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How Natural Language Generation Can Help Your Digital Marketing

Dec 30, 2020 | Blogging

You might be familiar with A.I. and what it can do. You might not know that there is a subset of it that plays a vital part in digital marketing. It’s called Natural Language Generation, and it’s more prevalent than you might think.

One of the best examples of natural language generation is Google’s Smart Compose. If you use Google’s email platform, you’ve already seen this in action: as you type out an email, Google provides suggestions to finish your sentence.

That’s natural language generation at work.

Natural language generation has some incredible implications for your digital marketing campaigns.

Before we go in-depth about natural language generation and how it can help your promotions, let’s take a minute to explain what precisely natural language generation is and what it does.

What Is Natural Language Generation (NLG)?

The definition of natural language generation is the “process of producing meaningful phrases and sentences in the form of natural language.”

Natural language generation comes from your structured data. It transforms the data you have into natural-sounding text. It can also produce a vast volume of written words far quicker than the average person.

However, its capabilities don’t stop there. Natural language generation allows the updating of existing text and automation. You can also use it to create:

Images
Graphs
Numerical data

You can see why natural language generation is attractive to marketers. However, natural language generation is beneficial for a range of other sectors, including:

Finance and data analysis: For report creation
Healthcare: For interpreting data and creating medical reports
E-commerce and retail: Produce accurate product descriptions and improve the overall customer experience
Journalism: Create and update news reports. The Associated Press and Bloomberg are just two organizations using data to expand their news coverage.
Logistics and travel: Keep passengers updated, write itineraries, and translate texts into various languages.
Customer service: Enables chatbots to answer questions and follow up with personalized emails for a more ‘human’ approach.
The insurance, energy, and telecommunications industries: These sectors have successfully introduced natural language generation to optimize their customer service, send out bills and invoices, and for report creation.

If the forecasts are correct, natural language generation will be used even more in the future.

Natural Language Generation and the Wider AI Picture

Now you know what natural language generation is and a bit about what it does. Let’s take a moment to explain how it fits into the overall AI picture.

If you’ve read about natural language generation before, you’re probably aware of similar terms like natural language processing and natural language understanding. These terms are often used in the same conversation, as they all fall under the AI umbrella.

There might be a bit of confusion on how these terms differ, so we’ve highlighted key differences below.

Natural Language Processing (NLP): While natural language generation’s purpose is to use your data to produce text, it’s NLP’s role to process and read the language.

NLP can also help with

Spelling corrections and grammar
Machine translation
Syntax and context

Natural Language Understanding: Another term you’re likely to come across is Natural Language Understanding (NLU). NLU analyzes text to understand intent and meaning. NLU enables:

Text summarization
Spelling correction
Sarcasm detection

Then there’s machine learning.

You’ll find a wide range of definitions out there, so let’s go with the one from the MIT Technology Review:

Machine Learning algorithms use statistics to find patterns in massive amounts of data.

Machine learning can process far more than just text. ML can also process images, figures, and clicks. Without it, we wouldn’t have the likes of Siri or YouTube.

Now we’ve covered these key terms, let’s delve into natural language generation and its possibilities for your digital marketing campaigns.

How Does Natural Language Generation Affect Digital Marketing?

Natural language generation has been with us since the 1960s, so it’s not new. However, what has changed is its commercialization and wide availability. You may have noticed there are numerous types of natural language generation software available now.

Plus, natural language generation’s capabilities have transformed recently, meaning improved accuracy. Its potential for the future of digital marketing is enormous.

From the first time a customer interacts with you to the follow-up and personalization of your marketing efforts, natural language generation can play a role in every stage.

Suppose you regularly employ a team to identify keywords, perform SEO, create product descriptions, and analyze marketing data. With natural language generation, you can automate all these tasks. Here are a few other ways natural language generation can help in your marketing.

Create Content

As a marketer, you know the importance of communicating regularly with your customers and would-be buyers. It can help build consumer trust, enhance customer loyalty, and allow you to demonstrate your expertise. It also goes a long way toward growing your brand.

Regularly producing fresh content that speaks to your customers is time-consuming and often costly. Natural language generation can solve this issue by allowing you to create keyword-optimized:

Blog posts
Web and landing pages
Newsletter and email content
Reports
General marketing content

However, it also helps create targeted content and enables teams to make sense of their data. Marketers can then use the information they have to create compelling, tailored campaigns for improved success.

But the real beauty of natural language generation? All it takes is three simple steps:

Get your data ready
Prepare/use a template
Let natural language generation choose your narratives and write your content

The Associated Press is an excellent example of how to use natural language generation well. AP uses natural language generation to produce financial reports and expand sports coverage, among other things. Additionally, the Washington Post uses natural language generation to generate hundreds of press releases.

