Call Routing With Real-Time Sentiment Analysis and Manager Alerts
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Solution Overview
Problem
Conventional call routing systems fail to provide accurate insights into customer sentiment during calls, relying on ineffective customer surveys and random representative allocation, leading to skewed satisfaction metrics.
Innovation Solution
Implement a call routing system that analyzes real-time conversations using machine learning to determine sentiment scores, allowing for proactive management interventions based on sentiment analysis during calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional call routing systems use random representative allocation and customer surveys, then call routing is simple, but customer sentiment measurement is inaccurate
Solution Approach 1:
The patent replaces manual customer surveys with an automated machine learning-based sentiment analysis system that processes call data automatically. This substitution eliminates the need for customers to complete surveys while providing continuous, objective sentiment measurements through automated text and voice analysis during and after calls.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the call routing system and sentiment measurement. These models analyze call transcripts, voice tones, and customer interactions to generate sentiment scores, serving as a mediator that translates raw call data into actionable sentiment insights without requiring direct customer input.
2Measurement precision
If machine learning sentiment analysis is implemented in real-time, then customer satisfaction measurement accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs sentiment analysis during the call itself rather than waiting for post-call processing. By analyzing customer sentiment in real-time as the call progresses, the system can provide immediate insights and alerts to representatives, eliminating the time delay associated with post-call survey completion and analysis.
Solution Approach 2:
The patent implements continuous sentiment analysis throughout the duration of the call, rather than conducting discrete measurements at specific intervals. This continuous monitoring provides uninterrupted sentiment data, allowing for dynamic adjustments and immediate intervention when negative sentiment is detected, maximizing the utility of the analysis throughout the entire call lifecycle.
3Adaptability or versatility
If real-time sentiment analysis is performed during calls, then proactive management intervention is enabled, but system complexity and computational requirements increase
Solution Approach 1:
The patent implements a feedback loop where sentiment analysis results are immediately communicated back to call representatives and management systems. Real-time sentiment scores and alerts are fed back to representatives during calls, enabling them to adjust their approach dynamically, while management receives aggregated sentiment data for proactive intervention and policy adjustments.
Solution Approach 2:
The patent enables the sentiment analysis system to automatically detect, analyze, and generate insights without requiring manual intervention. The machine learning models autonomously process call data, generate sentiment scores, and trigger alerts when necessary, reducing the need for complex manual analysis workflows and specialized personnel.
Data Source
AI summary
Machine learning technology can analyze in real-time the data from a call between a person and a customer service representative. Based on this analysis, a server can determine a sentiment score that describes a sentiment expressed by the person or the customer service representative. If the server determines that the sentiment score is less than or equal to a pre-determined value, the server can inform the customer service representative's manager so that the manager can take further action to help the person and/or the customer service representative.