What is NLP: How Chatbots Understand Your Customers

When a customer types "I want to know the price of the basic plan" to a chatbot and receives an accurate response with the requested information, behind that seemingly simple interaction is a sophisticated technology working in milliseconds. That technology is called NLP, or Natural Language Processing, and it is the building block that allows modern chatbots to understand what your customers really mean.

In this article we explain in an accessible way what NLP is, how it works, what are its main components and how this technology has evolved to turn chatbots into real intelligent assistants capable of holding natural conversations with your customers.

What is NLP or Natural Language Processing?

NLP (Natural Language Processing) is a branch of artificial intelligence that focuses on the interaction between computers and human language. Its main objective is to enable machines to read, interpret and generate text in a manner similar to that of a person.

In simple terms, NLP is the bridge between the language we humans use every day and the language computers understand. While we communicate with words, colloquial expressions, implicit context and even misspellings, computers process structured data and numbers. NLP translates between these two worlds.

Why is NLP important for business?

NLP is the technology that makes it possible:

• A chatbot understands a customer's question no matter how you ask it.
• A customer service system that automatically sorts messages by urgency
• An analytical tool to detect whether a customer is satisfied or frustrated
• A virtual assistant schedules appointments by interpreting phrases such as "Do you have availability on Tuesday afternoon?"

Without NLP, chatbots would only be able to respond to exact, predefined commands, making the user experience frustrating and limited.

Main Components of NLP

Natural Language Processing is not a single technology, but a set of processes that work together to analyze and understand text. These are the fundamental components:

Tokenization

Tokenization is the first processing step. It consists of dividing a text into smaller units called "tokens", which are usually individual words or sub-words.

Practical example:

The customer's message: "Hello, I would like to check the price of the premium plan".

Tokenized in: ["Hello", ",", "I want", "consult", "the", "price", "from", "plan", "premium"].

This step seems trivial, but it is critical so that the system can parse each element of the message separately and in context. Tokenization also handles challenges such as contractions, punctuation marks and compound words that are common in Spanish.

Intent Recognition (Intent Recognition)

Intent recognition is the process by which the system identifies what the user wants to achieve with your message. That is, it determines the purpose or desired action behind the words.

Practical example:

All these messages have the same intention ("consult_price"):

• "How much does the basic plan cost?"
• "I want to know the prices."
• "Could you pass me the rates?"
• "How much does the service cost?"
• "Prices plis"

Although each message is worded differently, uses different words and even has informalities, a good NLP system identifies that the intention behind all of them is the same: the customer wants to know prices.

3. Entity Extraction (Entity Extraction)

Entity extraction identifies and classifies the specific data mentioned in a message. Entities are the concrete elements such as names, dates, quantities, products or locations.

Practical example:

Message: "I would like to schedule an appointment for Tuesday 15th at 3:00 pm with Dr. Martinez".

Extracted entities:

• Action: schedule an appointment
• Date: tuesday 15
• Time: 3:00 PM
• Person: Dr. Martinez

Entity extraction allows the chatbot to not only understand that the customer wants to schedule an appointment, but also to capture all the details necessary to complete that action without having to ask additional questions.

4. Sentiment Analysis

Sentiment analysis evaluates the emotional tone of a message, generally classifying it as positive, negative or neutral. This capability is crucial in customer service to detect frustration, urgency or satisfaction.

Practical example:

• "Excellent service, thank you very much for the help."→ Positive sentiment
• "I need information about your plans" → Neutral sentiment
• "I've been waiting for three days and no one answers me, this is unacceptable." → Negative sentiment

When the system detects negative sentiment, it can prioritize that message, escalate the conversation to a human agent or adjust its response tone to be more empathetic and responsive.

5. Contextual Understanding

Beyond the individual components, modern NLP systems can maintain context throughout an entire conversation. This means that the chatbot remembers what was said before and can interpret ambiguous messages based on the history.

Practical example:

• Customer: "Do you have the product in blue?"
• Chatbot: "Yes, we have model X available in blue."
• Customer: "And in red?"

A system with contextual understanding understands that "And in red?" refers to the same product being discussed, without the need for the customer to repeat the entire question.

