Let’s face it, the world of chatbots is filled with jargon and acronyms like AI, ML and NLP. We’ve put together this chatbot terminology guide to help you understand what you’re reading.
When you read about chatbots, it can be easy to feel overwhelmed by the sheer variety of technical terms and acronyms that surround the technology.
Sure, you probably have a general understanding of what chatbots do and perhaps even know the programming basics behind them. But suppose you need to look for a chatbot provider or need to have an informed conversation about artificial intelligence (AI) and machine learning. In that case you need to beef up your chatbot vocabulary.
We’ve put together this chatbot terminology guide to save you from having to Google every developer-centric term and technical acronym you come across. This glossary will cover the basic chatbot terms you need to know and will be regularly updated with new entries.
First things first: a chatbot is a computer program that imitates and processes human conversations on websites, apps, and messaging platforms like Facebook Messenger and WhatsApp. They automatically process questions and provide relevant answers without human intervention.
While most people think of chatbots as being limited to written conversation, more advanced software can handle spoken language. These bots are sometimes referred to as voicebots.
Chatbots hold tremendous promise for customer service and stakeholder relations, with Mordor Intelligence projecting the chatbot market to reach $102.29 billion in value by 2026.
An Application Programming Interface (API) is a piece of software that allows two separate applications to interact. Think of it as a bridge that allows two apps to integrate.
3. Artificial intelligence (AI)
At its simplest, artificial intelligence (AI) is the simulation of human intelligence by machines, particularly computer systems.
In chatbot systems, AI enables chatbots to combine rule-based systems (i.e., predefined answers to specific questions) with context — the software analyses the context of conversations and compares them with historical data to provide relevant answers.
In other words, AI-powered chatbots perform messaging tasks that would typically require human intelligence for:
- Decision making
- Speech recognition
- Language translation
4. Chat widgets
Chat widgets are customisable chat windows that you can instantly deploy to your website. The chat widget is the primary interface linking your website’s visitors and your chatbot. Here’s an example of a chat widget on the Futr homepage.
5. Conversational AI
Conversational AI is the set of AI-based technologies that enable automated messaging and speech-enabled software to provide human-like conversations between computers and humans.
Conversational AI-powered chatbots use natural language processing and machine learning (both branches of AI) to understand customers and carry natural, human-like conversations.
6. Digital channel or conversational channel
Throughout our site, you’ll see us mention “deploying live chat and chatbots to your digital channels”.
These digital channels or conversational channels are places where we can launch your chat platform to interact with your customers or stakeholders. This could be anything from your website, app, Facebook Messenger, WhatsApp, Telegram or Slack, among others.
7. Machine learning
Machine learning (ML) is a technology under AI that uses algorithms (a set of rules or procedures for solving a problem) to identify and remember patterns in data — hence the word “learning”.
In the context of chatbots, machine learning allows bots to understand patterns in human language and better understand the context and intent of each keyword and query. The more data the chatbot parses, the more capable it becomes at conversing with humans.
8. Natural language processing (NLP)
Natural language processing (NLP) is another technology under AI, focused on understanding text and spoken words the way humans do. Together with machine learning, NLP combines rule-based models of human language with historical data to understand the intent and context of certain words and statements.
NLP also allows chatbots to translate text and speech from one language to another, as seen in platforms like Google Translate.
9. Natural language understanding (NLU)
Natural language understanding (NLU) is a subfield of NLP that seeks to understand human language. The difference between NLP and NLU boils down to objectives:
- NLP seeks to facilitate human-like communication between humans and computer systems.
- NLU focuses on a computer system’s ability to understand the context of human language. It seeks to decipher and rearrange unstructured language data so machines can understand them.
Think of it this way: before a chatbot can process a query and respond to it with a relevant answer, it must first use NLU to understand the query’s specific attributes.
10. Self-service or self-serve
Self-service/self-serve is a user experience (UX) feature that allows customers to complete an action or task without any assistance from your customer service agents.
Think of a petrol or charging station for cars, for example. These stations provide instructions on filling your tank or charging your electric vehicle — a relatively simple and pain-free process.
The same principle applies in the world of live chat and chatbots. By providing a list of your services and answers to frequently asked questions within the chat, customers can help themselves without speaking to your live agents.
Here’s an example of our chat widget signposting users to two service options: speaking to a Futr representative or booking a demo of our platform.
Self-service optimises your customer experience by giving users what they want when they need it. As counterintuitive as it sounds, many of your customers don’t necessarily want to speak to your company representatives. In fact, 67% of customers prefer using self-service options over speaking with a live agent.
11. Sentiment analysis
Sentiment analysis is a field under computer science that uses machine learning and NLP to understand the tone, intent and context of a text-based message or spoken language. It allows a chatbot to detect a user’s mood, largely by deciphering clues in their sentence structure and the words they use, connecting them to emotions like:
Sentiment analysis also enables chatbots to escalate complex conversations and queries by dissatisfied customers to live agents. If the chatbot detects words like “I’m tired,” “this is the nth time” and “I need…now”, it can understand the intent of the message, sense its urgency and immediately forward the conversation to your customer service team.
Sentiment analysis also plays a vital role in getting an overview of your customers’ overall mood based on common queries and the type of words they frequently use.
12. Software integrations
Software integration is the process of connecting two or more applications to work alongside each other. For example, with integration on Facebook Messenger, we can deploy your chatbot and live chat solution to your official Facebook page.
A more advanced example of software integration is enabling digital payments through chat. With Futr, for example, Stripe or PayPal integration allows users to pay for products and services without leaving the chat.
You can explore our list of 200+ live chat and chatbot integrations.
When in doubt, get in touch with the Futr team
This chatbot terminology guide covers the basic terms you’ll encounter in the world of chatbots. If you need to learn more about the technical nitty-gritty of bots and AI, don’t hesitate to contact the Futr team.
You can also follow the Futr blog to get insights on the landscape of chatbots and their potential use cases.