NLP chatbot is an AI-powered chatbot that enables humans to have natural conversations with a machine and get the results they are looking for in as few steps as possible. This type of chatbot uses natural language processing techniques to make conversations human-like.
In today’s cut-throat competition, businesses constantly seek opportunities to connect with customers in meaningful conversations. Conversational or NLP chatbots are becoming companies’ priority with the increasing need to develop more prominent communication platforms.
In this article, we’ll dive deep into:
- What is an NLP chatbot?
- How does an NLP chatbot work?
- NLP chatbots vs. rule-based chatbots
- How do NLP chatbots help businesses?
- What are the applications of an NLP chatbot?
You can jump right into the section you want to know more about.
What is an NLP Chatbot?
Let’s start by understanding what exactly is NLP. Natural language processing (NLP), in the simplest terms, refers to a behavioural technology that empowers AI to interact with humans using natural language. The aim is to read, decipher, understand, and analyse human languages to create valuable outcomes. It also means users don’t have to learn programming languages such as Python and Java to use a chatbot.
So why is NLP used in chatbots?
NLP is based on the deep learning concept that empowers the machine (in this case, chatbot) to decode the meaning from the input and respond to the user. The NLP engine uses natural language understanding to analyse the utterance (user query) and derive the intent to identify what the user wants the machine to do. Using natural language generation (NLG), the machine then responds to the user in a language that the user can understand.
Key terms to note
- Natural Language Understanding (NLU): a subfield of NLP that analyses sentences to understand the intent behind them. It decodes the message from sentence structure, phrases, idioms, contextual words, etc.
- Natural Language Generation (NLG): a subfield of NLP that generates a logical response from a machine that a user can understand.
With its intelligence, the key feature of the NLP chatbot is that one can ask questions in different ways rather than just using the keywords offered by the chatbot. Companies can train their AI-powered chatbot to understand a range of questions. For the training, companies use queries received from customers in previous conversations or call centre logs.
Today, NLP chatbots are highly accurate and are capable of having unique 1-1 conversations. No wonder, Adweek’s study suggests that 68% of customers prefer conversational chatbots with personalised marketing and NLP chatbots as the best way to stay connected with the business.
How does an NLP chatbot work?
To understand human language, NLP breaks down a sentence into multiple parts to gather context and meaning. The NLP engine of the chatbot uses various models to narrow the scope and reach the final understanding. We look at them below
1. Domain: first, the input is classified into a pre-set group of conversations for a particular domain. For example, an eCommerce domain will use the words buy, cart, refund, order, status, etc. In contrast, the real estate domain will use slots, booking, rent, villas, apartments, etc.
2. Intent: what the input means is determined by identifying the intent. For example, when a user says ‘check status of my order’, the intent is to track the user’s order.
3. Entity: at this step, the NLP tries to gather more information about the intent to understand the sentence’s meaning accurately. For example, in the sentence ‘what is the weather in Bangalore today?’, the intent is to know the weather forecast and entity is ‘Bangalore’ and ‘today’.
4. Role/Context: Sometimes, the entities alone can be confusing. So the NLP engine adds another model to differentiate different meanings of an entity based on context. For example, ‘business hours’ can further be classified as closed or open.
Training AI with the help of entity and intent while implementing the NLP in the chatbots is highly helpful. By understanding the nature of the statement in the user response, the platform differentiates the statements and adjusts the conversation.
Key differences between NLP chatbot and rule-based chatbot
The rule-based chatbot is one of the modest and primary types of chatbot that communicates with users on some pre-set rules. It matches KW in the user query to one in its database. It follows a set rule and if there’s any deviation from that, it will repeat the same text again and again. However, customers want a more interactive chatbot to engage with a business.
NLP chatbots give users more room for open-ended conversations. These chatbots understand users’ queries based on context, assess them, and respond to users in natural language. With machine learning algorithms, these chatbots learn from the users’ responses and improve their assessment in the future.
