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customer-feedback-taxonomy skill

/plugins/voice-of-customer/skills/customer-feedback-taxonomy

This skill standardizes customer feedback tagging across personas, lifecycle stages, drivers, and sentiment to accelerate insights.

npx playbooks add skill gtmagents/gtm-agents --skill customer-feedback-taxonomy

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: customer-feedback-taxonomy
description: Standardized tagging schema for personas, lifecycle stages, drivers,
  and sentiment.
---

# Customer Feedback Taxonomy Skill

## When to Use
- Normalizing surveys, interviews, support logs, or community chatter before synthesis.
- Auditing existing VoC datasets for drift or inconsistencies.
- Onboarding new teams to shared tagging standards.

## Framework
1. **Persona Layer** – map ICP, role, and influence level.
2. **Lifecycle Layer** – awareness, onboarding, adoption, expansion, renewal, advocacy.
3. **Driver Layer** – product, service, pricing, experience, relationship, outcomes.
4. **Sentiment Layer** – strength, urgency, confidence, sample size.
5. **Metadata Layer** – ARR, region, industry, channel, last touch.

## Templates
- CSV/Sheet taxonomy with dropdowns and validation rules.
- JSON schema for tagging automation or webhook ingestion.
- Governance checklist for quarterly taxonomy refresh.

## Tips
- Keep taxonomy lean (<30 drivers) to encourage adoption.
- Version every change so historical analyses remain comparable.
- Pair with `run-voc-listening-tour` to auto-tag new signals.

---

Overview

This skill provides a standardized tagging schema for customer feedback across personas, lifecycle stages, drivers, and sentiment. It helps teams normalize and structure survey responses, support logs, interviews, and community signals so insights are comparable and actionable. The taxonomy is production-ready and designed for automation, governance, and cross-team alignment.

How this skill works

The taxonomy organizes feedback into layered dimensions: Persona, Lifecycle, Driver, Sentiment, and Metadata. It includes CSV/Sheet templates with dropdowns, a JSON schema for automated tagging and webhook ingestion, and a governance checklist for versioning and refresh cadence. Outputs are designed to feed analytics pipelines, dashboards, and VoC synthesis workflows.

When to use it

  • Normalizing surveys, interviews, support logs, or community chatter before synthesis
  • Auditing existing Voice-of-Customer datasets for drift or inconsistent labels
  • Onboarding new teams to a shared tagging standard
  • Automating tagging for incoming feedback via webhooks or ingestion pipelines
  • Preparing data for trend analysis, root-cause work, or product prioritization

Best practices

  • Keep the driver list lean (under ~30) to maximize adoption and reduce tagging friction
  • Version the taxonomy and record change metadata to preserve historical comparability
  • Use dropdowns and validation rules in spreadsheets to avoid free-text drift
  • Pair the JSON schema with automated checks that flag unknown or ambiguous tags
  • Schedule quarterly taxonomy reviews with cross-functional stakeholders

Example use cases

  • Tagging NPS survey verbatims by persona, lifecycle stage, and driver for root-cause analysis
  • Auto-tagging support tickets on ingestion and routing urgent negative sentiment to escalation queues
  • Auditing a legacy VoC dataset to identify label drift and reconcile divergent labels
  • Onboarding customer success and product teams to a shared feedback language for unified reporting
  • Feeding tagged feedback into a prioritization model that weights ARR, urgency, and sentiment

FAQ

Can this taxonomy be automated with existing ingestion tools?

Yes. A provided JSON schema supports webhook ingestion and can be integrated with common ETL or message pipelines for automated tagging.

How often should the taxonomy be updated?

Quarterly reviews are recommended; version every change to maintain historical comparability and run drift audits between releases.