
SEO and Natural Language Processing
Search engines like Google—and by extension, SEOs—know that intent is the holy grail. If you can understand what a customer is looking for and why, then you can be there with a solution. And solutions build trust and brand loyalty.
To discover a person’s intent, Google uses Natural Language Processing as part or their solution. They have come to be the gold standard for search engines by creating extremely sophisticated algorithms that use NLP to really get to know the intent behind people’s searches to provide the most relevant and helpful results.
And that’s why, just like Google, you should start using NLP to go beyond just thinking about keywords and search volume, but also about syntax and semantics.
One way SEOs can use this technology is to identify common phrases that appear in top-ranking content about a particular topic. Then, you can apply NLP to reverse engineer at least part of Google’s algorithms. The results you get back are the phrases Google likely considers essential when you write about that topic. This allows you to provide more syntactic and semantic data to Google, in the way Google understands as relevant and helpful to answer the search query.
NLP and Me
To say the least, all of this is easier said than done! Not many Digital Marketing teams have a team of engineers hunkered down, creating in-house tools to reverse engineer Google’s algorithms. (Alright, alright: zero Digital Marketing teams have that.)
As with many technological problems facing SEOs, you should lean on your tech stack to start helping with this research.
Conductor is proud to start solving this particular problem with its new Content Guidance feature—now in beta. Content Guidance, which applies Natural Language Processing to topics you research, provides you with insights based on data that Conductor gathers with its enterprise platform.
How does Conductor do this? One example uses an NLP concept known as stemming, which lets Conductor quickly recognize common patterns across the content that ranks well for a topic you research. These patterns are what Google uses to determine that the top-ranked pages all share relevance with your researched topic. As a marketer, this approach lets you get into Google’s brain by working backwards from search results to determine the relevant and valuable keywords that may not be obvious without algorithmic analysis.
Don’t Forget to Put Your Customer First
Starting to think more like Google doesn’t mean you should lose sight of the people behind all those searches. They’re your potential customers, and it’s their intent you’re trying to discover. Just because we can try to use novel technological means to understand why and how Google decides content is a relevant answer to a query doesn’t mean we can forget there is a person behind that query.
Google knows this—and it’s why we can use them as a proxy to better understand customer intent through NLP.