How does this skill help with VoC data?

It provides a structured framework to tag feedback consistently, making it easier to analyze trends across different platforms and communication channels.

What layers are included in the taxonomy?

The taxonomy includes five layers: Persona (ICP/Role), Lifecycle (Stage), Driver (Product/Price), Sentiment (Strength/Urgency), and Metadata (ARR/Region).

Why is the driver layer limited to 30 items?

A lean taxonomy encourages higher adoption rates among team members and ensures that categories remain distinct and statistically significant for analysis.

Is it compatible with other GTM skills?

Yes, it is built to be paired with other skills in the GTM-Agents collection, such as the listening tour skill for auto-tagging new signals.

Can I use this for automated tagging?

Yes, the skill includes JSON schemas specifically designed for tagging automation and webhook ingestion into your data pipeline.

Customer Feedback Taxonomy

Name: Customer Feedback Taxonomy
Author: gtmagents

bygtmagents

•

分析与监控

Standardizes customer feedback tagging across personas, lifecycle stages, and sentiment to drive actionable GTM insights.

The Customer Feedback Taxonomy skill provides a robust framework for normalizing Voice of Customer (VoC) data from diverse sources like surveys, support logs, and interviews. By implementing a multi-layered schema—covering personas, lifecycle stages, value drivers, and sentiment—it enables teams to eliminate data drift and maintain consistent tagging standards across the entire customer journey. This skill is essential for Go-To-Market teams looking to automate feedback synthesis and ensure that historical data remains comparable during deep-dive analysis.

主要功能

01Governance checklists to maintain taxonomy health and prevent drift

02Multi-layered tagging for personas, lifecycle stages, and value drivers

03Metadata integration for ARR, region, and industry context

04Ready-to-use JSON schemas for automated webhook ingestion

05Standardized sentiment analysis with strength and urgency indicators

0629 GitHub stars

使用场景

01Normalizing fragmented feedback from support logs and community channels

02Auditing existing datasets to fix inconsistencies in customer tagging

03Onboarding new Customer Success teams to shared feedback standards

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add gtmagents/gtm-agents customer-feedback-taxonomy

For use in Claude.ai and ChatGPT

主要功能

01Governance checklists to maintain taxonomy health and prevent drift

02Multi-layered tagging for personas, lifecycle stages, and value drivers

03Metadata integration for ARR, region, and industry context

04Ready-to-use JSON schemas for automated webhook ingestion

05Standardized sentiment analysis with strength and urgency indicators

0629 GitHub stars

使用场景

01Normalizing fragmented feedback from support logs and community channels

02Auditing existing datasets to fix inconsistencies in customer tagging

03Onboarding new Customer Success teams to shared feedback standards

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add gtmagents/gtm-agents customer-feedback-taxonomy

For use in Claude.ai and ChatGPT