Feedback Analysis
Customer feedback analysis tools
AI sentiment analysis, duplicate detection, demand signals from voting, and advanced filtering. Turn raw customer feedback into the insights your product team needs to decide what to build.
AI duplicate detection for new submissions
When a user submits new feedback, the system should automatically check for similar existing posts and suggest them before creating a new entry.
This would reduce the number of duplicate posts our team has to manually merge and help users discover that their request already exists with votes they can add to.
Ideally it would use semantic matching, not just keyword matching, so "dark theme" and "night mode" would be recognized as the same request.
Users are requesting AI-powered duplicate detection that runs automatically when new feedback is submitted. The feature should use semantic matching to identify similar posts beyond simple keyword overlap, reducing manual triage work.
“Dark theme and night mode should be recognized as the same request.”
“Reduce the number of duplicate posts our team has to manually merge.”
Next Steps:
- –Evaluate embedding models for semantic similarity
- –Design inline suggestion UI for the submit flow
Auto-detect duplicate submissions on creation
Both request real-time duplicate checking when users submit new feedback to reduce manual triage...
Merge duplicate posts with vote consolidation
Both request the ability to merge duplicate posts while combining vote counts and notifying voters...
What it does
Feedback analytics built into your feedback tool
AI sentiment analysis
Every submission is automatically classified as positive, neutral, or negative. Filter by sentiment to spot frustration spikes after releases or identify your happiest power users.
Duplicate detection and merge
Hybrid scoring combines semantic similarity, full-text matching, and LLM verification. AI suggests merges with confidence scores and reasoning. One click to consolidate.
Trending and demand signals
A velocity-weighted algorithm surfaces requests gaining momentum right now, not just all-time leaders. Hot badges flag sudden spikes so you catch emerging patterns early.
Advanced filtering and saved views
Filter by status, board, tags, assignee, vote count, date range, or customer segment. Save filter presets for quick switching between analysis views.
Tags and categorization
Custom tags with colors let you slice feedback by product area, customer type, or priority. AI suggests tags based on content. Bulk-apply across multiple posts.
AI summaries with key quotes
For posts with long comment threads, AI generates summaries highlighting the key customer quotes and recommended next steps. Skim instead of reading every comment.
AI agents
Let AI agents do the feedback analysis for you
Quackback includes a built-in MCP server. Connect Claude, Cursor, or any MCP-compatible agent to search feedback semantically, identify trends, triage new submissions, and draft changelog entries from shipped items.
- Natural language search across all feedback and comments
- Auto-categorize, merge duplicates, and apply tags
- CSV and JSON export for any external analysis tool
- Full REST API with OpenAPI docs for custom integrations
quackbackio/quackback
Open-source feedback analysis
All included. No premium tier.
FAQ
Frequently asked questions
How does AI analyze customer feedback?
AI processes feedback text to detect sentiment, identify topics, flag duplicates, and surface trends. Quackback uses AI to categorize incoming submissions, merge duplicate requests, and generate summaries with key quotes — reducing the manual triage work for your team.
Can AI categorize feedback automatically?
Yes. Connect an AI agent to Quackback via the built-in MCP server and it can apply tags, assign categories, detect duplicates, and update statuses. You review the results before they go live.
What is sentiment analysis for customer feedback?
Sentiment analysis uses AI to classify feedback as positive, negative, or neutral. It helps your team spot frustration patterns, identify at-risk accounts, and prioritize fixes for the issues causing the most friction.
How accurate is AI feedback analysis?
Accuracy depends on the model and your data quality. Modern language models handle sentiment and topic classification well for product feedback. Quackback supports bring-your-own-key so you can choose the model that works best for your domain.
Stop guessing what your feedback means
Deploy in minutes. AI analysis, voting signals, and advanced filtering from day one. Free and open source.
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