As a content marketer, you may not realize it yet, but natural language processing (NLP) has already changed the game. Monster.com ranks it among the top three in-demand skills, and the global NLP market is projected to grow to 43 billion US dollars by 2025. That’s 14 times its size from 2017!
This field has a rich history dating as far back as the 1940s, but its popularity exploded in the last decade thanks to the rise of big data and machine learning.
From Siri to Google Autocomplete to Netflix movie recommendations, NLP is all-around us.
The majority of us just don’t realize it yet.
What is Natural Language Processing?
Natural language processing (NLP) studies how computers interact with human languages. In simple terms, it helps computers ‘read’ text and understand the meaning and context behind words, phrases, and sentences using machine learning algorithms.
NLP acts like a translator but for computers. It helps computers understand human language and is used to process and analyze data to extract meaning from text.
The goal of NLP is to create intelligent systems capable of understanding and producing natural language text.
What is natural language processing used for?
NLP has a plethora of real-world applications. These include:
Spam filters are software programs that scan incoming emails for suspicious content. For example, a spam filter may use NLP to detect phrases such as ‘double your income’ or ‘100% free’ in an email and flag the message as potentially fraudulent.
These filters prevent users from receiving unsolicited commercial emails (spam).
Information retrieval (IR) is the process of retrieving relevant data based on a query.
The simplest example is search engines such as Google or Bing. An NLP-powered search engine scans millions of pages and returns results ranked according to their relevance to a query.
For example, if you enter ‘how do I start a business’ into the Google search bar, it will return results that include information about starting a business. The search engine uses natural language processing to interpret your query and find pages that contain those words. It then ranks these pages according to their relevance to the question.
Voice assistants are becoming increasingly popular as they perform a myriad of tasks using speech recognition, such as making phone calls, setting reminders, streaming music, playing games, sending messages, controlling home appliances, and providing weather forecasts.
Alexa, Siri, Cortana, Google Assistant, and other voice assistants are programmed with natural language processing algorithms to understand voice commands and relay appropriate information.
Text mining is the task of extracting meaningful information from extensive unstructured data.
This could include analyzing news articles, tweets, emails, blog posts, white papers, research documents, or any other form of text.
Suppose you have a collection of ten thousand emails. If you want to find out the main topics discussed in the emails, you can use text mining to analyze them.
Automated Customer Support and Chatbots
Customers expect 24/7 personalized support when purchasing products and services online.
However, providing stellar customer service is often challenging and time-consuming.
With NLP, companies can quickly provide live customer support using automated chatbot systems. Chatbots are programs designed to converse with humans via messaging apps.
For example, if a customer asks, “When does my credit card expire?” the system immediately answers, “Your credit card expires on September 30th.”
Machine translation uses NLP to help computers translate text from one language to another.
For example, Google Translate can translate English to Spanish, French, German, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, Thai, Vietnamese, and Hindi.
Machine translation systems are used to translate websites, emails, and chat messages and can also create machine-generated subtitles for movies and TV shows.
Automatic Content Filtering
Automatic content filters help prevent children from viewing inappropriate content online. For example, it can block websites containing pornography.
An NLP program can answer questions by analyzing the context of the question and the information available.
For example, if a user types in ‘What is the capital of France?’ in Google Search, the program looks at other information available on the web to determine that Paris is the correct answer.
How is natural language processing transforming content marketing?
A digital strategy would be incomplete without content marketing. But it’s not easy to consistently come up with fresh, engaging content.
That’s why we’re excited to share four ways NLP is changing the game:
AI Content Creation Tools
NLP has made it possible for businesses to create new content for their websites or social media channels using AI writing tools. These tools can be leveraged by anyone with basic writing skills – from marketers who want to write blog posts to start-up founders who require web copy.
AI writers serve a wide range of use-cases, including ad copy, slogans, landing pages, emails, Quora or Reddit responses, video scripts, blogs, listicles, and even ebooks.
These tools help save time, publish consistent content faster, and improve productivity.
And if you’re new to the world of AI content creation tools, ContentBot is an excellent place to start! With 35+ fine-tuned AI use-cases, you’ll never have to stare at the blinking cursor on a blank page ever again.
Conversational marketing is a relatively new concept. It involves developing products and services that allow consumers to interact with brands in real-time.
For example, Domino’s Anyware lets you order a piping-hot pizza from wherever you crave it. Text a ‘pizza emoji’ to the registered phone number, Tweet #EasyOrder to @Dominos, ask Amazon Alexa or Google Home, order while watching a movie on your Samsung TV, or use Slack to order pizza on the grueling work-from-home days!
The rise of conversational marketing means brands can now talk directly to customers like a friend. This type of direct communication allows brands to build trust, reduce friction, and increase conversions.
NLP is also improving the general marketing scene. Marketers can use NLP to analyze data and create more targeted messages. For example, they can use NLP to segment customers into groups based on their interests. They can then send different types of marketing messages to each group.
Sentiment analysis refers to the task of determining whether a piece of text expresses positive or negative feelings. Sentiment analysis can be applied to many different types of documents, including news articles, product reviews, and movie reviews.
A key example is Grammarly which analyzes your written documents to decipher the tone and style.
In addition to this, NLP can partially help automate the editing process by checking for spelling, grammar, readability, and other issues with the rise of writing assistants such as Grammarly, ProWritingAid, and Hemingway Editor.
As a result, companies can spend less time manually reviewing content and more time creating high-quality content.
Personalization is among the most powerful trends in marketing today. It helps businesses deliver relevant messages to individual users. This is done through various methods, including personalizing email campaigns, optimizing website content, and tailoring customer service interactions.
But how does NLP fit into the personalization equation?
Companies leverage NLP to understand what people say and do online and then tailor their communications accordingly.
- Netflix recommends movies based on previous viewing history
- Spotify recommends music based on listening habits
- Airbnb suggests vacation rentals based on past travel preferences
- LinkedIn suggests jobs based on skillset
Marketing has always focused on connecting with potential customers. With the advent of NLP and AI, marketers can now communicate with customers in previously impossible ways.
Businesses need to embrace these technologies and utilize them to their full potential to stay ahead of the curve.
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