Customer Feedback Analyzer
Analyzes customer feedback from reviews, surveys, and tickets to extract sentiment, themes, specific issues, and actionable recommendations formatted for leadership.
When to use this prompt
Use this when you have accumulated customer feedback data that needs systematic analysis to identify patterns and priorities. This template works when you need to present findings to leadership in a structured format rather than raw feedback.
You are a customer insights analyst. Analyze this customer feedback and extract actionable insights: [FEEDBACK DATA - reviews, surveys, tickets, etc.] Provide: 1. Sentiment summary - Overall sentiment score - Trend over time (if data available) - Key emotional indicators 2. Theme extraction - Top positive themes - Top negative themes - Emerging themes 3. Specific issues - Most mentioned problems - Severity ranking - Frequency analysis 4. Verbatim highlights - Best quotes (positive) - Most concerning quotes (negative) - Suggestions worth considering 5. Recommendations - Quick wins (easy fixes) - Strategic improvements - Areas needing investigation 6. Competitive mentions - Competitor comparisons - Feature requests Format for presentation to leadership.
Free to use — the optimizer tailors this template to your exact task and target AI.
How to customize it
- [FEEDBACK DATA] — Paste the actual customer input, whether from product reviews, survey responses, support tickets, or a combination of sources
- Sentiment summary section — If your data includes timestamps, the template will analyze trends over time; otherwise it focuses on current overall sentiment
- Competitive mentions section — Only relevant if your feedback data contains references to competitors or comparative statements
- Format for presentation to leadership — The template already structures output for executive audiences, but you can specify additional formatting preferences if needed
What you'll get back
A structured report with six numbered sections covering sentiment scores and trends, positive and negative themes, ranked problems with frequency data, selected customer quotes, prioritized recommendations divided into quick wins and strategic improvements, and any competitor mentions found in the data.
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