Amazon Review Intelligence Extractor — Consumer Insights from 1B+ Reviews
Deep consumer insights from 1B+ pre-analyzed Amazon reviews. Extracts pain points, buying factors, user profiles, usage patterns, and differentiation opportunities across 11 analysis dimensions. Compares review sentiment across competitors and generates listing copy suggestions. Uses all 11 APIClaw API endpoints with cross-validation. Use when user asks about: review analysis, customer feedback, pain points, what customers say, review insights, sentiment analysis, consumer insights, product improvements, voice of customer, review comparison, negative reviews, customer complaints, buying factors, user profile. Requires APICLAW_API_KEY.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/apiclaw/amazon-review-intelligence-extractorAmazon Review Intelligence Extractor — 11 Dimensions, 1B+ Reviews
Pre-analyzed consumer insights. Pain points, buying factors, user profiles, differentiation gaps.
Files
- Script:
{skill_base_dir}/scripts/apiclaw.py— run--helpfor params - Reference:
{skill_base_dir}/references/reference.md(field names & response structure)
Credential
Required: APICLAW_API_KEY. Get free key at apiclaw.io/api-keys
Input (one of)
- Single ASIN: "Analyze reviews for B09V3KXJPB"
- Multi-ASIN: "Compare review pain points across these 5 competitor ASINs"
- Category-wide: keyword/category name → resolve via
categoriesfirst (need ≥3-level deep path)
API Pitfalls (see apiclaw skill for full list)
reviews/analysisneeds 50+ reviews — fallback torealtime/productratingBreakdown- labelType is NOT an API request parameter — the API returns all 11 dimensions in one call. Filter by
labelTypeclient-side from theconsumerInsightsarray. - Category mode needs precise path (≥3 levels) — broad categories = diluted insights
- Field name is
reviewRate(notreviewRate) for mention frequency - ASIN-specific endpoints don't need
--category; keyword-based ones do - Category auto-detection: categoryPath is auto-detected from target ASIN. If
category_sourcein output isinferred_from_search, confirm with user
11 Analysis Dimensions
painPoints · issues · positives · improvements · buyingFactors · keywords · userProfiles · scenarios · usageTimes · usageLocations · behaviors
Unique Logic
Analysis Modes
- Category mode: all reviews in category → market-level insights
- ASIN mode: specific products → competitive analysis
- Choose based on user intent. Category = broader, ASIN = deeper.
Pain Point Impact Ranking
Rank differentiation opportunities by: frequency × avg rating delta "Top pain point: durability — mentioned in 27/471 reviews (5.7%), avg rating 2.4 when mentioned"
| reviewRate | Frequency Level | Interpretation |
|---|---|---|
| >10% | 🔴 Critical | Mentioned by 1 in 10 buyers — must address in product design 📊 |
| 5-10% | 🟡 Significant | Common complaint — differentiator if solved 📊 |
| 2-5% | 🟠 Notable | Worth mentioning in listing if you solve it 📊 |
| <2% | 🟢 Minor | Edge case — deprioritize unless easy fix 🔍 |
| avgRating when mentioned | Severity |
|---|---|
| <2.5 | Severe — causes returns/1-star reviews 📊 |
| 2.5-3.5 | Moderate — disappoints but doesn't cause returns 🔍 |
| >3.5 | Mild — noticed but not deal-breaker 🔍 |
Differentiation Priority = High frequency + Low avgRating = Biggest opportunity 🔍. If top 3 pain points all have reviewRate >5% and avgRating <3.0, there is a clear product improvement opportunity 💡. If all pain points have reviewRate <2%, the category is well-served — differentiation through reviews is limited 🔍.
Metadata
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{
"plugins": {
"official-apiclaw-amazon-review-intelligence-extractor": {
"enabled": true,
"auto_update": true
}
}
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