Automated Call Routing for Agent Upskilling
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Solution Overview
Problem
Existing call center systems face challenges in efficiently retraining agents to perform additional types of work, as the current manual process is labor-intensive and relies heavily on supervisor monitoring, lacking an automated and data-driven approach to assess agent success and stress levels.
Innovation Solution
A method and apparatus that automatically direct a percentage of unskilled telecommunication calls to agents, measuring their success and stress levels through voice, textual, and physiological analysis, adjusting the percentage of unskilled calls based on predefined thresholds to optimize upskilling, and dynamically modifying the workload to ensure effective training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual monitoring and goal setting by supervisors is used for agent retraining, then personalized training can be provided, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The system enables agents to autonomously complete training tasks and demonstrate skill acquisition through automated assessments, eliminating the need for continuous supervisor monitoring. The automated call distribution system assigns training calls based on agent progress, allowing the system to self-manage the retraining process without manual intervention.
Solution Approach 2:
The patent replaces the mechanical manual monitoring process with an automated electronic system that uses software to track agent performance, assess skill levels, and dynamically assign training calls. This substitution transforms the labor-intensive manual process into an efficient automated system.
2Productivity
If automated call distribution is used to assign training calls, then training efficiency improves, but the system complexity increases
Solution Approach 1:
The existing automated call distribution system is extended to perform multiple functions: it not only routes customer service calls but also identifies training opportunities, assigns training calls, and tracks agent progress. This multi-functionality leverages the existing system infrastructure to reduce overall complexity while improving training efficiency.
Solution Approach 2:
The system incorporates automated feedback mechanisms that monitor agent performance on training calls and use this data to dynamically adjust future call assignments. This feedback loop enables the system to adapt to agent skill development without requiring complex manual intervention, maintaining efficiency while managing complexity.
3Reliability
If continuous monitoring of agent stress levels is implemented, then agent well-being is protected, but measurement and analysis complexity increases
Solution Approach 1:
The patent replaces complex psychological stress assessment with automated voice analysis technology that detects stress indicators through speech patterns, tone, and other vocal characteristics. This substitution simplifies the measurement process while maintaining reliability in assessing agent well-being during training calls.
Data Source
AI summary
A method and apparatus trains agents in a call center by directing a plurality of telecommunication calls to an agent by a controller wherein a percentage of the telecommunication calls are unskilled telecommunication calls that the agent is unskilled at processing; calculating by the controller average success of the agent in handling all of the unskilled telecommunication calls; calculating by the controller average stress of the agent in handling all of the unskilled telecommunication calls; increasing the percentage of unskilled telecommunication calls by the controller upon average success being greater than a predefined level of success and the average stress being less than a predefined level of stress; and stopping after the percentage equals a predefined percentage.