ML Interaction Quality Scoring for Consistent Agent Evaluation
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Solution Overview
Problem
Existing manual evaluation methods for agent-customer interactions in service-based industries are incomplete, unscalable, and prone to human bias, leading to inconsistent and incomplete assessments of agent performance, which can negatively impact customer satisfaction and business profitability.
Innovation Solution
A system utilizing machine learning models to automatically score interactions based on multi-dimensional analysis, including conversation and service scores, to provide a comprehensive and unbiased evaluation of agent performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual evaluation forms with multiple questions are used to assess agent performance, then comprehensive evaluation coverage is achieved, but evaluation consistency and reliability deteriorate due to human bias and variability
Solution Approach 1:
The patent replaces the manual human evaluation process with an automated machine learning-based evaluation system. The system uses natural language processing to analyze interaction transcripts and automatically generates scores for multiple dimensions including service quality, conversation quality, and compliance, eliminating human bias while maintaining comprehensive evaluation coverage
Solution Approach 2:
The system creates a digital copy of the evaluation process by using machine learning models to replicate and standardize evaluation criteria across all agents. The models learn from labeled interaction data and consistently apply the same evaluation standards, ensuring reliability while maintaining comprehensive assessment
2Measurement precision
If manual interaction evaluation is performed for each customer interaction, then detailed agent performance assessment is achieved, but productivity and scalability worsen due to high time consumption
Solution Approach 1:
The evaluation system performs self-service by automatically analyzing interaction transcripts without requiring human reviewer intervention. The machine learning models independently process transcripts, generate scores across multiple dimensions, and provide detailed performance assessments, enabling the system to evaluate numerous interactions simultaneously with high precision and scalability
Solution Approach 2:
The system enables continuous evaluation by processing interactions as they occur or are stored, rather than requiring batch manual review. The automated pipeline continuously analyzes transcripts, generates scores, and updates agent performance metrics, maintaining both detailed assessment quality and high productivity across large volumes of interactions
3Measurement precision
If comprehensive multi-dimensional analysis is performed to score interactions, then evaluation accuracy and completeness improve, but system complexity increases
Solution Approach 1:
The evaluation system segments the comprehensive analysis into distinct modular components: dimension scoring models that evaluate individual aspects (service quality, conversation quality, compliance), sub-score calculations for different interaction phases, and aggregated overall scores. This segmentation maintains evaluation accuracy while managing system complexity through organized, reusable modules
Solution Approach 2:
The machine learning evaluation system serves multiple functions simultaneously: it analyzes interaction transcripts, scores multiple dimensions, identifies compliance issues, provides performance feedback, and supports training decisions. This multi-functionality achieves comprehensive accurate evaluation without proportionally increasing system complexity, as the same core infrastructure supports all evaluation needs
Data Source
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AI summary
The present disclosure relates to automatically evaluating an agent-customer interaction utilizing aspects of machine learning to score the quality of the interaction. In some embodiments, one or more machine learning models are utilized to generate an interaction quality score which is a comprehensive evaluation of agent performance during the interaction. The interaction quality score is a combination of two sub-scores, a conversation score and service score which are each based on one or more dimension scores. The conversation score is a measure of how well the agent engages with the customer during the interaction. The service score is an evaluation of the quality of the agent's service during the interaction in terms of customer's perception of the agent's performance. Each of the conversation score and service score are determined by an analysis of one or more dimensions such as fluency, relevance, appropriateness, informativeness, assurance, responsiveness, empathy, compliance, and sentiment.