Feedback System Semantic Matching for Personalized Notifications
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Solution Overview
Problem
Customers often feel that their feedback is not being heard or acknowledged by companies, leading to a loss of faith and potential switching to competitors, as current methods lack personalized attention and notification when feedback is incorporated into products.
Innovation Solution
A feedback system that uses machine learning algorithms to parse and categorize user feedback, compare it with product update information, and generate custom notifications to users when their suggestions are implemented, utilizing natural language processing and word embedding techniques to ensure semantic matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated ML algorithms are used to categorize feedback, then productivity is improved, but loss of information occurs because personalized customer attention is lost
Solution Approach 1:
The system implements a feedback loop by notifying customers when their feedback is incorporated into product updates. The notification system provides feedback to customers about the status of their input, transforming the one-way feedback collection into a two-way communication system that maintains customer engagement while using automated processing.
Solution Approach 2:
The patent introduces an intermediary notification system that bridges the gap between automated feedback processing and personalized customer communication. This intermediary layer matches feedback items with product updates and generates personalized notifications, allowing automated processing to coexist with personalized customer attention.
2Ease of operation
If manual parsing of feedback is performed, then customer personalization is improved, but productivity deteriorates due to the time-consuming nature of manual review
Solution Approach 1:
The system segments the feedback processing task into two parts: automated categorization using ML algorithms for initial processing, and selective personalized notification for customer engagement. This segmentation allows high-volume automated processing while maintaining personalization for the customer communication aspect.
Solution Approach 2:
The notification system enables a form of self-service by automatically generating and sending personalized notifications to customers based on their feedback and product updates. The system serves customers automatically based on their preferences and feedback history, reducing the need for manual customer service intervention.
3Adaptability or versatility
If feedback is collected regularly, then customer engagement is improved, but loss of time occurs because customers feel no one reviews their feedback
Solution Approach 1:
The system performs preliminary action by proactively notifying customers when their feedback is incorporated into product updates, rather than waiting for customers to check or follow up. This preliminary notification action reduces customer time investment and maintains engagement by showing that feedback is actively reviewed and acted upon.
Data Source
AI summary
Systems and methods are described for creating a customized response to user feedback. In an example, a feedback system can receive user feedback about a product. The feedback system can parse the user feedback to extract keywords and assign categories to the keywords. The feedback system can also receive update information related to the product. The feedback system can parse the product update information in a similar manner to extract keywords and assign them to categories. The feedback system can compare the parsed user feedback and the parsed product update information and identify any matches that indicate that the product update addresses something mentioned in the user feedback. The feedback system can create a custom notification that highlights the portion of the product update information that matched to the user feedback.