Generate Product Descriptions

If you’re an e-commerce business owner, you understand the importance of well-crafted product descriptions to drive traffic to your site. However, it can be challenging to produce vast quantities of unique, SEO-orientated content that matches your company’s tone and voice.

While you could just go with the original, manufacturer-supplied descriptions, they’ll lack originality, which isn’t great for SEO.

Imagine if you had a way to take your unstructured data and insert it into a template and have the writing done for you?

That’s what natural language generation can do. It can transform your data into hundreds or even thousands of original descriptions that match your brand’s voice.

With natural language generation, you can create thousands of automated descriptions detailing:

Weight
Size
Key features
Colors

To get a better idea of what this looks like in practice, let’s use a free natural language generator tool.

You just enter your:

Product name
The problem it solves
Benefits and features
Objections
Product style

Then click “generate” and wait for your description to appear.

Write Ads

With its ability to personalize and tailor content, natural language generation has a lot of potential for writing ads. However, it’s not just about putting words on the page. Natural language generation aids ad creation by helping you understand your audience and their intent.

Natural language generation is even intelligent enough to adapt to a brand’s audience and use the language and tone most likely to resonate with them — all without compromising your ads’ creativity.

A further benefit of using natural language generation to create ads is it allows mass scaling while also sounding human and personalized. That all makes natural language generation ideal for:

Online paid ad (Google, Facebook, Amazon, etc.)
Landing pages
Individual customer offers

These are just examples. However, they should illustrate the possibilities for your digital marketing efforts.

Personalization

Personalization isn’t a buzzword or a passing trend: It’s one of the most critical elements of a successful marketing campaign.

When you apply natural language generation to your marketing efforts, there are several ways you can use it to tailor it to your customers, including:

Use natural language generation to understand individual customer’s metadata for the personalization of marketing materials.

Improve customer loyalty by targeting products, services, and special offers to your buyers.
Automatically updating and tailoring content for individual customers
Reach a broader customer base through localization

From these examples, it’s easy to see why brands turn to natural language generation to apply personalization to their campaigns.

Automated Updates

Relevant content is essential to online sales, and it’s vital for seasonal promotions. For example, when you want to promote your Black Friday sales or if someone is searching for Valentine’s gift.

However, keeping your content up-to-date is a challenge.

Updating seasonal content is another area where natural language generation shines. For example, you could automate category pages aimed at specific buyers by letting natural language generation create individualized content, making it targeted and more likely to convert.

By automating and updating content through natural language generation, you enhance the overall customer experience, increasing the likelihood of a sale.

Subject Line Generation

A good subject line is one of the essential parts of your email. After all, it can mean the difference between your message getting opened or not. Natural language generation can take your data and optimize subject lines to ‘speak’ to the receiver.

Natural language generation can convey and personalize sentiments, tone, and language, helping improve your email open rates. One example of this is the AI technology company Phrase.

The UK-based business’s natural language generation tool allows you to generate hundreds of effective subject lines. For instance, when Virgin Holidays wanted to improve customer experience and grow digital sales, it introduced Phrase to tweak its subject lines.

The result? A two percent increase in open rate, which may not seem like a huge increase, but it was worth millions to Virgin Holidays.

If you want to leverage the potential of natural language generation for your email campaigns, there are a few tools you can try:

First, Active Campaign has a free tool. Just choose a keyword category from the drop-down list, enter your keyword, and click the “generate subject lines” button.

We used “headaches” as our keyword, and these are the suggested subject lines:

Automizy is another free tool. It uses three steps to generator subject lines.

You copy and paste your email marketing content
Automizy will take your text and analyze it
Then, it generates a set of subject lines based on your copy.

From experience, we can say the above tools might not be perfect. However, they do illustrate the potential and give you a starting place.

SEO Optimization

According to Marketing Artificial Intelligence Institute, there are several ways marketers are using A.I. in SEO optimization. They’ve introduced it to:

Discover keywords and create topic clusters
Optimize content for better discoverability
Content analysis to find content caps
Create real-time targeted content
Write meta descriptions
Tag images
Write outlines and titles for your content calendar

Using natural language generation can help marketers drive more traffic, create more sales, and build their brands.

Conclusion

Natural language generation software has applications across a broad range of industries. Natural language generation allows users to create personalized, automated text. Still, its real power lies in adapting to individual brand voices, making it an ideal way for digital marketers to build relationships with a specific audience.

Digital marketers can also generate cost-effective, timely campaigns, optimize their ROI, and interact more authentically.

Despite some people’s fears, natural language generation is not going to steal anyone’s jobs. Rather, natural language generation complements copywriters’ work while freeing up vital time to concentrate on ROI-generating goals.

How could natural language generation help your business? Share your thoughts below.

How Natural Language Processing Affects Digital Marketing

Dec 30, 2020 | Blogging

“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:

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:

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.

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:

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 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.

Conclusion

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?