AI Multi-Skill Routing Matrix for Call Center Staffing
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Solution Overview
Problem
Existing call center optimization methods fail to accurately determine the number of agents needed to handle incoming calls efficiently, especially when agents have multiple skills, leading to suboptimal staffing and increased wait times.
Innovation Solution
An AI-driven system generates a multi-skill routing matrix to simulate various agent combinations, measures quality metrics, and recursively reduces the range of available agents to converge on the optimal number needed for each routing sub-problem, ensuring adequate staffing with agents having multiple skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional call center staffing methods are used, then staffing decisions are simple to make, but the accuracy of determining the number of agents needed is poor, leading to suboptimal staffing
Solution Approach 1:
The patent segments the complex multi-skill routing problem into smaller band routing matrices, each representing a specific skill or skill group. This segmentation allows the system to process and optimize staffing for each skill category separately while maintaining overall system accuracy, thereby improving measurement precision without overwhelming system complexity.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a mediator between call volume data and staffing decisions. This intermediary processes multiple variables including agent availability, skills, and call patterns to generate optimized staffing recommendations, significantly improving the accuracy of determining agents needed while managing complexity through automated intelligence.
2Adaptability or versatility
If more agents are hired to handle diverse skills, then service coverage improves, but resource allocation efficiency decreases
Solution Approach 1:
The patent implements multi-functionality by enabling agents to handle multiple skills and call types. The system creates a flexible routing matrix that can dynamically assign calls to agents based on their diverse skill sets, improving service coverage for diverse skills while maintaining resource allocation efficiency through optimized matching rather than hiring additional specialized agents.
Solution Approach 2:
The patent applies dynamics by making the routing matrix adaptive and flexible rather than static. The system continuously adjusts agent assignments based on real-time factors such as agent availability, current call volume, and skill requirements, allowing the call center to efficiently handle diverse skills without overstaffing any particular area.
3Measurement precision
If the full range of available agents is considered for each routing problem, then solution quality improves, but computational time increases
Solution Approach 1:
The patent segments the large agent pool into smaller groups based on skills and call types, creating multiple band routing matrices. This segmentation allows the system to evaluate solution quality for each segment separately and combine results, maintaining overall solution quality while significantly reducing computational time compared to evaluating all agents against all calls simultaneously.
Solution Approach 2:
The patent applies partial action by considering only the most relevant agents for each specific routing sub-problem rather than evaluating the entire agent pool for every decision. The system identifies and focuses on the subset of agents with the necessary skills for each call type, achieving high solution quality for each segment while reducing total computational burden.
Data Source
AI summary
Systems and methods for call handling optimization of a call center are disclosed. A system is configured to: obtain a plurality of agent demand variables; obtain an indication of available agents that are associated with a plurality of skills; generate a multi-skill routing matrix depicting a routing problem from the agent demand variables and the available agents; deconstruct the multi-skill matrix into one or more band routing matrices; and identify, for each band routing matrix, a number of agents from the available agents to be used in solving the band routing matrix. Identifying the number of agents includes recursively simulating a plurality of possible routing solutions, measuring a quality metric for each simulation, and reducing the range of available agents based on the quality metric. The system may indicate the number of agents to be used for each routing sub-problem (which may be at the staff group level).