In addition to monitoring data and intent, they can initiate conversations. Digital assistants can learn a user’s preferences over time, provide recommendations, and even anticipate needs. They apply predictive intelligence and analytics to enable personalization based on user profiles and past user behavior.

These chatbots are contextually aware and leverage natural-language understanding (NLU), NLP, and ML to learn as they go. Data-driven and predictive (conversational) chatbots are often referred to as virtual assistants or digital assistants, and they are much more sophisticated, interactive, and personalized than task-oriented chatbots.These are currently the most commonly used chatbots. Though they do use NLP so end users can experience them in a conversational way, their capabilities are fairly basic. Task-oriented chatbots can handle common questions, such as queries about hours of business or simple transactions that don’t involve a variety of variables. Interactions with these chatbots are highly specific and structured and are most applicable to support and service functions-think robust, interactive FAQs. Using rules, NLP, and very little ML, they generate automated but conversational responses to user inquiries. Task-oriented (declarative) chatbots are single-purpose programs that focus on performing one function.Driven by AI, automated rules, natural-language processing (NLP), and machine learning (ML), chatbots process data to deliver responses to requests of all kinds.

Whats a chatbot maker