We extract product listings, pricing signals, inventory status, and reviews across independent WooCommerce storefronts. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from WooCommerce. All fields typed and schema-versioned.
"store_domain": "example-store.com", "sku": "TSHIRT-001", "name": "Classic Cotton T-Shirt", "product_type": "variable", "price": 19.99, "regular_price": 24.99, "stock_status": "instock"
| # | store_domain | product_id | sku | name | permalink | product_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Inventory objects from WooCommerce. All fields typed and schema-versioned.
"store_domain": "example-store.com", "sku": "TSHIRT-001", "regular_price": 24.99, "sale_price": 19.99, "currency": "USD", "stock_quantity": 142, "stock_status": "instock"
| # | store_domain | sku | regular_price | sale_price | currency | stock_quantity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Tags objects from WooCommerce. All fields typed and schema-versioned.
"store_domain": "example-store.com", "category_id": 42, "name": "Men's Apparel", "slug": "mens-apparel", "parent_id": 0, "product_count": 384, "display_type": "products"
| # | store_domain | category_id | name | slug | parent_id | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Product Variations objects from WooCommerce. All fields typed and schema-versioned.
"store_domain": "example-store.com", "parent_sku": "TSHIRT-001", "variation_sku": "TSHIRT-001-L-BLU", "attributes": "Size: L, Colour: Blue", "regular_price": 24.99, "stock_status": "instock", "weight": "0.2 kg"
| # | store_domain | parent_sku | variation_sku | attributes | regular_price | sale_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from WooCommerce. All fields typed and schema-versioned.
"store_domain": "example-store.com", "review_id": "8472", "product_sku": "TSHIRT-001", "reviewer_name": "John Doe", "rating": 5, "verified_buyer": true, "date_created": "2026-03-14T10:22:00Z"
| # | store_domain | review_id | product_sku | reviewer_name | review_content | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our infrastructure normalises data from thousands of highly customised WooCommerce storefronts into a single, predictable schema.
Identify WooCommerce instances via Wappalyzer signatures and source code footprints across target domains.
Normalise disparate WooCommerce themes into a single unified product schema for consistent downstream analysis.
Detect and extract structured data directly from exposed /wp-json/wc/v3 endpoints when available.
Extract complex parent-child product relationships and variable pricing matrices from WooCommerce JSON payloads.
Monitor sale prices, regular prices, and dynamic discounts across competitor stores with high-frequency crawls.
Capture stock status, exact quantities if exposed, and backorder configurations directly from product pages.
Scrape user ratings, review text, and verified buyer flags across product pages and aggregated review widgets.
Extract product categories, tags, and custom attributes unique to specific themes and store configurations.
Run daily catalogue diffs or hourly price checks with change detection to minimise downstream processing.
Brief in. Clean data out.
Provide target domains, category URLs, or specific product links. We design the extraction schema together.
We configure Scrapy crawlers, REST API probes, proxy rotation, and WAF circumvention for the target stores.
Schema validation, null-rate checks, and multi-theme normalisation testing before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
WooCommerce is highly customisable, meaning no two stores look exactly alike. Here is how we build resilient pipelines across millions of domains.
WooCommerce stores use thousands of different themes. We build resilient selectors that target core WooCommerce classes and data attributes rather than relying on brittle, theme-specific DOM structures.
Many stores leave the WordPress REST API partially exposed. We probe /wp-json/wc/v3 endpoints first to extract clean, structured JSON before falling back to complex HTML parsing.
WooCommerce handles product variations via complex JSON payloads injected directly into the DOM. We extract and parse this raw data object rather than simulating browser clicks on dropdown menus.
High-value WooCommerce stores protect their data with Cloudflare or Wordfence. We deploy residential proxies and Playwright browser sessions to solve JS challenges and bypass WAF protections.
For large multi-store catalogues, we maintain a hash index of last-seen values per product. Subsequent runs only push diffs, reducing downstream processing load and storage costs.
Retailers track pricing and discount strategies across hundreds of independent competitor stores built on WooCommerce.
Brands monitor independent distributors and boutique stores for Minimum Advertised Price violations.
Niche marketplaces aggregate inventory and pricing data from independent sellers to build unified shopping portals.
Dropshippers monitor supplier stock levels and price changes to automatically update their own storefronts.
Machine learning teams use diverse product descriptions and category mappings to train retail classification models.
Analysts track the adoption of specific product categories and pricing trends across the long tail of independent retail.
"WooCommerce powers millions of independent storefronts, creating a highly fragmented but incredibly rich dataset of global retail pricing."
Scraping WooCommerce requires more than basic HTML parsing. Thousands of custom themes, Wordfence firewalls, and complex variable product JSON structures demand resilient infrastructure. DataFlirt handles the extraction so you can focus on data analysis.
Everything supported by our WooCommerce scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering and WAF circumvention. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies to bypass strict Wordfence and Cloudflare rate limits on target stores.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting across thousands of domains.
Data delivered to where your team already works — no new tooling required.
About WooCommerce scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and category data is generally permissible. DataFlirt targets only public frontend data and exposed, unauthenticated API endpoints. We do not bypass authentication walls to extract customer PII or backend order data.
While visual layouts change, the underlying WooCommerce data structures often remain consistent. We target core CSS classes, data attributes, and injected JSON payloads rather than relying on brittle, theme-specific DOM nodes.
Yes. Many stores leave the /wp-json/wc/v3 endpoints partially or fully exposed. Our pipelines probe these endpoints first to extract clean JSON data before falling back to HTML parsing.
We use ISP-grade residential proxies and full Playwright browser sessions to solve JavaScript challenges and present realistic TLS fingerprints, preventing WAF blocks.
If the store owner configures WooCommerce to display exact stock quantities on the frontend or exposes it via the REST API, we capture it. If hidden, we extract the binary 'instock' or 'outofstock' status.
We configure pipeline cadences based on your requirements. We support real-time hourly checks for high-priority competitor domains or daily catalogue refreshes for broader market analysis.
We typically start engagements with a defined list of target domains or specific product categories. Contact us with your target list for a scoped quote and sample data.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need to monitor 50 competitor domains or aggregate 10,000 independent storefronts, we build and operate the infrastructure. Tell us your requirements.
