ML Queue Adjustment for Self-Support Incentives
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Solution Overview
Problem
Existing customer support systems lack efficient mechanisms to incentivize users to perform self-support actions, leading to increased waiting times and resource consumption.
Innovation Solution
A support platform that utilizes machine learning to monitor a queue and adjust positions based on self-support actions performed by users, providing incentives for users to resolve issues independently and reducing the need for direct service center interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users perform self-support actions to resolve issues independently, then resource consumption is reduced and waiting times are decreased, but users require additional effort and guidance to perform these actions
Solution Approach 1:
The system provides real-time feedback to users by monitoring their self-support actions and dynamically adjusting support queue positions. Users receive immediate feedback on the impact of their actions (e.g., 'Your self-support action has moved you ahead in the queue'), which motivates them to perform useful actions while reducing waiting times.
Solution Approach 2:
The patent enables users to perform self-support actions independently to resolve their own issues. The system provides guidance and tools for self-service, allowing users to take control of their support needs and reducing dependency on manual service center interventions.
2Productivity
If the support queue is adjusted based on self-support actions, then resource allocation is optimized, but the system complexity increases
Solution Approach 1:
The patent replaces manual queue management with an automated machine learning-based system. The ML model automatically evaluates self-support actions, determines queue position adjustments, and manages resource allocation without human intervention, optimizing productivity while containing complexity through automation.
Solution Approach 2:
The system dynamically changes queue position parameters based on detected self-support actions. By adjusting priority levels and queue positions as real-time parameters rather than static assignments, the system achieves flexible resource allocation efficiency.
3Loss of time
If machine learning is used to monitor and adjust queue positions, then waiting times are reduced, but computational resources and energy consumption increase
Solution Approach 1:
The system applies partial action by monitoring only the necessary parameters of self-support actions rather than all possible user activities. The machine learning model processes only the minimal required data to determine queue adjustments, reducing computational energy consumption while maintaining effectiveness.
Data Source
AI summary
In some embodiments, a queue may be monitored to perform an automated adjustment related to a data item in the queue. In some embodiments, the data item may be associated with a product or service and include a code related to an access to the product or service. Based on the code, configuration information related to the product or service may be obtained. Based on a detected change related to the product or service, an indication of a set of self-support actions may be sent to a user device. One or more self-support actions (performed via the user device) may be determined, and an adjustment related to the access to the product or service may be performed based on the determined self-support actions. In some embodiments, the adjustment may include modifying a configuration of the access to the product or service based on the determined self-support actions.