Caller Routing System Using Performance-Based Agent Matching
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Solution Overview
Problem
Conventional contact center routing methods are largely random, failing to optimize caller-agent matching based on performance data and caller preferences, leading to suboptimal customer experience and operational efficiency.
Innovation Solution
Implementing a system that maps a portion of callers to agents based on performance data and pattern matching algorithms, allowing for adjustable fractions of callers to be routed using performance-based or pattern matching algorithms, with a control group for benchmarking, and providing a graphical interface to set and display the impact on outcome variables such as revenue, cost, and customer satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional round-robin routing is used to connect callers to agents, then the routing process is simple and fast, but the caller experience is suboptimal due to random matching
Solution Approach 1:
The system changes the routing parameter from random/round-robin selection to performance-based selection. It introduces agent performance metrics (such as customer satisfaction scores, resolution rates, and handling time) as the basis for routing decisions, transforming the routing mechanism from time-based to quality-based allocation
Solution Approach 2:
The system implements feedback loops where agent performance data is continuously collected, analyzed, and used to adjust routing decisions. Performance metrics are monitored in real-time and fed back into the routing algorithm to dynamically optimize caller-agent matching, creating a closed-loop system that improves over time
2Reliability
If performance-based routing is implemented for all callers, then caller experience improves, but system complexity increases
Solution Approach 1:
The system segments the caller population into different groups and applies different routing strategies to each segment. High-priority or complex calls may receive performance-based routing to specialized agents, while routine calls use simpler routing methods, reducing overall system complexity while maintaining quality improvements where most needed
Solution Approach 2:
The system implements performance-based routing partially rather than universally. It may apply advanced routing to a portion of callers (e.g., premium customers, complex inquiries) while using conventional routing for others, achieving quality improvements without the full complexity burden across the entire system
3Measurement precision
If pattern matching algorithms are used to match callers with agents, then matching precision improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing agent performance profiles, skill matrices, and compatibility metrics before actual routing decisions are needed. This pre-processing allows the routing algorithm to make rapid matching decisions based on pre-computed data rather than calculating everything in real-time
Solution Approach 2:
The system replaces complex mechanical pattern matching computations with simpler lookup operations and scoring mechanisms. Instead of performing exhaustive pattern matching in real-time, it uses pre-computed compatibility scores and performance metrics that can be quickly compared and ranked, substituting computational intensity with data retrieval and comparison
Data Source
AI summary
Techniques for handling contacts and agents in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for handling contacts and agents in a contact center system including associating an outcome of an interaction between a contact and an agent with an identification of a matching strategy used for matching the contact with the agent.