HOW MANY TYPES OF CHATBOTS EXIST?
The gaze of large and small companies is now projected towards the universe of chatbots, confirming analysts’ forecasts: “By 2027, chatbots will become the main customer support channel for about a quarter of organizations globally” (Gartner).
However, the word “chatbot” identifies different types of solutions that differ in “intelligence”, functioning, and applicability, but also in terms of conversational experience.
A chatbot for every need
Depending on the conversation objectives and the level of user experience to be achieved, technology offers various options: from basic models to more advanced models, up to hybrid models.
A keyword-based chatbot uses a system of canned responses based on common keywords and phrases. This type of chatbot uses natural language processing to recognize specific keywords or phrases entered by the user and provide an appropriate response.
Keyword chatbots are often used to answer common questions or provide background information. They are less sophisticated than AI-powered chatbots, but can still provide a useful and fast service for users looking for answers to repetitive and frequently asked questions.
A rule-based chatbot uses natural language processing based on a predetermined set of rules to generate answers to users’ questions.
The rules are established within a conversational flow defined in advance, to tell the chatbot how to understand and respond to users’ requests. These rules can be simple, such as: “if the user asks for a time, please provide the current time”, or more complex, such as: “if the user asks for a reservation, ask for dates, times places available, etc.”
Rule-based chatbots are relatively easy to implement and can be used to answer a wide range of common questions. However, they require a detailed definition of the rules and in case of more complex questions, which involve leaving the pre-established flow of conversation, they are not able to return an adequate answer.
MACHINE LEARNING Chatbot
A chatbot based on machine learning is able to continuously learn from the data it receives and from interactions with users, to improve its responses and the quality of the service offered. This type of chatbot is able to process natural language, understand the context and provide increasingly precise and personalized answers.
AI chatbots use historical conversation data to self-learn and improve the ability to understand and answer user questions, providing a highly personalized user experience, using natural language.
A generative chatbot is a type of chatbot that uses advanced artificial intelligence techniques to generate responses to user messages in a completely autonomous way.
Generative chatbots understand context and nuances of language to produce highly personalized responses naturally.
A generative chatbot uses machine learning algorithms, such as neural networks, which analyze large amounts of data to learn how to respond naturally and consistently to user questions.
Generative chatbots can use Large Language Models as part of their architecture to generate consistent and relevant answers to user questions and deliver a highly engaging user experience.
Hybrid models combine different artificial intelligence techniques to adapt to specific situations and needs.
In a hybrid chatbot model, one part of the system could be based on a predetermined rule-based natural language processing (NLP) approach, while another part could be based on machine learning techniques.
The advantage of hybrid models is that they can use the best natural language processing techniques available to adapt to the conversational context and provide a more fluid and natural conversation.
There are different chatbots, but also different needs.
The question to ask yourself to guide your choice is: “What should my chatbot be able to do?”
To answer a few common questions within a guided tour, a simple rules-based chatbot may be sufficient.
If you want to provide a complete conversational experience in natural language, technology today offers the performance of generative AI.
Hybrid models, on the other hand, allow you to combine different technologies to be able to adapt the chatbot’s responsiveness depending on the situation.