In-depth Amazon review analysis. Extract sentiment patterns, recurring complaints, feature requests, and competitive insights from product reviews. Turn customer feedback into product improvements and marketing opportunities.
Supported platforms: Amazon、Shopify、WooCommerce、Walmart、TikTok Shop、Etsy、eBay、BigCommerce。
How to use
分析我竞争对手瑜伽垫的评论（有2500条评论，4.2星）。客户抱怨什么？我能在我的产品中改进什么？
Features
- Sentiment analysis by star rating (positive/negative/neutral themes)
- Recurring complaint identification and severity ranking
- Feature request extraction from customer language
- Competitor review comparison (what customers like about competitors)
- User-generated content (UGC) mining for marketing copy
- Review-based product improvement priority matrix
How it works
Step 1: Gather information from the user message — product, platform, current situation, and goals.
Step 2: Ask all follow-up questions at once in multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a").
Step 3: Conduct research and analysis using the frameworks and methodologies below.
Multi-dimensional sentiment analysis model
- Star rating stratification: Analyze reviews by star rating tier (1-2 stars negative, 3 stars neutral, 4-5 stars positive). Identify specific sentiment themes within each tier (e.g., 5-star reviews may praise for different reasons such as "quality," "packaging," or "customer service").
- Complaint severity ranking: Rank complaints by frequency, severity (returns, A-to-Z claims), and distribution pattern (isolated vs. systemic issues). High-frequency systemic complaints = highest priority.
- Implicit feature request mining: Extract implicit needs from review language: "wish it came with a storage bag," "if only it were a bit bigger" — these reveal product improvement directions.
Competitor review comparison framework
- Identify points that competitor customers repeatedly praise = the baseline for your product.
- Find points that competitor customers complain about = your differentiation opportunities.
- Map competitive positioning based on review themes.
User-generated content (UGC) extraction
- Extract persuasive customer language from positive reviews for marketing copy.
- Identify viral review patterns for listing optimization.
- Find emotional trigger words that resonate with buyers.
Improvement priority matrix
- Plot identified issues/opportunities on an "impact vs. implementation difficulty" matrix.
- High impact + low difficulty = quick wins.
- High impact + high difficulty = strategic priorities.
Data sources
- Amazon review pages: Use web_fetch to scrape review content, titles, dates, and reviewer information.
- Note: Amazon pages load a limited number of reviews per load. Large-sample analysis may require multiple paginated scrapes.
Limitations
- Analysis quality depends on review volume. Products with fewer than 50 reviews have low statistical significance.
- Despite Amazon's filtering, fake/suspicious reviews may still exist.
- Consumers tend to leave extreme reviews (very satisfied or very dissatisfied). Neutral voices may be underrepresented.
- Implicit functional requirements are often vague and fragmented. Product domain knowledge is needed to translate them into actionable improvements.
- Review data only reflects post-purchase experience. It cannot provide insight into pre-purchase churn reasons (price, images, title).
Step 4: Provide structured, actionable output with specific recommendations rather than vague guidance.
Output format
- First provide a summary of findings
- Include specific data points and benchmarks (if any)
- Provide prioritized action items
- Estimates based on incomplete data must be marked with ⚠️
- End with specific next steps