Brand Personality Comparison Engine for Social Media Analysis
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Solution Overview
Problem
Current methods for assessing and managing brand personality are inadequate, as they rely on time-consuming and costly surveys, suffer from biases, and fail to consider the dynamic nature of brand perception in social media, leading to ineffective marketing strategies that do not align with consumers' perceptions.
Innovation Solution
A data processing system with a brand comparison engine that analyzes brand personality traits using crowdsource data to identify gaps between perceived and intended brand personalities, and recommends actions to bridge these gaps by leveraging social media insights and predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional survey methods are used to assess brand personality, then comprehensive brand perception data can be collected, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces traditional mechanical survey methods with automated computational analysis of social media data. The system uses natural language processing and machine learning algorithms to automatically analyze consumer perceptions across social media platforms, eliminating the need for manual survey administration while maintaining measurement precision.
Solution Approach 2:
The system creates a digital copy of brand personality assessment by analyzing social media mentions and consumer discussions. Instead of conducting new surveys, the system captures and analyzes existing consumer expressions about brands on social media platforms, providing real-time brand personality insights without time loss.
2Measurement precision
If traditional survey methods are used to assess brand personality, then brand perception data can be obtained, but the cost increases significantly
Solution Approach 1:
The system enables self-service brand personality assessment by automatically collecting and analyzing social media data without requiring human researchers to conduct surveys. The computational system performs data collection, processing, and analysis autonomously, significantly reducing labor costs while maintaining assessment accuracy.
Solution Approach 2:
The system uses existing social media content as a free or low-cost proxy for survey data. By analyzing publicly available consumer discussions, mentions, and posts about brands, the system obtains accurate brand personality measurements without incurring survey administration costs.
3Measurement precision
If static brand personality assessments are conducted, then brand characteristics can be measured, but the dynamic nature of brand perception in social media is ignored
Solution Approach 1:
The system implements dynamic brand personality assessment by continuously monitoring social media data streams in real-time. The computational model updates brand personality measurements as new consumer discussions emerge, capturing the evolving nature of brand perceptions rather than relying on static historical data.
Solution Approach 2:
The system incorporates feedback loops where social media consumer responses continuously inform and update brand personality measurements. The system analyzes new social media mentions, consumer sentiments, and discussions, feeding this information back into the brand personality model to maintain current and accurate measurements of dynamic brand perceptions.
Data Source
AI summary
Mechanisms are provided to implement a brand comparison engine. The mechanisms receive a request to compare brand personalities of a first specified brand and a second specified brand and obtain a first brand personality scale associated with the first specified brand and a second brand personality scale associated with the second specified brand. The mechanisms calculate at least one gap value indicating a difference between at least one personality trait in the first brand personality scale and a corresponding at least one personality trait in the second brand personality scale. The mechanisms also output an output indicating an aspect of the at least one gap based on the calculation.