Contact Center Agent Skill Promotion and Demotion
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Solution Overview
Problem
Current contact center systems face challenges in managing agent skill levels to meet fluctuating workloads, as they are complex to implement and only support adding reserve agents without considering removing agents from over-capacity skills, leading to inefficient resource allocation and potential service issues.
Innovation Solution
The system automatically adjusts agent skill levels by promoting or demoting them based on contact center state, allowing for dynamic skill level changes until the workload is brought into compliance, with features like randomization and hysteresis to prevent excessive switching and protect match rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual monitoring and moving of agents is used to handle workload fluctuations, then flexibility in resource allocation is improved, but labor intensity and operational complexity increase
Solution Approach 1:
The system enables self-service by automatically monitoring contact center state and adjusting agent skill levels without human intervention. The work assignment engine continuously evaluates service level objectives and autonomously promotes or demotes agents based on real-time conditions, eliminating the need for manual monitoring while maintaining operational flexibility.
Solution Approach 2:
The patent replaces the mechanical manual process of monitoring and moving agents with an automated electronic system. The work assignment engine uses computer algorithms to substitute human operators, automatically detecting when service level objectives are not met and executing skill level adjustments without manual labor.
2Reliability
If reserve agents are added to expand the eligible agent pool, then service level compliance is improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The system eliminates the need for complex reserve agent configuration by enabling agents to dynamically adjust their own skill levels based on real-time contact center state. Instead of requiring users to configure reserve agent pools, the work assignment engine automatically manages skill level adjustments, simplifying implementation while maintaining service level compliance.
Solution Approach 2:
The patent introduces dynamic skill level adjustment that allows agents to flexibly change their skill levels based on current workload conditions. This dynamic approach replaces static reserve agent configurations, enabling the system to adapt to changing conditions without complex pre-configuration of agent pools and skill assignments.
3Reliability
If automated utilization of reserve agents is implemented, then service level compliance is improved, but implementation complexity increases
Solution Approach 1:
The system achieves automated service level compliance through self-service mechanisms where agents autonomously adjust their skill levels based on real-time conditions. The work assignment engine monitors compliance and automatically triggers skill level changes without requiring complex reserve agent management or sophisticated automated utilization systems.
Solution Approach 2:
The patent simplifies automated compliance by changing the parameter being controlled from agent pool composition to individual agent skill levels. Instead of managing complex reserve agent configurations, the system adjusts skill level parameters dynamically, reducing implementation complexity while maintaining automated service level compliance.
4Productivity
If agent skill levels are adjusted to meet service level objectives, then resource allocation efficiency is improved, but match rate protection may be compromised
Solution Approach 1:
The system applies partial action by adjusting skill levels of only some agents rather than all agents simultaneously. When service level objectives are not met, the work assignment engine selectively promotes skill levels of specific agents based on current conditions, achieving resource allocation efficiency while minimizing impact on match rates for other agents.
Solution Approach 2:
The patent implements local quality by making skill level adjustments specific to individual agents and specific skill types rather than applying uniform changes across all agents. This localized approach allows the system to improve resource allocation efficiency for specific skills while preserving match rates for other skills where adjustments are not needed.
Data Source
AI summary
A contact center is described along with various methods and mechanisms for administering the same. The contact center proposed herein provides the ability to, among other things, selectively promote and demote agent skills to respond to various changes in contact center conditions. This enables the contact center to respond to unexpected changes in processing conditions without significantly impacting the overall performance of the contact center.