Caller State Metric Generation for Call Center Emotional Analysis
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Solution Overview
Problem
Customer service centers face challenges in effectively handling customer calls, particularly complaints, due to inadequate analysis of emotional and sentimental states, leading to unsatisfactory experiences and potential loss of customer relations.
Innovation Solution
A call center system equipped with recording and analysis modules that process voice recordings to determine emotional and sentimental states, generating a caller state metric to guide real-time call handling and provide feedback for improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice recordings are analyzed in real-time to determine emotional and sentimental states, then customer satisfaction and call handling quality improve, but system complexity and processing requirements increase
Solution Approach 1:
The system segments the voice recording analysis into distinct modules: emotion state analysis module that processes emotional states, sentiment state analysis module that processes sentimental states, and caller state metric generation module that combines both analyses. This segmentation allows each module to specialize in specific analysis tasks, improving overall reliability while managing system complexity through modular architecture.
Solution Approach 2:
The caller state metric serves as an intermediary that combines emotion and sentiment state metrics. This intermediary metric simplifies the complex analysis results into a unified measure that can be directly used for call handling decisions, reducing the complexity of interpreting multiple separate metrics while maintaining comprehensive analysis quality.
2Reliability
If comprehensive voice analysis is performed on all calls, then customer relations improvement increases, but processing time and computational resources increase
Solution Approach 1:
The system performs comprehensive voice analysis selectively rather than on all calls. By applying the full analysis only when necessary (e.g., for complaints or critical interactions), the system achieves improved customer relations for high-priority cases while avoiding unnecessary processing time and computational resource consumption for routine calls.
Solution Approach 2:
The automated voice analysis system processes calls independently without requiring manual review for every interaction. The system self-manages the analysis workflow, automatically generating emotion and sentiment metrics and combining them into caller state metrics, thereby reducing the time investment required from human agents while maintaining comprehensive analysis quality.
Data Source
AI summary
In a call center, voice recordings of calls with callers can be analyzed to determine an emotional state metric, e.g. based on the pitch, volume, etc. of the voice sample waveform. A sentiment metric can be determined from words spoken in the sample. The emotional state metric and the sentiment metric may be combined to generate a caller state metric. The caller state metric may trigger subsequent actions, such as transferring calls to call agents or supervisors, and identifying points within voice scripts that can be improved.