9 Ways You Should Be Using NLP in Customer Service to Boost Efficiency

In almost every home, you’ll find Amazon Alexa, Google Home, or Apple Siri. But the 69.9 million people who own smart home systems aren’t just using them to play their favorite songs or check the weather. 

A significant portion of our interactions with technology today involve having “conversations” with smart machines or conversational AI systems, and many people are using this machine learning technology to improve their customer service interactions. 

In fact, research shows that chatbots can handle 80% of customer communications. 

The reason this works so well is because chatbots use Natural Language Processing. NLP in customer service enhances customer experience by providing a fast, 24/7 response time and personalized interaction, which reduces costs and allows human agents to handle the more complex issues. 

In this post, we’ll go over nine ways you can use NLP in customer service to boost your contact center’s efficiency.

What Is NLP?

Natural Language Processing is a branch of artificial intelligence that enables computers and humans to converse through natural language – i.e. in a way that doesn’t sound like you’re talking to a robot from the ‘80s. 

NLP is a critical component of conversational AI, which humanizes AI interactions with customers 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.

In the context of call centers, NLP easily performs tasks such as text and sentiment analysis, language translation, speech recognition, and topic segmentation. It understands the words, sentences, and context of speech – or, in this case, your customer support queries – and provides a quick and accurate answer, all without human intervention.

Benefits of NLP in Customer Service

These days, most people have high expectations when it comes to customer service. They demand quick, accurate, and personalized responses, and they expect to interact with businesses through various channels (social media, chat, email, phone), making it challenging for even the best human agent to keep up. As a result, companies are forced to find better ways to meet these growing demands without compromising quality or efficiency.

NLP chatbots play a huge role in customer service because they enable automated systems to understand and respond to customer inquiries, and can take over routine tasks like answering frequently asked questions or directing customer calls to the right department. 

NLP enables chatbots to:

• Comprehend User Input: It analyzes and understands the text or voice input from users, including identifying the intent behind the message.
• Process Human Language: It handles various language constructs, such as grammar, syntax, and semantics, to make sense of the input.
• Generate Responses: It formulates appropriate and contextually relevant responses to user queries.
• Handle Multilingual Communication: It supports interactions in multiple languages, which opens up accessibility for a diverse user base.
• Learn and Improve: It continuously learns from interactions to improve accuracy and effectiveness over time.

So by automating basic or repetitive tasks and providing instant responses, NLP can help businesses: 

• Handle a higher volume of customer interactions without increasing their workforce
• Analyze large amounts of data to identify patterns to improve their service quality
• Provide more natural and human-like interactions between customers and automated systems so these conversations are not off-putting for the customer
• Improve workflows and processes by extracting valuable insights and patterns from unstructured data sources such as social media and reviews, and performing customer feedback analysis

In a nutshell, using NLP-powered conversational AI allows your call center’s chatbot to interpret user input, manage contextual queries, and provide accurate responses, ultimately enhancing user experience and operational efficiency in customer service.

Examples of NLP in Customer Service

You likely already know that companies like Amazon, Starbucks, and Netflix use this technology, but many banks also use NLP chatbots to assist customers with inquiries and support.

For instance, a bank’s chatbot can handle various customer service tasks, such as:

• Answering frequently asked questions (e.g. “What are your working hours?”)
• Providing account information (e.g. “What’s my current balance?”)
• Assisting with transactions (e.g. “Transfer $100 to my savings account”)
• Resolving common issues (e.g. “I lost my credit card, what should I do?”)

These chatbots understand and process the customer’s natural language input, and then provide quick and accurate responses, which is convenient for the customer and frees up human agents for more complex queries.

Another example is Uber, the on-demand ridesharing company. Uber’s smart reply system (or in-app chat) uses Natural Language Processing between drivers and passengers to facilitate easy communication. NLP helps interpret messages and then provide quick replies, even if there are language barriers and, with voice commands, allows drivers to keep their hands on the wheel at all times.

Uber has an extensive dataset and a huge engineering team, which means they’re well-equipped to implement and refine advanced technologies like NLP. The graphic below can help you visualize how NLP and machine learning create a better customer experience.

Top 9 Use Cases for NLP in Customer Service

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? If so, you were 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 appropriate department.

Customers are likely to use this system to contact your team. When conversational AI is the basis of the system, you can accurately divert their call to the most relevant line, and the IVR becomes an intelligent virtual assistant (IVA).

Why? Because NLP understands a caller’s request and therefore can assist them better. In other words, you don’t need to ask your customers to “listen to the following options” to send them in the right direction.

