Leveraging Machine Learning to Transcribe Calls and Perform Real-Time Call Analytics

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.

Solution

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.

Result

Assists human agents with caller identification, real-time keyword analysis, and custom entity extraction to respond quickly to customer requests
Allows agents to focus on more complex aspects of their interactions with callers by reducing the cognitive overhead and enabling higher accuracy

Quick Start: Quantiphi Real-Time Call Analytics on the AWS Cloud

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.