Real-Time Voice Analytics for Call Center Behavior
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Solution Overview
Problem
Existing technologies in call centers fail to effectively measure and influence employee behavior during calls, leading to poor on-call experiences, reduced productivity, and high attrition rates, as they focus on outcomes rather than behaviors and often degrade the employee experience by reducing autonomy.
Innovation Solution
A system and method that analyze real-time conversational dynamics using voice analytics to generate feedback and behavioral cues, enhancing situational awareness and engagement through game mechanics, providing immediate reinforcement and guidance to improve employee performance without reducing autonomy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If speech-to-text transcription and emotion detection are used to monitor conversational quality, then automated monitoring capability is improved, but measurement reliability deteriorates due to transcription inaccuracies and verbal communication ambiguities
Solution Approach 1:
The system provides real-time feedback to employees during calls through visual cues and auditory signals that indicate conversational quality metrics. This feedback loop allows employees to adjust their behavior immediately, improving measurement reliability by validating the automated monitoring data through human awareness and correction.
Solution Approach 2:
The patent introduces an intermediary layer between the automated speech analytics system and the employee. This intermediary processes the raw transcription data and emotion detection results, filtering out inaccuracies and presenting refined metrics to employees, thereby maintaining automation while improving measurement reliability.
2Productivity
If measurement, monitoring, and management technologies are increased to improve performance tracking, then productivity is improved, but employee engagement deteriorates due to reduced autonomy
Solution Approach 1:
The system enables employees to self-monitor and self-adjust their performance using real-time feedback from the analytics system. Instead of external monitoring imposing constraints, employees use the data themselves to improve their own performance, maintaining autonomy while enhancing productivity tracking.
Solution Approach 2:
The monitoring system is designed to be dynamic and adaptive rather than rigid. It adjusts its monitoring intensity and feedback frequency based on call context and employee performance levels, allowing flexibility that preserves employee engagement while maintaining effective performance tracking.
3Measurement precision
If speech analytics information is provided to front-line employees to improve behavior, then measurement capability is improved, but employee experience deteriorates when information is provided post-conversation
Solution Approach 1:
The system provides speech analytics information and feedback during the conversation itself, before the call ends. This preliminary action allows employees to adjust their behavior in real-time rather than receiving feedback too late to influence the interaction, maintaining both measurement precision and positive employee experience.
Data Source
AI summary
A method for improving a call-participant behavior, the method includes receiving an intensity data signal and an intensity variation data signal related to an ongoing call, receiving a pitch data signal and a pitch variation data signal related to the ongoing call, receiving a tempo data signal and a tempo variation data signal related to the ongoing call, receiving a channel comparison data signal related to the ongoing call, generating a real-time call progress signal based on the intensity data signal, the intensity variation data signal, the pitch data signal, the pitch variation data signal, the tempo data signal, the tempo variation data signal, and the channel comparison data signal, and sending the real-time call progress signal to a user device.