Customer Service Glossary

What Is a Chatbot?

A chatbot is an artificial intelligence-powered software agent that processes and simulates conversations (or “chats”) with customers. Chatbots leverage natural language processing (NLP) to communicate with users via computer chats, phone, websites, or messaging apps.

Chatbots vary in complexity. For example, some basic chatbot programs may answer user questions with a short, scripted answer. Other advanced “digital assistants” gather knowledge with ongoing use, programming, and testing. By doing so, they continually evolve to deliver human-like conversations to customers who have submitted a support request.

Why Are Chatbots Important for Customer Service?

Chatbots have become a customer support tool for businesses in virtually every industry. Forbes has even predicted that the chatbot market will reach a staggering $1.25 billion by 2025. Chatbots rely on ongoing testing, programming, and personalization to perform well. The advantages of the AI-powered bots include:

Reduced Costs

Chatbots offer unparalleled scalability, reducing the need for extensive customer service departments, new infrastructure, or increased human capital management. These digital agents can either escalate customer queries when necessary or assist with customer self-help.

Lower Waiting Times

Chatbots provide customers with immediate responses, allowing them to resolve questions or concerns without waiting for the next available agent. This is particularly helpful with post-sale inquiries.

24/7 Access

Unlike customer support representatives, chatbots can function 24 hours a day, seven days a week. With this technology, customers no longer have to wait until the office opens to request order information or purchasing assistance.

Automated Lead Qualification

Some businesses use chatbots to prequalify sales leads by moving customers through an automated funnel. In addition, chatbot engagement increases conversion rates through AI-powered presale support and other automated digital marketing functions.

Enhanced Engagement

Finally, chatbots can initiate a personalized conversation with customers based on shopper data and previous usage. The automation then uses its knowledge to improve the customer experience through customized digital solutions. While chatbots don’t always nail personalization or empathy the way a human can, the technology is improving.

Types of Chatbots

As AI technology continues to evolve, chatbots have become smarter and more life-like. As a result, businesses can now choose from countless iterations within two broad categories of bots:

Task-Oriented Chatbots

Also known as “declarative” or “rule-based” chatbots, task-oriented chatbots follow a decision tree to fulfill customer support requests within a set of defined guidelines. They typically serve one purpose and have limited, structured, and specific interactions with customers. With this in mind, task-oriented chatbots are well suited to essential customer support functions, such as answering FAQs.

Predictive Chatbots

Sometimes called “data-driven” or “conversational” chatbots, predictive chatbots use natural language understanding (NLU), natural language processing (NLP), and machine learning (ML) to evolve with use. Generally, they use previous customer behavior, extensive knowledge bases, and data-driven predictions to interact in a more natural, conversational way.

How Chatbots Work in Customer Service Departments

Chatbots respond to customer service requests via chat, text, email, or phone call. Requests may vary from yes-or-no questions to lead qualification and preliminary sales support. Regardless of the function, chatbot operations follow two steps:

1. Analyze Requests

After a customer submits a request, chatbots begin to analyze the text based on client intent and relevant keywords. To do so, chatbots utilize AI-driven functions such as NLP, NLU, and artificial intelligence markup language (AIML).

Artificial Intelligence Markup Language (AIML)

AIML allows chatbots to recognize patterns and create entity groupings within a customer service request. After a bot uses pattern matching to categorize the request, it can form a suitable answer.


NLU enables chatbots to comprehend human queries by converting text into data. To produce the data, NLU analyzes three concepts: entities, content, and expectations.


Similar to NLU, NLP converts written or spoken words into data that machines can understand, which a chatbot then uses to form an answer. NLP includes concepts such as sentiment analysis, dependency parsing, entity recognition, and tokenization.

2. Respond to Requests

After analyzing a customer query, chatbots provide a response based on several sources of content, including:

%u25CF     Generic or “canned” responses.

%u25CF     Knowledge bases.

%u25CF     Interaction history.

%u25CF     Data storage.

%u25CF     Interaction with another application.

In some cases, a chatbot may also ask straightforward questions to clarify user requests or redirect customers to a more suitable support channel for further assistance.