Composite Popularity Scoring for Demographic Targeting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current tools like website analytics and social media platforms do not provide sufficient insight to determine or predict which articles, topics, authors, or influencers are popular within a specific demographic, making it difficult for businesses to target their marketing effectively.
Innovation Solution
The development of composite scores that combine source scores from websites with social media scores to rank items like articles, topics, authors, and influencers, allowing for demographic-specific popularity analysis and prediction of future trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If website analytics and social media tools are used separately, then basic traffic data can be obtained, but sufficient insight into demographic-specific popularity cannot be determined
Solution Approach 1:
The patent combines website analytics data with social media data into a unified analysis system. The social media listening module integrates with existing website analytics to create composite popularity scores that incorporate both on-site behavior and social media engagement, thereby recovering the lost demographic-specific popularity information without requiring completely separate systems
Solution Approach 2:
The analysis system is designed to perform multiple functions: tracking website traffic, monitoring social media mentions, calculating popularity scores, and predicting future popularity trends. This multi-functional approach allows a single system to provide comprehensive demographic-specific insights that would otherwise require multiple separate tools
2Measurement precision
If composite scores combining multiple data sources are calculated, then accurate demographic-specific popularity prediction is achieved, but computational complexity increases
Solution Approach 1:
The popularity scoring system is divided into distinct components: a website popularity score based on traffic and engagement metrics, and a social media popularity score based on mentions and sentiment. These segmented scores are calculated separately using standardized formulas, then combined to produce the final composite popularity score. This segmentation makes the complex calculation process more manageable and interpretable
Solution Approach 2:
The system uses standardized formulas that transform raw data from different sources into normalized popularity scores. By changing the parameters through mathematical transformation (division by thresholds, logarithmic scaling), the system achieves accurate cross-source comparison while maintaining computational efficiency
3Adaptability or versatility
If social media data integration is implemented, then demographic targeting capability is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from social media data for popularity calculation, such as mention counts, engagement metrics, and sentiment scores. Rather than processing all available social media data, the system selectively extracts the key indicators needed for demographic-specific popularity assessment, thereby improving targeting capability while reducing overall data processing requirements
Data Source
AI summary
A method to analyze and determine which source content and user interactions are most popular is provided. The method generates scores for items, e.g., articles, topics, authors, or influencers, on a particular source based on data gathered from both the particular source and social media sources. The scores are used to rank items of the same type, and determine which items are the most popular. The method may also take demographic information as input. Using the demographic information, the system may determine the popularity of a particular item in a particular demographic. The method may also predict which demographic an item may be the most popular in. Furthermore, the method may give a recommendation on which author should write on a particular topic, which topic is most likely to be the most popular for a particular demographic, and which influencers should promote the article.