Improved Average Handling Time
Reduced Manual Effort
Improved Caller Experience
The customer provides management and support services to healthcare institutions across the United States. Their call center receives over 200k calls each month, covering close to 1 million minutes of conversation between the agents and customers. The agents are the single point of contact between healthcare institutes and customers, and must manually take notes on customers’ personal information, etc. This task is cumbersome and subject to error.
The customer wanted to provide a seamless experience for their call center agents and customers. Therefore, they sought to leverage machine learning to transcribe calls and perform real-time call analytics.
Quantiphi built a solution with real-time speech-to-text and Natural Language Processing (NLP) capabilities that assists call center agents with their day-to-day interactions with clients. The solution was integrated with their existing CISCO telephony systems and transcribes each conversation between customers and the agents. The script transcription is then used to extract entities like name, phone number, location. etc. At the end of the call, post call analysis is performed to summarize the call and understand the sentiments of the speakers. A custom User Interface was also built to display all the information captured, such as caller information, relevant keywords, and call summary.
Read more about Quantiphi’s Real-Time Call Analytics on the AWS Cloud. The Quick Start supports real-time call analytics using machine learning to transcribe and run live and post call data analytics, freeing support agents from manual note taking.