Dynamic Contact Center Routing Rules for Agent Allocation
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Solution Overview
Problem
The existing method of routing communications in contact centers using agent queues leads to inefficient use of resources, where agents in high-demand queues remain idle while those in low-demand queues are underutilized, resulting in suboptimal performance metrics such as wait time and customer satisfaction.
Innovation Solution
Monitoring performance metrics like wait time and abandonment rate, and adjusting the rules associated with each queue by adding or removing attributes and proficiency levels to optimize agent allocation, ensuring that more agents can handle communications, thereby improving queue performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If agents are placed in different queues based on communication topics, then routing precision is improved, but agent resource utilization deteriorates
Solution Approach 1:
The system dynamically adjusts queue assignments and routing rules based on real-time performance metrics and historical data. Agents can be moved between queues or have their routing attributes modified to optimize resource utilization while maintaining routing precision. The routing rules themselves are dynamically adjusted based on monitored performance data.
Solution Approach 2:
The system changes routing parameters and agent attributes based on performance metric thresholds. When metrics fall below thresholds, the system modifies routing rule attributes to improve both routing precision and agent utilization. This involves adjusting proficiency levels, queue assignments, and routing criteria.
2Reliability
If strict routing rules are applied to queues, then service quality is improved, but agent availability deteriorates
Solution Approach 1:
Routing rules are made dynamic rather than static. The system continuously monitors performance metrics and adjusts routing rule attributes in real-time. When service quality metrics fall below thresholds, the system relaxes or modifies routing rules to increase agent availability, and tightens them when quality is sufficient.
Solution Approach 2:
The system implements continuous feedback loops where performance metrics are monitored and fed back into the routing rule adjustment process. Historical performance data is used to inform rule modifications, ensuring that service quality is maintained while optimizing agent availability. The feedback mechanism allows the system to learn from past performance and adjust rules accordingly.
Data Source
AI summary
In one embodiment, one or more performance metrics for communication queues are monitored. The performance metrics may include metrics such as wait time, abandonment rate, and customer survey scores. Communications are retrieved from the queues and provided to agents based on rules associated with each queue. When a monitored performance metric for a queue falls below a threshold, the system determines one or more attributes of the rule associated with the queue that could be changed or removed to improve the performance metric. The determined attributes may be made based on the number of agents that will be available to handle communications from the queue after the attributes are changed, and historical performance metric data collected about the queue and agents that handled communications for the queue. The proposed changes to the rule may be presented to an administrator who may either accept or reject the proposed changes.