Real-Time User Reaction Scoring via ML Metrics
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Solution Overview
Problem
Current systems for monitoring user interaction with software programs only provide lagging indicators of user reaction, failing to offer real-time feedback on whether interactions are positive, negative, or neutral, which limits their ability to optimize user experience and product development.
Innovation Solution
A method and system that record user interactions in real-time, determine relevant metrics, and use machine learning algorithms to score user sessions, combining these metrics with post-session feedback to generate a holistic Product Success Score (PSS) for predicting user satisfaction and optimizing software features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional user interaction scoring systems are used, then user reaction can be assessed, but only after the user session has finished (lagging indicator)
Solution Approach 1:
The system performs preliminary actions by recording user actions and determining metrics during the user session itself, rather than waiting for completion. The processing device records actions, determines metrics, and measures user reactions in real-time as the session progresses, enabling assessment before the session ends.
Solution Approach 2:
The system implements continuous feedback by providing real-time scoring of user interactions during the session. The machine learning algorithm processes recorded actions and metrics continuously, generating ongoing feedback about user reaction rather than a single post-session evaluation.
2Productivity
If real-time monitoring of user interactions is implemented, then immediate feedback on user satisfaction is obtained, but system complexity increases
Solution Approach 1:
The system uses an intermediary processing device that acts as a mediator between user interactions and analysis. This device records actions, determines metrics, and feeds data to the machine learning algorithm, simplifying the architecture by centralizing real-time processing functions in a dedicated component.
Solution Approach 2:
The system replaces complex manual analysis mechanisms with automated machine learning algorithms. The ML model automatically processes recorded user actions and metrics to generate satisfaction scores, eliminating the need for manual real-time analysis and reducing operational complexity.
3Measurement precision
If comprehensive user action recording is performed, then accurate user reaction scoring is achieved, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features and metrics from recorded user actions for processing. Rather than analyzing all raw interaction data, the processing device identifies and extracts key metrics that correlate with user satisfaction, reducing the volume of data requiring intensive computational processing.
Solution Approach 2:
The system transforms raw user interaction data into standardized metrics and features that are more efficient for machine learning processing. By changing the parameters from raw action logs to structured metric data, the system reduces computational complexity while maintaining scoring accuracy.
Data Source
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AI summary
Systems, methods, and computer-readable media for scoring an individual interaction session between a user and a software program are disclosed. A method includes recording, by a processing device, one or more actions undertaken by the user with the software program to obtain recorded session data, determining, by the processing device in real-time, one or more metrics that correspond to at least one of the one or more actions undertaken by the user with the software program, measuring, by the processing device, the one or more actions undertaken by the user based on the one or more metrics to obtain metric data, and providing, by the processing device, the recorded session data and the metric data as an input for one or more of a machine learning algorithm and a predictive analytic algorithm. The one or more of the machine learning algorithm and the predictive analytic algorithm scores the individual interaction session.