7 Examples of What Businesses Can Achieve with Better Natural Language Processing

Natural language processing (NLP) is a branch of artificial intelligence that uses machine learning algorithms to help computers understand natural human language—not just what people are saying but also what they mean when they say it. There are examples of NLP in nearly every customer service process powered by AI.

Companies at the forefront of customer experience solve some of the most frustrating human-software interactions and stay ahead of today’s customer expectations by applying advanced NLP machine learning.

The result for customers is more natural and satisfying experiences and loyalty and revenue for companies.

Let’s take a look at natural language processing examples in customer service that take businesses above and beyond customer expectations.

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

Suppose your company uses conversational AI as a part of your voice channel.

Advanced NLP algorithms collect and learn from a diverse range of human voices, which means the speech engine can recognize a language no matter the accent or impediment. It can also help virtual assistants offer better sets of options that lead to a faster, more satisfying resolution.

Without advanced NLP, customers are more likely to get stuck in an unresponsive interactive voice response (IVR) menu. A non-native English-speaking customer, for instance, may not get the support they need if rudimentary speech recognition software can’t discern intent because of the customer’s accent. Instances like this are far too common among companies that don’t have advanced NLP, and they cause not only frustration and lost sales but also feelings of discrimination, which undermines trust in your brand.

With better voice recognition, NLP can help you overcome the language barrier and offer more inclusivity for customers who speak with accents or for whom English isn’t their first language. If the speech engine is still having trouble understanding the caller, the auto-attendant may connect them with a human agent or ask the customer if they would prefer to converse in their native language.

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

An NLP-powered virtual agent understands the semantics and context of keywords to respond more efficiently to mobile customer questions. This responsiveness and flexibility will help deliver tailored experiences, no matter which device customers are shopping on, or which digital channels they use in the app, mobile site, or desktop.

Your digital customers expect the same level of individual attention you give your in-store customers. When paired with an intelligent contact center platform to “recognize” repeat digital visitors, NLP can offer personalized greetings. It can even help chatbots and virtual agents pick up where conversations last left off. For buy-online, pick-up in-store orders, the virtual agent can supply human staff with crucial customer insights for more natural customer handoffs from virtual to human agents.

Natural language processing example

An example of a personalized greeting using UJET Virtual Agent

3. More empathetic responses to unhappy customers

NLP makes it possible for you to respond with more profound empathy to your customers’ situations and take more appropriate action to resolve issues. Using sentiment analysis and emotion recognition, NLP can flag heightened feelings on the customer side and areas for improvement on the agent side, so your company can take action to deliver a more timely or relevant response.

Imagine that a customer who is in a hurry calls into your contact center. Each time an agent asks the customer to hold for assistance, the customer shows growing impatience. But your agent doesn’t pick up on these tonal shifts in your customer as fast as they should.

In this scenario, advanced NLP software can recognize the urgency in your customer’s tone. It can infer from their wording that they’re short on time and fast-track the customer’s ticket so it has a higher priority. NLP software can also identify agents who may need more training and help managers gain better insights into where skills can be advanced. Calls can be automatically recorded and flagged for training purposes.

4. Tailored, specialized service for high-priority customers

An advanced NLP model can help your CRM and ticketing system “read” contextual cues beyond specific form fields to escalate a ticket and deliver it to the right person for the best response. By making automated support processes more flexible, NLP can also help your company deliver white-glove service to top-tier customers at scale.

Let’s say a CEO of a target account emails your support desk with a common problem. A typical automated workflow would send the customer an auto-response and treat the issue as low priority. The CEO gets average service for average outcomes, meaning they have a moderate chance of remaining a customer or upgrading their service.

In this situation, the NLP model could “see” from the customer’s email signature that they’re a CEO, send a more personalized response, prioritize the ticket, and route the ticket to a specialized agent. It can also help agents resolve tickets quicker by recommending answers based on similar questions.

All of this adds up to a superior experience for top-tier customers, which leads to higher retention rates and more revenue.

5. More satisfying site search and knowledge-base results

Your customers want better results when they look for help in self-service channels, such as site search and help centers. NLP can prevent self-service customers from becoming dissatisfied and taking their business elsewhere by interpreting the meaning of search queries and delivering more relevant autocomplete suggestions and results.

Suppose an electronic-device customer searches in a help center to troubleshoot a technical problem and can’t find an answer. In that case, they may decide to return the product and buy a different brand.

When a customer can’t find an answer using search, an NLP-powered chatbot can intervene and provide more personalized support or route the query to a human agent.

Better yet, the more customers use an NLP-supported knowledge base, the smarter your entire customer service system gets: knowledge-base searches “feed” the NLP model, providing chatbots and support agents with more relevant responses.

6. Fewer customer service runarounds

When customers turn to a company with a complicated issue, NLP can pick up contextual cues in a customer conversation. AI-driven automation can dynamically change CRM fields, and agents understand the customer’s situation right away.

People dread having to repeat themselves on customer support calls. It’s a nightmare for customers with complicated issues to explain their problem to a chatbot, then an agent, then their supervisor, then a specialist before finally getting a resolution.

NLP eliminates the need to repeat their problems and details. It collects, centralizes, and delivers the right customer information to the right people.

Plus, a chatbot powered by NLP can provide necessary backgrounds and details to a human agent at handoff, so the customer doesn’t have to repeat it, and the agent won’t have to spend time searching through records.

An example of a warm hand-off from UJET Virtual Agent to a human agent.

7. Stronger customer privacy protections — and more trust

New developments in privacy-preserving NLP mean that it will soon be possible to remove sensitive customer data from all records, even in the context of recorded customer service conversations.

Companies are offering more communication channels, where customers provide sensitive information like their contact info, birthdates, and payment account numbers. Hackers are finding more opportunities to decrypt and sell customer data.

Let’s say a customer gives their account number and birthdate to validate a customer service call. Later, a data breach leaks the files of customer service call recordings to a third party. Such a fiasco could lead to identity theft for your customer, and stiff penalties, class action suits, and PR nightmares for your company.

When customers share sensitive data with your company, NLP can detect and mask their identifying information to protect their privacy. This kind of protection helps your company comply with customer data security regulations, protecting customers from identity theft and your company from costly legal ramifications.

UJET’s Virtual Agent is one more example of what’s possible with natural language processing. 

UJET’s next-generation, natural language processing-powered solutions like Virtual Agent feature predictive and contextual routing and conversational web messaging. You can create one-of-a-kind experiences while preserving customer privacy and meeting other regulatory requirements. Learn more.