Today, 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. More businesses are applying those machine learning technologies to enhance customer service interactions. 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.
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, these natural language processing (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 this technology, gaining competitive advantages and pushing the whole industr better to integrate 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 companies can use it for instant and automated analysis of the customers’ sentences to make the most suitable decisions.
NLP vs. NLU vs. NLG
NLU (natural language understanding) is a technology that helps machines 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 decipher a speaker’s mistakes, as advanced NLP algorithms collect and learn from a diverse range of human voices and texts.
For example, customer service teams working with different countries can help non-native English-speaking customers. They may encounter difficulties getting support if rudimentary speech recognition software can’t discern intent because of their 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 have a speech disability. In such cases, the speech engine may still have trouble understanding the caller sometimes. In that case, the auto-attendant may connect them with a human agent, ask if they prefer to speak their native language or use chat.
2. Multimodal e-commerce experiences with an “in-store” feel
Digital customers, no matter which device or platform they’re using, 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 customer care. For example, NLP delivers personalized greetings to repeat digital visitors.
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 client insights from AI customer support for more natural 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 for a more timely or relevant response, which leads to increased customer satisfaction.
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. In addition, more flexible automated customer service and support processes help your company deliver white-glove service to top-tier customers at scale, leading to higher overall customer satisfaction, retention rates, and bigger revenue.
4. Less 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 making customer support calls. A survey of UK office workers found that 76% of millennials and 40% of baby boomers have anxious thoughts when their phone rings. Additional 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 repeat their problem to a chatbot, an agent, their supervisor, and finally a specialist before 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 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 by NLP communicates with 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, an interlocutor who is never impolite, tired, or burnt out. Thus it is a core piece of machine learning for your customer service department. As such, it can be applied in many various ways.
1. Chatbots and callbots
The most popular NLP applications in customer service are chatbots and a callbots. The popularity of platforms such as Facebook and WhatsApp has meant widespread adoption among companies for customer support chats.
Instead of employing humans to manage these communications, 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 costs and keep customer service representatives from burning out.
Furthermore, as employees are no longer bogged down answering simple questions, they might handle escalated customer issues requiring more expertise and time. Another benefit of call center automation with NLP is the organized collection of relevant data, facilitating the onboarding of new customer support agents. In addition, NLP will even help scale your customer service.
Let’s look at the numbers: 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 with a far shorter wait time, 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. a chatbot case study in telecom claims that the chatbot Annika handles 45% of all 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 particularly 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 consumer attitudes and take appropriate action.
3. Online conversations and social listening
Five years ago, businesses would have never thought of a profound data analysis on a forum thread containing witty comments. The fragmentation of media and communication has meant that online experiences are platform based, rather than traditional media and review outlets. Thus, increasingly, 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 posts reflecting 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, 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 it is accomplishing its goal.
5. Conversational systems for product recommendations
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 ticket routing
Customer support teams are usually overloaded with calls and tasks. It is a matter of time and money, of course.
For example, up to 50% of all help desk calls, according to Gartner, might be related to password reset requests. As Forrester Research notes, the average cost of a single password reset done by a help desk is about $70.
NLP algorithms can understand the request or support ticket topic and immediately provide the instructions for troubleshooting or, in complex cases, direct support tickets to a higher-tier agent.
This solution means reduced 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 as phrased in a person’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 text data by analyzing complaints from emails and through cancellation forms. Thus, your customer support team will get a notification before those complaints become a pervasive 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 without the budget for hundreds of 24/7 human customer service agents.
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 with proof of concept projects. Once you get the first results, you can expand from there.
Having delivered over 100 artificial intelligence projects including 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.
