A computer program anybody can talk to with normal language.
No matter what type of chatbot it is, they all have a similar purpose — to take regular human language input, understand what is being said and to provide a relevant, correct answer based on the knowledge it has. Chatbots excel at completing repetitive tasks and work around the clock. They can work alone or alongside humans, and are effective at completing 60–90% of an average human team’s workload, depending on the use case. They are also very effective at lead generation, as a conversational company wiki, for first-stage interviews in HR, and much more. We wrote an extensive guide to 25 Chatbot Use Cases to help you find the use case that matches your needs.
What Types of Chatbot Are Available?
There are two main types of chatbot. Button Bots and Conversational Chatbots.
Button Bots are based on predefined buttons and lead the user down a garden path with those button choices. You cannot talk to a button bot.
Conversational Chatbots understand user input. So a user should be able to type in a question and get a relevant answer from the chatbot. There are sub-types of Conversational Chatbot, which are covered more extensively in “How Do Chatbots Work”. Conversational Chatbots can, and generally do, use buttons as a backup function.
How Do Chatbots Work?
Under the categories of Conversational Chatbots and Button Bots, there are sub-variants of chatbot. Although how they work might change radically (and affect their overall quality), all chatbots have two basic high-level functions: “understanding” and “answering”.
Familiarizing yourself with these two functions is generally the most important factor when choosing a chatbot for your brand, as they dictate how much human work is necessary to maintain a chatbot.
A chatbot has to understand what you want. It can do this in four ways.
This is the most basic way a chatbot can understand intent. The chatbot builder puts buttons into the chatbot as question prompts for the user.
button bots don’t need to know or process any language, so they can work in any language they’re programmed for.
Chatbot builders can create very strict user flows for the chatbot. In other words, when you use a button bot, you are limited to the questions you can ask based on what the chatbot builder allows you to ask at any given moment.
No User Input
With more advanced understanding methods, chatbots can learn more easily. When a user gives them a question they don’t know the answer to, the chatbot builders have the option to add the answer. Buttons bots alone won’t give unanswered message data, so you can’t learn what your users want to know through button bot data.
Note: Button Bots can be incorporated as a feature in more advanced conversational AI chatbots. This useful feature can be used to confirm user intent at any time, when the AI is unsure or wants clarification. Keyword Rule Chatbot. Keyword Rule Chatbots take typed questions from the user and generate a response based on whether they use a certain keyword or not.
Chatbots that use keyword recognition rules are technically AI Chatbots, as they use a simple form of Natural Language Processing (NLP).
Users can type in their questions and get a response immediately, they don’t need to go through multiple clicks, like in button bots. Chatbot Builders get user feedback in the form of “Unanswered Questions”. This lets them improve the chatbot over time based on real user intent.
Keyword Recognising Chatbots depend a lot on the NLP engine that runs them. If it can’t recognise synonyms or compounds of keywords, then the user won’t get their answers if the human chatbot builder hasn’t considered these keywords for the chatbot
Intent-Based AI Chatbot
This combines NLP and Natural Language Understanding (NLU) to figure out the intent in the user’s question.
The difference between basic NLP and NLU can be shown in this example:
George asks the chatbot, “Where is my car?”, but the chatbot has only been taught to understand that question if the word “automobile” is used. So it cannot understand the question.
The chatbot has been taught the phrase, “Where is my automobile” and knows the answer to that. But George asks: “Where can I find my car?”. Even though the Intent-Based AI chatbot hasn’t been taught this phrase, it will see that George’s intent is the same and still give the right answer.
Autonomous AI Chatbots
This chatbot follows no rules, except for the ones it makes up for itself. This is the closest to the science-fiction AI most people imagine. It takes any information it can get from anywhere and creates questions and answers, based on its experience.
This is a baby in the field of AI, and publicly released versions of autonomous AI chatbot technology have been clear failures.
You don’t need people to train it.
You don’t need people to train it
In order to answer a question, it must be paired with an answer. All chatbots function on question-answer pairs. How they match questions to those answers differs a lot.
With a button bot, the button “questions” you present to users are paired with specific answers. So your chatbot builder has prepared a very strict garden path for the user to follow, and there is no room for unexpected questions or wrong answers.
Intent-Based AI Chatbot and Keyword Rule Chatbot
We bulk these together because the principle is very similar, but the execution is somewhat different.
Both Intent-Based AI Chatbots and Keyword Rule Chatbots give users an answer based on the answer criteria. With both forms of chatbots, you can use negative keywords to rule out certain answers. For example:
Mary asks about credit cards on a banking website. If she asks, “Where can I get a credit card statement?”, both types of chatbot should direct her to her credit card statement.
If she asks, “Where can I get a credit card?”, this clearly has a different intent, so we use negative keywords to make sure she’s directed to a credit card application, rather than getting a credit card statement.
For Intent-Based AI Chatbots, detecting this sort of intent is usually not difficult compared to a Keyword Rule Chatbot, but it’s an extra failsafe to make sure the user gets the correct information, every time.
Autonomous AI Chatbots
This will try to create an answer based on previous experience and data. It’s based entirely on what the AI thinks what the user intent is, with no failsafe like rule-based chatbots.
When the chatbot works without human agents as a backup, it automates conversations. This is often the case when the chatbot is providing customer service outside of office hours.
Customer service chatbots are taught to perform slightly differently outside of office hours. When it knows that there is no human agent back-up.
If the chatbot knows the answer to the question, then all is good. If it doesn’t know the answer to the question, it will apologize to the customer and offer alternative options like:
- leaving their details for a callback
- pointing the customer to knowledge articles related to their question for assisted self-service
- opening a ticket in the service desk’s ticketing system with details of the issue
- asking the customer to check back at a stated time when there is sure to be an agent online
Can I Build and Maintain AI Chatbots Myself?
In the past, AI chatbots were traditionally built by coders and developers hired by consultants, because it required more technical skills to build the chatbot. Some vendors still operate this way.
Language models are how well a chatbot knows any particular language. They are built by compiling huge amounts of written data, feeding it to the AI, and examining how it learns and understands the language over time. This is a form of machine learning, which is a branch of AI.
The more data the AI learns, the more accurate it is likely to be, like when your teacher told you to read more books to broaden your vocabulary in high school. It’s a similar principle.
English language models have seen the most development, simply because there is more public English language data available than any other. Google and Amazon both made great advances in English language modelling during the last few years.
Although the complexity of the language certainly plays a part, the less data available in any particular language, the more difficult it is to build a model for it.
For example, the Finnish language is complex because of its extensive use of compound words as well as altering words based on tense, pronouns, prepositions and whether it’s an interrogative sentence. And this is with the “proper” Finnish language, as opposed to more casual language. But, since it’s a relatively small language, it doesn’t have the same amount of data to build a language model, like English does.
So look for a chatbot vendor who knows their language models and uses ones that work for your preferred languages.
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