AI Skill Routing for Contact Center Agent Performance
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Solution Overview
Problem
Current methods for assigning and updating agent skills in contact centers are manual, time-consuming, and lack objective measures, leading to infrequent changes in agent skills and subjective training decisions, which affects communication routing efficiency.
Innovation Solution
The implementation of reinforcement learning (RL) and artificial intelligence (AI)/machine learning algorithms to automatically assign and update agent skills based on real-time analysis of interactions across various channels, using key performance indicators (KPIs) to quantify and monitor agent performance, eliminating the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual skill assignment by supervisors is used, then agent skills can be assigned based on subjective judgment, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical process of supervisor-based skill assignment with an automated AI/ML system that continuously monitors agent performance and automatically updates skill assignments. The system uses machine learning algorithms to analyze interaction data and make objective skill determination decisions, eliminating the need for manual intervention while maintaining accurate skill assessment.
Solution Approach 2:
The system enables self-service by allowing the AI/ML model to automatically monitor, measure, and update agent skills without requiring supervisor involvement. The system continuously collects performance data, analyzes it against predefined criteria, and autonomously updates skill assignments, making the process self-sustaining and efficient.
2Measurement precision
If continuous monitoring of agent skills is implemented, then accurate skill level determination is achieved, but no simple quantifiable measures exist for estimation
Solution Approach 1:
The patent implements continuous feedback loops where the system monitors agent performance metrics during interactions, compares them against predefined skill criteria, and automatically updates skill assignments. The system collects performance data, analyzes it in real-time, and provides continuous feedback to maintain accurate skill level determination without requiring complex manual assessment processes.
Solution Approach 2:
The system transforms subjective skill assessment into objective quantifiable parameters by defining specific measurable criteria for skill evaluation. The AI/ML model analyzes changes in performance parameters over time and uses these parameter changes to determine skill level updates, making continuous monitoring feasible through objective metrics rather than subjective judgment.
3Productivity
If AI/ML algorithms are used for automatic skill assignment, then productivity and accuracy are improved, but the system complexity increases
Solution Approach 1:
The patent segments the complex skill monitoring function into distinct modular components: data collection modules that gather interaction metrics, analysis modules that process performance data against predefined criteria, and execution modules that update skill assignments. This segmentation allows the AI/ML system to handle complexity through organized, manageable functions while maintaining high productivity.
Solution Approach 2:
The system performs preliminary action by pre-defining skill criteria and performance thresholds before actual skill assignment occurs. The AI/ML model is trained in advance with the criteria for skill evaluation, allowing it to automatically and efficiently make skill determination decisions during operation without requiring complex real-time analysis, thus reducing operational system complexity.
Data Source
AI summary
Methods for routing customers to an agent include receiving a customer communication; representing the customer communication as an array of one or more agent skills desired to handle the customer communication in one hot coding format or as a vector with an induced metric using an embedding algorithm; routing the represented customer communication to an agent having the one or more agent skills; measuring performance of the agent in relation to the one or more agent skills during or after the customer communication; updating, in real-time, one or more performance scores of the agent in a skill profile, wherein the one or more performance scores are related to the one or more agent skills; and routing subsequent customer communications based on the updated one or more performance scores.