By simply asking customers to describe their needs in their own words, IVAs can quickly analyze and route the call to the appropriate department or support agent. This not only streamlines the process, it also significantly improves the customer experience by reducing wait times and eliminating the frustration of navigating complex menu systems.

American Airlines saw significant results from using NLP for their customer service team. After revamping their IVR system, they:

• increased their call containment by 5% 
• saved the airline millions of dollars every year
• improved the overall customer experience

2) Quick routing of customer support tickets

You give customers a support ticket when they try to contact your customer service. This interaction then filters its way through to your support team’s queue. NLP can help streamline this process. 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.

Consider a scenario where a customer submits a ticket stating, “I need help changing my payment details.” In systems lacking NLP capabilities, this ticket would likely land in the general support queue and require manual intervention to identify and reroute it to the finance department.

On the other hand, a support platform equipped with NLP can immediately recognize the financial nature of the inquiry from keywords and phrases within the ticket. It can then autonomously direct the ticket to the appropriate team – in this case, the finance department. 

This automation speeds up the resolution process, reduces the workload on customer service agents, and ensures that customers receive timely and relevant assistance, ultimately enhancing the overall customer experience.

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. 

In fact, actively seeking and valuing customer feedback can significantly enhance a brand’s reputation – 83% of customers are loyal to brands that solicit and respond to their complaints.

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

NLP helps identify words or phrases commonly used in reviews, like “modern,” “intuitive,” and “expensive.” NLP can also find topics spoken about in feedback forms, such as “easy onboarding” or “affordable plans.”

You can combine NLP with sentiment analysis (more about this in number seven below) and get a top-level overview of customer opinions, making it a time-effective way to analyze customer behavior via feedback.

4)  NLP and customer service chatbots/live chat

An AI chatbot lets you communicate with your customers in the way they prefer and provides real-time support without having to wait for a response.

Why use live chat on your website? Because it’s the communication channel that customers prefer to connect with a company: 46% of would rather reach out via live chat, 29% for email and 16% for social media:

Keep in mind that although both live chat and chatbots are used for customer service, they are not exactly the same. Chatbots use artificial intelligence, including NLP, to handle initial queries, and live chat (human agents) address more complex issues. 

Many businesses use them together to provide a comprehensive customer support experience:

• Live Chat in Customer Service: Live chat provides real-time communication between customers and human customer service agents. This direct interaction is ideal for handling complex or nuanced queries that require a personal touch or simply can’t be found on FAQ-type pages. Customers appreciate the immediate responses and the ability to converse with a knowledgeable human rep who can address their specific concerns.
• Chatbots Powered by NLP in Customer Service: Chatbots, on the other hand, enable businesses to offer real-time support without the need for human intervention. By leveraging NLP, chatbots can respond to customer queries intelligently (most of the time!). This technology allows chatbots to interpret customer messages, even if they contain grammatical errors or incomplete sentences, so that they can get assistance at any time of day or night.

One of the key advantages of using both live chat and chatbots is the ability to manage high volumes of customer inquiries efficiently. When your customer support team is overwhelmed and can’t answer all queries in real time, an NLP-powered chatbot can step in to assist. The chatbot can handle routine questions and then pass customers off to human agents for more complex issues.

For instance, Cheapflights uses an NLP-powered chatbot to manage customer inquiries. This chatbot can understand and respond to a wide range of questions, ensuring that customers receive the help they need promptly. 

By combining live chat and NLP-powered chatbots, companies can provide the most robust customer support that meets the needs of 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 up with customer inquiries!By the way, you can calculate your average interactions per ticket to see how much time these interactions are costing you: 

An increasing number of agents are turning to machine learning software to cope with that high demand. Salesforce’s “State of Service” report discovered that 69% of high-performing service agents are actively looking for situations to use artificial intelligence.

Conversational AI can handle queries that don’t need much attention. This gives agents more time to handle complex queries that need a human touch. Your conversational AI could handle questions like:

• Technical Support: “Where is the HDMI input on my Samsung TV?”
• Order Status: “What is the status of my order?”
• Account Setup: “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 more complex or emotional questions like: 

• Account Issues: “My account got shut down, and I need help ASAP.”
• Billing Concerns: “I was charged incorrectly, and I need a refund.”
• Product Complaints: “My product arrived damaged, what can I do?”

