amazon-review-export

# 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 <asin> # structure reviews into exportable format review analyze <reviews> # full sentiment and pattern analysis review sentiment <reviews> # sentiment scoring breakdown review patterns <reviews> # find recurring themes and pain points review compare <asin1> <asin2> # compare review profiles between products review insights <reviews> # extract product improvement opportunities review competitive <comp-reviews> # analyze competitor review weaknesses review summary <reviews> # executive summary of review data review csv <reviews> # format reviews as CSV-ready data review report <asin> # 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: ```csv 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