What is Chatbot? A Definitive guide

What Is A Chatbot?

A chatbot is a software program designed to simulate a conversation with a human. It receives a message, processes what was said, and responds either by retrieving a pre-written answer, or by generating a response using artificial intelligence.

In other words, a chatbot is a program powered by AI (artificial intelligence) or rules that deliver a human-like chatting experience. No matter what type of chatbot you use (website bot or messenger bot), they all have one goal- take language input from humans, understand what they’re trying to say, and provide correct answers based on that. One of the best parts of using chatbots is that you don’t have to download them to use them. Unlike other applications, they won’t take up space on your phone’s memory. 

According to a recent study, the market size of chatbots will reach $1.3 billion by 2024. It will be a driving force behind all the business communication. Several startups and giant companies like Facebook, Skype, Telegram have adopted conversational bots for providing support to their customers. From this, we can pretty much assume that chatbots are expected to become more mainstream in the future. 

How does a chatbot work?

A chatbot works by following a process that turns your text input into a relevant response. The complexity of that process depends on the type of chatbot — but at a high level, every chatbot follows the same three steps: receive input, process it, and generate a response.

Step 1: Receiving input

When a user types a message to a chatbot, that message is the input. Most chatbots work with text, but more advanced ones also accept voice input, which is then converted to text before processing. 

Step 2: Processing the input

Once the chatbot receives the message, it analyses the intent behind the inquiry, and decides what response from its pre-written answers would be most relevant. This involves breaking down the sentence, identifying the key words and phrases, and matching them to the chatbot's knowledge base or set of programmed rules.

More sophisticated chatbots use a technology called Natural Language Processing (NLP). NLP is a computing technology that allows the chatbot to understand human language. 

For example, "how much does it cost," "what's the price," and "pricing please" are all asking the same question — NLP helps the chatbot recognise that, even though each phrase is different.

Step 3: Generating a response

Once the chatbot has processed the input, it generates a response. 

Based on how chatbots work, there are 2 major types: Rule-based chatbots and AI-based chatbots.

A rule-based chatbot pulls a fixed answer from its database. An AI-powered chatbot may generate a response dynamically, drawing on a broader knowledge base and the context of the conversation so far.

More advanced AI chatbots also learn over time using machine learning (ML). Each conversation adds to their understanding of how people phrase questions, which makes future responses more accurate.

Types of chatbots

Not all chatbots are built the same way. The type of chatbot a business uses depends on the complexity of the conversations it needs to handle and the resources available to build and maintain it. Here are the five main types.

1. Rule-based chatbots

A rule-based chatbot operates on a fixed set of pre-programmed rules. Through the user-interface (UI), it presents users with a menu of options or looks for specific keywords in a message, then retrieves the pre-written answer linked to that input. If a user types something the chatbot has not been programmed to handle, it cannot respond meaningfully.

Rule-based chatbots are straightforward to build and work well for simple, predictable tasks — such as answering frequently asked questions, collecting a name and email address, or guiding a user through a fixed process like booking an appointment.

2. AI-powered chatbots

An AI-powered chatbot uses machine learning and Natural Language Processing to understand the intent behind a message rather than just matching keywords. It can handle a wider range of questions, manage conversations that go in unexpected directions, and improve its responses over time as it processes more conversations.

AI-powered chatbots are better suited for complex tasks — such as troubleshooting a technical issue, handling multi-step support queries, or providing personalised product recommendations based on what a user has said earlier in the conversation.

3. Hybrid chatbots

A hybrid chatbot combines rule-based and AI capabilities. It uses a structured flow for predictable interactions and switches to AI when the conversation becomes more complex or goes outside the expected path. This gives businesses the reliability of rules-based responses where they are needed, and the flexibility of AI where they are not.

Most enterprise chatbots in use today are hybrid — they handle routine queries automatically and escalate to either a more capable AI model or a human agent when the situation requires it.

Explore Echo - A conversational AI chatbot that can resolve upto 60% inquiries without human intervention.

4. Voice chatbots

A voice chatbot can understand spoken input. It first converts speech to text, then processes the message using NLP, and responds — either in text or through a synthesised voice. Voice assistants such as Amazon Alexa, Google Assistant, and Apple Siri are well-known examples of voice chatbots in consumer settings. In business contexts, voice chatbots are used in call centre automation and phone-based customer support.

Phitku is another example of a company that uses voice chatbots in its phone-based customer support. 

Applications of chatbots

Chatbots are used across almost every industry and business function. The common thread is always the same — automating conversations that would otherwise require a human to handle manually. Here are the most common use cases.

