Agent Work Attribute Modeling for Contact Center Adherence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing contact centers face challenges in optimizing agent schedules to improve adherence metrics, leading to inefficiencies and increased costs due to suboptimal agent performance.
Innovation Solution
A computer-implemented method for modeling agent work attributes using an automated process to generate individualized work schedules that consider key shift parameters, leveraging a trained model to identify values that correlate with improved adherence metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling methods are used, then operational simplicity is maintained, but agent adherence performance deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical shift data, agent performance metrics, and scheduling patterns before generating optimized schedules. The automated modeling process pre-processes evaluation shifts and adherence scores to create training datasets that inform future scheduling decisions, enabling proactive optimization rather than reactive adjustments.
Solution Approach 2:
An automated modeling process acts as an intermediary between traditional scheduling systems and performance outcomes. This intermediary layer analyzes complex relationships between shift parameters and adherence metrics, translating historical data into actionable scheduling recommendations that improve performance without requiring complete system redesign.
2Productivity
If generic scheduling approaches are used, then administrative overhead is reduced, but agent performance and adherence deteriorate
Solution Approach 1:
The system applies local quality by customizing schedules for individual agents based on their specific performance patterns, preferences, and historical data. Each agent receives a personalized schedule optimized for their adherence patterns, rather than applying uniform scheduling rules across all agents. This localized approach maximizes individual performance while maintaining overall system efficiency.
Solution Approach 2:
The system dynamically changes scheduling parameters such as shift start times, break durations, and shift lengths based on analyzed performance data. The automated modeling process identifies optimal parameter combinations for each agent and adjusts schedules accordingly, enabling flexible adaptation to individual performance patterns without manual intervention.
3Reliability
If manual schedule optimization is attempted, then customization potential increases, but time consumption and operational costs increase
Solution Approach 1:
The scheduling system performs self-service by automatically analyzing historical data, identifying performance patterns, and generating optimized schedules without requiring manual intervention. The automated modeling process independently processes evaluation shifts, calculates adherence scores, and produces personalized scheduling recommendations, eliminating time-consuming manual optimization while maintaining high adherence standards.
Solution Approach 2:
The system implements continuous feedback loops where adherence metrics from executed schedules are fed back into the automated modeling process. This feedback mechanism allows the system to learn from actual performance outcomes and continuously refine scheduling recommendations, improving adherence over time while maintaining automated efficiency.
Data Source
AI summary
A method that includes an automated modeling process having the steps of: receiving shift data describing evaluation shifts worked by the agent and determining therefrom values for shift parameters; monitoring performance of the agent during each of the evaluation shifts in relation to an adherence metric and determining therefrom a score associated with the adherence metric for each; creating a training dataset that includes training samples for respective ones of the evaluation shifts, each training sample including the determined values of the shift parameters paired with the score achieved in relation to the adherence metric; and using the training dataset to train a work attributes model for the agent, the work attributes model configured to identify a key value for a key shift parameter that statistically correlates with the agent achieving a better score in relation to the adherence metric.