Dynamic Contact Center Workload Routing via Observed Complexity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Contact centers face challenges in efficiently managing multiple communication channels, such as voice and text-based work items, due to differing response thresholds and complexities, leading to workload imbalance and potential customer dissatisfaction.
Innovation Solution
A system that determines the initial complexity of work items and dynamically updates capacity based on observed complexity, routing work items to agents with necessary skills and capacity, allowing for simultaneous processing of multiple text chats while ensuring timely responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If agents process multiple text chat work items simultaneously, then productivity increases, but response time quality deteriorates
Solution Approach 1:
The system dynamically adjusts agent capacity based on observed complexity of work items. Capacity is not fixed but changes in real-time as work items are processed and their actual complexity becomes known. This allows the system to optimize the balance between processing multiple items and maintaining response quality.
Solution Approach 2:
The system monitors observed complexity of work items and uses this feedback to update agent capacity. The feedback loop continuously refines capacity estimates based on actual performance data, enabling the system to learn and adapt to the true complexity characteristics of different work item types.
2Speed
If initial complexity is used to route work items, then routing speed increases, but workload distribution quality deteriorates
Solution Approach 1:
The system performs preliminary routing based on initial complexity estimates to enable fast initial assignment. This preliminary action allows work items to be routed quickly without waiting for full complexity assessment, while subsequent monitoring and capacity updates ensure long-term distribution quality.
Solution Approach 2:
The system implements feedback mechanisms that monitor observed complexity and use it to update agent capacity and improve future routing decisions. This feedback loop gradually enhances workload distribution quality while maintaining the speed benefits of initial complexity-based routing.
3Device complexity
If agent capacity is fixed, then system simplicity is maintained, but adaptability to varying work item complexity deteriorates
Solution Approach 1:
The system transitions from fixed capacity to dynamic capacity that adapts to varying work item complexity. Capacity is updated based on observed complexity metrics, allowing the system to automatically adjust to different work loads and complexity levels without manual intervention.
Solution Approach 2:
The system performs self-adjustment of agent capacity based on monitored performance data. The capacity management mechanism serves itself by automatically learning from observed complexity and updating capacity allocations without requiring external configuration or manual tuning.
Data Source
AI summary
Contact center agents are often presented work items utilizing voice, video, and text. Text messages are often processed concurrently with other text or non-text messages. In order to avoid over or under utilizing agents, contact centers may determine an initial complexity for a work item and route the work item to an agent having the skills and capacity to accommodate the initial complexity. However, the initial complexity may differ from an observed complexity as the agent processes the work item. Accordingly, systems and methods are provided to monitor ongoing text message complexity and route subsequent work items to agents based on an observed complexity provided, at least in part, by the complexity of text-based work item current being processed, and the agents capacity to process the subsequent work item.