Real-Time User Segmentation via Multi-Dimensional ML Analysis
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Solution Overview
Problem
Current systems for user segmentation on online platforms lack granularity, preventing online platform providers from customizing marketing campaigns effectively and measuring their real-time effectiveness.
Innovation Solution
A computer-implemented method for granular-level user segmentation based on real-time activities on webpages, involving the receipt and analysis of data sets using machine learning algorithms to create segments and trigger marketing campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional user segmentation methods are used, then the system is simpler to implement, but the segmentation granularity is insufficient and cannot support customized marketing campaigns
Solution Approach 1:
The patent divides users into granular segments based on multiple dimensions including device characteristics, usage patterns, and behavioral data. This multi-dimensional segmentation approach enables precise user classification while managing system complexity through structured data organization and processing frameworks.
Solution Approach 2:
The system dynamically adjusts segmentation parameters by analyzing real-time usage data, device metrics, and user behavior patterns. This allows the segmentation granularity to be optimized continuously based on available data without requiring complete system redesign, thus improving precision while controlling complexity.
2Loss of time
If real-time data analysis is implemented, then marketing campaign effectiveness can be measured immediately, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis on user data by pre-processing and categorizing usage patterns, device characteristics, and behavioral metrics before marketing campaigns are executed. This advance preparation enables rapid real-time measurement during campaign execution, reducing the computational burden and time required for immediate effectiveness assessment.
3Adaptability or versatility
If comprehensive user data is collected for precise segmentation, then marketing campaign customization is improved, but data privacy concerns and security risks increase
Solution Approach 1:
The patent implements differential privacy and data anonymization techniques that preserve the utility of user data for segmentation and campaign customization while protecting individual privacy. By applying local quality modifications to data points (such as adding noise or generalizing identifiers), the system maintains adaptability for marketing purposes while mitigating privacy risks.
Data Source
AI summary
The present disclosure provides a computer-implemented method and system for granular level segmentation of users based on online activities on a webpage 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 using one or more machine learning algorithms. The user segmentation system creates one or more segments based on analysis performed on the first set of data, the second set of data and the third set of data. The user segmentation system initiates one or more marketing campaigns for the one or more segments.