What is a Callbot?
A callbot is an automated system that answers telephone calls. It uses speech recognition and predefined scripts to interact with callers and solve their problems or answer their questions. Calbots can be used in a variety of industries, such as customer service, retail, healthcare and financial services.
What is conversational AI and why is it essential in callbot?
Conversational AI adds value to a callbot by enabling it to understand and respond to callers in a more natural and fluent way. It can use automatic natural language processing techniques to understand and respond to user requests in a meaningful way, even if users use poorly worded terms or phrases.
Automatic natural language processing (NLP) models enable callbots to understand complex questions and contexts, and provide more accurate responses. This increases customer satisfaction and reduces calls transferred to human agents.
In addition, conversational AI can also be used to enhance the user experience by tailoring the response based on the user's situation. This can include features such as personalizing responses or using geolocation to provide local information.
Conversational AI offers significant benefits in improving the user experience and solving user problems more efficiently, maximizing costs and efficiency for businesses that use callbots.
What is the difference between NLU and NLP
NLU (Natural Language Understanding) and NLP (Natural Language Processing) are two related but distinct terms that refer to different tasks in automatic natural language processing.
NLP (Natural Language Processing) is the set of computer techniques that allow the processing and understanding of texts written in natural language. It includes tasks such as sentence segmentation, word recognition and text generation. NLP models are used for tasks such as speech recognition, machine translation, meaning understanding and text generation.
NLU (Natural Language Understanding) is a subset of NLP that focuses on semantic analysis and meaning understanding of natural language sentences. It includes tasks such as intent recognition, feature extraction, relationship recognition and summary generation. NLU models are used for tasks such as intention recognition, question understanding or emotion recognition.
In sum, NLP allows to process and understand texts in their raw form (syntax and structure) while NLU allows to understand the hidden meaning behind texts (semantics, intention, emotion...). Both are necessary to create an efficient natural language understanding system.
How to make a callbot perform for a customer service?
- The quality of speech recognition: good speech recognition is essential to understand and respond to user requests in a relevant way.
- The quality of natural language understanding: a good natural language understanding (NLU) model is needed to understand user requests, even if users use poorly worded terms or phrases.
- Quality of scripts: Predefined scripts must be well designed and maintained to ensure that the answers provided are accurate and useful.
- Ability to handle exceptions: callbots must be able to handle unusual requests and exceptions, and transfer calls to human agents when necessary.
- The ability to adapt to the user: callbots must be able to adapt to the user according to his interaction history, his geographical location, his profile, etc. to provide a personalized response.
- The ability to manage multiple communication channels: phone, chat, email, etc. for a better user experience
- The ability to evolve: callbots must be able to adapt to new business needs and market changes.
