Real-Time Call Center Analytics
Life SciencesBusiness Impacts
Improved Average Handling Time
Reduced Manual Effort
Improved Caller Experience
Customer Key Facts
- Location : North America
- Industry : Healthcare
Problem Context
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.
Challenges
- High call volumes – 200,000+ calls per month
- Agents had to take notes manually making the process cumbersome
- Lack of important/relevant information captured from calls
- Long query resolution process led to more wait times and customer dissatisfaction
Technologies Used
Amazon S3
Amazon DynamoDB
Amazon Fargate
Amazon Transcribe
Amazon Comprehend
Amazon Kinesis Video Streams
Amazon Chime Voice Connector
Amazon API Gateway
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.