Other ways that NLP can help agents enhance their operational efficiency include:

• Suggested Responses: Providing agents with suggested responses based on a customer query to make sure they can offer fast but accurate replies.
• Knowledge Base Integration: Quickly searching through a knowledge base to present relevant information to the agent for faster resolution of customer issues.
• Call Transcriptions: Converting spoken conversations into text for record-keeping, training, and quality assurance purposes.
• Conversation Summarization: Automatically summarizing any long customer interaction, helping agents understand the context without reading through entire conversation logs.
• Multilingual Support: Translating messages in real time, allowing agents to communicate with customers in their preferred language without needing to be fluent in multiple languages.

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 lay out common trends for your team to follow.

Consider a scenario where your business receives numerous complaints via email or a “Why did you leave us?” questionnaire included in your cancellation form. And let’s say you have 150 complaints to file. Your cancellation form asks people to check one of the following boxes:

• Confusing onboarding process
• It’s too expensive
• I don’t have time

People might tick the wrong box, leading to misinterpretation of the issues. For instance, you might think the primary problem is the cost because many people selected the “too expensive” option. However, there might actually be a deeper issue with the billing process that customers miscategorized.

As a result, you might consider increasing your prices based on the feedback, thinking it’s an acceptable move. But in reality, the core issue is something else, such as confusion with the billing process. NLP helps accurately categorize and analyze customer feedback so that you address the actual issues rather than misinterpreted data.

In another example, let’s say there’s a sudden spike in questions about a new product feature or a recent update. NLP can alert your team to investigate further. Understanding these trends allows your business to respond swiftly to potential issues, forecast future support needs, and adjust resources accordingly.

7)  Sentiment analysis and customer satisfaction

You’ve probably got customer feedback filtering its way through to your support team. But how do you know whether, on the whole, people are happy with your product or service? You likely don’t have time to comb through all this data 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!”

Then 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 better understanding of how well you’re performing.

And the best part is that 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 hoped.

Plus, NLP can analyze customer messages to detect emotions and sentiment in real time, alerting agents to frustrated or angry customers so they can prioritize and handle these interactions with extra care.

8) Speech-to-text applications

Voice search is on the rise: 50% of people worldwide search by voice on a daily basis.

And part of the reason is speech-to-text devices. We ask our personal assistants – including Google Home, Amazon Alexa, and Siri – to plan the best route to a friend’s house, to remind us of important events and appointments, and to play our favorite music or podcasts.

But what does that mean for your customer service? Well, you can use voice recognition systems to:

• Allow customers to access their account with their voice
• Translate a customer’s query from their native language to yours
• Integrate your software with a voice assistant

None of these situations works without NLP, which interprets the spoken word. Then you can use speech analytics (or voice analytics), one of the less common analytics that more call center should take advantage of, to analyze and improve customer satisfaction.

9)  Built-in search bars in knowledge bases

The search bar on your website is basically a mini search engine. And a significant portion of website visitors goes straight to the search bar when they land on a site, especially, but not limited to, e-commerce sites. The results for these queries must display relevant information. If not, users will leave your website, which 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 it isn’t in proper English, contains grammatical errors, or is misspelled.

Here are a few reasons why using NLP in your site’s search bars can improve customer service:

• NLP helps deliver more accurate search results by understanding the context and intent behind user queries, so users find what they’re looking for quickly.
• Users can type queries in natural, conversational language, and NLP processes these queries effectively, so customers won’t have to use specific keywords or technical jargon.
• When users find relevant information quickly, they are less likely to leave the site immediately, reducing the bounce rate and keeping potential customers engaged.
• By addressing customer queries accurately, NLP-driven search bars help users find products or information that meet their needs, increasing the chances of making a purchase.
• By analyzing these search queries, businesses can gain insights into customer preferences, popular products, and common issues, helping to inform their strategies and improve product offerings. 

Integrating NLP into your search bar means that your website will meet the needs of visitors much better and thus improves customer satisfaction.

Related: How to Use Conversational Analytics Software Effectively

Nextiva + NLP = Better Customer Experience

Natural Language Processing in customer service is a core piece of machine learning you should use in your contact centers. 

Nextiva integrates NLP technology into our products to help businesses transform their customer service operations. Our NLP-powered solutions enable businesses to automate routine inquiries, analyze customer sentiment, and provide real-time assistance to support agents.

By adopting an NLP solution, your customer service team can better understand and address customer needs, leading to higher satisfaction rates, increased loyalty and, ultimately, a stronger bottom line. Embrace the power of NLP with Nextiva to stay ahead in the competitive landscape and deliver exceptional customer service that meets and exceeds expectations.

NLP in Customer Service FAQs