Table of Contents
1. What is Natural Language Processing (NLP)?
4. Challenges in Natural Language Processing
5. The Impact of NLP on Customer Experience
Imagine your internet connection provider has an NLP-powered contact center, where you call when your internet is slow. You describe the issue like you would to a human customer service agent. After that, the NLP bot deconstructs your language, understands your intent, and guides you through the troubleshooting steps.
The process eliminates customer frustration that generally happens with IVRs and conventional bots. It delivers a more subtle, on-point, and humanized interaction to keep customers hooked.
NLP helps businesses improve customer service tremendously with functionalities such as sentiment analysis, intent recognition, automated responses, multilingual support, contextual understanding, etc. In fact, Gartner suggests that 80% of companies that use NLP for sentiment analysis report an improvement in customer satisfaction.
But how does NLP actually work? Let’s drill deeper into that question and explore what this technology is, what its key components are, and how it can empower your AI agents.
What is Natural Language Processing (NLP)?
NLP is a field in computer science that uses AI and machine learning to equip computers with the ability to understand and communicate with natural spoken language.
This technology enables computers to recognize, understand, and generate humanlike text and speech. Computers use rule-based modeling, statistical modeling, and machine learning to achieve humanization in their responses.
This capability allows NLP to power your chatbots by enabling spoken commands, question-answering digital assistants, and much more.
For example, you can employ NLP models to analyze customer sentiment and prioritize negative feedback for quick redressal. It also helps you transcribe customer calls and intelligent search for your knowledge base.
Key Components of NLP
NLP functions with five major components:
1. Morphological and Lexical Analysis
This breaks down the words in human language and analyzes them to extract meaning.
For example, NLP chatbots understand the word “unhappy” by breaking it into two parts – “un,” which they interpret with negativity, and “happy.”
2. Syntactic Analysis
This component ascribes meaning to each word in a phrase. Here, the NLP program looks at a word’s literal and grammatical meaning rather than contextual.
For instance, the computer interprets the phrase “My thermostat isn’t starting up; I’ve tried everything” using the phrases “thermostat” and “Starting up.” However, it cannot provide relevant guidance yet because there is a lack of context in its understanding.
3. Semantic Analysis
This component gives context to a customer’s sentence. It looks at the entire sentence to derive meaning and intent after the syntactic analysis provides a basic grammatical structure for interpretation.
In this step, the computer can understand the phrase “My thermostat isn’t starting up; I’ve tried everything” by looking at the second part of the sentence, inferring that the customer has already gone through basic troubleshooting and needs further assistance.
The NLP chatbots refine their understanding in this way over time.
4. Discourse Integration
This component interprets multiple sentences sequentially for meaning.
For example, “I’m unhappy with the product. I will discontinue” has two codependent sentences. To a computer, they don’t make sense individually. Discourse integration pieces the implied meaning together from both sentences to develop understanding.
5. Pragmatic Analysis
This component is responsible for deriving context and meaning to understand the purpose of a sentence.
For example, NLP apps such as Alexa and Siri are experts at understanding a user and leveraging it to deliver accurate and relevant responses.
How NLP Powers AI Agents?
According to Chatbot Magazine, NLP-driven functions could potentially reduce customer support costs by 30% in the automotive sector.
There are countless ways in which NLP powers AI agents. Let’s go over some of them below:
1. Understanding User Input
Using stemming, tokenization, and part-of-speech tagging techniques, NLP enables AI agents to break down user text into meaningful components. It helps the AI agents grasp the meaning behind words and phrases that customers use during their interactions with chatbots, voice bots, etc.
2. Context Awareness
NLP allows AI agents to take the entire conversation history into account, enabling them to generate relevant and contextual responses. It eliminates the need for a customer to repeat themselves over and over again with their requests.
3. Sentiment Analysis
NLP empowers AI agents to detect the emotional tone of a user’s message.
For example, AI agents can associate the use of punctuation marks with certain keywords that imply frustration, disappointment, dissatisfaction, etc. They will flag the “Extremely disappointed!” phrase as a negative emotion and prioritize it for resolution by identifying the keywords “extremely” and “disappointed” as a high degree of negative emotion, paired with the exclamation point to highlight the possibility of escalation.
In this scenario, the NLP chatbot would then respond with something like, “I am extremely sorry to hear that. Let me know from the options below what I can do to resolve your issue.”
4. Natural Language Generation
NLP enables AI agents to generate humanlike responses in addition to understanding and interpreting human language. Instead of delivering robotic responses, NLP crafts a natural conversation that feels humanized.
