AI Agent Interaction Analysis for Contact Center Sentiment
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Solution Overview
Problem
Current contact center agent evaluation processes rely on manual, academic assessments that do not accurately reflect real-life performance, leading to a disconnect between training and actual interaction skills.
Innovation Solution
A system utilizing artificial intelligence to analyze real-time agent interactions by processing transcripts and determining call adherence, agent positivity, client and agent sentiment, and multiple skill levels, providing a combined performance score and personalized feedback to improve agent sentiment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual academic assessments are used to evaluate agent skills, then the evaluation process is simple to implement, but the assessment accuracy does not reflect real-life performance
Solution Approach 1:
The patent replaces manual academic assessments with an AI-based automated evaluation system that analyzes real interaction transcripts. The system uses natural language processing and machine learning models to objectively measure agent performance across multiple dimensions including skill application, sentiment, and adherence, substituting human-graded quizzes with automated computational analysis of actual work behaviors.
Solution Approach 2:
The system enables self-service evaluation by automatically processing agent transcripts and generating performance scores without requiring external evaluators. The AI model independently analyzes interaction data, calculates skill levels, and provides feedback, allowing the evaluation process to serve itself rather than relying on manual human assessment.
2Measurement precision
If real-time AI analysis is implemented to accurately measure agent performance, then assessment accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing transcript data in structured formats, pre-training AI models on historical interaction data, and preparing evaluation frameworks in advance. This allows the system to quickly analyze new transcripts without starting from scratch, reducing real-time processing delays while maintaining comprehensive analysis accuracy.
Solution Approach 2:
The evaluation process is segmented into multiple independent analysis components that process different aspects of agent performance separately (skill application, sentiment analysis, adherence measurement). Each segment can be processed in parallel, reducing overall processing time while maintaining comprehensive evaluation accuracy through aggregated results from all segments.
3Adaptability or versatility
If comprehensive AI modeling is used to analyze multiple agent skills and sentiments, then evaluation thoroughness improves, but system complexity increases
Solution Approach 1:
The comprehensive evaluation system is divided into separate specialized AI models or analysis modules, each focused on a specific dimension such as skill assessment, sentiment analysis, or adherence measurement. This segmentation allows each module to be optimized independently while collectively providing thorough multi-dimensional evaluation coverage.
Solution Approach 2:
The AI evaluation system is designed with universal components that can handle multiple evaluation functions through a unified framework. A single processing pipeline can analyze different transcript features and generate multiple performance metrics simultaneously, achieving comprehensive evaluation versatility without proportionally increasing overall system complexity.
Data Source
AI summary
A method for agent interaction analysis using artificial intelligence according to an embodiment includes receiving a transcript for a real-time agent interaction between a contact center agent and client, processing the interaction using at least one artificial intelligence model to determine a call adherence score, an agent positivity score, a client sentiment, an agent sentiment, and a plurality of agent skill levels associated with respective agent skills, determining a combined agent performance score based on the call adherence score and the agent positivity score, transmitting the combined agent performance score to an agent device for display on a gamification dashboard, retrieving agent-specific content of the contact center agent in response to determining that the agent sentiment is negative, and transmitting the agent-specific content to the agent device for display in conjunction with the real-time agent interaction to improve the agent sentiment.