We extract product listings, 618 and 11.11 promotional signals, monthly sales volumes, flagship store intelligence, and multimedia reviews from Tmall. 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 tmall.com. All fields typed and schema-versioned.
"item_id": "673829104823", "title": "Xiaomi 14 Pro 5G Smartphone Leica Optics", "brand": "Xiaomi", "original_price": 5499.0, "promo_price": 4999.0, "monthly_sales": 45000, "stock_count": 1284, "shop_name": "Xiaomi Official Flagship Store"
| # | item_id | title | brand | category | original_price | promo_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for SKU Variations objects from tmall.com. All fields typed and schema-versioned.
"sku_id": "51283940182", "item_id": "673829104823", "properties_name": "Color: Titanium Special Edition; Storage: 16GB+1TB", "promo_price": 6499.0, "stock": 312, "sku_status": "in_stock", "promotion_tags": "['11.11 Pre-sale']"
| # | item_id | sku_id | properties_name | original_price | promo_price | stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from tmall.com. All fields typed and schema-versioned.
"review_id": "11928374650", "user_nick": "t***8", "user_tier": "T4", "content": "Battery life is excellent. The Leica camera performs well in low light.", "created_at": "2026-05-10T14:23:00Z", "rate_type": "positive", "append_days": 14
| # | review_id | item_id | user_nick | user_tier | content | created_at |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Shop Intelligence objects from tmall.com. All fields typed and schema-versioned.
"shop_id": "103847291", "shop_name": "Dyson Official Flagship Store", "shop_type": "Brand Flagship", "description_score": 4.9, "service_score": 4.8, "logistics_score": 4.9, "follower_count": 3205000
| # | shop_id | shop_name | seller_nick | shop_type | description_score | service_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from tmall.com. All fields typed and schema-versioned.
"keyword": "noise cancelling headphones", "position": 1, "item_id": "58291038471", "title": "Bose QuietComfort Ultra", "price": 2299.0, "monthly_sales": 3200, "is_mall": true, "scraped_at": "2026-05-12T08:11:22Z"
| # | keyword | sort_type | position | item_id | title | price |
|---|---|---|---|---|---|---|
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Our Tmall scraper handles every layer of the platform: flagship store listings, dynamic promotional pricing, monthly sales volumes, and the review corpus — with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, specifications, warranty details, high-resolution images, and every metadata field Tmall surfaces — scraped at the item level.
Capture original price, promotional price, 11.11/618 discounts, and coupon requirements — timestamped per crawl.
Extract the visible 30-day sales volume indicator for every item, critical for market share and demand estimation.
Extract pricing, stock levels, and specific imagery for every individual SKU combination (colour, storage, configuration).
Full review text, user tiers, appended comments, and associated image/video URLs — paginated across review endpoints.
Shop DSR scores (description, service, logistics), follower counts, and full item catalogues from brand flagship stores.
Track organic position for any keyword and sort parameter (relevance, sales, price) across Tmall search results.
Extract data from cross-border listings on Tmall Global, including shipping origin and import tax indicators.
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 item IDs, store URLs, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for tmall.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.
Alibaba invests heavily in scraping detection. Here is how we stay resilient — and why teams choose managed infrastructure over DIY.
Tmall's bot detection operates on TLS fingerprints, browser headers, and strict IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Tmall product pages and promotional pricing are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering to capture data that headless HTTP clients miss entirely.
Frequent requests trigger Alibaba's slider CAPTCHAs. We integrate automated solving APIs via CapSolver with precise mouse trajectory simulation to clear these challenges without manual intervention.
Tmall changes its DOM structure frequently, especially during major shopping festivals. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline overnight.
For large store 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.
Global electronics brands monitor grey market sellers and track official flagship store performance.
eCommerce teams track competitor pricing strategies across normal trading days and major events like 11.11.
Analysts aggregate monthly sales indicators to estimate market share and revenue for competing product lines.
Product managers mine Tmall reviews and appended comments to identify hardware defects and feature requests.
Brands analyse Tmall Global listings to understand pricing parity and import tax impacts on consumer demand.
Legal teams audit third-party store listings for unauthorised use of brand assets and suspicious pricing.
"Tmall dictates the electronics market in China, but accessing its promotional pricing and sales volume requires bypassing some of the strictest anti-bot systems globally."
Most teams underestimate the investment required: reliable Tmall scraping requires regional residential proxies, full JavaScript rendering, slider CAPTCHA solving, and constant selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our tmall.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 CN/HK 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 tmall.com scraping, legality, and pipeline operations.
Ask us directly →We use regional residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Slider CAPTCHAs are solved automatically via CapSolver integration. We monitor for block rate spikes in real time and trigger pool rotation automatically.
Yes. Our pipelines capture the base price, the promotional price, and the specific event tags associated with major shopping festivals. We adjust crawl frequency during these events to capture flash sales and dynamic coupon impacts.
Yes. We capture the visible 30-day sales indicator displayed on Tmall product listings. This is a critical metric for estimating market share and competitor revenue.
Yes. We support extraction from Tmall Global, capturing specific cross-border data points such as shipping origin, import taxes, and customs clearance indicators.
Tmall listings often contain dozens of SKU combinations (e.g., colour, storage size). We extract the specific price, stock level, and image associated with each individual SKU combination on the parent listing.
Yes. Our review extraction includes the text content, user tier, appended comments (follow-up reviews), and the URLs for any images or videos uploaded by the consumer.
Our smallest packages start at a defined item list (typically 1,000-50,000 items) or specific flagship stores with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off store catalogue dump or a continuous price-monitoring feed across major electronics brands — we scope, build, and operate the pipeline. Tell us what you need.