The Evolution of NLP: From Keywords to LLMs

The way chatbots understand language has undergone a radical transformation in recent years. Understanding this evolution helps to understand why today's chatbots are so superior to their predecessors.

First Generation: Keyword Matching

The first chatbots worked with simple rules: if the customer's message contained certain keywords, the system would respond with a predefined answer.

Limitations:

• If the client used synonyms or different expressions, the system did not understand.
• There was no real understanding of the context and intent.
• The answers were rigid and often irrelevant.
• The user experience was frustrating and robotic.

Second Generation: Classical NLP Models

With the advent of machine learning, chatbots began to use statistical models trained on data from real conversations. These models could better generalize and understand variations in the way people ask questions.

Improvements:

• Ability to understand synonyms and variations
• Classification of intentions with greater precision
• Basic entity extraction
• Better handling of spelling errors

Limitations that persisted:

• They needed large amounts of specific training data.
• Response generation was still limited
• Difficulty with complex or multi-intentional questions
• Each new topic required manual retraining.

Third Generation: LLMs and Advanced NLP (Current Era)

Large Language Models (LLMs) such as GPT-5 and other state-of-the-art models represent a quantum leap in NLP capabilities. These models have been trained with massive amounts of text and can understand and generate language with unprecedented accuracy.

Current capabilities:

• Deep understanding of context and intent, even with ambiguities
• Generation of natural, coherent responses that do not sound robotic
• Reasoning and problem-solving skills
• Multiple language proficiency without language-specific training
• Understanding of colloquial expressions, regionalisms and jargon
• Ability to follow complex instructions and maintain consistent personality

How Chatbots Use NLP to Understand Messages

When a customer sends a message to an advanced NLP-powered chatbot, complete processing occurs in a matter of milliseconds:

1. Reception and preprocessing: The message arrives and is cleaned up (characters are standardized, emojis are handled, obvious errors are corrected).

2. Tokenization and analysis: The text is divided into tokens and the grammatical structure is analyzed.

3. Identification of intent: The system determines what the customer wants to accomplish (buy, inquire, complain, schedule, etc.).

4. Entity extraction: The specific data mentioned (products, dates, quantities, names) are identified.

5. Sentiment analysis: Emotional tone is assessed to adjust the response appropriately.

6. Context consultation: The conversation history is reviewed for consistency.

7. Response generation: With all this information, the system generates a natural and relevant response.

8. Validation: The response is checked against the business policies and knowledge base before being sent.

Practical Examples of NLP in Customer Service

Example 1: Product inquiry with informal language

Customer: "hey, do you have that new samsung cell phone? the one with the good camera".

NLP process:

• Intention: check product availability
• Entities: brand (Samsung), feature (high quality camera), condition (recent model)
• Sentiment: neutral/positive (buying interest)

Chatbot response: "Hi! Yes, we have available the latest Samsung models with excellent camera. I can show you the Galaxy S25 Ultra which has 200MP camera, would you like to see the specs and price?"

Example 2: Complaint with multiple problems

Customer: "I bought a product a week ago, it still hasn't arrived and they charged me double. I need this resolved now"

NLP process:

• Multiple intentions: shipment tracking + duplicate collection claim
• Entities: time (one week), problem 1 (delay of shipment), problem 2 (double charging)
• Feeling: negative (frustration, urgency)

Chatbot response: "I understand your frustration and I am very sorry for the inconvenience. I will help you with both issues. Let me check the status of your shipment and review the duplicate charge. Could you share your order number with me so I can locate your purchase quickly?"

Example 3: Natural language scheduling

Customer: "I need an appointment with the dentist, if it can be next week in the morning, all the better."

NLP process:

• Intention: to schedule an appointment
• Entities: specialty (dentist), time preference (next week), time preference (morning)
• Feeling: neutral

Chatbot response: "I would be happy to help you schedule your dental appointment. I have availability next week on Tuesday and Thursday mornings, which of these days works best for you. The times available are 9:00 AM, 10:00 AM and 11:30 AM."

How Aurora Inbox Uses Advanced NLP

Aurora Inbox integrates state-of-the-art NLP technology into its artificial intelligence agents, allowing chatbots to truly understand what your customers want to communicate, no matter how they express it.