1. Customer support

Customer support is the most widespread use of chatbots. A chatbot can answer frequently asked questions, help users troubleshoot common issues, track orders, process returns, and escalate complex queries to a human agent when needed. Because chatbots are available around the clock, they allow businesses to provide 24/7 support without maintaining a full support team at all hours.

2. Lead generation

Businesses use chatbots on their websites to engage visitors before they leave. A chatbot can ask a visitor what they are looking for, qualify them based on their responses, collect their contact details, and either connect them to a sales representative or direct them to the most relevant product or pricing page. This replaces a passive browsing experience with an active conversation that moves the visitor closer to a decision.

3. Feedback collection

Chatbots make feedback collection significantly less friction-heavy than traditional survey forms. Instead of presenting a respondent with a long list of questions, a chatbot asks one question at a time in a conversational format — which feels more natural and produces higher completion rates. Businesses use chatbots to collect NPS scores, post-purchase feedback, employee satisfaction data, and event feedback.

4. E-commerce and product recommendations

Retail and e-commerce businesses use chatbots to guide customers through the buying process. A chatbot can ask about preferences, budget, and intended use, then recommend the most relevant products — replicating the role of a sales assistant in a physical store. Brands such as H&M and eBay have used chatbots this way to reduce decision fatigue and increase conversion rates.

5. Booking and scheduling

Chatbots are widely used to handle appointment bookings, reservations, and scheduling without requiring human involvement. A user interacts with the chatbot, selects a time slot, provides their details, and receives a confirmation — all within the conversation. This is common in healthcare, hospitality, and professional services.

6. Internal business use

Chatbots are not only customer-facing. Businesses use them internally to handle IT helpdesk queries, answer HR policy questions, onboard new employees, and manage routine administrative tasks. An internal chatbot reduces the volume of repetitive queries that reach support or HR teams, freeing them to focus on higher-value work.

Why are chatbots important for businesses?

Chatbots solve two problems that every customer-facing business has: the cost of handling large volumes of repetitive conversations, and the expectation that support is available at any hour. A chatbot addresses both without requiring a proportional increase in headcount.

1. They reduce the cost of customer support

A significant portion of customer support queries are repetitive — the same questions about pricing, delivery times, account access, and product features asked hundreds of times a day. A chatbot handles these queries automatically, at any scale, without adding to your support team's workload. This frees your people to focus on the complex, high-stakes conversations that actually require judgment and empathy.

2. They are available around the clock

Customers do not limit their questions to business hours. A chatbot is available 24 hours a day, seven days a week, without breaks or shift changes. For businesses with customers across multiple time zones, this is particularly valuable.

3. They improve response times

The speed of a first response has a direct impact on customer satisfaction. A chatbot responds instantly — there is no queue, no hold music, and no wait for an agent to become available. 

For straightforward queries, the customer gets their answer in seconds. For more complex ones, the chatbot can collect the relevant details upfront before routing to a human, which reduces the time the agent needs to spend on context-gathering.

With AI in the mix, conversational AI agents are making it more easier for companies to resolve inquiries automatically. 

4. They scale without friction

A human support team has a fixed capacity. During a product launch, a sale, or an unexpected service disruption, query volumes can spike significantly — and a team that is sized for normal volumes will struggle. A chatbot handles spikes in volume without any change in response time or quality. It scales up and down automatically based on demand.

5. They collect useful data

Every conversation a chatbot handles is a data point. Over time, the questions customers ask most frequently, the issues they raise most often, and the points in the journey where they get stuck all become visible. This data is useful beyond the support function — it informs product decisions, content strategy, and the design of the customer experience more broadly. 

Key features of an effective chatbot

The features a chatbot has determines what it can handle, how well it handles it, and how useful it is to the business over time. Here are the features that matter most.

Natural Language Processing (NLP)

NLP is what allows a chatbot to understand the intent behind a text or voice command. A chatbot without NLP can only respond to exact matches or pre-set menu options. 

Whereas, a chatbot with NLP can handle the same question phrased in ten different ways and still produce the right response. For any chatbot that needs to handle open-ended conversation, NLP is non-negotiable.

With NLP, chatbots take a keyword-recognition approach. For example, "I'd like to request for a refund" can be interpreted by the chatbot the same as "where is my refund?" using keyword-recognition, but with an extra layer of sentiment detection. The bot can understand that the second phrase sounds more angrier or frustrated than the first phrase.

