How to Change Customer Service Through Natural Language Processing

changing customer service

Usage of the Amazon Alexa, Google Home, and Apple Siri are all on the rise. But the 66.4 million people who own smart speakers aren’t just using them to learn about the weather.

More people are using the same machine learning technology to improve their customer service interactions.

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As a result, Gartner predicts that 30% of interactions with technology would be through “conversations” with smart machines — many of them by voice. Plus, research has found that chat can handle 80% of customer communications.

Changing Customer Service

This is happening through the development of Natural Language Processing (NLP). It humanizes customer language and solves their queries without human input.

(Think of it as an intelligent virtual agent. Your customers can use NLP chatbots to obtain quick answers without actually speaking to a person on the other end.)

9 Ways to use NLP in Customer Service

What is NLP?

Natural Language Processing is a type of machine learning. It understands the words, sentences, and context of your customer support queries. As a subset of artificial intelligence (AI), NLP interprets and analyzes customer’s verbal statements. Once it does, it provides them with an answer—all without human intervention.

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NLP can come in chatbot form. This is another type of machine learning software that handles customer conversations.  More than 67% of consumers used a chatbot for customer support in the past year.

Examples of NLP in Customer Service

Uber, the on-demand ridesharing leader, employs NLP in between drivers and passengers to improve their communication. As illustrated below, NLP can reduce uncertainty and mental labor in replying. One tap of a quick reply is much faster than typing a response.


It’s an understatement to say that Uber has an extensive dataset and a huge engineering team. However, you can visualize how NLP and Machine Learning helps facilitate a better customer experience.

Mastercard had made it easy for their banking partners to serve consumers over Facebook Messenger. In the example pictured below, you can see how consumers achieve an immediate benefit without talking to a live representative. The NLP component here analyzes the conversation as a whole versus only the verbatim input from a customer.


Regardless of which conversational AI you’re using for customer service, both need NLP to understand what the customer is asking for in the first place.

NLP Process Flow

Natural Language Processing helps machines understand human language. But the English language alone is one that has undergone millions of changes over the years. How can a machine keep up?

Languages have several layers that help people communicate, such as:

NLP begins by collecting tons of data around the language you use. This data isn’t structured. But the AI system starts to categorize it based on the layers before giving it a numerical value.  (This happens because computer language is numerical).

A deeper example of this is found in certain content analysis tools. TF-IDF, or “Term Frequency?—?Inverse Document Frequency” is used by certain content analysis tools to identify the key information in a document. It works by assigning a numerical value to show the importance of words and phrases.

By this point, the machine learning system now understands data within the numerical language. A classifier is then used to convert the data back to plain English. Then, the AI  flits back and forth to understand the qualitative data it is given.

NLP in Customer Service: Top 9 Use Cases

1)  Accurate call routing with IVR systems

Have you ever called a customer support line and needed to say “Billing” to reach the finance department? You’re talking to an Interactive Voice Response (IVR) system. IVRs are the foundational technology that converts phrases (“update my credit card” or “make a payment”) into transferring you to the billing department.

Customers are likely using this system to contact your team. But when conversational AI underlies the system, you can accurately divert their call to the most relevant line. Why? Because NLP understands their request.

You don’t need to ask your customers to “listen to the following options” to send them in the right direction.

Conversational AI within IVR systems can simply ask your customers to explain what they need help with. They can do this in their own words before NLP sends them on their way.

American Airlines saw significant results from this NLP use case for their customer service team. After revamping their IVR system, they increased their call containment by as much as 5%—saving the airline millions of dollars.

2) Routing support tickets

You’re giving people a support ticket when they try to contact your team. This interaction then filters its way through to your support team’s queue.

NLP can help streamline this process. Why? Because conversational AI can understand the topic of the ticket. It can divert support tickets to the most relevant person, helping to resolve issues faster.

Let’s say that your customer sends a support ticket. Their message has the words “I need help changing my payment details.” A management platform without NLP passes it through to your general support desk.

A person would then need to divert the customer towards your finance department. This needs to be done manually. (You could miss the hour-long deadline that customers expect a response to their email within.)

A platform using NLP will spot that the customer needs financial-related help. It will automatically send the support ticket to your finance team.

3) Understanding customer feedback

Customer feedback is valuable data for businesses. It can help you fix flaws with your product and identify which aspects people are loving. Both of which are excellent foundations for your marketing and advertising campaigns.

(Not only that, but it could improve your reputation. 77% of consumers view brands more favorably if they invite and accept customer feedback.)

You don’t need to spend hours manually combing through this type of qualitative data.

NLP helps identify words or phrases commonly used. For example, words like “modern,” “intuitive,” and “expensive,” which could indicate your customers see you as a luxury, high-end brand.

NLP can also find topics spoken about in feedback forms. This might be words like “easy onboarding” or “affordable plans.”

You can combine NLP with sentiment analysis and get a top-level overview of customer opinions, making it a time-effective way to analyze customer feedback.

4)  NLP and customer service chatbots

Research has found that 42% of consumers would rather connect with a company through live chat, versus 23% for email or 16% for social media:


That’s because 70% of millennials prefer chatbot interactions for its self-service approach.

