How Natural Language Processing Affects Digital Marketing

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how natural language process affects digital marketing

“Natural language processing” (NLP) sounds complicated, but its applications are simple. Chances are, you already use NLP dozens or even hundreds of times per day.

For example:

Global Natural Language Processing value

What exactly is natural language processing? What do you need to know about it? What impact does it have on digital marketing? Let’s find out.

What Is Natural Language Processing (NLP)?

Language is natural to humans, but the same can’t be said for computers. Understanding the context behind our words is a huge challenge for them. NLP is all about changing that.

Natural Language Processing is an area of artificial intelligence (AI) that leans on disciplines like computer science and computational linguistics to enable computers to interpret, comprehend, and manipulate the often arbitrary, ruleless world of human language. As such, its ultimate goal is to help computers make sense of the things we say in a way that adds value.

As I noted above, NLP has a ton of use cases, many completely embedded in our everyday life. For instance:

  • Translation tools like Google Translate use it to produce translations between languages that make sense, rather than just a literal word-for-word translation
  • Word processors (think Microsoft Word and Google Docs) use it to assess the grammatical accuracy of written text
  • Call centers use interactive voice response applications to respond to certain customer requests

It’s also the driving force behind search engines like Google becoming “smarter.” While keywords are still highly valuable, search behavior is becoming more complex because we expect search engines to understand what we mean. Consider the following search:

natural language processing google search

As humans, we understand the searcher is Brazilian and wants to know if they need a visa to visit the US.

Previously, Google struggled to discern the true meaning, so it served an unhelpful result for US citizens visiting Brazil. However, advances in NLP now allow it to understand the importance of the common word “to” in this query, thereby enabling it to provide a more relevant result.

NLP vs. AI vs. Machine Learning

To a non-computer scientist, NLP sounds a lot like machine learning and AI. In reality, all three are closely intertwined, but subtly different. To understand their relationship, you need to understand a third term: deep learning.

  • Artificial intelligence encompasses anything we do to make machines smarter, whether that’s a software application, a smart fridge, or a car.
  • Machine learning is a subset of artificial intelligence covering anything to do with systems learning for themselves, free of human intervention.
  • Deep learning is a subset of machine learning, applied specifically to large data sets.
Natural language processing vs AI vs machine learning

Where does natural language processing fit in? Well, it’s a part of AI, but it also overlaps with both machine learning and deep learning.

The Evolution of Natural Language Processing

While it sounds hyper-modern, natural language processing has existed in one form or another for several decades, although it’s come a long way since the early days.

The History of Natural Language Processing

  • Started in the 1950s as machine translation, when linguist Leon Dostert of Georgetown University used an IBM 701 computer to translate Russian to English.
  • The Soviet Union soon launched its own competing machine translation project to translate English into Russian. By 1964, the USSR had become the world leader in machine translation.
  • In 1966, Joseph Weizenbaum programmed the first chatbot, named Eliza. It was only capable of holding very limited conversations, mostly based on reordering the user’s input to form questions.
  • Whereas these early examples of NLP were held back by the need to develop complex sets of handwritten rules and parameters, in the late 1980s the field was revolutionized by early forms of machine learning.

How it Is Now: The Effects of NLP on Digital Marketing

Marketing has always been about context; getting into the heads of our audience to understand what they are (and aren’t) telling us. It helps us answer questions like:

  • What persuaded them to click our ad?
  • What made them bounce off the landing page?
  • What made them add to cart, then abandon?

NLP gives us more context by helping us understand not just the exact words being used, but what they mean. That makes it hugely applicable to marketing. For instance, voice search is wholly dependent on NLP, as it uses complex algorithms to understand a user’s commands and discern the most helpful response.

How to Use Natural Language Processing in Marketing

By now, you’ve probably started to understand just how useful NLP is to marketers, but in reality, the use cases are likely more substantial than you’ve imagined! Here are some of the most relevant and fascinating.

Understanding Customer Sentiment

Whether you’re a household name or a tiny startup, you need to know when people are speaking about you online and what they’re saying.

NLP software helps by analyzing social posts, reviews, and user-generated content related to your brand. Hootsuite’s sentiment analysis tool, which analyzes the language used in brand mentions on social media, is a super simple example of how this looks in practice:

Hootsuite sentiment analysis natural language processing

There are many more complex, dedicated tools that use natural language processing to monitor sentiment across digital channels, from social media and review sites to blogs and forums. Examples include:

  • MonkeyLearn
  • Lexalytics
  • Brandwatch 
  • Social Searcher 
  • Aylien
  • Social Mention
  • Critical Mention

Sentiment analysis tools are powered by one of the following three types of algorithms:

  • Rule-based: These use a set of manually determined rules to automatically predict the sentiment of a given social mention, review, blog post, etc.
  • Automatic: Automatic algorithms rely solely on machine learning techniques to understand user sentiment.
  • Hybrid: These systems combine both of the above approaches, often producing more accurate results.

