Chatbots have become a cornerstone of modern customer service, streamlining interactions and enhancing user experiences. But not all chatbots are created equal. Those powered by Natural Language Processing (NLP) stand out for their ability to understand context, interpret intent, and deliver more human-like responses.
More than 70% of CX executives believe that personalization is the key to attracting the highest customer engagement and satisfaction. This indicates the growing importance of using chatbots that can understand user language.
In this article, we will be exploring every aspect of chatbot using NLP, the comparison with other technology, how it works, the types of NLP chatbots, and the components needed to deploy one such chatbot.
What is a chatbot using NLP?
A natural language processing (NLP) chatbot is a software program designed to understand and respond to human speech. These AI-powered bots, often called AI agents, enable seamless communication between people and computers in a natural, conversational manner that mimics real-life interactions.
Benefits of Using an NLP Bot
AI agents designed for customer experience (CX) do more than just enhance customer support—they bring several additional advantages to the table. Here are some of the most impactful benefits of NLP AI agents:
Reduce Operational Costs
NLP AI agents can handle a majority of customer requests independently, significantly reducing the need for additional human resources.
They help businesses lower costs while increasing efficiency by:
• Resolving customer issues faster.
• Reducing wait times.
• Scaling support operations without increasing headcount.
Offer Non-stop Multilingual Service
AI agents operate around the clock, ensuring 24/7 availability. Their ability to support multiple languages boosts customer satisfaction and loyalty. For instance, Switzerland’s first online casino, Jackpots.ch, uses multilingual AI agents to provide consistent support in German, English, Italian, and French.
Personalize Every Interaction
By integrating with backend systems like CRMs or e-commerce platforms, NLP AI agents gain access to customer data. This allows them to deliver personalized, context-rich responses, ensuring a more engaging and satisfying experience for users.
Elevate Your Team’s Role
With AI resolving up to 80% of customer inquiries, your support team can shift focus to more complex, meaningful tasks. This transformation turns agents into AI managers and supervisors, enhancing their productivity and enabling them to handle cases requiring a human touch.
Provide Admins with Actionable Insights
AI-driven analytics offer valuable performance metrics, such as resolved versus unresolved cases and areas for further automation. These insights empower leaders to optimize processes, confidently automate tasks, and address a wider range of customer issues effectively.
What are the applications of chatbot using NLP?
Before getting into the nitty-gritty details of the intricacies surrounding chatbot using NLP, let’s quickly understand the applications first. This will help you understand the practical use cases of this technology effectively.
NLP chatbot vs. Rule-based chatbots
When you think of a chatbot, the image that often comes to mind might be the outdated, rigid rule-based chatbots. These bots operate on a simple framework, relying on predefined keywords or pattern matching rather than using AI to grasp the full context of a customer’s message.
Take this example: a rule-based bot might answer, “What is the price of your membership?” accurately, provided the question matches a programmed pattern. But if the phrasing changes even slightly, the bot could fail to understand or respond correctly. To make it functional, developers must manually add every variation of possible questions—a time-consuming and limiting process.
This rigidity often leads to misunderstandings, leaving rule-based chatbots struggling to meet customer needs. To address these limitations, businesses are now turning to NLP chatbots.
These advanced bots use intent recognition and structured dialogue flows to interpret customer queries more effectively. Unlike their rule-based counterparts, NLP bots can track conversational context, learn from interactions using machine or deep learning, and improve their accuracy over time.
Types of NLP chatbot
NLP chatbots come in various forms, each designed to address customer needs differently. There are basically two different types of NLP chatbot. We will learn about the two types of NLP chatbot in this section.
Generative AI NLP Bots
Generative AI-powered NLP chatbots are a step ahead, offering personalized responses tailored to the user’s context. These bots can:
• Handle a wider range of queries.
• Deliver accurate, relevant information.
• Continuously learn from every interaction, becoming smarter and more efficient over time.
For example, Kenyt.AI utilizes OpenAI’s GPT-4 model to pull human-like responses from a business’s knowledge base. This makes the bots three times faster at resolving issues, providing more accurate, satisfying customer interactions.
AI Agents
AI agents represent the evolution of generative AI NLP bots, designed to autonomously manage complex customer interactions while maintaining a personal touch. Their advanced capabilities include:
• Training on industry-leading AI models and vast datasets of real customer interactions.
• Accurately identifying customer needs and responding with the empathy and precision of a human agent.
• Providing end-to-end resolutions while collaborating with human agents for a seamless workflow.
Take Grove Collaborative as an example. This brand relies on AI agents to handle 68,000 tickets monthly with only 25 human agents, achieving a remarkable 95% customer satisfaction score. From addressing routine FAQs to managing intricate inquiries, their AI agents streamline operations without adding to the team’s workload.
How do chatbot using NLP works?
Chatbots powered by conversational interfaces or large language models (LLMs) follow a structured process to understand, analyze, and respond to human language. For NLP-based chatbots, an additional step of entity recognition can further enhance their functionality.
The different steps how the chatbot using NLP works is described below:
Normalizing
The bot begins by cleaning up the input. It removes irrelevant details, converts words into a standardized format, and adjusts inputs to lowercase for consistency.
Tokenizing
The language input is divided into smaller pieces, called tokens, while unnecessary elements like punctuation are stripped away. This makes the input easier to process.
Intent Classification
Once the input is normalized and tokenized, the bot uses AI to analyse the message and identify the user’s intent. This step determines what the user is asking or trying to achieve.
Recognizing Entities
In this optional step, the chatbot extracts specific details mentioned in the message, such as order numbers, email addresses, or transaction IDs, for more personalized responses.
Response Generation
Advanced NLP bots generate multiple response options based on the processed input. The AI model then selects the most relevant reply and delivers it to the user.
What are the components of NLP chatbot necessary while deploying it practically
Now that we have come to conclusion, let’s learn how to deploy NLP chatbots and the components necessary for deploying them. This will help you get a head start in building and scaling up your business.
Conclusion
Deploying an NLP chatbot can transform your customer interactions, making them more intuitive, efficient, and personalized. By understanding the intricacies of NLP and leveraging the right tools, businesses can stay ahead in delivering exceptional customer experiences.
At Kenyt.AI, we specialize in creating customized and trainable NLP chatbots tailored to your unique business needs. Contact our sales team today and take the first step toward smarter automation.
Frequently Asked Questions
An NLP chatbot uses natural language processing to understand the context and intent behind customer messages, providing more accurate and conversational responses. Unlike rule-based chatbots, NLP bots don’t rely on fixed keywords or patterns and can handle varied phrasing more effectively.
NLP-powered chatbots offer several advantages, such as reducing operational costs, providing 24/7 multilingual support, personalizing customer interactions, and empowering support teams to focus on complex tasks.
Businesses can integrate NLP chatbots with their CRM, e-commerce platforms, or backend systems to enable seamless customer data access and enhanced contextual support. API integrations and custom-built solutions are often used for this purpose.
NLP chatbots leverage machine learning and real-world conversation data to continually learn and refine their responses. This ongoing training ensures they become more accurate, efficient, and adaptive to customer needs over time.
Aaron Jebin is an enthusiastic SAAS technical content writer interested in writing for new and existing technologies, platforms, and tools. With an experience of over 4 years in technical writing, he is keenly focused on developing articles to provide readers with complete solutions to the common problems that arise in the everyday workplace. His writing mostly focused on team building, work ethics, business analysis, project management, automation, AI, customer and employee engagement methodologies. He has an interest in baking cakes and making stained glass art. He is currently honing his drifting skills.
