Dynamic Alert Threshold Adjustment via User Feedback
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Solution Overview
Problem
Determining appropriate alert trigger thresholds for business metrics is challenging, as thresholds that are too early or too late can diminish notification credibility and lead to overburdened recipients, causing them to ignore alerts.
Innovation Solution
A method and system for automatically adjusting alert notification trigger thresholds based on user feedback, where a processor sends notifications when thresholds are met and adjusts the thresholds based on received feedback from users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If alert trigger threshold is set to be sensitive (low threshold), then early detection of metric deterioration is improved, but notification credibility deteriorates due to premature alerts
Solution Approach 1:
The system collects feedback from users about alert notifications (whether they took action, found it useful, etc.) and uses this feedback to dynamically adjust future alert behavior. This feedback loop allows the system to learn from premature alerts and improve notification credibility over time by adjusting threshold sensitivity based on actual user responses.
Solution Approach 2:
The alert trigger threshold is not fixed but dynamically adjustable based on feedback received from users. The system can modify the threshold sensitivity over time, making it more or less sensitive depending on the credibility and effectiveness of previous alerts, thereby resolving the contradiction between early detection and maintaining credibility.
2Reliability
If alert trigger threshold is set to be conservative (high threshold), then notification credibility is improved, but detection time increases causing late detection
Solution Approach 1:
User feedback about the timeliness and usefulness of alerts is collected and used to adjust the trigger threshold. If alerts are consistently found to be too late, the system can lower the threshold to improve detection timing while monitoring for premature alerts that would reduce credibility.
Solution Approach 2:
The threshold is dynamically adjusted based on historical feedback data, allowing the system to optimize the balance between early detection and credibility. The threshold can evolve over time to find the optimal sensitivity level for each specific metric and user context.
3Loss of time
If multiple alert notifications are sent frequently, then detection coverage is improved, but recipient engagement deteriorates due to overburdening
Solution Approach 1:
The system tracks user responses to multiple alerts and uses this feedback to adjust future notification frequency and timing. If users are overburdened, the system can learn to send fewer, more targeted alerts at optimal times, improving engagement while maintaining detection coverage.
Solution Approach 2:
The alert notification system dynamically adjusts frequency, timing, and targeting based on user feedback patterns. This allows the system to optimize the balance between comprehensive detection coverage and maintaining recipient engagement by adapting to individual user preferences and responses.
Data Source
AI summary
Arrangements relate to the automatic adjustment of an alert trigger threshold associated with a metric. In response to a determination that a predetermined alert trigger threshold associated with a metric is met, an alert notification can be sent to a plurality of users associated with the metric. Feedback on the alert notification can be received from one or more of the plurality of users. The alert trigger threshold can be adjusted based on the received feedback. Such adjusting can be performed using a processor.