Automating customer communication has several advantages. Potential customers today are not prepared to deal with complicated ways of contacting you or wait forever for a response. Customers' problems should be solved quickly and precisely before they turn to another company. However, since good customer service also requires a lot of personnel, many companies are now relying on chatbots and conversational AI. They optimize internal processes and relieve employees by, for example, answering frequently asked questions, acting as a guide for visitors and creating a better user experience through constant availability.
In order to use Conversational AI correctly, it is important to understand as precisely as possible what you are dealing with and how this artificial intelligence works.
The term "Conversational AI" is first and foremost germany whatsapp data a collective term for various dialogue systems that are all based on the same basis and artificial intelligence. The underlying technology is Natural Language Processing, or NLP for short.
NLP is responsible for processing natural language by capturing and processing letters, words and sentences by an AI. It does not matter whether it is text or speech content, as the technology is designed to interpret the context of speech or text input. To do this, the system accesses large amounts of data with which it compares and analyzes the input. NLP is based on the basic idea of machine learning, which means that Conversational AI analyzes large amounts of data and derives, recognizes and learns patterns from it. The aim is to give the customer the impression that they are communicating with a real person.
The AI that works in the background is able to understand requests based on predefined categories and provide an appropriate answer. In order to be able to offer this service to your customers, two components must be considered:
Recognizing the intent
Intent recognition is able to distinguish between different types and contents of texts. This requires a dataset that contains different categories with corresponding text examples. The system can then learn through machine learning to assign different texts to the corresponding categories. The better the quality of this dataset, the better the AI can work.