Context-Based Feedback System Using Temporal Triggers
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Solution Overview
Problem
Current feedback platforms suffer from decayed quality of feedback data over time, noisy and confusing data aggregation, and ineffective presentation, leading to compromised data quality and increased false information, particularly due to their reliance on community dynamics and prolonged data collection periods.
Innovation Solution
A computer-implemented system and method for context-based feedback collection and aggregation, using a temporal/location/demographic trigger model to request feedback in real-time, allowing for immediate data storage and tailored reporting, with dashboards providing actionable insights to sponsors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feedback is collected using traditional survey platforms with prolonged time periods (1-7 days), then users have more time to provide thoughtful responses, but feedback data quality decays exponentially and false information increases
Solution Approach 1:
The system sends push notifications to users at predetermined time intervals (e.g., 1 hour, 24 hours, 7 days) after purchase, proactively requesting feedback before the user naturally forgets the experience. This preliminary action captures feedback at optimal moments while the experience is still fresh in the user's mind, preventing data quality decay.
Solution Approach 2:
The feedback collection system dynamically adjusts its approach based on user behavior and timing. Instead of a static collection period, the system adapts by sending targeted push notifications at varying intervals, allowing feedback collection to be both timely and flexible according to user engagement patterns.
2Productivity
If feedback platforms use community-based data broadcast models (e.g., Yelp, Facebook), then feedback aggregation becomes easier and more widespread, but feedback quality decreases due to community dynamics promoting contrarian and less genuine opinions
Solution Approach 1:
The system extracts feedback collection from the noisy community broadcast environment and delivers it directly to individual users through personalized push notifications. By taking out the feedback request from the communal context, the system eliminates the distorting influence of community dynamics while maintaining efficient aggregation through centralized collection.
Solution Approach 2:
The feedback platform acts as an intermediary between the product/service provider and the user, mediating the feedback collection process through push notifications. This intermediary approach bypasses the need for community-based broadcasting while maintaining efficient data aggregation, as the platform directly communicates with users and collects responses systematically.
3Measurement precision
If feedback requests are sent frequently to ensure real-time data collection, then data freshness and relevance improve, but user annoyance and response fatigue increase
Solution Approach 1:
The system applies partial action by sending push notifications at selective, predetermined time intervals (e.g., 1 hour, 24 hours, 7 days) rather than continuously. This partial approach captures feedback at critical moments without excessive contact, balancing data freshness with user tolerance by requesting feedback only when most likely to be valuable.
Solution Approach 2:
The feedback collection follows a periodic pattern with predetermined time intervals between notifications. This periodic action ensures real-time data collection at key moments while providing natural breaks between requests, reducing user annoyance by establishing a predictable, non-intrusive rhythm of communication.
Data Source
AI summary
A computer-implemented interactive system, methods, and platform are herein disclosed to allow for the aggregation of context based feedback data according to a selected feedback context model. In an illustrative implementation, the computer-implemented interactive system/platform comprises an exemplary server computer environment, an instruction set comprising one or more instructions to request from the one or more other computing environments feedback data (e.g., such requests being made on a temporal basis according to one or more feedback context models), and a data store. The platform may be used to aggregate feedback data for various feedback sponsors/requestors, by way of example, as a user/customer is experiencing a product/service, in real time. The frequency, timing, and trigger for feedback data requests from users, illustratively, can be tailored by the one or more instructions of the one or more feedback context models that take into account a baseline use/experience of given product/service.