Contact Center Queue Routing With Dynamic Visibility Thresholds
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Solution Overview
Problem
Contact centers face inefficiencies and high costs in managing complex routing profiles with thousands of queues and agents, leading to errors and suboptimal performance due to manual configuration and dynamic traffic patterns.
Innovation Solution
Implementing a routing service that dynamically adjusts queue priorities and visibility thresholds using control systems approaches, such as PID controllers, to optimize agent assignments and improve queue performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of routing profiles is used, then administrators can control queue assignments, but the system becomes error-prone and inefficient as contact centers grow to thousands of queues and agents
Solution Approach 1:
The system enables self-service through automated routing profile configuration. The machine learning model automatically generates routing profiles based on historical data and performance metrics, eliminating the need for manual administrator intervention. The system self-adjusts queue assignments and routing rules dynamically, reducing human error and improving efficiency as the contact center scales to thousands of queues and agents.
2Reliability
If more administrators are hired to manage routing configurations, then configuration accuracy improves, but operational costs increase significantly
Solution Approach 1:
The patent replaces the mechanical system of manual administrator configuration with an automated machine learning-based system. The ML model processes routing data, analyzes performance metrics, and automatically generates optimized routing profiles without human intervention. This substitution eliminates the need for multiple administrators while maintaining or improving configuration accuracy, thereby reducing operational costs significantly.
3Device complexity
If static routing profiles are used, then configuration simplicity is maintained, but the system cannot adapt to dynamic traffic patterns and queue performance changes
Solution Approach 1:
The system implements dynamics by continuously monitoring queue performance metrics and traffic patterns, then automatically adjusting routing profiles in real-time. The machine learning model processes incoming data streams and dynamically reconfigures routing rules to optimize agent assignments based on current conditions. This dynamic adaptation maintains configuration simplicity from the administrator's perspective while enabling the system to respond flexibly to changing traffic patterns.
4Adaptability or versatility
If thousands of queues and routing profiles are created to handle all scenarios, then service coverage improves, but system complexity and management burden become unmanageable
Solution Approach 1:
The patent implements universality by creating a single automated machine learning-based routing profile generation system that handles all queue configurations and routing scenarios. Instead of requiring separate manual configurations for each queue and routing profile, the ML model universally processes all routing decisions based on learned patterns from historical data. This multi-functional approach provides comprehensive service coverage while maintaining manageable system complexity through automation.
Data Source
AI summary
Systems and methods are described relating to distributing agents to different queues provided by a call or contact center. A contact service may define a number of different queues for processing different customer requests, such as may be routed to different agents, with each queue associated with a priority value (higher priority get assigned agents first) and a visibility threshold (how long a request will sit before it is picked up by an agent). The described techniques add a time-to-service level (SL) goal, per queue to determine when queues are over and underperforming. In the case queues are overperforming, various techniques, including control systems approaches may be used to determine adjustments to the visibility threshold (e.g., at least one of increasing the thresholds for overperforming queues and decreasing the thresholds for underperforming queues) to increase performance in meeting the customer SL goals.