Real-Time Sentiment Analysis for Call Center Complaint Resolution
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Solution Overview
Problem
Current computer-based text/speech analyzers fail to properly recognize tone, changes in tone, underlying emotions, and emotional states in interactive communications, leading to potential misinterpretation of intended meaning by receivers.
Innovation Solution
A system that uses machine learning models to analyze customer sentiment in real-time during calls, routing calls to appropriate agents, automatically punctuating speech, and providing agents with suggested phrases based on successful resolutions of similar customer issues to improve customer interaction and sentiment management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to analyze customer sentiment in real-time, then customer experience and sentiment management are improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments sentiment analysis into multiple specialized machine learning models: a first ML model for detecting dissatisfaction tone, a second ML model for predicting complaint likelihood, and a third ML model for generating responses. This segmentation allows each model to specialize in a specific aspect of sentiment analysis, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary sentiment analysis by continuously analyzing customer utterances as they occur during the call. The first ML model analyzes tone in real-time before complaints are fully articulated, allowing early detection and intervention. This preliminary action enables the system to predict potential complaints and prepare appropriate responses in advance.
2Measurement precision
If real-time analysis of customer tone and emotions is implemented, then misinterpretation of intended meaning is reduced, but processing time and computational load increase
Solution Approach 1:
The system implements continuous real-time analysis of customer sentiment throughout the entire call duration. The machine learning models process customer utterances as they occur, providing ongoing sentiment assessment rather than post-call analysis. This continuous action ensures timely detection of tone changes and emotional states, enabling immediate intervention while maintaining accurate measurement.
Solution Approach 2:
The system provides real-time feedback to call center agents about customer sentiment, tone, and predicted complaint likelihood. This feedback loop allows agents to adjust their communication style immediately based on analyzed sentiment data, improving the accuracy of tone interpretation while the system continues processing without significant delay to call flow.
3Productivity
If automated assistance is provided to call center agents, then call resolution success rate improves, but automation extent and system complexity increase
Solution Approach 1:
The system provides automated assistance that empowers call center agents to handle complaints more effectively through real-time suggestions and recommended responses generated by the third ML model. The system serves itself by continuously learning from call outcomes and improving its suggestions, reducing the need for human expertise in sentiment analysis while maintaining high resolution success rates through agent support.
Solution Approach 2:
The system replaces manual sentiment analysis and complaint prediction with machine learning-based automated analysis. Instead of relying on agent intuition or manual review of call recordings, the third ML model automatically generates suggested responses based on analyzed sentiment patterns, substituting mechanical human processes with automated intelligent systems while improving productivity.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process incoming call-center calls based on inferred sentiments. An incoming call is routed to a call agent based on an inferred topic, classified based on one or more inferred sentiments of a current caller's speech, determining, based on the call classification, that a complaint has been articulated and initiating an automated assistance by searching for one or more similar callers to the current caller. Based on finding a successful call outcome associated with one or more similar callers, the system suggests one or more phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments.