How to Use NLP in Marketing to Improve Customer Experience
What is NLP and How Can Digital Marketers Use NLP in Marketing to Improve the Customer Experience? Find out here.
Image Source: Pixabay
How Digital Marketers Can Use NLP to Improve Customer Experience
Many digital marketers are already harnessing the power of NLP to improve their marketing campaigns. The biggest benefits of NLP in marketing is focused on improving customers’ experience. If you’re not familiar with NLP…
What exactly is NLP and how can it be used by digital marketers?
You may not realize it, but you see NLP in action every day — when you use voice search; when you talk to a chatbot; or when you get a recommendation on Netflix or Amazon based on your previous views.
You might be intrigued and want to find out more, but not sure where to begin. Maybe you already know about it but are having trouble implementing it in your own business. In this article, we will analyze how marketers can apply NLP in marketing to improve customer experience.
What Does NLP Have to Do With Digital Marketing?
Ways To Use NLP To Enhance Customer Experience
What is NLP?
Let’s start with the basics. NLP (Neuro-Linguistic Programming) is a form of artificial intelligence (AI) that allows machines to analyze human language and understand it. The idea of breaking down language into its most basic elements — words, phrases, and structures — is called computational linguistics.
In the case of search, Google isn’t looking at entire web pages to determine if they’re relevant to your query. It’s crawling through the text on those pages, searching for specific terms and phrases that indicate quality content.
Consider Voice Assistants like Google Assistant, Alexa, Siri, etc. These assistants use NLP to recognize your intent from what you speak. Or think about chatbots that can answer your queries on a website, or a virtual assistant that can help you book flights and hotels. All these are powered by NLP algorithms and techniques.
The evolution of NLP
Like other fields in AI, NLP has also evolved over time with significant advancements seen in two key areas – rule-based or symbolic approach, and statistical approach.
Rule-based or symbolic approach uses a rule-based approach to look for linguistic terms (such as ‘love’) in sentences to derive their sentiment (positive or negative).
The statistical approach uses ML-powered statistical techniques to train algorithms to understand or predict sentiments. This approach relies on a training dataset that is labeled accordingly (the input sentence is associated with a sentiment).
But how can NLP be used in marketing?
Image Source: PXhere
NLP in marketing can help us deliver more relevant content and personalized experiences for our customers. In fact, research by PwC found that three-quarters of consumers want brands to personalize their communications with them.
As a digital marketer, you can use this same approach in your own analysis of your site’s content. You can break down your content into keywords and phrases that customers might be searching for and integrate these terms into your pages to optimize your site for search engine rankings. You can also use NLP to better understand how customers are interacting with your brand online and respond accordingly.
As digital marketers, what do we want?
To reach the right audience.
Digital marketing is all about reaching the right audience. Whether you’re selling shoes or services, you have to find the people who are most interested in buying what you’re selling.
To do that, you need to know who your target market is and how to reach them. This may seem like an obvious point, but it’s one that many marketing teams get wrong — especially in the digital space.
To engage them.
Marketers have a real opportunity to engage with consumers, but we don’t want to do it in a way that’s off-putting. Take the example of a website visitor who is ready to make a purchase. But say a pop-up appears asking for the visitor’s email address before completing the purchase. It’s likely that the visitor will leave and never return.
We have to make sure that our content is relevant and engaging, and that we are able to get our customers’ attention without annoying them. As such, a new digital marketing term was born: engagement marketing.
And to convert them.
We want to make sure that we’re using the right content strategies and the right kind of resources to ensure that we get people who will actually buy our product.
To achieve these goals, we create content. But what makes the content successful?
The answer is that it should resonate with your target audience. It should convey the right emotions and connect with people on a deeper level.
This is where natural language processing (NLP) comes into the picture. According to research from Stanford University, NLP can help marketers understand what their customers expect and tailor their content accordingly to improve customer experience.
Ways To Use NLP To Enhance Customer Experience
NLP in marketing goes hand in hand with customer experience go hand-in-hand because it helps businesses understand what customers want and why they want it. Think about how many times you’ve had a bad customer experience because an automated system didn’t understand your question or request.
That’s where NLP comes in.
You don’t need an advanced degree in computational linguistics to get started analyzing your content with NLP.
NLP can be used by digital marketers to vastly improve customer experience. Here are some ways you can use NLP to enhance customer experience:
Analyze user intent
One of the most popular NLP applications is text classification, which is a subcategory of NLP that allows us to categorize and label text according to its content. Text classification can be used for a variety of purposes, but in this post, we’ll demonstrate how it can be used to analyze user intent.
User intent is a critical metric for businesses because it indicates whether users are satisfied with their experience on your website or app. If you receive a lot of negative feedback from users about your product, it may mean that either you’ve misinterpreted their needs or didn’t provide them with the solution they were looking for. Either way, this means that your customers are unhappy with your product and you need to take action before they decide to stop using it altogether.
One of the most amazing applications of NLP in marketing is their capability to understand the sentiments of humans. They can identify the emotion behind a text, whether it is happy, sad, angry or frustrated. This way your customer service team can identify a customer’s emotions and respond accordingly. But before you can start with that, you need to train your system on what a certain text means. For instance, if there is an exclamation mark in a sentence. It means that the user is excited about something or angry.
Also, these systems are trained on social media data which may have features like sarcasm or humor that makes it difficult for NLP to detect emotions accurately. However, this will improve with time as more data will be available for training these systems which will make them intelligent enough to distinguish between different emotions that a customer could be feeling at any given point in time.
If you want to cater to a global audience, you need to translate content into the languages your customers speak. Text translation is a very common task for NLP systems. Usually, the documents to be translated are not small paragraphs but large texts (a book or an article).
There are two ways to translate content:
- Rule-based MT: rule-based machine translation. It uses language-specific rules to replace words and phrases with their equivalents in another language.
- Statistical MT: statistical machine translation. It works by analyzing a lot of human-translated documents and learning from them to make its own translations.
Recommending content and products is usually done using collaborative filtering. Basically what this means is that people who listen to music similar to yours will probably like songs you also like.
NLP can be used in a similar way, where if you have a chat with a customer service bot, it can recommend products that are related to the messages you’ve sent.
This is really helpful for smaller businesses that don’t have the resources to build out a recommendation system just based on the customer’s browsing history. Instead, they can use data from their customer service inquiries and use that to make recommendations.
A lot of companies are using NLP to generate reports. These reports provide a better customer experience, as they are made based on the customer’s requirements and preferences. This is possible because of the ability of NLP to understand the context.
In the end, these are only a few examples of how digital marketers can use NLP in marketing. Obviously, there are many other ways that this technology can be utilized for business and marketing communications, but the key takeaway is this: those who are willing to apply this technology to their messaging will be at an advantage in the digital marketing field. If you want businesses to thrive in the coming years, you want them to understand their customers—and digital marketers who utilize NLP to gain greater insight into customer communication will be critical in that endeavor.