Native Spanish Comprehension

Unlike many platforms that were designed primarily for English and then translated, Aurora Inbox is optimized to understand the particularities of Latin American Spanish:

• Regionalisms and idioms: Understands local expressions from different Spanish-speaking countries
• Spelling variations: Correct processing of messages without accents, with abbreviations or common errors
• Informal language: Correctly interpret Spanglish, emojis and colloquial expressions used by customers on WhatsApp.
• Cultural context: Understands cultural references and forms of communication typical of Latin America.

NLP Enhanced with RAG (Recovery Augmented Generation)

Aurora Inbox agents combine advanced NLP with RAG technology, which means that they not only understand the customer's message, but also search for the precise answer in your company's knowledge base:

• Company documents, catalogs and manuals
• Return, warranty and shipping policies
• Frequently asked questions and approved answers
• Updated information on products and services

Sentiment Analysis for Intelligent Scaling

Aurora Inbox uses real-time sentiment analysis to detect when a customer is frustrated or when the query requires human intervention. The Human-in-the-Loop system automatically transfers the conversation to a human agent with full context, ensuring that the customer does not have to repeat their problem.

Unified Multi-Channel Processing

Aurora Inbox's NLP engine works consistently across WhatsApp, Facebook Messenger and TikTok, maintaining the same quality of comprehension regardless of which channel the client writes through.

Benefits of Advanced NLP for your Business

Implementing an advanced NLP chatbot like Aurora Inbox generates tangible benefits:

• Reduction of the 70% in response time: Customers get immediate answers 24 hours a day
• Increased customer satisfaction: Responses are relevant and natural, not robotic.
• Lower operating load: Human team focuses on complex cases while chatbot solves frequent queries
• Scalability without linear cost: The chatbot can handle hundreds of simultaneous conversations.
• Consistency in communication: All responses follow the company's policies and tone.

Frequently Asked Questions about NLP and Chatbots

What is the difference between NLP and a rule-based chatbot?

A rule-based chatbot works with predefined "if the customer says X, respond Y" flows. It can only handle scenarios that were explicitly programmed. In contrast, a chatbot with NLP understands the intent behind the message no matter how it is formulated, can handle infinite variations of the same question, and generates contextual and natural responses. NLP allows the chatbot to "understand" the language instead of simply looking for exact matches.

Does NLP work as well in Spanish as it does in English?

Historically, NLP technologies were more developed for English. However, current language models (LLMs) have excellent support for Spanish. Platforms like Aurora Inbox are specifically optimized for Latin American Spanish, understanding regionalisms, idioms and the particularities of communication in each country. The key is to choose a platform that has been designed considering Spanish as the primary language, not as a secondary translation.

How accurate is NLP in understanding misspelled messages?

Modern NLP systems are highly tolerant to misspellings, abbreviations and informal language. This is especially important in channels like WhatsApp where clients write fast, use abbreviations ("xq" instead of "porque", "tmb" instead of "tambien") and frequently omit accents. A good NLP system like Aurora Inbox can correctly interpret these messages without losing accuracy in understanding the client's intention.

Can NLP understand voice messages or only text messages?

NLP works primarily with text. However, many modern platforms combine NLP with Speech-to-Text technology to process voice messages. The audio is automatically transcribed to text and then the NLP engine processes that text in the same way it would process a written message. Aurora Inbox supports the processing of voice notes in WhatsApp, transcribing and parsing the content automatically.

How do I know if my current chatbot has good NLP?

There are several signs that your chatbot has poor NLP: it answers "I didn't understand your question" frequently, gives irrelevant answers when the customer uses synonyms or informal expressions, cannot handle questions with multiple intentions, and requires the user to follow a specific format to be understood. A chatbot with good NLP should understand questions phrased in different ways, handle informal language, and maintain the context of the conversation across multiple messages.

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NLP is the technology that makes it possible for chatbots to go from being simple decision trees to true intelligent assistants. If you want to offer your customers an automated customer service experience that really understands what they need, you need a chatbot with advanced NLP.

Want to see how Aurora Inbox NLP understands your customers? Try our platform and discover how our AI agents understand Spanish messages accurately, no matter how your customers formulate them.