Behavioral Pairing Model Evaluation in Contact Centers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Behavioral pairing model generation and validation in contact centers are time-consuming and resource-intensive, requiring manual intervention or fine-tuning to improve pairing strategies for efficient contact-agent pairing.
Innovation Solution
A method for behavioral pairing model evaluation using computer processors to partition contact data, applying locality sensitive hashing (LSH) and randomized sets to determine the quality of partitioning, and selecting the best partitioning method to generate a behavioral pairing model that enhances contact center performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If behavioral pairing model generation and validation are performed manually, then model quality can be improved, but time consumption and resource requirements increase
Solution Approach 1:
The system performs self-evaluation of behavioral pairing models through automated computer-implemented methods. The processor automatically determines contact data, partitions it into types, calculates quality measures, and selects optimal models without requiring manual intervention, thereby reducing time consumption while maintaining model quality assessment capability
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with computer-implemented automated methods. The system uses processors to automatically perform data partitioning, quality measurement calculation, and model selection, substituting human manual work with computational algorithms that achieve both speed and accuracy
2Measurement precision
If behavioral pairing model generation and validation are performed manually, then model accuracy can be improved, but computational resources increase
Solution Approach 1:
The system evaluates multiple partitioning methods and selects the optimal one based on quality measures, rather than exhaustively analyzing all possible models. This partial action approach achieves sufficient model accuracy without requiring excessive computational resources by focusing evaluation on the most promising partitioning strategies
3Productivity
If automated partitioning methods are used, then processing speed is improved, but model selection accuracy may deteriorate
Solution Approach 1:
The system calculates quality measures for different partitioning methods and uses this feedback to select the optimal partitioning approach. The automated process incorporates evaluation feedback loops where quality measures inform subsequent model selection decisions, ensuring both speed and accuracy are maintained
Solution Approach 2:
The system changes parameters such as the number of partitioning methods evaluated and the quality measure thresholds to optimize the balance between processing speed and model selection accuracy. By adjusting these parameters, the system can adapt to different operational requirements while maintaining effective automation
Data Source
AI summary
Techniques for behavioral pairing model evaluation in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing model evaluation in a contact center system and communicatively coupled to the operating system comprising determining, by at least one computer processor configured to operate in the contact center system, contact data; partitioning, by the at least one computer processor, the contact data into a first plurality of types; determining, by the at least one computer processor, a first measure of the quality of the partitioning of the contact data into the first plurality of types; and outputting, by the at least one computer processor, a computer-processor generated behavioral pairing model based on the quality of the partitioning.