Amazon Review Export

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name: amazon-review-export description: "Amazon product review export and analysis agent. Extract, organize, and analyze Amazon reviews — export to structured format, identify sentiment patterns, surface product insights, and generate competitive intelligence from review data. Triggers: amazon review export, review analysis, export reviews, review data, review csv, sentiment analysis, review insights, customer feedback analysis, review scraper, product reviews, review patterns, voc amazon" allowed-tools: Bash metadata: openclaw: homepage: https://github.com/mguozhen/amazon-review-export

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Amazon Review Export & Analyzer

Extract intelligence from Amazon product reviews — organize into structured data, analyze sentiment patterns, identify product improvement opportunities, and generate competitive insights from customer voice data.

Commands

review export               # structure reviews into exportable format
review analyze           # full sentiment and pattern analysis
review sentiment         # sentiment scoring breakdown
review patterns          # find recurring themes and pain points
review compare      # compare review profiles between products
review insights          # extract product improvement opportunities
review competitive  # analyze competitor review weaknesses
review summary           # executive summary of review data
review csv               # format reviews as CSV-ready data
review report               # comprehensive review intelligence report

What Data to Provide

Review text — paste reviews directly (as many as possible)

Star rating distribution — number of reviews at each star level

ASIN — product identifier

Competitor reviews — for competitive analysis

Time period — recent reviews vs. older reviews for trend analysis

Review Analysis Framework

Review Export Format

Structure raw reviews into:

Date,Rating,Title,Review Text,Verified,Helpful Votes,Reviewer
2024-01-15,5,"Great product","Very satisfied with...",Yes,12,Customer123
2024-01-10,2,"Disappointing","Expected better...",Yes,3,Customer456

Sentiment Analysis Framework

5-star rating interpretation:

⭐⭐⭐⭐⭐ (5-star): Delighted — read for what exceeds expectations
⭐⭐⭐⭐   (4-star): Satisfied — note any "but" qualifiers
⭐⭐⭐     (3-star): Neutral — mixed feelings, often most useful insights
⭐⭐       (2-star): Dissatisfied — specific complaints, high value for improvement
⭐         (1-star): Angry — often extreme cases, filter for systemic vs. one-off

Sentiment scoring:

Positive signals (+): "love", "perfect", "great", "amazing", "exactly what I needed"
Negative signals (-): "disappointed", "broke", "doesn't work", "waste", "returned"
Neutral signals (=): "okay", "fine", "average", "as expected", "decent"
Net Sentiment Score = (Positive reviews - Negative reviews) / Total reviews × 100
Target: Score > 60 = healthy product sentiment

Theme Identification (Qualitative Coding)

Categorize all reviews into themes:

Product quality themes:

□ Build quality / durability
□ Materials / finish quality
□ Sizing / dimensions (accurate vs. listing)
□ Performance (does it work as claimed?)
□ Longevity (how long does it last?)

Customer experience themes:

□ Packaging / unboxing experience
□ Instructions / ease of setup
□ Customer service experience
□ Shipping / delivery condition
□ Value for money perception

Use case themes:

□ Intended use (matches expected use case)
□ Alternative uses (how customers use it unexpectedly)
□ Gifting (bought as a gift)
□ Replacement (replacing specific previous product)
□ Professional vs. personal use

Frequency Analysis

Count mentions of each theme:

Theme                    Mentions    % of Reviews    Sentiment
Durable/sturdy           45          42%             Positive
Easy to assemble         38          35%             Positive
Instructions unclear     22          20%             Negative
Size smaller than shown  15          14%             Negative
Great value for money    52          48%             Positive

Priority fix threshold: Any negative theme appearing in >10% of reviews requires action.

Pain Point Extraction

From negative reviews, extract specific pain points:

Pain Point              Frequency   Severity    Fix Category
Product breaks quickly  23 mentions High        Product quality
Wrong size/dimensions   15 mentions Medium      Listing accuracy
No instructions         12 mentions Low         Packaging insert
Hard to clean           8 mentions  Low         Product design

Severity classification:

High: Safety, complete product failure, cannot use product

Medium: Significant disappointment, reduced usefulness

Low: Minor inconvenience, still satisfied overall

Competitive Review Intelligence

From competitor reviews, extract:

Competitor weaknesses (from their negative reviews): → These are your differentiation opportunities

Competitor strengths (from their positive reviews): → Baseline expectations you must meet or exceed

Competitor Pain Points → Your Product Claims
"Instructions are confusing" → "Clear 10-step illustrated guide included"
"Flimsy material" → "Reinforced with aircraft-grade aluminum"
"Customer service ignores" → "24/7 support with 1-hour response guarantee"

Review Trend Analysis

Compare recent vs. older reviews:

Period          Avg Rating    Top Complaint        Top Praise
Last 90 days:   4.1           Size issues (18%)    Easy use (42%)
6-12 months:    4.4           No issues dominant   Quality (55%)
12+ months:     4.6           Rare complaints      Durability (60%)
Trend: Rating declining → investigate recent product/supplier change

VOC (Voice of Customer) Summary

Generate a customer perspective summary:

WHAT CUSTOMERS LOVE (keep and amplify in marketing):
1. [Most praised attribute + quote]
2. [Second most praised + quote]
3. [Third most praised + quote]
WHAT CUSTOMERS WANT IMPROVED (product/listing fixes):
1. [Top pain point + specific ask]
2. [Second pain point + ask]
3. [Third pain point + ask]
WHAT SURPRISES CUSTOMERS (unintended uses or unexpected positives):
1. [Unexpected use case]
2. [Unexpected benefit]

Review-to-Listing Optimization

Map review insights directly to listing improvements:

Review insight → Listing change
"Sturdy, holds 50lbs easily" → Add to bullets: "HEAVY-DUTY CONSTRUCTION — tested to hold up to 50 lbs"
"Works great as a gift" → Title: add "Perfect Gift" / create gift-focused image
"Instructions confusing" → Add instruction image to image gallery
"Looks exactly as shown" → Emphasize "true-to-photo" in listing

Workspace

Creates ~/review-data/ containing:

exports/ — structured CSV exports per ASIN

analyses/ — full review analysis reports

themes/ — coded theme frequency data

competitive/ — competitor review intelligence

voc/ — voice of customer summaries

Output Format

Every review analysis outputs: 1. Rating Distribution — star breakdown with percentage for each level 2. Net Sentiment Score — overall sentiment health (0-100) 3. Top 5 Positive Themes — what customers love most (with frequency) 4. Top 5 Negative Themes — main pain points (with frequency + severity) 5. VOC Summary — customer voice in plain language 6. Listing Optimization Map — review insights → specific listing improvements 7. Product Development Signals — engineering/sourcing changes implied by feedback 8. CSV Export — structured data ready to paste into spreadsheet