Predictive Feedback Model for Accurate Target Group Response Ranking
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Solution Overview
Problem
Existing methods for obtaining feedback on content items, such as advertisements, often result in rushed or inaccurate responses due to user inconvenience, leading to ineffective targeting and resource wastage for inquirers.
Innovation Solution
A system and method that involves presenting content items to two groups of users, calculating a comparison value from the second group's responses, and rewarding users whose answers closely match this value, thereby encouraging thoughtful responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional survey methods (telephone, mail, online surveys) are used to obtain feedback, then users can provide responses, but the responses are rushed and inaccurate due to user inconvenience and time consumption
Solution Approach 1:
The system implements a feedback mechanism where users are shown actual aggregate responses from other users and can adjust their predictions accordingly. This creates a loop where users refine their answers based on observed patterns, improving accuracy without requiring extensive time investment from individual users.
Solution Approach 2:
The system allows users to self-select their level of engagement. Users can quickly make predictions without detailed analysis, or spend more time refining their answers if they choose to. The system accommodates different time investments while maintaining data quality through the aggregate feedback mechanism.
2Reliability
If traditional survey methods are used, then feedback can be collected, but significant resources are wasted obtaining unreliable feedback from unfocused users
Solution Approach 1:
By showing users aggregate responses from other users and allowing them to adjust their predictions, the system creates a self-correcting mechanism that filters out unreliable responses. Users who invest time and effort to understand the content better tend to provide more accurate predictions, which are then weighted more heavily in the aggregate results.
Solution Approach 2:
The system changes the parameter of user engagement from passive response collection to active prediction refinement. Users transition from simply answering questions to iteratively improving their predictions based on feedback, which naturally selects for more thoughtful and reliable responses while reducing waste from rushed answers.
3Loss of information
If users are required to provide detailed feedback, then more accurate information can be obtained, but users may rush through answers to complete surveys quickly
Solution Approach 1:
The system provides immediate feedback to users showing aggregate responses from other users. This allows users to quickly adjust their predictions without needing to invest excessive time in analysis, maintaining both information quality and completion speed. The feedback loop enables rapid iteration on predictions.
Solution Approach 2:
Users can invest varying amounts of time in refining their predictions without penalty. The system accepts partial effort from users who want to complete surveys quickly while still capturing valuable data, and accommodates users who invest more time for potentially higher accuracy, without requiring full engagement from all participants.
Data Source
AI summary
Apparatus and methods present a content item and question from an inquirer to a group of users in a first feedback session with a requirement for the users to provide predictions of how a member of a distinct target group will respond to the content item, receiving, during the first feedback session, the first group's prediction of the target group's responses, presenting, during a second feedback session, the same content item and the same question to the target group with a requirement for the target group to provide responses directed to their own responses, constructing a predictive model of the target group based on responses received from the target group during the second feedback session, measuring accuracy of the first group's predictions using the target group predictive model and providing the inquirer access to an ordered visual representation of the first group users arranged as a function of accuracy.