Automated Feedback Prioritization via NPS Uplift Analysis
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Solution Overview
Problem
Current online user feedback platforms are inefficient in identifying and prioritizing areas for improvement, as they overwhelm marketers and UX designers with large volumes of unfiltered feedback, delaying response times and lacking interactive features to address user concerns.
Innovation Solution
A system that uses machine learning for semantic analysis to categorize and prioritize user feedback, automatically identifying topics, moving feedback into actionable groups, and calculating Net Promoter Score (NPS) uplift to determine priority topics, enabling instant responses and resource allocation to key issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If marketers and UX designers manually identify and prioritize user feedback areas, then they can invest resources in key improvement areas, but the process delays response time due to the large volume of feedback requiring quantification and prioritization
Solution Approach 1:
The patent replaces the manual mechanical process of reading and analyzing feedback with automated text analytics and machine learning algorithms. The system automatically processes, quantifies, and prioritizes feedback using computational methods, eliminating the time-consuming manual review while maintaining accurate identification of key improvement areas through structured analysis of feedback patterns and sentiment.
2Quantity of substance
If current online user feedback platforms collect large volumes of user feedback, then they gather comprehensive user insights, but they overwhelm marketers and designers with unfiltered feedback lacking actionable prioritization
Solution Approach 1:
The patent extracts and isolates the most critical and actionable feedback from the large volume of incoming user comments. Using text analytics and prioritization algorithms, the system separates high-impact feedback requiring immediate attention from less critical inputs, presenting only the most relevant prioritized items to marketers and designers. This extraction process transforms overwhelming raw data into a manageable set of actionable insights.
3Device complexity
If online user feedback platforms focus on feedback collection without follow-up, then they simplify platform functionality, but they fail to be responsive and interactive with customers, preventing users from providing feedback when requested
Solution Approach 1:
The patent implements a closed-loop feedback system where the platform not only collects user feedback but also tracks resolution status and communicates back to users. The system monitors whether identified issues have been addressed, notifies users of resolutions, and encourages further feedback based on resolution outcomes. This feedback loop transforms the platform from a simple collection tool into an interactive system that engages users throughout the issue resolution process, improving overall feedback effectiveness.
Data Source
AI summary
A system and method for online user feedback management are provided. The method includes receiving online user feedbacks for a product from a plurality of users. A plurality of topics for the product are identified from the online user feedbacks. For each topic, the received online user feedbacks are categorized into a plurality of groups based on a rating score provided in each online user feedback for the product and semantic analysis of each online user feedback for the product. A net promoter score (NPS) uplift for each topic is calculated, where the NPS uplift measures an improvement in a NPS for the product if issues related to the topic are resolved. A priority topic is identified based on the NPS uplift for each of the topics. The priority topic is then prioritized in resolving issues related to the topics included in the online user feedbacks.