ML Queue Positioning for Support Call Prioritization
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Solution Overview
Problem
Current support systems often prioritize calls based solely on receipt time, leading to nuisance users being rewarded, users with simple issues being penalized, and inefficient resource allocation, resulting in prolonged wait times and wasted resources.
Innovation Solution
A machine learning-based support platform that processes communication data, including historical and current call data, to determine factors and weights for each call, allowing for dynamic reordering of the support queue to prioritize users with simple issues and penalize nuisance users, thereby reducing wait times and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If calls are prioritized based solely on receipt time, then first-come-first-served fairness is maintained, but nuisance users are rewarded and users with simple issues are penalized
Solution Approach 1:
The system changes the parameters used for queue positioning from solely temporal (receipt time) to include behavioral parameters (call history, issue complexity, user patterns). The machine learning model analyzes multiple parameters including past call frequency, time of day, day of week, and issue types to dynamically adjust queue position, thereby resolving the contradiction between fairness and efficiency.
Solution Approach 2:
The system implements feedback loops where call outcomes and user behavior are continuously monitored and fed back into the machine learning model. This feedback mechanism allows the system to learn from past interactions and refine queue positioning decisions, preventing nuisance users from being rewarded while ensuring legitimate users receive timely support.
2Device complexity
If traditional FIFO queueing is used, then implementation simplicity is maintained, but wait times are prolonged and resources are wasted
Solution Approach 1:
The system replaces the mechanical FIFO queueing mechanism with an intelligent machine learning-based positioning system. Instead of simple first-come-first-served processing, the system uses predictive analytics to dynamically determine queue positions based on multiple factors including user behavior patterns, issue complexity, and historical data, thereby reducing wait times while managing complexity through automation.
Solution Approach 2:
The system performs preliminary analysis of call characteristics and user behavior before assigning queue positions. By pre-processing and evaluating multiple parameters upfront using machine learning models, the system can make informed queue positioning decisions that reduce overall wait times without requiring complex real-time adjustments during call processing.
3Productivity
If queue positions are dynamically adjusted based on user behavior, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it analyzes user behavior patterns, predicts issue complexity, determines queue positioning, and provides feedback for continuous improvement. This multi-functionality allows the system to achieve dynamic resource allocation efficiency while consolidating complexity into a single integrated platform rather than requiring separate systems for each function.
Solution Approach 2:
The system implements self-service mechanisms where the machine learning model automatically adjusts queue positions based on analyzed data without requiring manual intervention. The model continuously learns from incoming data and autonomously optimizes queue management, reducing the operational complexity burden on human operators while maintaining high productivity through automated intelligent decision-making.
Data Source
AI summary
A device receives, from a user device, a communication associated with a support issue encountered by a user of the user device and assigns the communication to a position in a support queue based on when the communication is received, wherein the support queue includes data identifying positions of other communications received from other users, and data identifying when the other communications were received. The device processes data identifying the communication and historical communication data describing prior communications associated with the user, with a model, to determine an average time spent on hold by the user for the prior communications. The device modifies the position of the communication in the support queue based on the average time and performs one or more actions based on modifying the position of the communication in the support queue.