Advocacy Filtering via User Influence Scoring
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Solution Overview
Problem
Current online social media platforms lack an intelligent mechanism to effectively track and promote advocacy among users, particularly for brands, as only a small percentage of fans actively advocate for brands beyond liking their social media pages, and there is a need to identify and engage true brand advocates within their fan base.
Innovation Solution
An online community advocacy management platform that filters and promotes user-generated content based on advocate levels, trend rates, and recent activity, using algorithms to gauge the likelihood of future influence and provide incentives for users to become influential advocates, while also allowing brands to evaluate and reward user-generated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional social networking platforms allow unrestricted user-generated content sharing, then user participation and content volume increase, but the ability to identify and promote true brand advocates deteriorates due to lack of intelligent filtering mechanisms
Solution Approach 1:
The system implements feedback loops where user actions (likes, shares, comments) generate data that feeds into advocacy scoring algorithms. These scores then determine content promotion and user engagement strategies, creating a continuous improvement cycle for identifying true advocates while maintaining high participation levels
Solution Approach 2:
The patent replaces manual content review and advocate identification with automated algorithmic systems that analyze user behavior patterns, engagement metrics, and content performance to objectively measure and promote brand advocacy at scale
2Adaptability or versatility
If the platform promotes all user-generated content equally, then content diversity increases, but the effectiveness of brand advocacy promotion deteriorates due to lack of intelligent selection
Solution Approach 1:
The system applies different promotion strategies to different users based on their calculated advocacy scores and content quality metrics. High-value advocate content receives prioritized promotion while maintaining overall content diversity, ensuring that brand advocacy effectiveness is enhanced without eliminating content variety
3Measurement precision
If the platform implements comprehensive tracking of user actions, then advocate identification accuracy improves, but system complexity and data processing requirements worsen
Solution Approach 1:
The system segments user actions into categorized events (likes, shares, comments, clicks) with associated weights, and processes these segmented data points through modular scoring algorithms. This segmentation enables comprehensive tracking while maintaining system manageability and computational efficiency
4Measurement precision
If the platform provides detailed insights and filtering options, then brand evaluation capability improves, but ease of operation deteriorates due to increased interface complexity
Solution Approach 1:
The system provides comprehensive filtering and analysis capabilities, but implements default views that show only the most relevant advocate content and key metrics. Users can access detailed filtering options when needed, but the default interface remains simple and easy to operate, applying partial action to maintain usability
Data Source
AI summary
For an online community advocacy management platform, the techniques herein intelligently provide mechanisms and user interfaces for filtering (triaging) the view of comments and content based on advocate levels, trend rates, consumer participation, hot topics, recent activity, etc. by gauging the likelihood of user-generated actions to cause future influence based on their algorithmic similarities to historically influential actions, and weighted based on an aggregate, compounding history of individuals throughout time.