Subscriber Behavior Profiling via IP Mapping and Data Scrubbing
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Solution Overview
Problem
Current systems lack effective methods to analyze and characterize user behavior based on network connectivity requests to provide targeted advertising and identify potential churners among subscribers.
Innovation Solution
A system that logs and analyzes data requests from multiple subscribers, using a profiler and behavior analyzer to determine subscriber characteristics by mapping IP addresses to unique identifiers, scrubbing personally identifiable information, and comparing requests to behavior models to identify interests and switching propensities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data requests from multiple subscribers are analyzed to determine subscriber characteristics, then targeted advertising and churn identification capabilities are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments data requests by organizing them into sets based on subscriber identifiers, allowing the analyzer to process and characterize each subscriber's behavior patterns separately. This segmentation enables complex analysis of individual subscriber characteristics while managing overall system complexity through structured data organization.
Solution Approach 2:
The system introduces a mapping mechanism that maps IP addresses to unique subscriber identifiers as an intermediary layer. This intermediary allows the system to track and associate data requests with specific subscribers without directly exposing complex subscriber information, simplifying the analysis process while enabling comprehensive characterization.
2Measurement precision
If IP addresses are mapped to unique subscriber identifiers to track subscriber behavior across time periods, then subscriber characterization accuracy is improved, but data processing and storage requirements increase
Solution Approach 1:
The system extracts and stores only the essential mapping information between IP addresses and subscriber identifiers in the mapping data structure. By taking out only the necessary mapping relationships rather than storing complete subscriber profiles or all request details, the system achieves accurate subscriber identification while minimizing data storage requirements.
Solution Approach 2:
The unique subscriber identifier serves multiple functions: it identifies subscribers across different time periods, associates data requests with specific subscribers, and enables consistent tracking even when IP addresses change. This multi-functionality reduces the need for multiple separate data structures, thereby reducing overall data volume while maintaining identification accuracy.
3Object-affected harmful factors
If data scrubbing is performed to remove personally identifying information, then privacy protection is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs data scrubbing as a preliminary action during the data collection and organization phase, removing personally identifying information before the analysis phase begins. By completing the scrubbing operation upfront when data is first processed and organized into subscriber sets, the system minimizes repeated processing and reduces overall time expenditure while ensuring continuous privacy protection.
Data Source
AI summary
Requests for data received from multiple subscribers are accessed. At least some of the requests for data originate from one or more addresses associated with a particular subscriber of the multiple subscribers. The accessed requests for data are organized into sets of requests based on the one or more addresses such that a set of requests corresponds to the particular subscriber, and a characteristic of the particular subscriber is determined based on aspects of the set of requests corresponding to the particular subscriber and a behavior model.