Chatbots should have a cozy spot in your tech stack for that reason. They let you communicate with customers in the way they prefer, and also provide real-time support, without having to hang around for a response.

But what happens if your customer support team is jam-packed and can’t answer a support query in real-time through the live chat on your website?

With NLP, you can create a chatbot that not only understands a customers’ query, but answers it for them, too. Here’s an example from Cheapflights:


It doesn’t matter whether your support inquiry has grammatical errors or incomplete sentences. NLP is smart enough to understand the concept of the message and respond without human intervention.

It’s no wonder that by 2020, 80% of businesses are expected to have some form of chatbot integration available to their customers.

5) NLP for agent support

Did you know that the average customer support agent can only handle 21 support tickets per day? It’s easy to see how agents struggle to keep on top of customer inquiries!

(You can calculate your average interactions/ticket to see how much time these interactions costs.)

An increasing number of agents are turning to machine learning software to cope with that high demand.  Salesforce discovered 69% of high-performing service agents are actively looking for situations to use artificial intelligence (AI).

Conversational AI can handle queries that don’t need much attention. This leaves agents with more time to handle complex queries that need a human touch.

Your conversational AI could handle questions like:

  • “Where is the HDMI input on my Samsung TV?”
  • “What is the status of my order?”
  • “How do I connect my Google Analytics account?”Those support tickets will make up a considerable chunk of tickets. But with them already handled, your agents can answer emotional questions like “my account got shut down and I need help ASAP.”

    6) Business data analysis

    Earlier, we mentioned how NLP allows businesses to analyze qualitative data from customer feedback. It can also mine information from elsewhere, and layout common trends for your team to follow.

    This works especially well with customer complaints. Whether they’re coming directly via email or through the “why did you leave us?” box on your cancellation form, NLP can pick out the trends within this data, and notify your team before they become a problem.

    Let’s put that into practice and say you’ve got 150 complaints to file through. Your cancellation form asks people to check one of the following boxes:

  • Confusing onboarding process
  • It’s too expensive
  • I don’t have timePeople might tick the wrong box. This means you think the problem is in one area. But in reality, complaints have been filed incorrectly.As a result, you might increase your price because people tick the box saying it’s too high. But there’s actually a problem with their billing process.

7)  Sentiment analysis and customer satisfaction

You’ve got customer feedback filtering its way through to your support team. How do you know whether, on the whole, people are happy with your product or service? You don’t have time to comb through it yourself.

Sentiment analysis uses NLP to determine the underlying emotion in a message. For example: If you get these responses from feedback forms:

  • “The agent I spoke to was awesome.”
  • “My order arrived quicker than I expected.”
  • “It’s easy to sync my data. Thanks for putting together your onboarding docs!”Sentiment analysis will take over and interpret those words as emotions. In the case above, those words might be “awesome,” “quicker,” or “easy.”The machine learning system will then tell you that the vast majority of feedback is positive. This gives you a rough understanding of how well you’re performing.The best part? You can use the AI system to scan for mentions of your brand. Then, you can use sentiment analysis to determine whether the coverage you’re getting is as good as you’d hope.

8) Speech-to-text applications

You’ve probably heard the statistics that say voice search is on the rise. Some researchers predict that 30% of all searches will be done without a screen by 2020.

Applications helping bring that statistic to life is speech-to-text devices. Devices like Google Home, Amazon Alexa, and Siri are our personal assistants. We ask them to plan every minute of our day—from planning the best route to your friend’s house, to ordering more cereal that you’ve just run out of.

…But what does that mean for your customer service?

You can open the floor to voice recognition systems by:

  • Allowing customers to access their account with their voice
  • Translating a customer’s query in their native language to yours
  • Integrating your software with a voice assistantNeither of these situations would work without NLP, which interprets the spoken word. That gives you the chance to introduce speech-to-text applications and offer better customer service.

9)  Built-in search bars in knowledge bases

The search bar on your site is SEO’s younger sibling. They act similarly to search giants like Google. A user types what they’re looking for, and the search bar retrieves a list of links relevant to their query.

Some 50% of users go directly to the search bar as soon as they arrive on a website. Chances are, their search queries aren’t complete sentences. They’ll be short, snappy words and phrases related to the thing they’re looking for, like “blogging advice” or “Fujifilm Instax.”

The results for your users’ query must display relevant information. If not, they’ll leave your website. This impacts key metrics like bounce rate, conversions, and time on site.

But your site’s search bar won’t show relevant information for those queries without some form of NLP.

The machine learning software interprets the meaning of those queries. It understands what the user is looking for—even if that isn’t in plain English, contains grammatical errors, or is misspelled.

NLP = Better User Experience & Personalization

NLP is a core piece of machine learning you should use in your customer service departments.

Your support agents get a machine that can save hours of their time, but your customers will benefit, too. Why? Because they’re able to communicate in a way that suits them, catering to the personal assistants they would no longer be without.


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One Reaction
  1. Does this mean that you can use AI to process customer service inquiries? I know it is done but it certainly cannot cover everything.