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voc-analysis

Automate VOC analysis and competitive intelligence from public platform feedback.

42|7|Updated Apr 17, 2026
Search Tags:#competitive intelligence#market research#sentiment analysis#user feedback#product benchmarking#voc analysis#pain point mining
One-click install
npx skills add https://github.com/addxai/enterprise-harness-engineering --skill voc-analysis-addxai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: voc-analysis
Source: https://github.com/addxai/enterprise-harness-engineering/tree/main/skills/voc-analysis
Command: npx skills add https://github.com/addxai/enterprise-harness-engineering --skill voc-analysis-addxai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

Manually collecting and analyzing scattered user feedback across social media, e-commerce, and app store platforms is time-consuming, prone to bias, and fails to deliver comprehensive, data-backed market insights. This Skill automates the entire voice of customer (VOC) analysis and competitive intelligence workflow to eliminate manual effort and deliver accurate, actionable research findings.

Core Features & Use Cases

  • Multi-Platform Data Collection: Gather user feedback from Reddit, Twitter, Amazon, App Store, YouTube, and 8+ other platforms via Apify Agent Skills and web search fallback.
  • Bias-Free Semantic Analysis: LLM processes 100% of raw data with no sampling, performing context-aware semantic tagging, pain point mining, and sentiment analysis.
  • Feature-Level Competitive Benchmarking: Calculate per-feature mention rates, positive/negative rates, and average ratings with noise filtering to compare your product against competitors objectively.
  • Use Case: A product manager can use this Skill to analyze user feedback for their new smart wearable, compare it against 3 top competitors, and generate a full Chinese-language market research report with English user quotes and clickable source links.

Quick Start

Use the voc-analysis skill to run a full VOC and competitive intelligence analysis for your smart home device, collecting user feedback from 5+ platforms, identifying top pain points, and generating a data-driven market insight report with feature-level competitor benchmarking.

Frequently Asked Questions about voc-analysis

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

FAQPage Schema
How do I automate voice of customer analysis from multiple social media platforms?▼

Automating voice of customer analysis involves scraping user feedback from platforms like Reddit, Twitter, and Amazon, then applying LLM semantic tagging and Python counting to generate data-driven market insight reports. This eliminates manual collection and provides bias-free competitive intelligence.

What is the best way to do feature-level competitive benchmarking for SaaS products?▼

Feature-level competitive benchmarking calculates per-feature mention rates, positive and negative rates, and average ratings from user feedback. By applying noise filtering and LLM processing to 100% of raw data, you objectively compare your SaaS product against competitors.

How does sentiment analysis and pain point mining work on raw user feedback?▼

Sentiment analysis and pain point mining use LLMs to perform context-aware semantic tagging on raw user feedback without sampling. This processes 100% of collected data to accurately identify and quantify specific user issues across product market research.

Can I use Apify scraping tools for collecting e-commerce and app store reviews?▼

Yes, Apify scraping tools gather user feedback from e-commerce and app store platforms like Amazon and the App Store. The workflow integrates web search fallback to ensure comprehensive multi-platform data collection for market research.

Does automated VOC analysis provide source attribution for user feedback quotes?▼

Automated VOC analysis delivers market insight reports featuring full source attribution. It includes clickable source links and original English user quotes within the generated Chinese-language reports to ensure data-backed, verifiable research findings.

What are the limitations of using LLMs for competitive intelligence on consumer electronics?▼

While LLMs process 100% of raw data for bias-free semantic analysis, the competitive intelligence quality depends on public platform data availability. It focuses on consumer electronics, software, and SaaS products, utilizing Apify scraping with web search fallback for collection.

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