Building Chatbots for Customer Service and Lead Gen

Why do people use chatbots? Well, as this study shows, there are a bunch of reasons. They’ve become a key customer service tool and an invaluable part of the buying process, helping people find quick answers before connecting to a real human for more in-depth discussion.

natural language processing chat bot uses

Natural language processing is the technology that powers chatbots. Without it, they’d be limited to extremely simple interactions. Sure, it’s normally pretty clear that you’re speaking to a bot rather than a person, but this doesn’t seem to be a problem for users. In fact, 54 percent would always choose a chatbot over a human if doing so would get them an answer 10 minutes faster.

Identifying Trends with Natural Language Processing

You’ve probably used a news aggregator or RSS feed before to find regular information about a specific brand, product, or topic area.  Well, NLP takes things a lot further by finding that information, then summarizing all the key points in just a split second. That’s invaluable if you’re trying to identify the next big trend in your market.

Scaling Content Creation

Artificial intelligence is capable of writing fiction and plausible news stories, so it’s no surprise that it’s also capable of much simpler content creation tasks.

I’m not saying you should turn your whole content marketing strategy over to robots. For now, at least, you’re best leaving anything more creative in the hands of humans.

What about content creation at scale though? Say you’ve got a huge e-commerce site with thousands of products; creating descriptions for all those individual pages would be a copywriter’s worst nightmare!

That’s where AI-driven content, underscored by natural language processing, becomes invaluable. Indeed, e-commerce giant Alibaba has already introduced an AI copywriter capable of handling all that labor-intensive writing. Clothing brands like Dickies and Esprit use it to create Chinese-language product descriptions.

Leveraging NPL for Voice Assistants

About a quarter of US adults own a smart speaker.

While we’ve barely scratched the surface when it comes to realizing the marketing potential of these devices, there have been a few standout examples. Amazon Echo users were given the chance to explore the dystopian setting of the TV show Westworld, while Netflix promoted the second series of Stranger Things by allowing Google Home users to “chat” with the character Dustin.

Of course, as I’ve already discussed, none of that would be possible without natural language processing to translate speech into text, semantically match that text with the device’s knowledge base, then provide a helpful answer.

NLP Marketing Case Study: Tenable Doubles Conversion Rates

While the phrase “natural language processing” might be new to a lot of us, the technology itself has been around for a long time. So it’s no surprise that brands are already using it to deliver impressive results.

One great example is the cybersecurity company Tenable. It was facing two big problems with its sales process:

  • Leads were taking too long to reach a sales development representative (SDR)
  • SDRs faced a bottleneck in engaging with leads outside office hours, or at points in the day when they were busy or away from their desks

“If you’re not following up with them, there’s a good chance that a person’s going to say ‘I don’t even remember filling out that form,’ or, ‘I don’t even remember going to your website,’” noted Matt Mullin, Tenable’s Senior Director of Global Marketing Operations and Technology.

By implementing a business development strategy that placed smart chatbots front and center on its website, the brand saw a 30% upturn in the quality and length of conversations with prospects, while conversion rates doubled.

Uses for Natural Language Processing Besides Marketing

NLP didn’t start as a marketing solution, and its use cases extend way beyond marketing. Here’s just a handful of other uses for the technology:

Detecting Coronavirus

That’s right: NLP isn’t just about marketing, it’s been helping us fight the pandemic. Alibaba Group’s R&D institute, the DAMO Academy, built an NLP-based system capable of using chest scans and deep data to diagnose Covid-19 infections in just 20 seconds, with 96% accuracy.

Identifying & Analyzing Competitors

Every business uses some degree of competitor analysis to inform strategic direction. However, in an increasingly globalized world, it’s not always obvious who your biggest rivals are.

You might think you’re competing with the brand down the road, when in reality your customers are being poached by a company on the other side of the planet.

Again, NLP has a solution. Tools like Zirra (and many others) are capable of automatically mapping the competitor landscape, creating a list of companies ranked by how closely related they are to your brand.

Assessing Creditworthiness

Lenders use credit scoring to understand whether an individual or business is a safe bet for a loan or some other form of borrowing.

However, that’s not always possible in emerging markets, where key records may not be as readily available.

Now, brands like Lenddo are using natural language processing to make lending decisions based on non-traditional data sources that encompass an applicant’s entire digital footprint, from their browsing habits and social media usage to e-commerce transactions and even psychometric profiling.

Hiring Talent

For years, recruiters and HR teams have been using technology to scan resumes and cover letters for certain keywords.

NLP is a logical extension of this. Rather than fixating on specific phrases, it’s capable of analyzing and extracting the information that’s most relevant to the specific role.

That allows employers to automate the lengthy process of sifting through CVs, safe in the knowledge that those who make it through will be up to the task.


Natural language processing certainly sounds advanced, but it’s based on the old-fashioned marketing principle of understanding our customers better.

Rather than directly asking your audience what they think about your brand or product, what challenges they’re facing, or what their goals are, NLP helps you to discern their feelings, motivations, and opinions from the words they use.

NLP is another step toward removing the guesswork from our marketing decisions, enabling us to reach the right people, at the right time, with the right messaging.

How are you planning to use national language processing in your marketing strategy?

The post How Natural Language Processing Affects Digital Marketing appeared first on Neil Patel.

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