Brand Equity Index Computation via Multi-Platform Data Enrichment
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Solution Overview
Problem
Existing methods fail to provide a unified, accurate, and real-time analysis of brand equity across multiple platforms, leading to unclear insights and ineffective strategies due to the diverse and distracting nature of online content, lacking a comprehensive benchmark for social media and industry trends.
Innovation Solution
A system and method that continuously procures and enriches data from various platforms, classifies it, determines sentiment and engagement metrics, and computes a weighted average-based social equity index for brands, using a hardware processor and modules for data procurement, enrichment, classification, sentiment analysis, and index formulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is continuously procured from multiple platforms, then the comprehensiveness of brand analysis is improved, but the complexity of data processing increases
Solution Approach 1:
The system segments the complex data processing task into distinct functional modules: data procurement module for collecting data from multiple platforms, data enrichment module for cleaning and standardizing data, classification module for categorizing content, sentiment analysis module for evaluating brand perception, and engagement metrics module for measuring interaction levels. This modular segmentation allows comprehensive multi-platform data analysis while managing processing complexity through specialized sub-systems.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw data collection and final equity index calculation. This intermediary layer includes data enrichment and classification modules that standardize and organize diverse platform data before analysis, acting as a mediator that transforms heterogeneous data from multiple platforms into a unified format suitable for comprehensive brand analysis.
2Loss of time
If real-time analysis is implemented, then the timeliness of brand insights is improved, but the computational resources required increase
Solution Approach 1:
The system performs preliminary data enrichment and classification actions as data is being procured from multiple platforms, rather than waiting to collect all data before processing. This preliminary action prepares data in real-time for subsequent analysis, enabling timely brand insights while distributing computational load across time rather than concentrating it in a single resource-intensive batch processing operation.
Solution Approach 2:
The patent implements continuous data procurement and processing operations that run continuously in the background, maintaining an ongoing analysis of brand equity across multiple platforms. This continuous useful action ensures real-time insights are always available without requiring periodic intensive computational bursts, optimizing resource utilization while maintaining timeliness.
3Measurement precision
If multiple categories and metrics are analyzed, then the accuracy of equity index is improved, but the difficulty of interpretation increases
Solution Approach 1:
The patent merges multiple analysis categories (content classification, sentiment analysis, engagement metrics) and their respective metrics into a single unified brand equity index. This combination integrates comprehensive multi-dimensional brand analysis data while simplifying interpretation by presenting a consolidated quantitative score that reflects overall brand equity, making the results accessible and actionable for business decision-making.
4Reliability
If noise is removed from input data, then the quality of insights is improved, but the processing time increases
Solution Approach 1:
The system performs data enrichment and noise removal as a preliminary action during the data procurement phase, cleaning and standardizing data before it enters the main analysis pipeline. This preliminary data preparation improves the quality of subsequent insights while minimizing the processing time impact on core analysis operations, as the noisy data filtering occurs in parallel with data collection rather than sequentially after complete data gathering.
Data Source
AI summary
The present disclosure relates to analysis of content to determine an equity index of a brand by building a data record by procuring input data from at least one platform; enriching the data record to remove noise from the input data to obtain an enriched data record; classifying the enriched data record into at least one category of one or more categories to obtain a classified data record; determining a sentiment ratio for the classified data record; determining an engagement metrics for the brand; and determining the equity index for the brand based on the at least one category and the sentiment ratio, the sentiment ratio and one or more variables.