Simulated Contact-Agent Pairing for Balanced Contact Center Routing

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Contact centers face challenges in optimizing contact-agent pairings, as existing strategies like FIFO and PBR often lead to uneven agent workload and reduced training opportunities, while benchmarking methods fail to capture statistically significant effects due to resource-intensive and error-prone log tracking.

Innovation Solution

Implement computer-implemented systems and methods to simulate contact-agent pairings and analyze performance, allowing real-time tracking of alternative strategies and determining their effectiveness through simulated sequences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If performance based routing (PBR) is used to maximize contact center performance, then overall performance improves, but agent workload becomes uneven and lower-performing agents receive fewer contacts

Engineering Contradiction:

Improvecontact center performanceVSAvoidagent workload distribution

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system changes the parameters used for agent ranking by considering multiple factors (performance metrics, workload balance, agent preferences, skill levels) rather than relying solely on performance metrics. This allows the routing system to adjust the weighting and combination of parameters dynamically to achieve both performance optimization and equitable workload distribution.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The routing system implements dynamic adjustment of agent rankings and contact assignments. Instead of static PBR rankings, the system continuously updates agent positions based on current performance data, workload status, and other relevant parameters, allowing flexible optimization that adapts to changing conditions while maintaining balance.

Inventive Principle:

Principle #15Dynamics

2Ease of operation

If FIFO strategy is used for contact assignment, then agent workload is evenly distributed, but contact center performance may be reduced due to suboptimal pairings

Engineering Contradiction:

Improveagent workload distributionVSAvoidcontact center performance

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system performs preliminary actions by pre-calculating and pre-ranking agents based on multiple criteria before contacts arrive. This allows the system to have agents ready in optimized positions according to their current state, enabling both balanced workload distribution and high-performance pairings when contacts are assigned.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where routing decisions are continuously refined based on outcomes of previous assignments. Performance data, agent responses, and contact resolution metrics feed back into the ranking algorithm, allowing the system to learn and improve its pairing strategy over time while maintaining workload balance.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If benchmarking methods are used to evaluate pairing strategies, then performance comparison is possible, but resource consumption increases and measurement precision is reduced due to log tracking errors

Engineering Contradiction:

Improveperformance measurementVSAvoidresource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

Instead of tracking and analyzing extensive real logs for benchmarking, the system creates simplified copies or models of contact and agent data that capture the essential characteristics needed for performance evaluation. This reduces computational resources required while maintaining measurement accuracy by focusing on key parameters rather than complete log details.

Inventive Principle:

Principle #26Copying

4Productivity

If PBR orders agents by highest performance, then expected outcome of each interaction is maximized, but higher-performing agents become overworked and lower-performing agents idle longer

Engineering Contradiction:

Improveexpected outcome of interactionVSAvoidagent idle time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system applies partial action by not always assigning contacts to the top-ranked high-performing agents. Instead, it deliberately distributes some contacts to lower-ranked agents to provide them with training opportunities and development experience, accepting that this may slightly reduce immediate performance in exchange for long-term agent development and reduced idle time.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250365367A1Computer-implemented systems and methods for evaluating contact center performance using simulated contact-agent pairings

Publication Date: 2025.11.27 AFINITI AI LTD

AI summary

Computer-implemented systems and methods are disclosed for simulating contact-agent pairings and analyzing contact center performance based on the simulated contact-agent pairings. An exemplary method includes obtaining a first set of available contacts and available agents; determining a first contact-agent pairing based on the first set of available contacts and available agents and a first pairing strategy; and establishing a first connection between a first agent device and a first contact device for the first contact-agent pairing. The exemplary method further includes storing a first set of pairing data corresponding to the first contact-agent pairing; generating, by a simulation model implementing a simulated pairing algorithm, a second contact-agent pairing based on the first set of available contacts and available agents and a second pairing strategy; and storing a second pairing data corresponding to the second contact-agent pairing.