Analyze Amazon seller stores and product portfolios. Estimate revenue, identify hit products, assess growth trajectories, and reveal strategic patterns. Suitable for competitive intelligence and market research.
Supported platforms: Amazon、Shopify、WooCommerce、Walmart、TikTok Shop、Etsy、eBay、BigCommerce。
How to use
分析这个亚马逊卖家的策略：他们卖厨房小工具，大约有50个产品。帮我理解他们的打法。
Features
- Seller store analysis (product count, categories, pricing strategy)
- Revenue estimation based on BSR and category benchmarks
- Product portfolio assessment (hit products, long tail, new listings)
- Review growth rate and quality score
- Growth trajectory estimation
- Competitive positioning analysis
How it works
Step 1: Collect 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 abbreviated answers (e.g., "1b 2c 3a").
Step 3: Conduct research and analysis using the framework and methodology below.
Seller store structure analysis model
- Product portfolio tiering: Classify the seller's products by sales contribution (using BSR as a proxy indicator). Head products (core hit products), mid-tier products (steady contributors), tail products (strategic experiments). The distribution reveals whether the strategy is "hit-driven" or "long-tail coverage."
- Category span assessment: Analyze category coverage and cross-category relationships. Cross-category sellers have stronger supply chain integration but higher operational complexity.
- Variation strategy identification: Determine whether variations are "horizontal expansion" (multiple variations satisfying different preferences) or "vertical depth" (each variation pursuing the highest quality).
Revenue estimation engine
- BSR-to-sales mapping model: Use category-specific conversion rate benchmarks to map BSR rankings to estimated monthly sales. The relationship between BSR and sales varies greatly across categories.
- Category benchmark calibration: Calibrate the model using known reference products from industry reports or public data.
- Price-weighted revenue calculation: Multiply each product's estimated sales by its current price, then aggregate to obtain the seller's monthly revenue range (output as a range to reflect uncertainty).
- Seasonal adjustment: If the analysis period is an off-season, apply a seasonal factor.
Product lifecycle positioning
- New product identification: Identify recently listed products through listing time, review count, and BSR trends.
- Mature product identification: Products with stable BSR, sufficient reviews, and slowing growth = profit pillars but at risk of being overtaken.
- Declining product identification: Products with continuously declining BSR and stagnant review growth = candidates for clearance or discontinuation.
Strategy pattern identification
- Pricing patterns: Frequent price adjustments = use of automated repricing tools. Stable prices = brand premium positioning.
- Advertising intensity inference: Correlate BSR changes with ranking changes to infer advertising dependence. Stable ranking + large BSR fluctuations = organic traffic driven. Synchronized changes = advertising driven.
- Listing cadence analysis: Detect whether listings are launched in bursts or continuously, and identify seasonal patterns.
- Review acquisition strategy: Analyze review growth patterns to identify use of the Vine program, the request-a-review feature, or other compliant review strategies.
Data sources
- Amazon seller storefront page: Use web_fetch to scrape product listings — title, price, review count, rating, listing date.
- Product detail page: Independent BSR data for each product requires a separate web_fetch.
- Built-in BSR model: Industry statistical data and empirical parameters for BSR-to-sales mapping.
Limitations
- All analysis is based on publicly visible information. Private operational data cannot be obtained.
- BSR-to-sales estimates have an inherent margin of error (typically ±30% to ±50%).
- Large stores require substantial data collection time (one BSR scrape per product).
Step 4: Provide structured, actionable output with specific recommendations rather than vague guidance.
Output format
- Start with 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