We extract product listings, pricing signals, discount depths, trend rankings, seller data, reviews, and category intelligence from Shein. 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 shein.com. All fields typed and schema-versioned.
"goods_id": "sg-11203571, "title": "SHEIN EZwear Floral Print Wrap Midi Dress", "category": "Women Dresses", "price": 12.99, "original_price": 22.99, "currency": "USD", "discount_pct": 43, "rating": 4.3, "review_count": 3847, "is_new_arrival": true, "in_stock": true
| # | goods_id | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Promotions objects from shein.com. All fields typed and schema-versioned.
"goods_id": "sg-11203571", "price": 12.99, "original_price": 22.99, "discount_pct": 43, "flash_sale_price": 9.99, "flash_sale_ends_at": "2026-05-13T23:59:00Z", "app_exclusive_price": 11.49, "coupon_eligible": true, "price_timestamp": "2026-05-12T08:22:00Z"
| # | goods_id | price | original_price | discount_pct | discount_abs | flash_sale_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from shein.com. All fields typed and schema-versioned.
"review_id": "rv_sh_4928710", "goods_id": "sg-11203571", "star_rating": 5, "verified_purchase": true, "review_title": "Perfect summer dress, runs true to size", "helpful_votes": 84, "fit_feedback": "true_to_size", "review_date": "2026-04-29"
| # | review_id | goods_id | reviewer_name | verified_purchase | star_rating | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Category & Trends objects from shein.com. All fields typed and schema-versioned.
"category_id": "cat_dresses_midi", "category_name": "Midi Dresses", "trending_rank": 3, "new_arrivals_count": 1482, "avg_price": 14.20, "avg_discount_pct": 38, "avg_rating": 4.2, "scraped_at": "2026-05-12T08:30:00Z"
| # | category_id | category_name | parent_category | trending_rank | new_arrivals_count | total_products |
|---|---|---|---|---|---|---|
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Our Shein scraper handles every layer of the platform: product catalogues, dynamic pricing, flash sale windows, trend rankings, and the review corpus — with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, description, material, care instructions, images, size options, and every metadata field Shein surfaces — scraped at SKU level with full variant mapping.
Capture price, original price, flash sale windows, app-exclusive rates, new-user discounts, and coupon eligibility — timestamped per crawl.
Extract trending rank, new arrival flags, bestseller positions, and wish-count signals across categories — track what's rising in real time.
Full review text, star ratings, fit feedback, size purchased, helpful votes, and reviewer body metrics — paginated across all review pages.
Complete category tree with product counts, average pricing, average discount depth, and top-ranked items per sub-category.
Track organic position and sponsored placement for any keyword — with new-arrival, trending, and curated-collection badge capture.
shein.com, shein.co.uk, shein.de, shein.com.au, shein.com.mx and 20+ regional storefronts — all from a unified schema with localised pricing.
Monitor flash sale eligibility windows, countdown timers, stock depletion rates, and coupon stacking — useful for competitive pricing and trend alerting.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, keyword sets, or goods ID lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for shein.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Shein's platform is heavily JavaScript-rendered with aggressive bot detection. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Shein's bot detection operates on TLS fingerprints, browser headers, and IP reputation scoring. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management — trained on real user behaviour patterns.
Shein product pages, category feeds, and flash sale pages are fully JavaScript-rendered single-page applications. We run full Playwright browser sessions with lazy-load triggering, scroll simulation, and dynamic price widget hydration — capturing data that headless HTTP clients miss entirely.
Shein iterates its frontend rapidly. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, text-pattern matching, and structured data extraction — so a layout change doesn't break your data pipeline overnight.
For large SKU 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. You get a clean changelog rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops — and respond before you notice. SLA uptime is contractual, not aspirational.
Fashion retailers and D2C brands monitor Shein's aggressive discount cadence, flash sale windows, and price floors to benchmark their own positioning.
Buyers and merchandisers track new arrival velocity, rising category ranks, and wish-count growth to identify emerging micro-trends weeks before mass adoption.
Analysts map category saturation, average price points, and discount depth across thousands of sub-categories to identify whitespace and investment opportunities.
ML teams use Shein datasets to train fashion recommendation engines, visual similarity models, and NLP classifiers on apparel descriptions.
Sourcing teams correlate material descriptions, pricing, and review velocity to benchmark supplier costs and identify fast-moving product attributes.
PE firms and analysts track category leaders, new arrival frequency, and review growth curves to evaluate fast-fashion platform dynamics.
"Shein lists millions of new SKUs every week — making it the fastest-moving fashion dataset on earth. But none of that trend signal is usable unless you build the pipeline."
Most teams underestimate the complexity: reliable Shein scraping requires residential proxies, full JavaScript rendering, dynamic session handling, and daily selector maintenance. DataFlirt absorbs that infrastructure complexity so your analysts can focus on the fashion intelligence — not the plumbing.
Everything supported by our shein.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 scroll interactions. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US/UK/AU/DE 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 shein.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Shein is generally permissible under applicable law in India, the US, and the UK — consistent with the hiQ v. LinkedIn ruling and similar precedents. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls. We recommend clients review Shein's ToS independently and consult legal counsel for specific use cases.
We use residential ISP proxies that appear as real consumer traffic, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes don't break the pipeline. We monitor for block-rate spikes in real time and trigger pool rotation or solver queues automatically.
We support shein.com, shein.co.uk, shein.de, shein.com.au, shein.com.mx, shein.com.br, shein.fr, shein.it, shein.es, shein.com.sg, and 15+ additional regional storefronts — all from a unified schema with market-normalised pricing.
Latency depends on your agreed cadence. Real-time streaming pipelines achieve sub-60-minute latency for price and flash-sale signals on a defined SKU set. Full catalogue refreshes at daily cadence complete within a 6–12 hour window. New-arrival feeds can be ingested within hours of Shein publishing them.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per goods ID for price, trending rank, review count, and wish count. New arrival timestamps allow you to calculate trend velocity from day of listing.
Our smallest packages start at a defined SKU list or category set (typically 5,000–100,000 items) with weekly delivery. For larger catalogues, ongoing trend monitoring, or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Yes — including reviewer-submitted images, size and colour purchased, fit feedback labels (true to size, runs small, runs large), and self-reported height and weight where provided. This makes Shein review data particularly valuable for fashion-fit modelling.
Absolutely. We provide a sample run of up to 1,000 SKUs or 20 category pages as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off trend catalogue snapshot or a continuous flash-sale monitoring feed across 3M SKUs — we scope, build, and operate the pipeline. Tell us what you need.