Challenges in Natural Language Processing
NLP technology is evolving rapidly as it learns more and more from user interactions. However, it does pose some challenges during implementation and training:
1. Language Ambiguity
NLP models frequently slip when it comes to decoding nuanced words and phrases with multiple meanings.
For example, the phrase “Can you handle this?” is ambiguous and does not offer a clear query or instruction for the NLP program to work on. The program is likely to get confused whether the customer wants a representative to work on something or if they are expressing dissatisfaction. Therefore, such phrases can be tricky for an NLP-enabled AI agent to solve.
2. Contextual Understanding
The NLP programs that haven’t been properly trained on high-quality data struggle to establish contextuality in customer conversations. Outdated or biased training data can also lead to poor contextual interpretation. Such models often end up delivering irrelevant or inaccurate service, leading to further customer dissatisfaction.
This challenge can be navigated by ensuring high-quality, real-world data is available to train your models.
3. Slang and Colloquialisms
NLP models tend to get confused when customers use slang or informal language. For example, the phrase “Stop beating around the bush and solve my problem” can confuse an NLP chatbot.
You can resolve this issue by training your AI models on a conversational dataset.
4. Complex Queries
NLP models may not be able to handle complex customer queries efficiently.
For example, a customer explaining their problem to a chatbot using multiple sentences, with intricate detail using specifics, may confuse the NLP chatbot. In these scenarios, human agents are better suited to handle multifaceted queries that have more than one layer.
You can resolve this issue by introducing NLP-based AI models that enhance the context-awareness of your service agents.
The Impact of NLP on Customer Experience
NLP has extensive application across sectors, delivering tangible impact in enhancing customer experience. Here are some key areas:
1. Improved Customer Interactions
NLP-powered AI agents deliver customers a more hands-on service experience by conversing in a more natural language rather than using robotic, stiff phrases. This makes the conversations more engaging and even enjoyable for customers.
The enhanced comprehension capabilities of natural language processing allow chatbots to address a more significant proportion of customer queries through natural dialogue.
2. Personalized Experiences
NLP chatbots are able to maintain conversation histories with every single customer, empowering brands to create profiles with personalization details for all their customers.
This way, whenever a customer initiates a conversation with the brand, NLP interfaces automatically apply all the personalization to the conversation.
3. Proactive Support
NLP AI agents are able to measure customer sentiment during the flow of conversation, enabling them to understand negative emotions and take proactive measures to prevent escalation.
Additionally, they can offer personalized incentives or alert service teams about a conversation that is taking an unfavorable turn.
For example, when a customer says, “I am shocked at the bad condition in which the product arrived,” the NLP bot is able to sense customer churn and responds with, “I am sorry to hear that. I have updated the shipping team about your issue. To express our apologies, would you like to accept a 10% discount on your next purchase with us?”
4. Humanized Service
NLP enables AI agents to generate outputs in natural, spoken language, which humanizes the entire conversation.
For example, when a customer asks, “I need help with bookings,” the NLP bot may use “Sure! Let me know which part of booking you need help with from the options below”.
This is much more natural as compared to the response, “Choose an option from the menu below.” Customers find it more engaging to interact with a naturally flowing, conversational AI agent rather than a stiff bot. It helps them stay loyal to the brand and improve brand perception through the humanization of interactions.
In addition to enhancing customer experience in the front end, NLP also helps businesses reduce the operational costs of their customer support department. Basic queries are delegated to NLP-powered AI agents, while human agents reserve their time and effort for the more complicated and high-priority service cases.
Conclusion
The integration of AI has led to the development of countless useful technologies, and NLP is one of the most critical for the customer service sector. Brands can gain a competitive edge in the market using NLP in their service departments to rise above competition and offer their customers lasting value.
You can implement the power of NLP in your AI agents today with the help of Kapture CX, a comprehensive, AI-first customer experience platform. It delivers end-to-end, tailored support for industries such as BFSI, retail, utilities, and more.
Some of its useful features include:
- Omnichannel Ticketing & Helpdesk: Consolidates customer queries from multiple channels (email, chat, social, phone, etc.) into one interface.
- Contact Center Solutions & CRM Integration: Offers a single customer view, enabling your NLP-powered agents to access relevant information in real time.
- AI & Chat/Voice Bots: Uses NLP to interpret user intent, provide contextual responses, and automate repetitive tasks.
- Knowledge Base Management: A centralized repository of FAQs, product or service details, and troubleshooting steps that can feed into your AI agent’s responses.
Schedule a personalized demo to learn more!
