Website Performance Cost Correlation System
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Solution Overview
Problem
Conventional website performance monitoring, analytics, and application health monitoring systems lack the ability to correlate and draw intelligent conclusions about the cause-and-effect relationships between website performance, analytics, and application health, making it difficult for businesses to determine the cost of website slowdowns and application health issues.
Innovation Solution
A system and method that correlates website performance data, analytics data, and application health data to visually compare and draw intelligent conclusions, determining the cost of website slowness by analyzing historical traffic and revenue data during slow periods, and identifying the root cause of issues within the interconnectivity of the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional monitoring systems are used to collect website performance, analytics, and application health data separately, then each system can specialize in its specific metrics, but the systems cannot correlate cause-and-effect relationships between the different datasets
Solution Approach 1:
The patent merges three separate monitoring systems (website performance monitoring, web analytics monitoring, and application health monitoring) into a single integrated platform. This integration allows the system to collect and correlate data from all three sources simultaneously, enabling cause-and-effect analysis while maintaining the specialized monitoring capabilities of each subsystem through modular architecture.
2Ease of operation
If conventional disparate systems generate separate data results, then each system can operate independently, but the website owner must manually make sense of the disparate data results
Solution Approach 1:
The system implements automated feedback mechanisms that continuously monitor all three datasets, correlate them in real-time, and provide actionable insights about cause-and-effect relationships. The system automatically identifies performance issues, determines their root causes across different layers (website, analytics, application), and presents consolidated results that reduce manual interpretation effort while maintaining independent system operation.
3Device complexity
If conventional systems cannot determine cause-and-effect relationships, then system complexity remains low, but the cost of website performance slowdowns cannot be determined
Solution Approach 1:
The patent adds a new dimension of analysis by correlating temporal patterns across the three datasets. The system analyzes data at multiple time granularities (real-time, hourly, daily) to identify causal relationships between website performance, analytics, and application health metrics. This temporal dimension enables the system to determine when performance slowdowns occur and what their business impact was, thereby calculating the cost of performance issues.
4Measurement precision
If historical analytics data is maintained and compared during slow periods, then the cost of website slowness can be determined, but data storage and processing requirements increase
Solution Approach 1:
The system implements selective data retention strategies, maintaining historical analytics data only for the duration necessary to calculate cost metrics. The system processes and analyzes data in batches, retaining detailed historical records only when needed for cost calculation and discarding or aggregating older data. This approach maintains measurement precision for cost determination while managing data storage requirements through intelligent data lifecycle management.
Data Source
AI summary
A system and method for visually and mathematically correlating website performance datasets with analytics datasets and optionally with back end application performance datasets in order to determine if variations in one dataset are caused by or related to variations in another dataset and simultaneously determines the cost of slow or unavailable websites by comparing expected traffic to actual traffic during the slow or errant period.