We extract footwear, apparel, accessories, daily deals, and size-level inventory from 6pm.com. 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 6pm.com. All fields typed and schema-versioned.
"product_id": "9482103", "brand": "Nike", "title": "Revolution 6 Next Nature", "category": "Shoes", "sub_category": "Sneakers & Athletic Shoes", "gender": "Men", "material": "Textile upper", "page_url": "https://www.6pm.com/p/nike-revolution-6-next-nature-black-white-iron-grey/product/9482103"
| # | product_id | sku | brand | title | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Clearance objects from 6pm.com. All fields typed and schema-versioned.
"sku": "9482103-3", "current_price": 45.5, "msrp": 70.0, "discount_percentage": 35, "discount_absolute": 24.5, "currency": "USD", "is_clearance": true, "price_timestamp": "2026-05-12T10:15:00Z"
| # | sku | current_price | msrp | discount_percentage | discount_absolute | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Sizing objects from 6pm.com. All fields typed and schema-versioned.
"sku": "9482103-3", "color_name": "Black/White/Iron Grey", "size": "10", "width": "D - Medium", "in_stock": true, "low_stock_warning": "Only 2 left in stock", "out_of_stock": false
| # | sku | color_id | color_name | size | width | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brand & Category Data objects from 6pm.com. All fields typed and schema-versioned.
"brand_name": "Nike", "brand_url": "https://www.6pm.com/nike", "total_products": 1245, "active_categories": "['Shoes', 'Clothing', 'Accessories']", "average_discount": 32.5, "max_discount": 65
| # | brand_id | brand_name | brand_url | total_products | active_categories | average_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search & SERP Results objects from 6pm.com. All fields typed and schema-versioned.
"keyword": "running shoes", "position": 4, "product_id": "9482103", "brand": "Nike", "price": 45.5, "msrp": 70.0, "discount_badge": "35% OFF", "scraped_at": "2026-05-12T10:15:33Z"
| # | keyword | position | product_id | brand | title | price |
|---|---|---|---|---|---|---|
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Our 6pm scraper navigates complex variant matrices, aggressive inventory turnover, and dynamic sizing grids. We map every colourway, size, and width combination to its specific clearance price.
Extract complex variant matrices. We map every size, width, and colour combination to its specific SKU and price point.
Capture the exact retail price (MSRP), current 6pm price, and calculated discount percentages across the entire catalogue.
Track in-stock status and low-stock warnings at the most granular level. Know exactly which sizes are selling out.
Monitor entire brand catalogues on 6pm. Track Zappos-affiliated inventory liquidation for specific designers and labels.
Capture flash sales and daily clearance events before inventory depletes. High-frequency polling for fast-moving stock.
Extract primary images, alternate angles, and product video URLs directly from the 6pm CDN.
Maintain the exact category and sub-category hierarchy to map 6pm's internal classification to your own database.
Clearance inventory moves fast. We use hash-based diffing to emit only changed records, reducing downstream processing costs.
Access 6pm.com via US residential proxies to ensure accurate pricing and prevent regional blocking.
Brief in. Clean data out.
Provide target brands, categories, or search terms. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for the 6pm network.
Schema validation, null-rate checks, and variant mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
6pm shares backend infrastructure with Zappos, meaning strict bot protections and complex JSON payloads for product variants. Here is how we extract it reliably.
6pm utilises strict edge protections shared with the broader Amazon/Zappos network. Our crawlers use US residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass TLS fingerprinting.
Apparel and footwear data is deeply nested. A single shoe might have 12 sizes, 3 widths, and 5 colours. We parse the frontend state objects and flatten these matrices into queryable row-level SKUs.
Clearance items sell out fast. We capture dynamic DOM elements indicating 'Only 2 left' or out-of-stock statuses at the size level, providing accurate inventory velocity metrics.
For large brand catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, discount outliers, and coverage drops — and respond before you notice.
Discount retailers monitor 6pm clearance pricing to optimise their own promotional calendars and markdown strategies.
Premium brands audit 6pm to track how their excess inventory is being liquidated and at what discount depth.
Analysts track size-level stock depletion rates to calculate sales velocity for specific footwear models and apparel lines.
Fashion eCommerce platforms benchmark their category depth and brand coverage against 6pm's available catalogue.
Retail analysts track average discount percentages across categories to understand broader macroeconomic trends in apparel liquidation.
Secondary market platforms ingest daily deal data to identify high-margin arbitrage opportunities on premium brands.
"6pm.com holds the definitive dataset for fashion clearance pricing, but extracting size-level stock requires handling complex Zappos-backend variant structures."
Most teams underestimate the investment required: reliable 6pm scraping requires handling aggressive inventory turnover, dynamic sizing grids, and anti-bot protections shared with the Zappos network. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our 6pm.com 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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About 6pm.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from 6pm is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and inventory data. We do not extract personal data or circumvent authentication walls. Clients should consult legal counsel for specific use cases.
6pm embeds variant matrices within the page's frontend state. We intercept and parse these JSON objects during the Playwright session, flattening them into individual SKU rows containing the specific size, width, colour, and stock status.
Yes. By running high-frequency pipelines against specific SKUs, we track size-level stock depletion and low-stock warnings, allowing you to calculate estimated sales velocity for clearance items.
For daily deals and flash sales, we configure pipelines to poll the target categories at hourly or sub-hourly intervals, ensuring you capture maximum discount events before inventory runs out.
Yes. Every pricing record includes the original MSRP, the current 6pm clearance price, and the calculated discount percentage and absolute dollar value.
Our selector strategy relies on extracting structured data from the frontend state objects rather than fragile CSS selectors where possible. When DOM extraction is necessary, we use multi-layer fallback chains and alert on schema drift immediately.
Yes. We provide a sample run of up to 500 products or a specific brand category as part of the pre-engagement scoping process — so you can validate schema fit and variant mapping before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off brand catalogue dump or a continuous clearance-monitoring feed — we scope, build, and operate the pipeline. Tell us what you need.
