Automated Testing Analytics Engine for Real-Time Data Mining
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Solution Overview
Problem
Current business environments face inefficiencies in leveraging testing analytics to optimize investment strategies and maximize customer relationships due to organizational silos, invalid testing, and manual data mining procedures, which result in undetected valuable test information and inability to track performance across multiple business lines effectively.
Innovation Solution
An automated system for collecting and storing test data, determining test performance indicators like lift and confidence across multiple segments of the test participant population, and delivering test result information, which includes a computing platform with a testing analytics module for real-time statistical calculations and data synchronization, enabling efficient data access and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual test data mining procedures are used, then individual calculations can be performed, but valuable test information goes undetected and efficiency is insufficient
Solution Approach 1:
The system performs self-service by automatically executing comprehensive test data mining and performance indicator calculations without requiring manual intervention. The automated engine processes entire datasets, identifies patterns, and generates insights independently, eliminating the limitation of manual procedures that can only handle individual calculations and miss valuable information.
Solution Approach 2:
The patent replaces manual mechanical data mining processes with an automated computational system. The testing analytics module uses computer algorithms to process test data, mine patterns, and calculate performance indicators across multiple segments, substituting human manual operations with automated mechanical/electronic systems that are both more efficient and more thorough.
2Measurement precision
If manual calculations are performed for test performance indicators, then specific calculations can be executed, but all required calculations to uncover performance results cannot be performed
Solution Approach 1:
The automated testing analytics module provides universal functionality by executing all required calculations across multiple segments, test metrics, and population groups simultaneously. Rather than performing individual calculations manually, the system universally processes the entire dataset to uncover performance results across all dimensions, maintaining both precision and productivity.
Solution Approach 2:
The system maintains continuous useful action by automatically and continuously performing all necessary calculations without interruption. The automated engine processes data continuously across multiple segments and metrics, ensuring that all required calculations are completed thoroughly and efficiently, unlike manual calculations that are intermittent and selective.
3Adaptability or versatility
If testing analytics are not automated, then manual control over calculations is maintained, but organizational changes cause testing results to be lost or ineffective
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor and adapt to organizational changes. The automated analytics module receives updated data about organizational structure and adjusts its analysis accordingly, providing feedback loops that ensure testing results remain valid and effective despite changes in organization, eliminating the loss of information caused by manual processes that lack such adaptability.
Solution Approach 2:
The testing analytics system is designed to be dynamic rather than static. It automatically adapts its calculations and analyses in response to changing organizational conditions, maintaining relevance and effectiveness. The system's dynamic nature allows it to withstand organizational changes without losing testing results, unlike manual processes that are rigid and easily disrupted by changes.
4Adaptability or versatility
If data is not synchronized across business lines, then data isolation is maintained, but performance tracking across multiple business lines is impossible
Solution Approach 1:
The system applies segmentation by organizing data into distinct but interconnected segments across different business lines. The automated analytics module processes each business line's data as a separate segment while maintaining the ability to track and analyze performance across segments, achieving cross-business line tracking without requiring complete data homogenization that would increase complexity.
Solution Approach 2:
The automated testing analytics module serves as an intermediary that bridges different business lines. It receives data from various business line sources, standardizes and synchronizes the data, and then provides unified performance tracking across all business lines. This intermediary function enables cross-line tracking while managing the complexity through structured data handling.
Data Source
AI summary
Embodiments of the invention relate to systems, methods, and computer program products for testing analytics. Embodiments herein disclosed provide for automating the collection and storage of test data, automating test performance calculations across a multitude of segments within the test group and automating test result information delivery.