Brand Attraction Score System for Holistic Performance Monitoring
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Solution Overview
Problem
Conventional methods for monitoring brand performance are data source and economically centric, failing to provide a holistic view that incorporates investment or expenditure factors, requiring separate monitoring of performance and return on investment for each data source/service provider.
Innovation Solution
A system and method that dynamically pulls organic data from multiple online sources, scores brand performance in real-time against competitors using a weighted formulation, incorporating both influence and exposure factors to calculate a brand's mass attraction score, allowing for a holistic view of market performance and investment impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional data source centric methods are used to monitor brand performance, then performance metrics from individual sources can be tracked, but a holistic view of brand performance and investment return cannot be achieved
Solution Approach 1:
The patent merges multiple data sources (social media, search engines, display networks, video platforms) into a single unified monitoring system that aggregates exposure data, influence metrics, and investment information to provide a holistic brand performance view, eliminating the need for separate monitoring systems for each data source
Solution Approach 2:
The system performs multiple functions simultaneously: tracking brand exposure across diverse platforms, calculating influence metrics, monitoring investment expenditure, and providing comparative analysis against competitors, all within a single universal platform that serves comprehensive brand monitoring needs
2Measurement precision
If separate monitoring of performance and ROI is conducted for each data source, then detailed source-specific metrics are available, but efficient holistic brand assessment is compromised
Solution Approach 1:
The system segments brand performance into distinct measurable components (exposure metrics from various platforms, influence scores, investment data) that can be independently tracked and precisely measured, then integrates these segments into a unified assessment framework that maintains measurement precision while improving efficiency
Solution Approach 2:
The patent introduces intermediary calculation layers (exposure metrics as intermediate data, influence scores as intermediate results) that bridge raw data from multiple sources and final ROI assessments, enabling efficient processing while maintaining precise measurement through structured intermediate representations
Data Source
AI summary
An interactive algorithm and sequential methodology dynamically that pulls numerous organic data sets from online social, search, sentiment and paid media sources against a weighted formulation to determine with statistical significance a brand's market performance and characterization defined as a “Brand Attraction Score” in real time against key competitors. Consumer and media behaviors are measured using billions of data signals from multiple API sources distributed downstream through a series of influence (I) and exposure (E) weighted formulations to a master algorithm to determine the net performance and attraction scores and ranking. These statistical sets are then conformed into a series of scores and rankings in a predetermined competitive set in an Exposure to Attraction coordinate system, thereby allowing a Brand to easily visualize their comparative Attraction among their competitor brands.