![]() If customer queries fall outside the pre-defined rules, these chatbots fall short of recognizing conversation context and won’t be able to identify advanced scenarios.Īrtificial intelligence (or machine learning) chatbots, on the other hand, use natural language processing (NLP) technologies to understand the intent behind the question and solve the customer’s problem without any human assistance. In general, these are simple chatbots that highly depend on user input. Sometimes, these types of chatbots are also keyword-based (or have keyword recognition functionality), reacting to specific terms, but these are limited to typos and may not provide appropriate responses, which can cause very frustrating customer experiences. In most cases, these types of chatbots are built with graphical or conversational interfaces reacting to the user pressing the chatbot’s menu buttons, which activates the next layer of the decision tree. These chatbot types are often split into two tracks: a sales track for capturing contact details and setting up a call or a meeting and a support track for giving generic answers or sending a website link containing the necessary information. Guided by a decision tree, the customer is given a set of pre-defined options that lead to the appropriate answer. Such types of chatbots are used to answer questions that are often simple – for instance, booking a table in a restaurant, buying tickets to the cinema, or using online delivery services. They are also called “button-based” or “menu-based” chatbots (usually seen in automated phone menus). Rule-based chatbots became very popular after Facebook launched its Messenger platform where chatbots performed automated customer support for businesses. Rule-based chatbots vs Artificial intelligence chatbots
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