Automated Agent Interaction Evaluation and Training Recommendation System
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Solution Overview
Problem
Current technologies for evaluating and training agents in contact centers are ineffective due to reliance on small, unrepresentative samples of interactions, scalability issues, and challenges in providing consistent and objective evaluations.
Innovation Solution
The development of a system that evaluates agent performance based on all recorded interactions, using objective and unbiased criteria, and generates personalized training recommendations through generative artificial intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of agent interactions is performed, then evaluation depth and expertise can be applied, but scalability is limited and evaluation consistency becomes difficult to maintain
Solution Approach 1:
An automated evaluation system acts as an intermediary between agent interactions and quality assurance outcomes. The system processes interactions through machine learning models and evaluation frameworks, producing standardized assessments that maintain consistency while enabling evaluation of large volumes of interactions across multiple agents simultaneously.
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with automated computational systems. Machine learning models, natural language processing, and automated scoring algorithms substitute human evaluators, enabling scalable evaluation while maintaining or improving consistency through standardized automated criteria application.
2Loss of time
If random sampling of interactions is used for evaluation, then evaluation time is reduced, but representativeness and comprehensiveness of training recommendations deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically evaluating all agent interactions in advance using machine learning models. Evaluation results, including performance metrics and training recommendations, are generated and stored before being retrieved for coaching sessions, eliminating the need for time-consuming random sampling while ensuring comprehensive coverage.
Solution Approach 2:
The automated evaluation system serves itself by continuously processing interactions through predefined evaluation frameworks. The system automatically generates evaluation results and training recommendations without requiring manual intervention for each interaction, enabling comprehensive evaluation while maintaining efficiency through automated workflows.
3Measurement precision
If all interactions are evaluated manually, then comprehensive assessment is achieved, but resource requirements and evaluation cost increase significantly
Solution Approach 1:
The patent replaces resource-intensive manual evaluation with automated computational systems. Machine learning models process interactions efficiently, requiring minimal human resources while achieving comprehensive evaluation of all agent interactions. The automated system scales without proportionally increasing human resource requirements.
Solution Approach 2:
The system changes the parameters of evaluation by using automated computational metrics and machine learning-based assessment criteria. This enables comprehensive evaluation of all interactions while reducing resource requirements, as automated systems can process large volumes of data more efficiently than manual human evaluation.
4Productivity
If automated evaluation systems are implemented, then scalability and consistency are improved, but ability to evaluate complex interactions and provide personalized recommendations may be reduced
Solution Approach 1:
The automated evaluation system applies local quality by providing personalized training recommendations tailored to each agent's specific performance gaps and needs. While the evaluation framework is standardized and automated, the output is customized for each agent based on their individual interaction patterns, strengths, and areas for improvement, maintaining adaptability within the scalable automated system.
Data Source
AI summary
A system for evaluating agent performance in interactions and generating training recommendations for agents based on the evaluated agent performance may include a computing device; a memory; and a processor, the processor configured to: create a plurality of evaluation prompts for evaluating interaction data items of one or more interactions; generate evaluation results for the interaction data items using the plurality of evaluation prompts and machine learning; create training recommendation prompts for the evaluation results; and generate training recommendations from training categories using the training recommendation prompts and machine learning.