5 reasons to immediately start using NLP in customer service

Irina Kolesnikova
September 25th, 2022

NLP in customer service: Titan holds a chatbot, connecting to clients

66.4 million people use smart speakers such as Amazon Alexa, Google Home, and Apple Siri for more than just learning about the weather. Machines that understand human language and are capable of conversation have become an essential part of everyday life.

Customer service is, no doubt, crucial for business, as research shows. Most consumers agree they are willing to purchase more products (87%) and are willing to recommend a company to others (81%) if they have an exceptional customer service experience.

More businesses are applying those machine learning technologies to enhance customer service interactions. For example, Gartner predicts that 30% of interactions with technology will be through “conversations” with machines — including those made by voice. Moreover, researchers claim that smart chats can handle up to 75% of customer conversations.

In addition, NLP applications pay off quickly and provide significant results in a short time. No wonder the natural language processing market will be worth US$ 28.6 Bn in 2026. This means more companies will adopt that technology, getting competitive advantages, so it might be better to apply natural language processing in customer service earlier. This article highlights five reasons why you should adopt NLP for your customer service immediately.

What is NLP?

NLP means natural language processing. It is a branch of data science and a subfield of artificial intelligence. Natural language processing allows machines to analyze and understand human language and generate reactions by transforming unstructured data into conversations.

In other words, natural language processing allows humans and machines to speak to each other in the same language. So that companies can use it for instant and automated analysis of the customers’ sentences to make the most adapted decisions.

NLP vs. NLU vs. NLG

NLU (natural language understanding) is a technology that helps understand data thoroughly, identifying the meaning and intentions of human language, written or spoken. NLG (natural language generation) is an AI technology that automatically creates reactions and answers in a particular language, written or spoken. Both are NLP techniques and could be applied together or separately.

Reasons to use NLP in customer service right now

The benefits of NLP are saving time and money, providing flexibility, and a possibility to stand out as a brand. It frees the customer from the annoying and long waiting time or other triggers like ending up talking with the wrong department or an agent non-suited to resolve the issue. These features alone can improve the customer experience.

1. Communications more inclusive of language, culture, and ability

Artificial intelligence can learn to recognize a language with any accent or mistakes. One way to apply it is to create different bots for each language. For example, it helps customer service teams working with different countries. Moreover, as advanced NLP algorithms collect and learn from a diverse range of human voices and texts, they can understand non-native speakers.

For example, a non-native English-speaking customer may meet difficulties getting support if rudimentary speech recognition software can’t discern intent because of the customer’s accent. Unfortunately, instances like this are far too common among companies that don’t have advanced NLP. These cause frustration, lost sales, and feelings of discrimination, which undermine trust in your brand.

With better voice recognition, NLP overcomes the language barrier and offers more inclusivity for customers who speak with accents or for whom English isn’t their first language. However, the speech engine may still have trouble understanding the caller sometimes. In that case, the auto-attendant may connect them with a human agent or ask the customer if they prefer to speak their native language.

2. Multimodal e-commerce experiences with an “in-store” feel

Digital customers, no matter which device customers are shopping on, or which digital channels they use in the app, mobile site, or desktop, expect the same level of individual attention a business gives its in-store customers. An NLP-powered virtual agent that understands the semantics and context of keywords to respond more efficiently can provide that level. For example, added to the recognition of repeat digital visitors, NLP delivers personalized greetings.

Moreover, it might even remember the whole conversation and thus help chatbots, voice assistants, and virtual agents pick up where conversations last left off. Tailored customer experience will also benefit if human staff get crucial customer insights from AI virtual customer support for more natural customer handoffs from virtual to human agents.

3. Customer satisfaction

NLP can help customer support service respond with more profound empathy to your customers’ situations and take better action to fix issues. Using emotion recognition and sentiment analysis, the NLP model detects tension on the customer side and areas for improvement on the agent side. Thus your company might take action to take a more timely or relevant response.

An advanced NLP algorithm helps detect contextual signals beyond specific form fields, such as tone of voice, email signature, or trigger words. Then it can prioritize calls or support tickets and deliver them to the right person for the best response. For example, more flexible automated customer service and support processes help your company deliver white-glove service to top-tier customers at scale, thanks to NLP.

These NLP benefits add a superior experience for top-tier customers, leading to higher overall customer satisfaction, retention rates, and bigger revenue.

4. Fewer customer service runaround

When customers have a complicated issue, NLP step in identifying contextual signals in a customer conversation. In addition, AI-driven customer service support may dynamically change CRM fields, thus making agents understand the customer’s situation faster.

People dread having customer support calls. The survey of UK office workers found that 76% of millennials and 40% of baby boomers have anxious thoughts when their phone rings. More research suggests that phone anxiety is related to a fixation on what the other person thinks of them. It’s a nightmare for many customers to explain their problem to a chatbot, an agent, their supervisor, and a specialist before finally obtaining a solution.

NLP collects, processes, and delivers the right customer information to suitable agents. So, no need for customers to repeatedly describe the issue, and the agent won’t have to spend time searching through records.

5. Stronger customer privacy protections — more trust

NLP can protect privacy by detecting and masking sensitive customer data, such as contact info, birthdates, and payment account numbers. This protection helps your company comply with customer data security regulations, protecting customers from identity theft and your company from costly legal ramifications.

NLP already makes it possible to remove sensitive customer data from all records, even in recorded customer service conversations.

How can businesses use NLP in customer service?

Customer service enhancement is just one of the natural language processing use cases. A system empowered with NLP communicates to customers in a way that suits them best. At the same time, it saves hours of support agent time, helping to find the answers quickly.

