The Product Database endpoint allows you to query the comprehensive Jungle Scout product catalog using sophisticated filtering capabilities to identify products that meet specific criteria. This endpoint provides access to over 30 data points for each product, making it essential for detailed product research, competitor analysis, and market opportunity identification.
Why Use This Endpoint: The Product Database serves as your primary tool for high-level product research, offering detailed metadata about Amazon products including 30-day sales trends, pricing information, and comprehensive listing details. Whether you're looking for products in specific price ranges, recently launched items, or analyzing competitor performance, this endpoint provides the data foundation needed for informed business decisions.
Endpoint Details
Request Type: POST
URL: /api/product_database_query
Available Markets: US, UK, DE, IN, CA, FR, IT, ES, MX, JP
What You Can Input (Per API Call)
Required Parameters
- Marketplace: Country code (us, uk, de, in, ca, fr, it, es, mx, jp)
Optional Parameters
- Sort: Multiple sort options including name, category, revenue, sales, price, rank, reviews, lqs, sellers (default: name)
- Pagination: Page size (max 100 results, default 50) and cursor for additional pages
Request Body Filters
- Product Tiers: oversize, standard
- Seller Types: amz (Amazon), fba (Fulfilled by Amazon), fbm (Fulfilled by Merchant)
- Keywords: include_keywords and exclude_keywords arrays
- Brand Control: exclude_top_brands option
- Availability: exclude_unavailable_products option
- Price Range: min_price and max_price
- Net Profit Range: min_net and max_net
- Sales Rank Range: min_rank and max_rank
- Sales Volume Range: min_sales and max_sales
- Revenue Range: min_revenue and max_revenue
- Reviews Range: min_reviews and max_reviews
- Rating Range: min_rating and max_rating (1-5 scale)
- Weight Range: min_weight and max_weight
- Seller Count Range: min_sellers and max_sellers
- Listing Quality Score Range: min_lqs and max_lqs (1-10 scale)
- Date Range: min_updated_at and max_updated_at
Sample Request
{
"data": {
"type": "product_database_query",
"attributes": {
"product_tiers": ["oversize", "standard"],
"seller_types": ["amz", "fba", "fbm"],
"include_keywords": ["pasta", "spaghetti"],
"exclude_keywords": ["sushi", "ramen"],
"exclude_top_brands": true,
"exclude_unavailable_products": true,
"min_price": 10,
"max_price": 6000,
"min_revenue": 10,
"max_revenue": 1000000,
"min_lqs": 1,
"max_lqs": 10
}
}
}What You Get Back
Product Information
- Marketplace/ASIN: (e.g., us/B07ZZJLZT2)
- Title: Full product title
- Price: Current listing price
- Brand: Product brand name
- Category: Primary Amazon category
- Breadcrumb Path: Full category hierarchy
- Parent ASIN: Parent product identifier
- Variant Information: Related product variants
- Availability Status: Current availability
Performance Metrics
- Reviews: Total number of reviews
- Rating: Average customer rating (1-5 scale)
- Product Rank: Overall sales rank in category
- Subcategory Ranks: Detailed ranking within subcategories
- Approximate 30-Day Revenue: Monthly revenue estimate
- Approximate 30-Day Units Sold: Monthly sales volume estimate
Product Details
- Weight: Product weight with unit
- Dimensions: Length, width, height with unit
- Image URL: Product image location
- Date First Available: Launch date information
- Fee Breakdown: FBA fees, referral fees, variable closing fees, total fees
- Listing Quality Score: Proprietary 1-10 rating of listing quality
- EAN List: European Article Numbers
- ISBN List: International Standard Book Numbers
- UPC List: Universal Product Codes
- GTIN List: Global Trade Item Numbers
Seller Information
- Seller Type: FBA, FBM, or Amazon
- Number of Sellers: Total sellers for this product
- Buy Box Owner: Current buy box holder
- Buy Box Owner Seller ID: Seller identifier
Data Quality and Coverage Notes
- Variant Reviews: May contain null values for ASINs below monthly sales thresholds
- Sales Data Aggregation: 30-day units sold represents parent ASIN total, not individual variant
- Revenue Calculation: Calculated as units sold × variant-specific price
- Update Frequency: Most ASINs updated daily, with variants potentially updated at different times
More detailed explanations for data quality and coverage are included at the bottom of this article in the Technical Specifications section.
Sample Response Output
{
"data": [
{
"id": "us/B07ZZJLZT2",
"type": "product_database_result",
"attributes": {
"title": "Charging Dock for Apple Watch & iPhone (Apple Certified), ONEDock Power Station w/Built-in Original Apple Lightning Connector for Docking, Made for Series, 5,4,3,2,1, AirPods, iPod",
"price": 39.99,
"reviews": 1162,
"category": "Cell Phones & Accessories",
"rating": 4.3,
"brand": "Press Play",
"product_rank": 25990,
"listing_quality_score": 64,
"approximate_30_day_revenue": 2399.4,
"approximate_30_day_units_sold": 60,
"fee_breakdown": {
"fba_fee": 8.37,
"referral_fee": 5.99,
"total_fees": 14.37
}
}
}
],
"meta": {
"total_items": 4
}
}Data Highlights
- Data Granularity: Over 30 data points per product for comprehensive analysis
- Data Structure: JSON API format with detailed product attributes
- Search Method: Flexible filtering with multiple criteria combinations
- Response Volume: Up to 100 products per API call with pagination support
- Market Coverage: Single marketplace per query across 10 major Amazon markets
Use Cases & Applications
1. Curating Product Lists to Specific Criteria
Business Goal: Utilize extensive filtering options to create tailored product lists that meet specific research requirements, ensuring analysis is focused and efficient.
