Demographic Segmentation in Consumer Rating Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current consumer rating systems fail to account for the diverse experiences of users from different demographics, leading to an incomplete representation of a business's inclusiveness, as they do not consider user demographics when aggregating reviews.
Innovation Solution
A consumer rating system that provides custom match scores and ally scores based on users' demographics, allowing users to input demographic information and inclusion questions, which are used to generate personalized scores and reviews, enabling users to find more welcoming businesses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general consumer rating system aggregates all reviews into a single overall score, then the system is simple to operate and provides a quick summary, but it fails to reflect the different experiences of users from different demographics
Solution Approach 1:
The patent segments the overall rating system into multiple demographic-specific rating categories. Instead of a single aggregated score, the system divides ratings by demographic groups (e.g., race, gender, age, disability status) to provide nuanced representations of business inclusiveness. This segmentation allows each demographic group to see ratings that reflect their specific experiences, resolving the contradiction between measurement precision and system complexity by organizing complexity in a structured, user-friendly manner.
Solution Approach 2:
The patent adds demographic dimensionality to the traditional one-dimensional rating system. By introducing multiple rating dimensions (different demographic categories) alongside the traditional overall score, the system transforms a simple linear rating into a multi-dimensional rating structure. This allows the system to maintain simplicity in presentation while capturing complex demographic-specific experiences through additional rating layers.
2Measurement precision
If the system collects detailed demographic information from users to provide personalized ratings, then the accuracy of match scores improves, but the complexity of user input and data processing increases
Solution Approach 1:
The patent implements preliminary action by providing users with demographic category selections during the review submission process. Instead of requiring complex real-time analysis, the system pre-establishes demographic categories and allows users to select their identity group(s) when submitting reviews. This preliminary structuring of demographic data collection simplifies the processing burden while maintaining the precision needed for accurate match scores and ally scores.
Solution Approach 2:
The system uses feedback mechanisms where users' demographic selections directly influence the calculation of match scores and ally scores. The feedback loop allows the system to continuously refine ratings based on demographic patterns observed in reviews, improving measurement precision while managing data complexity through iterative learning from user inputs.
3Adaptability or versatility
If the system provides demographic-specific ratings and reviews, then users can find businesses more welcoming to their demographic group, but the system requires more sophisticated algorithms to calculate match scores
Solution Approach 1:
The patent applies local quality by providing different rating presentations tailored to each user's demographic profile. Instead of a uniform rating system, the system customizes the display and calculation of ratings based on local demographic contexts. For example, a user from a marginalized demographic group sees ratings and reviews specifically relevant to their experience at that business, while other users see different highlighted information. This local customization achieves high adaptability while managing algorithmic complexity through targeted, context-specific calculations.
Solution Approach 2:
The system dynamically changes rating parameters based on user demographics. Match scores and ally scores are calculated using different weightings and criteria depending on the user's demographic profile. This parameter flexibility allows the system to be highly adaptable to different user needs while managing complexity through systematic parameter adjustment rather than entirely separate algorithms for each demographic group.
Data Source
AI summary
The consumer rating system provides a custom match score to users for businesses and individual reviews for those businesses that takes the user's background into account. The consumer rating system also provides ally scores to the user which take into account preselected ally criteria selected by the user. The user is also able to provide responses to inclusion questions when leaving reviews that are taken into account when generating the match scores and ally scores for other users.