Reputation Determination System Using Demographic Sampling
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Solution Overview
Problem
Current reputation determination methods in e-commerce are inaccurate and biased, relying on unrealizable data that fails to provide a reliable measure of entity or industry reputation, which is crucial for managing stakeholder expectations and improving consumer experience.
Innovation Solution
A system and method that determines reputation by selecting a demographically representative sample with a predefined level of familiarity, assessing emotional connection and practical thinking through categorized survey questions, weighting for cultural and demographic biases, and aggregating scores to provide a comprehensive reputation perception and factor score, while identifying key drivers of reputation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current reputation determination methods are used, then data collection is simple, but measurement precision is poor due to biased and unrealizable data
Solution Approach 1:
The patent segments the reputation measurement process into distinct components: sample selection module, survey administration module, data weighting module, and score aggregation module. Each module handles a specific aspect of the measurement process, allowing for precise control over data quality while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent applies preliminary action by pre-determining demographically representative sample sizes with confidence intervals before data collection begins. This preliminary planning ensures that the data collected will be statistically valid and representative, improving measurement precision without requiring complex post-processing corrections.
2Measurement precision
If demographically representative sampling with confidence intervals is implemented, then measurement precision improves, but loss of time increases due to extensive survey processes
Solution Approach 1:
The patent applies partial action by collecting survey data from a carefully calculated sample size that provides sufficient statistical power (95% confidence intervals) without requiring exhaustive surveying of entire populations. This partial sampling approach achieves adequate measurement precision while significantly reducing the time and resources required compared to comprehensive surveys.
Solution Approach 2:
The patent changes the parameter of confidence level (setting at 95%) and sample size to optimize the balance between measurement precision and time investment. By adjusting these statistical parameters, the system achieves reliable reputation measurements without requiring excessive survey time, finding an optimal point on the precision-time tradeoff curve.
3Measurement precision
If survey data is collected without demographic weighting, then ease of operation is high, but measurement precision deteriorates due to cultural and demographic biases
Solution Approach 1:
The patent implements feedback mechanisms where survey responses are continuously weighted and adjusted based on demographic representation compared to target population distributions. This feedback loop automatically corrects for cultural and demographic biases in the collected data, improving measurement precision while the automated nature of the weighting process maintains ease of operation.
Solution Approach 2:
The system applies self-service by automatically performing demographic weighting and bias correction calculations without requiring manual intervention. The software autonomously adjusts survey data based on pre-defined demographic parameters and confidence interval requirements, maintaining measurement precision while keeping the operation simple and automated.
Data Source
AI summary
A system and method for determining and managing reputation of an entity or industry includes cleaning data to derive a highly reliable, demographically representative sample of a survey population, sized to provide a ninety-five percent or greater confidence interval from individuals familiar with the entity; determining a reputation perception score of the entity; determining reputation factor scores; determining reputation driver scores; and determining a reputation driver weight and reputation driver order of importance to entity reputation.