Multiphase Optimization for Service Agent Assignment
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Solution Overview
Problem
Existing response management systems face challenges in efficiently resolving customer-encountered issues due to limited resources and inefficient assignment of service agents, leading to increased time and cost in resolving these issues.
Innovation Solution
A multiphase optimization procedure is implemented to identify and rank qualified service agents based on their skills, past performance, and current workload, ensuring that the most suitable agent is assigned to resolve customer-encountered issues within prescribed goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If service agents are assigned to resolve customer-encountered issues without optimization, then the system can operate with simpler processes, but the time to resolution increases and productivity decreases
Solution Approach 1:
The patent segments the service agent selection process into multiple phases: identifying qualified agents based on skills, ranking them by efficiency metrics, and selecting the optimal agent. This segmentation transforms a complex monolithic assignment system into manageable discrete steps, resolving the contradiction between productivity improvement and system complexity.
Solution Approach 2:
The system performs preliminary actions by pre-identifying and pre-ranking service agents based on their skills and past performance before actual issue assignment occurs. This advance preparation enables faster, more accurate agent selection during issue resolution, improving productivity without proportionally increasing operational complexity.
2Productivity
If more service agents are deployed to handle issues, then the capacity to resolve issues increases, but the cost and resource requirements increase
Solution Approach 1:
The patent changes the parameter of agent selection from random or simple first-come-first-served to an optimized selection based on multiple parameters including skill matching, efficiency metrics, and workload balance. This parameter transformation allows the system to achieve higher resolution capacity with the same or fewer agents, resolving the contradiction between productivity and resource quantity.
Solution Approach 2:
The system enables service agents to effectively serve themselves by providing them with ranked assignments based on their capabilities and current workload. This self-service mechanism optimizes resource utilization automatically without requiring additional management overhead, increasing capacity while controlling the number of agents needed.
3Reliability
If service agents are assigned without considering their skills and performance, then the assignment process is faster and simpler, but the quality of resolution and reliability decrease
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring service agents' past performance and using this information to rank and select agents for new issues. This feedback loop ensures that agents with proven success rates in similar scenarios are prioritized, improving resolution quality and reliability while maintaining a manageable evaluation system through automated data collection and analysis.
Solution Approach 2:
The system replaces manual, complex evaluation processes with automated computational algorithms that assess agent skills and performance. This substitution transforms the mechanical complexity of human judgment into streamlined digital processing, achieving high reliability in agent selection without proportionally increasing system complexity.
4Reliability
If service requests are escalated frequently when agents fail to resolve issues, then the chances of resolution may improve, but the time to resolution increases and productivity decreases
Solution Approach 1:
The system performs preliminary matching of service agents to issues based on skill compatibility and historical success rates before assignment. This preliminary action ensures that the most suitable agent handles each issue from the start, maximizing the probability of first-contact resolution and minimizing the need for time-consuming escalations, thereby resolving the contradiction between reliability and time loss.
Solution Approach 2:
The patent introduces an optimization system as an intermediary between the issue and the service agent, mediating the assignment process through intelligent matching. This intermediary analyzes multiple factors including agent skills, workload, and performance history to make optimal assignments, improving first-contact resolution rates while preventing unnecessary escalations and time delays.
Data Source
AI summary
Methods and systems for managing customer-encountered issues are disclosed. To manage the customer-encountered issues, a multiphase optimization process may be implemented to select a service agent to resolve each customer-encountered issue. The multiphase analysis may include a process of identifying service agents qualified to attempt to resolve each customer-encountered issue. The multiphase optimization may also include a process of ranking the qualified service agents based on their past performance and experience. The multiphase optimization may also include a process for estimating the likelihood of each of the qualified service agents resolving each customer-encountered issue within prescribed goals.