The growth in chatbot technology has been as dynamic as the evolution of chatbot capabilities. For now, chatbots can be broadly categorized into three types. The three types are differentiated by their technical complexity, namely:
1. Simple chatbots,
2. Smart chatbots, and
3. Hybrid chatbots.
Simple chatbots have limited capabilities, and are usually called rule-based bots. They are task-specific. Think of them as IVRS on chat. This means the bot poses questions based on predetermined options and the customer can choose from the options until they get answers to their query. The chatbot will not make any inferences from its previous interactions. These chatbots are best suited for straightforward dialogues. They are very simple to build and train.
Example: Ordering Pizza
When a customer interacts with a chatbot to order pizza, the flow of the conversation is set. Just like an operator asks for your order over the phone, the chatbot will pose the questions in the same way. Starting from the size of the pizza, to the crust, toppings and amount of cheese. It will then request the address and payment method. The steps are logical and only requires the customer to click through to complete their order.
AI-enabled smart chatbots are designed to simulate near-human interactions with customers. They can have free-flowing conversations and understand intent, language, and sentiment. These chatbots require programming to help it understand the context of interactions. They are much harder to implement and execute and need a lot of data to learn.
Example: Virtual Assistants
Virtual assistants are a modified version of smart chatbots. Siri, for instance, learns from every human interaction. It can also engage in small talk which is an added benefit of smart chatbots. While smart chatbots are trained to give the most relevant response with the help of an open domain resource, they learn best by collecting information in real-time. Note that companies are yet to build a bot to the extent to which virtual assistants work because it requires massive data. But theoretically, smart chatbots would work like virtual assistants within web apps.
They are a combination of simple and smart chatbots. Both simple and smart chatbots are extremes in the chatbot spectrum. There will constantly be a need for simple chatbots to be smarter and smart chatbots to be simpler. Hybrid chatbots meet that middle ground. Hybrid chatbots have some rule-based tasks, and they can understand intent and context. This makes them a balanced tool for businesses to interact with customers.
Example: Medical Diagnosis
Chatbots that help with a medical diagnosis combine the capabilities of both simple and smart chatbots. Visitors will be able to voice their health-related questions and the bot can narrow down possible conditions by asking for symptoms in a rule-based format. Visitors will be able to go back and forth, choose different options and give more details until the bot narrows down on their condition and prescribes remedies for the same.
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