According to Gartner, 15% of consumer interactions will be handled entirely by the AI, including in the chatbot. While both the chatbots operate in quite a similar manner, we list the key differences between the NLP chatbot and the rule-based chatbot below:
|The NLP one is based on understanding the context
|The rule-based chatbot function on keywords
|Offers scalability in the type of questions answered
|Scalability depends on how thoroughly the script has been added
|Operate on various questions explicitly shared by the customers
|It depends on the predefined set of rules
|Resolve the queries even when there are spelling mistakes or partial sentences
|Finds it hard to identify the words not listed in the keywords or with a slight deviation
|Can speak in multiple languages based on customer’s need
|Respond in only one language
Benefits of Chatbots using NLP
The key benefits or utility of chatbots using NLP are:
1. Natural conversations
While conversing with customer support, people wish to have a natural, human-like conversation rather than a robotic one. While the rule-based chatbot is excellent for direct questions, they lack the human touch. Using an NLP chatbot, a business can offer natural conversations resulting in better interpretation and customer experience.
2. More automation
One of the limitations of rule-based chatbots is their ability to answer a wide variety of questions. By and large, it can answer yes or no and simple direct-answer questions. Companies can automate slightly more complicated queries using NLP chatbots. This is possible because the NLP engine can decipher meaning out of unstructured data (data that the AI is not trained on). This gives them the freedom to automate more use cases and reduce the load on agents.
3. Contextual engagement
One of the customers’ biggest concerns is getting transferred from one agent to another to resolve the query. Or ending up with a live agent without any context. This leaves the customer with a bitter experience.
NLP chatbot identifies contextual words from a user’s query and responds to the user in view of the background information. And if the NLP chatbot cannot answer the question on its own, it can gather the user’s input and share that data with the agent. Either way, context is carried forward and the users avoid repeating their queries.
4. Improved user experience
If a user gets the information they want instantly and in fewer steps, they are going to leave with a satisfying experience. An NLP chatbot delivers on these points. Over and above, it elevates the user experience by interacting with the user in a similar fashion to how they would with a human agent, earning the company many brownie points.
5. Time and cost-efficient
NLP chatbots are quick with their responses and resolution. And the more they interact with the users, the better and more efficient they get. On top of that, NLP chatbots automate more use cases, which helps in reducing the operational costs involved in those activities. What’s more, the agents are freed from monotonous tasks, allowing them to work on more profitable projects.
NLP chatbot use cases
The best NLP chatbot examples or use cases are
1. Banking and Insurance
The customer’s most common questions w.r.t. to the banking and insurance industry are linked to the balance enquiry, account status, refund status, and card-associated queries. What’s challenging with banking and insurance queries is the urgency. And urgency leads to typos, incomplete sentences, etc. NLP chatbots can identify the urgency and hand over the chat to agents and decipher the meaning of the utterances despite the typos and incomplete sentences.
2. Customer Care
One of the most common use cases of chatbots is for customer support. AI-powered chatbots work based on intent detection that facilitates better customer service by resolving queries focusing on the customer’s need and status.
If there is one industry that needs to avoid misunderstanding, it’s healthcare. NLP chatbot’s ability to converse with users in natural language allows them to accurately identify the intent and also convey the right response. Mainly used to secure feedback from the patient, maintain the review, and assist in the root cause analysis, NLP chatbots help the healthcare industry perform efficiently.
4. Hospitality and Travel
NLP chatbots are pretty beneficial for the hospitality and travel industry. With ever-changing schedules and bookings, knowing the context is important. Chatbots are the go-to solution when users want more information about their schedule, flight status, and booking confirmation. It also offers faster customer service which is crucial for this industry.
5. Supply Chain and Logistics
NLP can comprehend, extract and translate valuable insights from any input given to it, growing above the linguistics barriers and understanding the dynamic working of the processes. Offering suggestions by analysing the data, NLP plays a pivotal role in the success of the logistics channel.
NLP chatbot: a win for customers and companies
NLP chatbots can help to improve business processes and overall business productivity. AI-powered chatbots have a reasonable level of understanding by focusing on technological advancements to stay in the competitive environment and ensure better engagement and lead generation.
The NLP market is expected to reach $26.4 billion by 2024 from $10.2 billion in 2019, at a CAGR of 21%. Also, businesses enjoy a higher rate of success when implementing conversational AI. Statistically, when using the bot, 72% of customers developed higher trust in business, 71% shared positive feedback with others, and 64% offered better ratings to brands on social media.
If you are looking for effective and efficient natural language processing chatbots, Verloop.io is one of the best partners that offers seamless integration and smartly designed bots for the customer support needs of the business. Though chatbots cannot replace human support, incorporating the NLP technology can provide better assistance by creating human-like interactions as customer relationships are crucial for every business.