Subscriber Identification System for Targeted Channel Advertising
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Solution Overview
Problem
Channel owners face challenges in attracting new subscribers as traditional advertising methods do not allow for targeted advertising based on specific channel content, leading to inefficient promotion of their media items.
Innovation Solution
A method and system that identifies candidate subscribers by analyzing activity data related to online media items and comparing it to subscriber data, using classifier models to determine matching features and direct relevant content to potential subscribers, thereby allowing targeted advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional advertising methods are used to promote channel content, then channel owners can reach a broad audience, but the advertising is not targeted based on specific channel content leading to inefficiency
Solution Approach 1:
The patent segments the advertising system by dividing the audience into candidate subscribers based on their activity data and feature matching with channel subscribers. This segmentation allows targeted advertising to specific groups rather than broad undifferentiated audiences, improving advertising efficiency while maintaining manageable system complexity through automated classification
Solution Approach 2:
The patent performs preliminary actions by pre-identifying candidate subscribers through activity data analysis and feature comparison before advertising is executed. The classifier model is trained in advance on subscriber data to establish feature patterns, enabling efficient targeted advertising without requiring complex real-time decision-making during ad delivery
2Adaptability or versatility
If activity data analysis and feature comparison are performed to identify candidate subscribers, then targeted advertising can be implemented, but data processing complexity increases
Solution Approach 1:
The patent transforms complex activity data into simplified feature representations that can be compared against subscriber profiles. By changing the parameters from raw activity data to extracted features (such as content preferences, engagement patterns), the system achieves high targeting precision while reducing processing complexity through dimensionality reduction and feature abstraction
Solution Approach 2:
The patent introduces a classifier model as an intermediary between raw activity data and candidate subscriber identification. This intermediary processes and interprets activity data, comparing it against trained subscriber patterns to produce clear classification results. The classifier model acts as a mediator that handles the complexity of data analysis while providing simple, actionable identification of candidate subscribers
Data Source
AI summary
A server computer system identifies activity data for an entity. The activity data for the entity relates to one or more online media items. The server computer system compares the activity data to subscriber data for a channel. The subscriber data includes sets of features for subscribers of the channel. The server computer system determines that the entity is a candidate subscriber for the channel when the activity data corresponds to at least one of the plurality of sets of features.