NLP = Better User Experience & Personalization

A machine empowered with natural language processing chats or speaks to customers, but it is never unpolite, tired, or burnt out. Thus it is a core piece of machine learning for your customer service department. However, it can be applied in many various ways.

1. Chatbots and callbots

The most popular NLP applications in customer service are a chatbot and a callbot. For example, Facebook and WhatsApp as chat platforms have more than 1 billion users each.

So, the trend is apparent: sooner or later, everyone will be using some platform for chats. Instead of employing humans to manage these inbound customer chat requests, the chat can be automated by NLP. As a result, customer service bots enhanced with NLP will be available 24/7. At the same time, it will seriously cut down your costs and keep your customer service representatives from burning out.

ai chatbot an example pf NLP in customer service

Furthermore, as your employees are no longer bogged down answering simple questions, they might handle escalated customer issues requiring more expertise and time. Another benefit of chatbot and call center automation with NLP is the facilitation of new customer support agent onboarding. In addition, it will even help scale your customer service.

Let’s make some calculations: one customer service agent can deal with one phone call at a time, and five chats, whereas customer service chatbots or callbots can answer thousands of customers simultaneously.

Last but not least, your customers receive immediate and accurate answers whenever they contact your customer service to limit the demand on traditional call centers, which means automated chatbots improve customer experience.

For example, Elisa, a leading telecommunications and entertainment company serving thousands of customers across northern Europe, is well aware of the need for extraordinary customer service. The chatbot case study in telecom claims that the chatbot Annika handles 45% of all the inbound contacts, saving thousands of euros.

2. Reputation and sentiment analysis

To learn about customer needs, opinions and intentions, many companies use the web and digital media as sources. NLP enables organizations to listen to and understand their customers’ voices online and the sentiment behind them, as it often determines customers’ choices and decisions. Companies get an opportunity to automate the web search for brand mentions and product references to understand and take appropriate action. This has proven favorable for businesses, thus, they can better understand their customers and make the right business decisions.

NLP in customer service for sentiment analysis

3. Online conversations and social listening

Businesses would have never thought of a profound data analysis on a forum thread containing witty comments. Online communications have already started changing, so brands’ only means of communication with customers is text mediated.
Today, online conversations, such as messages and social media comments, are the sources of great value for NLP analysis. Businesses can learn much from people’s comments about their opinions and intents.

As people spend more time of their lives online, there is a growing need for high-quality NLP on social media posts, chat logs, and forum replies.
Katie Bauer , Senior Data Scientist at Reddit

For example, analyzing social media, Visa created a customizable database of SMB users by searching Twitter bios for terms like “I manage,” “I run,” and “I own.” Further social listening to their Twitter conversations provided valuable insights into the specific needs of this segment. Since adopting this strategy, customers’ sentiment about Visa has grown 50-60% more positive – which means they are accomplishing their goal.

5. Conversational systems for product recommendation

Chatbots and NLP tools can also enhance product recommendations.

For instance, NLP algorithms can quickly analyze and sift requests, then reply automatically — or route customers to the right human agent.
Afterward, the artificial intelligence system can recommend similar products or services.

For example, AI chatbots can remember customers’ past conversations, even if they occurred weeks or months earlier.

Then the system might use such data to learn and thus convey more suitable and well-tailored content. With those recommendations, companies can even anticipate customers’ future needs.

6. Agent’s ticket routing

Customer support teams are usually overloaded with calls and tasks. It is a matter of time and money, of course.

Nlp in customer service: ticket routing

For example, Forrester Research estimates that the average cost of a single password reset done by a help desk is about $70.

Moreover, those password reset requests might reach up to 50% of all help desk calls, as Gartner estimates.

NLP algorithm can understand the request or support ticket topic and immediately provide the instruction for troubleshooting – or direct support tickets to a higher-tier agent in complex cases.

This solution means reducing bottlenecks and errors, consequently alleviating the CS load.

7. Accurate call routing with IVR systems

NLP applications include not only text chats but also voice conversations. For example, have you ever called customer support, saying “Make a payment” to reach the finance department? That was an interactive voice response (IVR) system. Using IVR, your customers don’t need to “listen to the following options” to get the answer or talk to the right person. Why? Because NLP understands their request. Conversational IVR goes even further. It will simply ask a customer to explain their issue and then understand the inquiry, even vocalized in human’s own words.
For example, American Airlines reported an increase in their call containment by as much as 5% after applying NLP. That helped save the airline millions of dollars.

8. Business data analysis

Apart from analyzing qualitative data from customer feedback, NLP allows businesses to process any information from elsewhere. This analysis can lay out trends to follow.

For example, NLP is able to identify trends within such text data by analyzing complaints from emails and through the cancellation form. Thus, your customer support team will get a notification before those complaints become a problem.

In conclusion: from nice-to-have to necessity

If you have a loaded customer support team and receive or make repetitive calls, you definitely might consider an NLP application, especially if you fight for the best hands-on support 24/7 and customer satisfaction without a budget for hundreds of human customer service agents.

MindTitan team working on big picture AI strategy

Good news: the NLP application does not have to be a huge investment from the very beginning. You will clearly understand whether NLP in customer service provides benefits even starting smaller with proof of concept projects. Once you get the first results, you could expand from there.

Having delivered over 80 artificial intelligence projects involving successful AI upgrades of customer service departments, we are convinced that NLP-enhanced data processing can provide significant efficiency, productivity, and profitability growth.

MindTitan specializes in providing NLP services that solve intricate business problems when off-the-shelf solutions do not work or when complex integration with other AI models is required.

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