Dynamic User Segmentation via Machine Learning Analysis
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Solution Overview
Problem
Conventional systems for managing application features and experiments are limited in uncovering underlying usage patterns associated with desired or undesired actions, often relying on demographic and usage data, which may not reveal patterns in search terms and their correlations, and require lengthy experimental periods for statistically valid results, leading to delays in implementing effective changes.
Innovation Solution
The system collects and evaluates application usage data and metadata, using machine learning to predict actions and generate synthetic traffic to mimic user interactions, allowing for real-time analysis and dynamic segmentation of user cohorts, and streaming analysis of experiment data to provide immediate metrics and optimize feature rollouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional systems rely on demographic and usage data for segmentation, then basic user categorization is achieved, but underlying usage patterns and correlations (such as search term patterns) remain uncovered
Solution Approach 1:
The patent replaces conventional mechanical/statistical data analysis methods with machine learning algorithms that can automatically discover hidden usage patterns and correlations in application usage data, thereby uncovering information that traditional methods miss without proportionally increasing system complexity
Solution Approach 2:
The patent introduces an intermediary machine learning model that processes raw application usage data and metadata, extracting hidden patterns and correlations (such as search term patterns) that directly connect user behavior to desired or undesired actions, serving as a bridge between raw data and actionable insights
2Measurement precision
If conventional systems conduct lengthy experiments to achieve statistically valid results, then measurement reliability is improved, but implementation time increases significantly
Solution Approach 1:
The patent performs preliminary actions by collecting and analyzing application usage data and metadata before conducting formal experiments, using machine learning to identify usage patterns and generate hypotheses in advance, which reduces the time needed for subsequent experimental validation while maintaining statistical rigor
Solution Approach 2:
The system performs self-service by automatically analyzing its own collected data through machine learning models to identify patterns and generate experimental hypotheses, reducing reliance on lengthy manual experimental processes and enabling faster iteration cycles
3Measurement precision
If the system collects and analyzes extensive application usage data and metadata, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent replaces complex manual data processing procedures with machine learning algorithms that automatically handle extensive application usage data and metadata, improving prediction accuracy while the automated nature of ML processing prevents proportional increases in operational complexity
4Ease of operation
If conventional systems use fixed user segments for experiments, then experiment setup is simplified, but adaptability to changing usage patterns is reduced
Solution Approach 1:
The patent transforms static user segments into dynamic, adaptive segments that automatically adjust based on changing usage patterns detected through machine learning analysis of application usage data and metadata, maintaining ease of operation through automated segment updates rather than manual reconfiguration
Data Source
AI summary
Systems and methods are provided for dynamic segmentation of users during an experiment based on changes to application data collected during the experiment. Data regarding application interactions and associated application metadata may be collected from users during application experiments that involve testing different variants of a feature or otherwise different user experiences. The data regarding application interactions and associated application metadata may be evaluated to discover segments of users and/or usage patterns (e.g., “cohorts”). During the experiment, the users may be dynamically re-segmented into new/different cohorts based on new application data being collected.