Multi-channel deployment

Customers interact with businesses across multiple channels — a website, a mobile app, WhatsApp, Facebook Messenger, and email. An effective chatbot can be deployed across all of these from a single platform, so the experience is consistent regardless of where the conversation starts. A chatbot that only works on your website leaves a significant portion of your customer interactions unaddressed.

Seamless handoff to a human agent

A chatbot should know its limits. When a query is too complex, too sensitive, or too important to handle automatically, the chatbot should be able to transfer the conversation to a human agent — along with the full context of what was already discussed. 

A handoff that forces the customer to repeat themselves from the beginning is worse than no chatbot at all. 

A lot of companies also faced the issue of handoffs without context retention. This causes customers to repeat information to the agent when they'd already done so to the chatbot. That's a frustrating process no customer should ever go through. It can also reduce customer satisfaction for resolutions. 

Conversation history and context retention

An effective chatbot remembers what was said earlier in a conversation and uses that context to inform its responses. If a customer says "I want to return the blue jacket I ordered last week," the chatbot should not ask "what would you like to return?" three messages later. Context retention is what makes a conversation feel coherent rather than disjointed.

Integration with existing tools

A chatbot that operates in isolation from the rest of your technology stack has limited value. The most useful chatbots integrate with your CRM, helpdesk, e-commerce platform, and analytics tools — so they can pull in customer data, log conversations, update records, and trigger workflows automatically. Integration depth is one of the most important practical considerations when evaluating a chatbot platform.

Analytics and reporting

A chatbot should tell you more than just how many conversations it handled. Effective analytics cover resolution rate, handoff rate, the most common queries, drop-off points in the conversation flow, and customer satisfaction scores. This data is what allows you to improve the chatbot over time and identify gaps in your broader customer experience.

No-code or low-code setup

Building and maintaining a chatbot should not require a developer for every change. The best chatbot platforms allow non-technical teams to create conversation flows, update responses, and add new use cases through a visual interface. This is particularly important for marketing and customer success teams who need to move quickly without depending on engineering resources.

Multilingual support

If your business operates across multiple markets, your chatbot needs to communicate in the languages your customers use. Multilingual support allows a single chatbot to serve customers in different regions without requiring a separate build for each language. This is increasingly important as businesses expand globally and customer expectations for localised experiences rise.

Data security

Data security is another crucial must-have feature for any good AI chatbot. That’s why it’s important to use a bot that provides data security. According to a 2019 report, an average of 4,800 websites is hit by form-jacking attacks per month. So chatbots should have the features of password protection and end-to-end encryption to prevent such data breaches. 

The difference between chatbots and conversational AI

The terms "chatbot" and "conversational AI" are often used interchangeably, but they describe different things. Understanding the distinction helps you make a more informed decision about what your business actually needs.

A chatbot is a tool. It is a software program designed to simulate conversation, typically within a defined scope — answering FAQs, collecting information, or guiding a user through a fixed process. Most chatbots operate within boundaries set by the person who built them. They handle what they were designed to handle, and little else.

Conversational AI is the underlying technology that makes more advanced chatbots possible. It refers to the combination of Natural Language Processing, machine learning, and dialogue management that allows a system to understand intent, maintain context across a conversation, and improve its responses over time. Conversational AI is what separates a chatbot that matches keywords from one that understands what you are actually asking.

The simplest way to think about it: all conversational AI systems can be described as chatbots, but not all chatbots use conversational AI. A rule-based chatbot that presents a menu of options and retrieves pre-written answers is a chatbot — but it uses no conversational AI. A system that understands "I need to change the address on my order from last Tuesday" and acts on it without being given a specific command is powered by conversational AI.

As conversational AI matures, some systems also layer in voice responses using tools like Async Voice API, allowing chatbots to speak replies instead of relying only on text, making interactions feel more natural and accessible.

Explore which to choose: A chatbot or a conversational AI agent?

Automate most of your resolutions with Echo

Chatbots have moved well past the novelty stage. They are now a practical tool that businesses of every size use to handle support at scale, collect feedback, generate leads, and automate conversations that would otherwise require human time.

The type of chatbot you need depends on what you are trying to accomplish. A rule-based chatbot is sufficient for simple, predictable tasks. An AI-powered or hybrid chatbot is better suited for complex, open-ended conversations. The key is matching the tool to the actual complexity of your use case — not choosing the most sophisticated option by default.

SurveySparrow's conversational chatbot, Echo - lets you collect feedback, qualify leads, and engage customers in a format that feels natural rather than transactional. Built on a no-code platform, it deploys across your website, app, and messaging channels without requiring developer involvement.