Contact Center Queue Reassignment for Service-Level Compliance
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Solution Overview
Problem
Contact centers face challenges in managing human and AI resources to meet operational, strategic, and contractual needs due to the lack of real-time adjustments in Automated Call Distribution (ACD) technologies, leading to potential service-level compliance issues and financial penalties.
Innovation Solution
A computer-implemented method and system for modifying queue assignments using a queue recommendation engine that determines staffing changes based on real-time and historical data, service metrics, and constraints to align with long-term business and contractual requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional ACD technologies are used for routing calls, then real-time routing decisions can be made, but long-term strategic queue targets and service-level compliance cannot be ensured
Solution Approach 1:
The system performs preliminary actions by calculating projected queue metrics and determining staffing changes in advance based on historical data and service targets. The queue recommendation engine computes net staffing attributes and recommends staffing adjustments before the service period ends, enabling proactive rather than reactive queue management that ensures service-level compliance while maintaining real-time routing capabilities
Solution Approach 2:
The system implements dynamic queue assignment modifications by adjusting queue targets and staffing recommendations in real-time based on changing conditions. The queue recommendation engine continuously monitors service metrics and updates staffing recommendations dynamically, allowing the contact center to adapt to varying call volumes and service requirements while maintaining compliance with strategic queue targets
2Reliability
If queue assignments are modified frequently to meet service targets, then service-level compliance improves, but system complexity and difficulty of management increase
Solution Approach 1:
The queue recommendation engine operates as a self-service system that automatically calculates projected queue metrics, determines net staffing attributes, and generates staffing recommendations without requiring complex manual intervention. The system uses historical data and service targets to autonomously compute optimal staffing changes, reducing management complexity while ensuring service-level compliance through automated, data-driven decisions
Data Source
AI summary
A method, system, and article of manufacture for modifying a queue assignment for a contact center are provided. At least one computing device determines, by solving for constraints, at least one staffing change for at least one of the first queue or the second queue, using as inputs (a) first time-based service metrics, (b) second time-based service metrics, (c) data describing eligible staffing changes, and (d) at least one predetermined constraint. The at least one staffing change is a modification to the queue assignment of a subset of the computing device instances that is predicted to improve net staffing relating to service targets selected from (a) an average speed of answer (ASA) specifying an average time for one of the computing device instances to initiate handling of a particular task after the particular task has been assigned to the relevant queue and (b) a service level specifying a percentage of tasks for which handling is initiated within a specified time frame after the tasks are assigned to the queue. The at least one staffing change can be implemented by modifying the queue assignment of the subset of the computing device instances.