What deliverables does it produce?

Structured, quantified insights with benchmarks and prioritized recommendations to guide roadmaps.

Can it support NPS and churn analysis?

Yes. It processes NPS results, churn drivers, and product feedback to inform improvements.

customer-feedback-analyzer

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npx machina-cli add skill Yarmoluk/cognify-skills/customer-feedback-analyzer --openclaw

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SKILL.md

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Customer Feedback Analyzer

Analyzes customer feedback from surveys, reviews, support tickets, and interviews to identify themes, prioritize improvements, and quantify sentiment. Use when processing NPS results, analyzing churn reasons, reviewing product feedback, understanding support ticket patterns, or building a product or service improvement roadmap.

What This Skill Produces

Structured, quantified deliverables with specific dollar amounts
Industry-aware analysis with built-in benchmarks
Actionable recommendations with prioritized next steps

Access

This skill definition is available to Cognify clients and partners.

To learn more or request access: cognify.com | LinkedIn

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Learn to create Agent Skills like this one: Custom Skill Developer Guide

Source

git clone https://github.com/Yarmoluk/cognify-skills/blob/main/.github/skills/customer-feedback-analyzer/SKILL.mdView on GitHub

Overview

Analyzes customer feedback from surveys, reviews, support tickets, and interviews to identify themes, quantify sentiment, and prioritize improvements. It helps with processing NPS results, churn analysis, and building product or service improvement roadmaps by delivering structured, benchmarked insights and actionable recommendations.

How This Skill Works

The skill ingests diverse feedback sources, applies theme extraction and sentiment analysis, and outputs structured insights. It benchmarks results against industry norms and provides prioritized recommendations with clear next steps.

When to Use It

Processing NPS results to identify drivers of satisfaction and dissatisfaction.
Analyzing churn reasons to prioritize retention improvements.
Reviewing product feedback from surveys, reviews, and interviews.
Understanding support ticket patterns to surface recurring problems.
Building a data-driven product or service improvement roadmap.

Quick Start

Step 1: Aggregate feedback from surveys, reviews, support tickets, and interviews into a single dataset.
Step 2: Run theme extraction and sentiment analysis to surface themes and sentiment scores.
Step 3: Review the structured deliverables and implement the highest-priority actions with owners and timelines.

Best Practices

Ingest diverse feedback sources (surveys, reviews, tickets, interviews) to capture a complete signal.
Standardize data with consistent segments, timestamps, and currencies where relevant.
Use consistent sentiment scoring and align with industry benchmarks.
Prioritize actions using impact vs effort and include owner accountability.
Validate impact by tracking changes over time and through follow-up feedback.

Example Use Cases

An e-commerce platform clusters feedback by theme and quantifies sentiment to guide a UI overhaul.
A SaaS company identifies top churn drivers and prioritizes fixes based on impact.
Support teams detect recurring issues and reduce ticket volume by addressing root causes.
Product teams benchmark sentiment against industry norms to set improvement targets.
A services firm tailors onboarding improvements by analyzing NPS segments and feedback patterns.

Frequently Asked Questions

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