Near-Real-Time Negative User Experience Alert System
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Solution Overview
Problem
Current solutions fail to provide real-time or near-real-time communication of enriched and actionable user behavior information on websites, limiting the ability of website owners to optimize user experiences in a timely manner.
Innovation Solution
A system and method for near-real-time communication of negative user experiences, involving the analysis of in-page interaction information to identify interaction patterns, compute user experience scores, and generate alerts when a negative experience is detected, allowing immediate notification and potential adjustments to enhance user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time analysis and alerting of user interactions is implemented, then user experience optimization capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The system pre-defines multiple interaction patterns (e.g., rage clicking, repeated back navigation, form abandonment) and their associated user experience scores before deployment. These patterns are stored in a database and automatically matched against live user interactions, eliminating the need for complex real-time analysis algorithms and reducing system complexity while maintaining reliable detection capability
Solution Approach 2:
The system implements a feedback loop where user interactions are continuously monitored, analyzed against predefined patterns, and alerts are generated when negative experiences are detected. This real-time feedback enables immediate optimization actions, improving the reliability of user experience management without requiring overly complex predictive models
2Measurement precision
If comprehensive interaction pattern analysis is performed, then detection accuracy of negative user experience is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments user interactions into distinct, predefined interaction patterns (e.g., navigation patterns, clicking patterns, form interaction patterns). Each pattern type is analyzed independently using simple matching rules rather than comprehensive complex analysis, maintaining high detection accuracy for specific negative experiences while reducing overall processing time through parallel evaluation of segmented interaction types
Solution Approach 2:
The system uses predefined thresholds and scoring parameters for different interaction patterns (e.g., number of rapid clicks, time spent on page, navigation depth). By changing and adjusting these parameters rather than performing complex real-time analysis, the system achieves accurate detection of negative user experiences with minimal processing time and computational overhead
Data Source
AI summary
A system and method for near-real-time communicating negative user experience of users interacting with a website are provided. The method includes identifying at least one interaction pattern by analyzing an in-page interaction information of a user interacting with at least one page of the website; computing a user experience score for each of the at least one identified interaction pattern; generating an alert indicating that the user experience score determined for a respective identified interaction pattern demonstrates a negative user experience; and sending the alert immediately upon identifying the interaction pattern demonstrating a negative user experience.