Hierarchical Contact Routing for Resource Allocation
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Solution Overview
Problem
Existing contact center resource allocation systems using rules engines face bottlenecks when adapting to changing conditions, leading to increased response times and reduced flexibility due to the processing burden of continuously re-evaluating rules, which can result in not requeueing contacts to avoid these issues but at the cost of reduced system flexibility.
Innovation Solution
A network of nodes is maintained, where contacts are enqueued and dequeued based on routing recommendations, with service nodes and resource nodes allowing intelligent queuing and resource allocation, using attributes like priority and age to determine conflict resolution and optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a rules engine is used to continuously re-evaluate rules and requeue contacts, then adaptability to changing conditions is improved, but response time increases due to processing burden
Solution Approach 1:
The system segments the contact center into multiple hierarchical queues organized by skill levels (e.g., L1, L2, L3 support). Each queue operates with its own routing logic, allowing local decision-making without requiring continuous global rules evaluation. This segmentation enables adaptability at each queue level while reducing the processing burden on a centralized rules engine.
Solution Approach 2:
The system pre-configures routing rules and queue hierarchies before contacts arrive. Routing decisions are based on pre-established criteria such as contact attributes, skill requirements, and queue capacities. This preliminary configuration allows rapid routing decisions without requiring continuous rules re-evaluation, thus maintaining adaptability while reducing response time.
2Loss of time
If contacts are not requeued once assigned to avoid rules engine bottleneck, then response time is reduced, but system flexibility is reduced
Solution Approach 1:
The system implements dynamic monitoring of queue conditions and contact status. If a contact's attributes change or queue conditions evolve after initial assignment, the system can dynamically re-evaluate routing decisions through event-driven triggers rather than continuous processing. This allows flexibility to be restored without the performance penalty of continuous rules engine processing.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor contact progression through the queue hierarchy and queue status changes. When specific conditions are met (e.g., contact waiting time thresholds, skill availability changes), feedback triggers selective re-evaluation of routing decisions. This feedback-driven approach restores flexibility only when necessary, maintaining fast response times while enabling adaptability when conditions change.
3Productivity
If multiple hierarchical queues are implemented for intelligent queuing, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The system implements a universal queue hierarchy model that can accommodate multiple contact types, skill levels, and routing scenarios using the same structural framework. The hierarchical queue design with standardized attributes (skill requirements, priority levels, capacity thresholds) serves multiple functions: initial routing, overflow management, skill-based distribution, and load balancing. This universality improves resource utilization across diverse scenarios while avoiding the complexity of implementing separate systems for each function.
Data Source
AI summary
Resource allocation in a contact center can be performed using a network of nodes. Such a network of nodes can be organized according into resource nodes, domain nodes, and service nodes, with paths from the domain nodes, through the service nodes, to the resource nodes being used in the allocation.