Ad Performance Monitoring System with Dynamic Threshold Adjustment
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Solution Overview
Problem
Current ad delivery systems lack the ability to monitor and respond to ad performance in real-time, failing to optimize ad effectiveness as it is displayed.
Innovation Solution
A system that calculates an expected performance value for an ad during an initialization phase and continuously monitors its performance in intervals, replacing the ad if it falls below certain performance limits or exceeds them, recalculating limits based on recent performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ad performance is monitored using traditional post-analysis methods, then data processing is simpler, but real-time optimization capability is lost
Solution Approach 1:
The system pre-calculates expected performance values and establishes performance limits before ad delivery begins. During ad delivery, it continuously monitors actual performance and compares against pre-established thresholds, enabling immediate detection and response to performance deviations without waiting for post-campaign analysis
Solution Approach 2:
The system implements a closed-loop feedback mechanism where ad performance data is continuously collected, compared against expected values, and used to trigger real-time actions. When performance falls below thresholds, the system automatically replaces underperforming ads with alternative creatives, creating a dynamic optimization loop that continuously improves ad effectiveness during the campaign
2Productivity
If ad performance monitoring is implemented in real-time with continuous checks, then ad effectiveness is improved, but system complexity increases
Solution Approach 1:
The monitoring system divides ad performance evaluation into discrete time intervals (e.g., hourly or daily checkpoints). At each interval, the system performs a focused comparison of key metrics against pre-established thresholds, rather than continuously analyzing all performance data. This segmentation reduces computational complexity while maintaining real-time optimization capability
Solution Approach 2:
The system transforms complex performance evaluation into simple threshold comparisons by pre-calculating expected performance values and establishing upper/lower performance limits. Instead of analyzing multiple performance dimensions simultaneously, the system monitors whether actual performance falls within acceptable ranges, simplifying the monitoring logic while maintaining comprehensive optimization
Data Source
AI summary
The disclosure is directed to a system, method, and computer program product for detecting and responding to changes in the performance of an ad. In a first phase, an expected performance value for an ad is calculated. In a second phase, the performance of the ad is monitored in intervals, wherein for each interval, the response rate of the ad during the interval is calculated and compared to the expected performance value, an upper performance limit, and a lower performance limit. The ad is replaced or the expected performance value is recalculated, depending on how the response rate compares to the expected performance value, the upper performance limit, and the lower performance limit.