Machine Learning Model for Dynamic User Segment Generation
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Solution Overview
Problem
Conventional segmentation systems face complexity in user interfaces, inefficiency in resource utilization, and limited flexibility in analyzing diverse datasets, leading to overly complex and resource-intensive processes that often result in less meaningful segment generation.
Innovation Solution
A specially trained machine-learning model generates an emerging user segment based on target outcomes and respondent attributes from digital surveys, allowing for dynamic prediction of users with similar characteristics, even if they haven't responded, and integrates this segment into graphical user interfaces for targeted actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional segmentation systems analyze all digital input traits to build segments, then comprehensive segment coverage is achieved, but computing resources are wasted on non-deterministic or unrelated traits
Solution Approach 1:
The system extracts and analyzes only the deterministic subset of input traits that are actually relevant to forming the target user segment, rather than processing all available digital input traits. This extraction approach eliminates waste on non-deterministic or unrelated traits while maintaining segment accuracy.
Solution Approach 2:
The system applies different processing quality to different traits: deterministic traits receive full analytical attention while non-deterministic traits are excluded or minimally processed. This local differentiation optimizes resource allocation based on the actual contribution of each trait to segment formation.
2Adaptability or versatility
If conventional segmentation systems provide many tools and options in user interface, then user customization capability is improved, but user interface complexity increases
Solution Approach 1:
The system performs automatic segment generation using machine learning models without requiring users to manually configure complex parameters or navigate multiple interface options. The AI system serves itself by autonomously identifying patterns and creating segments, eliminating the need for complex user-facing configuration tools.
Solution Approach 2:
The machine learning model provides a universal solution that handles diverse segmentation needs through a single interface, replacing the need for multiple specialized tools and options. One system accomplishes what previously required numerous separate functions and configuration choices.
3Measurement precision
If conventional segmentation systems are highly customized for specific features, then feature-specific analysis precision is improved, but scope and application flexibility are limited
Solution Approach 1:
The machine learning model is designed to handle multiple feature domains and dataset types through a unified approach. Rather than creating separate customized systems for website visits, webpage visits, or other features, a single ML model adapts to analyze diverse datasets while maintaining precision through learning from data patterns.
Solution Approach 2:
The system dynamically adapts to different feature domains and dataset structures rather than being statically configured for specific features. The machine learning model adjusts its analysis approach based on the input data characteristics, providing both precision and flexibility.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a specially trained machine-learning model to generate an emerging user segment based on a target outcome for digital survey responses and respondent attributes of respondents to such digital surveys. In some cases, for instance, the emerging user segment includes a group of users that share the same or similar characteristics as the subset of respondents. By analyzing respondent attributes of digital survey respondents that match a target outcome, the disclosed systems can use the specially trained machine-learning model to dynamically predict users that likely have (or are at risk of having) the same or a similar target outcome—even if such users did not respond to the relevant digital survey.