| name | customer-feedback-analysis |
| description | Analyze NPS, CSAT, and qualitative customer feedback to extract themes, identify trends, and generate actionable insight reports. Use when the user requests customer feedback analysis or provides relevant inputs for this workflow. |
| license | MIT |
| metadata | {"author":"community","version":"1.0"} |
Customer Feedback Analysis
Transform raw customer feedback from NPS surveys, CSAT responses, support interactions, and app store reviews into structured insights. This skill extracts recurring themes from open-text responses, calculates quantitative score distributions, identifies emerging trends over time, and produces reports that connect customer sentiment to specific product areas and business outcomes.
Workflow
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Collect feedback data — Aggregate feedback from all available sources: NPS survey responses (score + open text), CSAT ratings from support interactions, in-app feedback widgets, app store reviews, social media mentions, G2/Capterra reviews, and sales call notes. Tag each response with metadata: date, customer segment, plan tier, account tenure, and source channel. Ensure consistent schema across all sources.
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Clean and normalize — Deduplicate responses from the same customer across channels. Standardize rating scales (convert 1-5 CSAT to 1-10 for cross-comparison). Strip PII from open-text responses. Handle multilingual responses by detecting language and translating to English while preserving the original. Remove bot/spam responses using pattern detection (identical text, suspicious timing, single-word noise).
