Contact Center Agent Demand Modeling With Ensemble Selection
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Solution Overview
Problem
Contact centers face challenges in accurately predicting staffing needs due to varying user volumes and demand, leading to issues such as understaffing or overstaffing, which can result in long wait times or excessive costs.
Innovation Solution
Utilizing artificial intelligence and machine learning to train multiple modeling engines, including neural networks and time-series forecasting engines, to predict staffing needs based on historical data, combined through a combination engine that determines the most accurate prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple modeling engines are trained and combined to predict staffing needs, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system divides the prediction task into multiple specialized modeling engines, each trained on different aspects of historical data (e.g., time-series patterns, neural network patterns, regression models). Each engine handles a specific segment of the prediction problem independently, then their results are combined through a combination engine to achieve higher overall accuracy while maintaining manageable complexity through modular design
Solution Approach 2:
Multiple independent modeling engines are merged into a unified prediction system through a combination engine that aggregates their outputs. This merging allows the system to leverage the strengths of different algorithms (time-series, neural networks, regression) simultaneously, improving prediction accuracy by combining diverse analytical approaches rather than relying on a single model
2Reliability
If adequate staffing is ensured through accurate prediction, then service quality is improved, but operational costs increase if overstaffing occurs
Solution Approach 1:
The system uses historical contact center data including actual staffing levels, service quality metrics, and cost information as feedback to continuously refine predictions. By analyzing past performance data and comparing predicted versus actual staffing needs, the model learns to optimize the balance between maintaining service quality and minimizing unnecessary staffing costs, adapting to changing patterns over time
Solution Approach 2:
The system performs preliminary prediction of staffing needs before demand occurs by analyzing historical patterns and forecasting future requirements. This advance planning allows contact centers to staff optimally in advance, ensuring adequate coverage during high-demand periods while avoiding the costs of maintaining excessive staff during low-demand periods, thereby balancing service quality with cost efficiency
Data Source
AI summary
A contact center server obtains historical contact center data of a contact center by tracking contact center conditions. The contact center server trains, based on the historical contact center data, multiple modeling engines to generate agent demand data representing a number of agents working at a given time. The contact center server trains, based on the historical center contact center data and performance data of the multiple modeling engines, a combination engine to generate a combination of one or more modeling engines from the multiple modeling engines. The contact center server provides an output representing the trained combination engine and the multiple modeling engines.