Real-Time Interaction Quality Scoring via Segmented Text Analysis
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Solution Overview
Problem
Existing interaction monitoring systems in contact centers lack the ability to accurately assess the quality of interactions in real-time, often relying on keyword alerts rather than comprehensive analysis, which can lead to delayed or inadequate supervisor intervention.
Innovation Solution
The system employs real-time monitoring using machine learning models to score text components and time periods of interactions, producing a score history that calculates a real-time indication of interaction quality, allowing for earlier detection of unsatisfactory interactions and timely supervisor intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If keyword-based monitoring is used, then the system complexity is reduced, but the measurement precision of interaction quality deteriorates
Solution Approach 1:
The interaction text is segmented into multiple time periods, and each time period is scored independently based on text components within it. This segmentation allows comprehensive analysis without requiring complex global analysis of the entire interaction, thus maintaining measurement precision while managing system complexity.
Solution Approach 2:
The system pre-processes text components by scoring them individually before aggregating into time period scores. This preliminary scoring action enables efficient real-time processing and reduces the complexity of final quality assessment while maintaining high measurement precision through cumulative analysis.
2Measurement precision
If comprehensive text analysis is performed, then the measurement precision of interaction quality improves, but the processing time increases
Solution Approach 1:
The interaction is divided into periodic time periods, and scoring is performed periodically for each segment. This periodic approach enables real-time monitoring and early detection of quality issues without requiring analysis of the entire interaction, thus reducing processing time while maintaining measurement precision through cumulative scoring.
Solution Approach 2:
The system processes text components autonomously by automatically scoring them and aggregating results without human intervention. This self-service processing reduces manual review time while maintaining high measurement precision through consistent automated evaluation of all text components.
3Measurement precision
If all text components are monitored, then the measurement precision improves, but the quantity of data to be processed increases
Solution Approach 1:
The large volume of text data is segmented into manageable time periods with specific score thresholds. This segmentation reduces the effective data volume that requires detailed processing at any given time while maintaining measurement precision by analyzing all components through cumulative scoring across multiple segments.
Solution Approach 2:
The system transforms raw text components into scored numerical values for each time period. This parameter change from qualitative text to quantitative scores reduces data complexity and volume for processing while preserving measurement precision through the scoring mechanism that captures essential quality indicators.
Data Source
AI summary
Systems and methods for automatic real-time monitoring of interactions, carried out by at least one computer processor, including: producing a score for each text component of a text representation of an interaction; producing, based on the score for each text component, a score for each of a plurality of time periods of the interaction; producing a score history, including a plurality of the time period scores; and calculating, based on the score history, a real-time indication of the quality of the interaction.