Abstract:
Using voice-enabled conversational capabilities, the created Con- voBot is an inventive application in the field of medical insurance campaigns. Easy to integrate with the popular Vici dialer used in contact center settings, the system shows how to handle cus- tomer interactions in an organized manner. ConvoBot initiates calls and uses a sequence of scripted questions to dynamically interact with customers. The pre-recorded voice prompts are stored in a dataset file that can be accessed via a JSON file interface. The speech recognition library in Python is used by the system to ef- fectively process user responses and extract text from audio inputs. Next, Natural Language Processing (NLP) methods made possible by the nltk library are applied to ascertain the intent of the user, allowing for accurate response classification. After intents are iden- tified, they are compared to a database of preset response variations, which determines the next course of investigation. When a customer does not show interest and is politely refused, the call is effectively ended. On the other hand, in the event that a client indicates inter- est, the call is automatically transferred to a human representative, guaranteeing that support and direction will continue. ConvoBot also has built-in access to microphone and system audio features, which increases its flexibility and control over dialer functions like call transfer and disposal. This all-inclusive structure represents a noteworthy development in contact center automation, simplifying client communications and increasing operational effectiveness in the medical insurance sector.
