Contact Center Agent Call Logging Evaluation System
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Solution Overview
Problem
Ineffective agent call logging in contact centers leads to inefficient data collection, incomplete customer interaction insights, and reduced operational efficiency, impacting customer satisfaction and agent performance evaluation.
Innovation Solution
A computerized method and system that utilizes a speech-to-text algorithm to summarize customer interactions and a semantic comparison unit to calculate a Sentence Similarity Score (SSS) and Logging Effectiveness Score (LES), alerting users to mismatches and forwarding scores to quality management systems for improved coaching and performance evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated quality management systems monitor all agent interactions, then agent performance evaluation is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system creates automated copies of customer interactions through speech-to-text conversion and generates summarized text representations. These copies enable automated analysis without requiring human evaluators to listen to every call, thus improving measurement precision while reducing the operational complexity of the monitoring system.
Solution Approach 2:
The patent replaces manual mechanical evaluation (human listeners analyzing recorded calls) with automated computational processes. Speech-to-text algorithms and semantic comparison units automatically evaluate call logging effectiveness, substituting human cognitive processes with machine-based analysis that reduces system complexity and resource consumption.
2Quantity of substance
If agents manually log customer interactions, then data collection is improved, but logging accuracy and completeness deteriorate due to human error and time constraints
Solution Approach 1:
The system automatically creates text copies of customer speech through speech-to-text conversion, eliminating the need for agents to manually transcribe. This copying process captures complete interaction data with high accuracy, overcoming human limitations in manual logging while maintaining volume of data collection.
Solution Approach 2:
The system implements automated feedback by comparing agent-generated call logging against actual customer speech transcripts. This feedback mechanism identifies discrepancies and enables continuous improvement of logging accuracy, ensuring that the volume of logged data maintains high precision without relying on human perfection.
3Loss of information
If speech-to-text algorithms are used to convert customer speech, then data capture completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs speech-to-text conversion and text summarization automatically during or immediately after call completion, before evaluation is needed. This preliminary action ensures complete information capture is achieved in advance, allowing subsequent analysis to proceed without time delays and reducing the perception of processing time impact.
Solution Approach 2:
The system applies speech-to-text conversion selectively to only the necessary portions of customer speech that require analysis, rather than transcribing every word of every call. This partial action approach maintains information completeness for critical evaluation while reducing overall processing time and computational resource consumption.
4Measurement precision
If semantic comparison is performed on each sentence, then logging effectiveness measurement precision is improved, but computational complexity increases
Solution Approach 1:
The system segments the call logging evaluation into individual sentence-level comparisons rather than analyzing entire calls as single units. This segmentation allows precise measurement of logging effectiveness at the sentence level while managing computational complexity by breaking down the analysis into smaller, more manageable discrete comparison tasks.
Solution Approach 2:
The system changes the parameter of analysis from holistic call evaluation to sentence-level semantic comparison. By transforming the evaluation parameter to focus on individual sentence similarity between customer speech and agent logging, the system achieves high measurement precision while using computational methods optimized for text comparison that manage complexity effectively.
Data Source
AI summary
A computerized method for evaluating agent-call-logging, in a contact center, is provided herein. The method includes operating an evaluating agent-call-logging module. The operating of an evaluating agent-call-logging module includes: (i) receiving a recorded interaction between an agent and a customer, stored in the database of recorded interactions and a corresponding agent-call-logging, having ‘n’ sentences, stored in the database of call loggings; (ii) operating a speech-to-text algorithm on the received recorded interaction to yield a summarized text thereof. The yielded summarized text is comprised of sentences expressed by the customer; and (iii) operating a semantic comparison unit, to compare each sentence in the yielded summarized text with a corresponding sentence in the agent-call-logging, to yield a Sentence Similarity Score (SSS) for each sentence of the agent-call-logging.