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How Analytics With NLP Can Help Unlock SEO Wins

 Analytics With NLP

What can you do to unlock hidden search engine optimization wins?  

Let’s assume that you’re already doing everything else right: You’ve made your site fast and secure, developed a strong architecture, done the expected keyword research, engaged in strategic link building, and stayed on top of the latest algorithm updates. You are also diligent about smaller optimizations and emerging trends: You’ve embraced local SEO best-practices, started thinking about the impact of voice search, and ensured your website content and experience is mobile-first. 

What Next? 

One approach that can be effective, and which is often overlooked, is to harness the capabilities of NLP to widen the scope of your SEO efforts from a keyword-based approach to a topic-based approach. This can be done using an analytics platform that uses natural language processing (NLP) to analyze and understand the content on your website and how your audience engages with it. 

For those unfamiliar with the term, NLP is simply the ability of computers to effectively understand how humans use language. Of course, this relatively easy-sounding task is incredibly complex. Because the ways in which people communicate involve so many rules, exceptions, variations, and nuances, it requires quite a bit of power and sophistication to be able to correctly parse language consistently. 

That’s why NLP was a futuristic vision for a long time. However, thanks to relatively recent advances in computing, machine learning, and artificial intelligence, it is now a reality (if still an imperfect one). This has opened up a wide range of exciting avenues, including significantly improved search engine optimization.

Analytics with NLP

How NLP in Analytics works? 

Why is NLP relevant to SEO? Largely because it increasingly lies at the heart of how search itself operates. 

Since they were created, the ultimate goal of search engines has been to connect individuals with the information they want as seamlessly as possible. That’s why companies such as Google have invested so heavily in natural language processing: it bridges the gap between traditional stilted queries (“How can I book a last-minute inexpensive hotel deal in Las Vegas for October 20, 2018?”) and how people actually speak (“I need a cheap place to crash in Vegas tonight.”). Taken even further, it anticipates what people want by delivering results not even asked for (such as Vegas hotel results to someone looking for articles about gambling). 

Over the past few years, as NLP has advanced, it has been integrated into nearly all search engine features: it is now an instrumental part of everything from delivering contextual results within Universal Search to surfacing the appropriate Knowledge Graph snippets. 

Because of this reliance, utilizing an analytics suite with NLP enables you to approach your own content as a search engine would. 

Specifically, a tool like this allows you to do two key things: 

Categorize Your Content into Relevant Topics:

Because SEO has been keyword-focused for so long, we forget that this isn’t exactly how people think or speak. The reality is that human curiosity often tends to start with interests/topics. This doesn’t mean that individual terms aren’t important, but rather that they live within broader categories.

Analytics with NLP can help identify these topic interest accurately. Rather than relying on imprecise and/or incomplete tagging by your team, pieces can properly be classified into their highly-specific parent categories. The taxonomy of keywords that make up a topic, synonyms, related words, topic-specific descriptors becomes a valuable tool to widen your content creation efforts.  

Provide Fresh Insights into Your Audiences:

Surfacing insights such as topic affinities are important because it gives you a much better understanding of your audiences. Think of it as being able to take a step higher with your analytics: you can see past low-level connections — specific search keywords leading to specific pieces — and determine which concepts are sparking engagement. Essentially, just as NLP enables Google to better link queries with the underlying intent, analytics with NLP enables you to connect audiences with underlying content interests. 

The basic idea is that your analytics work like a modern search engine: you are simply applying the similar strategy and technology — you are reverse-engineering the way search approaches your offerings — to understand how your audiences engaged. 

What is important to understand is that this approach can surface insights that are beyond what you’re currently receiving. A good analytics tool with NLP can deliver: 

  • Unknown audience interests and topic affinities: It doesn’t take a sophisticated platform to suggest that fans of NBA content may be fans of NCAA March Madness content. What’s much more difficult is to make non-obvious connections, such that your basketball audiences are also interested in European politics. It’s in surfacing these non-obvious affinities that analytics with NLP is immensely helpful.
  • Measurement of quality of engagement: Analytics tools that use NLP are currently most popular with digital media publishers such as broadcast media, news websites etc. This is no surprise given the volume of content they create and the importance of audience engagement in their business model. So these tools can also offer Engagement Quality metrics that allow you to prioritize topics, measure performance of campaigns & optimization efforts more accurately.
  • Hidden relevant keywords: Again, the importance of topics does not mean that keywords are not still valuable. Rather, the concept is that individual terms are nested within larger categories and that each is useful in their own ways. It also means that when unexpected links are made between topics, unexpected keywords are brought to the fore. This should deliver a set of target terms that are currently not being surfaced. For example – a lawyer’s website could include the usual keywords – legal, advocate, attorney, lawyer, counsel, legal advice etc. but can also be expanded to include thematic keywords such as civil rights, labor law, anti-discrimination lawsuits which allow you to widen the scope of content that can be created.

Audience Interest Treemap

Audience Interest Treemap on NativeAI Analytics for Publishers

Imagine being able to visualize your audience’s content consumption interests on your website in this form, without you having to tag content with all the related keywords.

Fresh Websites for Link-Building Opportunities:

A good analytics tool with NLP can recommend high-authority sites for link-building opportunities. As with topics and keywords, the benefit is in the unexpected: the platform is identifying hidden connections and delivering properties that you may not have had on your radar. 

The core idea is that you are not getting more of the same data: powerful computing combined with machine learning and artificial intelligence can deliver unanticipated, ever-changing recommendations that you can execute upon to garner constant marginal search performance gains. 

Ultimately, analytics with natural language processing is capable of supercharging your SEO: it applies the power of AI and machine learning to your content, turns your own technology into a version of a sophisticated search engine, and finds valuable new opportunities for engagement.

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Karthik Balachander

Karthik leads marketing for the News360 personalized news app & NativeAI analytics platform. He is a seasoned tech marketer with diverse international experience in using his engineering background, affinity for tech & flair for understanding user behavior to evangelize new technology such as AI, Cloud infrastructure & online identity.
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  • Never heard about this NLP topic. Thus, thank you for the information, Karthik. I’ll check you the service you are introducing in your article as they offer a trial version

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