Customer Service & Retention AI

Voice of Customer

Voice of Customer is an ecommerce AI skill for LeroyCreates, built for teams working with OpenClaw. Use it to you are handling recurring pre-sale or post-sale…

Provider
LeroyCreates
Platforms
OpenClaw
View original link · ClawHub

What this skill helps you do

Builds a structured Voice of Customer analysis framework that transforms feedback from reviews, support tickets, surveys, and social media into actionable insights. Features a seven-step workflow: scope definition, multi-channel feedback collection and normalization, hierarchical taxonomy building (3 levels with cross-category tagging), aspect-level sentiment analysis with intensity scoring, quantified theme extraction with trend direction and revenue impact estimation, action mapping to product/marketing/CX/ops teams with assigned owners, and ongoing monitoring with KPI thresholds and review cadences. Includes a detailed DTC skincare brand example walking through the complete methodology.

Install and get started

Copy the full instructions into your AI tool. Test one low-risk example before connecting real store data.

Original Skill instructions

You are a Voice of Customer analyst. Seven-step framework: (1) Define scope—product, time period, channels, questions, audience. (2) Collect/normalize feedback from reviews, support tickets, surveys, social mentions, forums into unified format with source/date/rating/segment. (3) Build 3-level hierarchical taxonomy with multi-tagging. (4) Analyze aspect-level sentiment (not document-level) with 1-5 intensity; extract verbatim quotes, flag emotional triggers. (5) Quantify by frequency, sentiment, trend direction, severity, revenue impact. (6) Map insights to team actions with owners, timelines, success metrics. (7) Establish ongoing monitoring with KPI thresholds, alerts, review cadences (weekly pulse, monthly deep dive, quarterly strategic review).

Useful tasks

  • Analyzing product reviews to identify top themes driving satisfaction and dissatisfaction
  • Connecting support ticket patterns to upstream product or process fixes
  • Extracting customer language for marketing copy and messaging refinement
  • Detecting early churn signals from sentiment trend shifts across feedback channels
  • Building ongoing VoC monitoring dashboards with KPI thresholds and alerting

How to use it

  • Use aspect-level sentiment, not document-level—a review can praise quality but criticize shipping
  • Build a 3-level taxonomy with multi-tagging; feedback often touches multiple themes
  • Score priority by frequency × severity × revenue impact, not just mention count
  • Map every insight to a specific team, owner, timeline, and success metric
  • Set alert thresholds proactively (e.g., 'defective' mentions exceed 5% of total reviews)

More skills for this workflow

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