All relevant filtering criteria
How to Implement:
- Define clear product criteria based on business objectives
- Apply multiple filters simultaneously to narrow results
- Use price, revenue, and review filters for market positioning
- Combine seller type and brand filters for competitive analysis
- Leverage listing quality score filters for benchmark identification
Key Metrics to Focus On:
- Price range alignment with target market segments
- Revenue and sales volume thresholds for market validation
- Review count and rating criteria for quality assessment
- Seller count analysis for competitive landscape evaluation
- Listing quality scores for performance benchmarking
Business Impact: Streamline product research by focusing only on products that meet specific business criteria. Reduce analysis time by eliminating irrelevant products early in the research process. Enable data-driven decision making by working with pre-qualified product sets.
2. Gathering Comprehensive Data for Selected ASINs
Business Goal: Collect detailed information for specific ASINs to enable thorough product analysis and competitive intelligence gathering.
How to Implement:
- Use the include_keywords parameter to input specific ASINs
- Retrieve over 30 data points for each target product
- Analyze fee structures and profit margins for each ASIN
- Compare performance metrics across selected products
- Build comprehensive product profiles for strategic planning
Key Metrics to Focus On:
- Detailed fee breakdowns for profit margin calculations
- 30-day sales and revenue trends for performance assessment
- Seller information and buy box ownership patterns
- Product dimensions and weight for logistics planning
- Date first available for market timing analysis
Business Impact: Enable deep-dive analysis of specific products without manual data collection. Support competitive intelligence efforts with comprehensive product insights. Facilitate informed product sourcing and private label development decisions.
Important Considerations: When analyzing variant data, remember that 30-day sales figures represent parent ASIN totals, while revenue calculations use variant-specific pricing. Use updated_at timestamps to ensure you're working with the most current data when comparing variants.
3. Identifying Top Products Based on Listing Quality Score
Business Goal: Discover products with superior listing quality to establish benchmarks and identify optimization opportunities in your market space.
How to Implement:
- Apply high LQS filters (7-10 range) to identify top-performing listings
- Sort results by LQS to prioritize highest-quality products
- Analyze listing elements of high-scoring products for best practices
- Compare your products against high-LQS benchmarks
- Identify market gaps where high-quality listings are missing
Key Metrics to Focus On:
- Listing Quality Score distribution across product categories
- Correlation between LQS and sales performance
- Review patterns and rating consistency for high-LQS products
- Image quality and title optimization strategies
- Category-specific LQS benchmarks and standards
Business Impact: Improve your own listing quality by learning from top performers. Identify market opportunities where listing quality is consistently low. Establish competitive advantages through superior listing optimization strategies.
Technical Specifications
Rate Limits
- Endpoint follows standard API rate limits (300 requests/minute, 15/second)
- Each call can return up to 100 products with pagination support
- Response volume depends on filter specificity and market size
Filter Combinations
- Multiple filters can be applied simultaneously for precise targeting
- Logical AND operation between different filter types
- Array-based filters (keywords, seller types) use OR logic within arrays
Response Format
- JSON API format with consistent data structure
- Comprehensive metadata including total item counts
- Pagination links for large result sets
Data Freshness
- Product data updated regularly with updated_at timestamps
- 30-day sales and revenue estimates based on recent performance
- Listing quality scores calculated using current listing data
Important Data Considerations
Variant Reviews Coverage: Null values in the 'variant_reviews' column occur because variant review data is not collected for every ASIN. Collection focuses on ASINs that surpass specific monthly sales thresholds, ensuring good coverage for high-demand products while not all ASINs are covered. Coverage expansion is ongoing to include more ASINs over time.
30-Day Units Sold Aggregation: The "approximate_30_day_units_sold" figure represents total sales for the entire variant group (parent ASIN), not individual variant sales. When querying a variant ASIN, you receive variant-specific data points (title, price, image_url) but parent-level sales aggregation. This figure is calculated at query time, reflecting the most recent 30-day period from the update timestamp.
Revenue Calculation Method: The "approximate_30_day_revenue" is calculated by multiplying "approximate_30_day_units_sold" by the variant's "price." Since variants can have different prices, revenue figures may vary across variants even with identical unit sales. For parent ASIN revenue analysis, average variant prices across all variants to calculate more cohesive revenue figures.
Update Frequency and Timing: Most ASINs undergo daily data collection and updates, though lower-sales-frequency products may update less frequently. Variants under the same parent ASIN may not update simultaneously within the day, potentially causing minor discrepancies in 30-day sales data when comparing variants. Use the "updated_at" timestamp to identify the most recently updated data for optimal accuracy.
Error Handling
- 400 Bad Request: Invalid filter parameters or request format
- 403 Forbidden: API key lacks marketplace permissions
- 422 Validation Error: Filter value validation errors
Additional endpoint documentation is available here: https://developer.junglescout.com/api#endpoints