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customer-feedback-analysis

Analyze customer feedback to extract themes, identify trends, and generate insight reports.

147|32|Updated Feb 11, 2026
Search Tags:#sentiment analysis#insight generation#customer feedback#customer satisfaction#theme extraction#NPS analysis
One-click install
npx skills add https://github.com/seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customer-feedback-analysis
Source: https://github.com/seb1n/awesome-ai-agent-skills/tree/main/customer-success/customer-feedback-analysis
Command: npx skills add https://github.com/seb1n/awesome-ai-agent-skills --skill customer-feedback-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill transforms raw, unstructured customer feedback into actionable insights, helping businesses understand customer sentiment, identify key themes, and drive product improvements.

Core Features & Use Cases

  • Theme Extraction: Identifies recurring topics and sentiment from open-text feedback.
  • Quantitative Analysis: Calculates NPS, CSAT, and score distributions across customer segments.
  • Trend Identification: Tracks changes in feedback over time and correlates them with business events.
  • Insight Reporting: Generates comprehensive reports with executive summaries and recommended actions.
  • Use Case: Analyze NPS survey results to pinpoint why detractors are unhappy and what promoters love, then use these insights to prioritize product roadmap items.

Quick Start

Analyze our Q4 NPS survey results (850 responses) and break down scores by plan tier.

Frequently Asked Questions about customer-feedback-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze NPS survey results and extract customer sentiment from open-text feedback?▼

NPS analysis and sentiment extraction are performed by cleaning raw feedback, applying topic modeling to qualitative comments, and calculating score distributions to pinpoint why detractors are unhappy and what promoters love.

What is the best way to identify recurring themes and trends in unstructured customer feedback?▼

Identifying recurring themes in customer feedback involves normalizing qualitative comments, applying topic modeling to extract key subjects, and tracking sentiment changes over time to correlate them with business events.

Can I calculate CSAT and NPS score distributions across different customer segments?▼

You can calculate CSAT and NPS score distributions across customer segments by processing quantitative survey data and breaking down scores by specific attributes like plan tier or customer cohort.

How do I generate actionable insight reports from customer satisfaction data?▼

Generating actionable insight reports from customer satisfaction data involves synthesizing quantitative scores and qualitative themes into structured outputs with executive summaries and recommended actions for product roadmap prioritization.

Does this customer feedback analysis approach work with mixed quantitative scores and qualitative comments?▼

This customer feedback analysis approach works with mixed data by processing quantitative scores from CSAT and NPS surveys alongside qualitative open-text comments to produce comprehensive insight reports.

How do I track customer satisfaction trends over time and correlate them with product changes?▼

Tracking customer satisfaction trends over time requires analyzing sequential feedback data, identifying sentiment shifts, and mapping those changes against business events to understand the impact of product updates.

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