Contact Center Queue Wait-Time Estimation With Historical Data
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Solution Overview
Problem
Contact centers often face challenges in accurately estimating wait times for users attempting to communicate with agents, leading to poor user experience and inefficient use of time.
Innovation Solution
A server-based system calculates an estimated wait time by determining the number of devices in a user queue and available agents, using a combination engine that incorporates previous wait times and statistical models to provide real-time updates to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional queue management systems are used, then system simplicity is maintained, but wait time estimation accuracy deteriorates
Solution Approach 1:
The system segments wait time estimation into multiple independent calculation methods: (1) queue position-based estimation using device queue position and average service time, (2) historical data-based estimation using statistical analysis of previous wait times, and (3) real-time monitoring-based estimation. Each segment handles specific aspects of the estimation problem, improving overall accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The server implements multiple estimation methods that serve different functions simultaneously: queue position method provides immediate rough estimates, historical data method provides statistically refined estimates, and real-time monitoring provides dynamically adjusted estimates. This multi-functionality allows the system to adapt to different scenarios and maintain high accuracy across varying contact center conditions.
2Ease of operation
If real-time wait time calculation is implemented, then user experience is improved, but computational resource consumption increases
Solution Approach 1:
The system performs wait time calculations periodically rather than continuously. The server updates wait time estimates at scheduled intervals and triggers recalculation events based on specific conditions (e.g., when queue position changes or new devices join). This periodic approach provides real-time user experience improvements while significantly reducing computational resource consumption compared to continuous calculation.
Solution Approach 2:
The system uses historical wait time data and statistical models that automatically refine estimates without requiring intensive real-time computation. Once historical data is collected, the statistical analysis performs self-service by continuously improving estimation accuracy using stored data, reducing the need for heavy computational resources during real-time operations.
3Measurement precision
If multiple estimation methods are used, then estimation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary calculations by pre-processing historical data and establishing statistical models in advance. Queue position-based estimates are calculated using pre-determined average service times. When a new request arrives, the system combines these pre-prepared elements with current queue state, significantly reducing processing time while maintaining high accuracy through the integration of multiple estimation methods.
Data Source
AI summary
A server determines a number of devices preceding a user device in a user queue of devices for communication with a contact center agent device. The server determines a number of contact center agent devices available for the communication. The server calculates an estimated wait time for the user device based on the number of devices preceding the user device, the number of contact center agent devices, and wait times of user devices, distinct from the user device, requesting communications with the contact center agent device.