Real-Time User Segmentation via Multi-Source Data Fusion
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Solution Overview
Problem
Current systems for online platform providers fail to segment users at a granular level, customize marketing campaigns effectively, measure campaign effectiveness in real-time, analyze performance, and predict campaign outcomes for granular user segments.
Innovation Solution
A computer-implemented method that receives and analyzes user data using machine learning algorithms to segment users into granular segments, assign segment goals, initiate marketing campaigns, and predict campaign performance in real-time, utilizing data from past and live events across various online platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user segmentation is performed at a coarse level using traditional methods, then the system complexity is low, but the marketing campaign effectiveness and user targeting precision deteriorate
Solution Approach 1:
The patent applies segmentation by dividing users into granular micro-segments based on multiple dimensions including device characteristics, application behavior, and demographic data. This creates highly specific user groups that enable precise marketing targeting while managing complexity through automated machine learning algorithms.
Solution Approach 2:
The system changes parameters by incorporating multiple data types (device info, app events, demographic data) and using machine learning models to dynamically adjust segmentation criteria. This allows the system to achieve high segmentation precision by analyzing numerous parameters simultaneously rather than relying on single coarse categories.
2Reliability
If real-time data analysis is implemented for marketing campaigns, then the campaign effectiveness measurement is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user data from multiple sources before campaigns begin. Historical user behavior, device characteristics, and demographic information are collected and organized in advance, enabling rapid real-time analysis during actual campaigns without excessive processing delays.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor campaign performance in real-time and provide immediate insights. This allows marketers to measure effectiveness accurately and adjust campaigns dynamically, with the system processing and returning performance data quickly through optimized analytical pipelines.
3Adaptability or versatility
If granular-level user segmentation is performed, then the marketing campaign customization is improved, but the data processing complexity and time requirements increase
Solution Approach 1:
The patent applies universality by creating a multi-functional data processing system that handles multiple data types (device information, application events, demographic data) through a unified machine learning framework. This single system performs segmentation, prediction, and campaign optimization functions, reducing overall complexity despite the granular nature of the analysis.
Solution Approach 2:
The system uses machine learning models as intermediaries that automatically process and synthesize complex multi-source data. These algorithms act as mediators between raw data from various sources and the final segmentation results, managing the complexity of data integration and enabling granular customization without manual intervention.
4Measurement precision
If multiple data sources are integrated for user analysis, then the prediction accuracy is improved, but the data integration complexity increases
Solution Approach 1:
The patent merges multiple data sources including device characteristics, application event data, and demographic information into a unified user profile structure. This consolidation enables comprehensive analysis and accurate prediction while managing integration complexity through standardized data formats and centralized processing pipelines.
Data Source
AI summary
The present disclosure provides a computer-implemented method and system for running high performance marketing campaigns for granular-level segments of users in real-time. The computer-implemented method and system corresponds to a user segmentation system. The user segmentation system receives a first set of data associated with a plurality of users. The user segmentation system fetches a second set of data. The user segmentation system obtains a third set of data. The user segmentation system analyzes the first set of data, the second set of data and the third set of data. The user segmentation system enables segmentation of the plurality of users. The user segmentation system assigns one or more segment goals. The user segmentation system creates a plurality of micro-segments. The user segmentation system triggers initialization of one or more marketing campaigns. The user segmentation system predicts performance of each of the one or more marketing campaigns.