Anomaly Detection Model for Live Marketing Campaign Data
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Solution Overview
Problem
Digital marketing platforms lack the capability for continuous, unsupervised monitoring of live marketing campaign data, making it difficult for marketers to detect and respond to real-time anomalies in campaign performance effectively.
Innovation Solution
A digital marketing platform equipped with an anomaly detection model that uses machine learning to identify anomalies in live data, allowing for real-time detection and recommendation of actions to address them, while maintaining ongoing monitoring without requiring user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a marketer manually monitors campaign performance metrics on the platform, then they can detect anomalies in campaign performance, but they cannot continuously monitor or respond in real-time due to human limitations
Solution Approach 1:
The system enables self-service anomaly detection by automatically monitoring campaign performance metrics and identifying anomalies without requiring continuous human intervention. The anomaly detection model autonomously analyzes performance data and generates alerts, allowing the system to serve itself in detecting issues while maintaining high response speed.
Solution Approach 2:
The patent replaces the mechanical human monitoring process with an automated anomaly detection model that uses machine learning algorithms to analyze performance metrics. This substitution eliminates human limitations in continuous monitoring and real-time response, enabling the system to detect and alert about anomalies instantaneously without human intervention.
2Loss of time
If the platform presents aggregated performance metrics for a period, then the marketer can review overall performance, but they cannot detect real-time anomalies as data enters the platform
Solution Approach 1:
The anomaly detection model continuously analyzes performance metrics as data enters the platform, maintaining uninterrupted monitoring without requiring aggregation into periodic reports. This continuous analysis enables real-time detection of anomalies while the system processes incoming data streams, eliminating the time loss associated with periodic aggregation while maintaining detection accuracy.
3Measurement precision
If the marketer devotes attention to continuously monitoring the campaign, then they can detect anomalies promptly, but they cannot simultaneously modify the campaign
Solution Approach 1:
The system performs self-service anomaly detection and generates automated alerts, freeing the marketer from continuous monitoring duties. This allows the marketer to simultaneously focus on campaign modification and optimization without sacrificing anomaly detection capability, as the system autonomously monitors performance and notifies the marketer of issues.
4Device complexity
If digital marketing platforms lack continuous unsupervised monitoring capability, then the system is simpler to implement, but it cannot automatically detect and recommend actions for anomalies
Solution Approach 1:
The patent implements automatic anomaly detection by replacing manual monitoring processes with machine learning-based anomaly detection models. These models automatically analyze performance metrics, identify anomalies, and generate recommendations for actions, enabling a high degree of automation while managing system complexity through modular architecture and integration with existing marketing platforms.
Data Source
AI summary
Techniques for detecting anomalies in live marketing campaign data are disclosed, including: obtaining baseline data associated with one or more digital marketing campaigns; configuring an anomaly detection model to detect anomalies in digital marketing data, based at least on the baseline data; receiving a live stream of a set of digital marketing data associated with a particular digital marketing campaign that is currently being executed; while the particular digital marketing campaign is being executed: applying the anomaly detection model to the set of digital marketing data, to determine if the set of digital marketing data includes an anomaly relative to the baseline data; prior to completion of the particular digital marketing campaign and responsive to determining that the set of digital marketing data includes the anomaly relative to the baseline data, executing an action to address the anomaly.