Resource Selection Computing System for Contact Center Routing
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Solution Overview
Problem
Conventional contact center routing schemes do not consider the relationship between customers and resources, leading to inefficient resource selection and potential discomfort for customers when sharing personal information with unfamiliar agents.
Innovation Solution
A resource selection computing system that monitors parameters associated with communication sessions, computes a connection score between customers and resources based on stored parameters, and selects resources for handling communication sessions based on this score, ensuring a more personalized and efficient routing of customer requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional routing schemes (skill based routing, work based routing) are used to select resources, then resource selection efficiency is maintained, but customer comfort and satisfaction deteriorate due to forced interaction with unfamiliar agents
Solution Approach 1:
The system performs preliminary actions by monitoring and analyzing customer-resource interaction parameters before the actual communication session occurs. Connection scores are pre-computed based on historical data, enabling the routing system to quickly match customers with compatible resources without sacrificing selection efficiency.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring communication session parameters and updating connection scores based on actual interaction outcomes. This feedback loop allows the system to learn from past interactions and improve future routing decisions, balancing customer comfort with operational efficiency.
2Measurement precision
If connection score computation is added to the routing process, then customer matching accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex matching process into distinct modules: a monitoring module that collects parameters, a computation module that calculates connection scores, and a routing module that uses scores for decision-making. This segmentation allows each module to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The connection score acts as an intermediary metric that bridges customer preferences and resource capabilities. Rather than directly comparing complex customer profiles with resource attributes, the system uses the pre-computed connection score as a simplified intermediary value that captures the essence of compatibility.
3Measurement precision
If monitoring of communication session parameters is implemented, then connection score accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant parameters from communication sessions for monitoring and analysis. Rather than processing all possible data, the system identifies and extracts key interaction metrics that have the greatest impact on connection scores, reducing data processing requirements while maintaining accuracy.
Data Source
AI summary
A resource selection computing system for selecting at least one resource for one or more communication sessions in an enterprise is disclosed. The resource selection computing system includes a monitoring module for monitoring one or parameters associated with at least one communication session from at least one customer. The system further includes a database for storing the one or more monitored parameters. The system further includes a computation module for computing at least one connection score for the at least one customer with each of a plurality of resources based on the one or more stored parameters. The system further includes a resource selection module for selecting the at least one resource from the plurality of resources based on the at least one computed connection score.