We extract product listings, pricing signals, flash sale windows, LazMall seller intelligence, reviews, and search rankings from Lazada. 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 lazada.com. All fields typed and schema-versioned.
"item_id": "1234567890", "title": "Samsung Galaxy Buds2 Pro True Wireless Earphones", "brand": "Samsung", "price": 3990.00, "currency": "THB", "discount_pct": 22, "lazmall_badge": true, "rating": 4.7, "review_count": 6821, "sold_count": 15200, "in_stock": true
| # | item_id | title | brand | seller_name | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from lazada.com. All fields typed and schema-versioned.
"item_id": "1234567890", "price": 3990.00, "original_price": 5099.00, "discount_pct": 22, "flash_sale_price": 3490.00, "flash_sale_end": "2026-05-12T18:00:00+07:00", "coins_cashback": 39, "free_shipping_eligible": true, "price_timestamp": "2026-05-12T10:05:00Z"
| # | item_id | price | original_price | discount_pct | discount_abs | flash_sale_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from lazada.com. All fields typed and schema-versioned.
"review_id": "lzd_rv_9918827", "item_id": "1234567890", "star_rating": 5, "verified_purchase": true, "review_title": "Amazing sound quality, fast delivery", "helpful_votes": 84, "review_date": "2026-04-21", "seller_reply": false
| # | review_id | item_id | reviewer_name | verified_purchase | star_rating | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Intelligence objects from lazada.com. All fields typed and schema-versioned.
"seller_id": "samsung-th-official", "seller_name": "Samsung Thailand Official Store", "lazmall_official": true, "positive_rating_pct": 98, "response_rate": 99, "ship_on_time_rate": 97, "follower_count": 182400, "active_listings_count": 342
| # | seller_id | seller_name | seller_url | lazmall_official | positive_rating_pct | response_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from lazada.com. All fields typed and schema-versioned.
"keyword": "wireless earphones", "position": 1, "item_id": "1234567890", "sponsored": false, "lazmall_badge": true, "free_shipping_badge": true, "price": 3990.00, "scraped_at": "2026-05-12T10:05:44Z"
| # | keyword | country | position | item_id | title | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Lazada scraper handles every layer of the platform: product listings, dynamic flash sale pricing, LazMall seller intelligence, review corpus, and SERP rankings — across all six Southeast Asian markets.
Title, specs, description, images, SKU variants, sold counts, and every metadata field Lazada surfaces — scraped at item level with full variant mapping.
Capture flash sale prices, countdown windows, voucher codes, bundle deals, Lazada Coins cashback, and free-shipping eligibility — timestamped per crawl.
Seller rating, positive feedback percentage, on-time shipping rate, chat response rate, follower count, and official store verification status.
Full review text, star ratings, helpful votes, verified purchase flags, SKU reviewed, seller replies, and review images — paginated across all review pages.
Track organic vs sponsored position for any keyword across TH, MY, ID, PH, SG, and VN — with LazMall and voucher badge capture.
lazada.co.th, lazada.com.my, lazada.co.id, lazada.com.ph, lazada.sg, and lazada.vn — all from a unified schema with market-normalised pricing.
Parent product to child SKU relationships with full option matrix — colour, size, storage, and any custom attribute Lazada sellers configure.
Monitor 11.11, 12.12, Mid-Year Sale, and other mega-campaign price movements at the item level — with pre/during/post campaign snapshots.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or campaign-triggered cadences with change-detection diffing.
Brief in. Clean data out.
Provide item ID lists, category URLs, keyword sets, or seller IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and anti-bot handling for Lazada.
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.
Lazada's platform spans six countries with different anti-bot configurations. Here's how we stay resilient across every market.
Lazada's bot detection varies significantly by country. We maintain dedicated residential ISP proxy pools for TH, MY, ID, PH, SG, and VN — rotating per request with country-specific browser fingerprints and cookie session management.
Lazada product pages, flash sale countdowns, and search results are heavily JavaScript-rendered. We run full Playwright browser sessions to capture price widgets, promotion overlays, and lazy-loaded review data that HTTP clients miss entirely.
Lazada's frontend differs by country. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, JSON-LD, and API intercept — so a storefront layout change in one market doesn't break the others.
For large item catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and storage bloat. 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.
Brands and 3P sellers monitor pricing, flash sale windows, and competitor promotions to reprice dynamically and protect margin across SEA.
Brands audit unauthorised resellers, MAP violations, and counterfeit listings — protecting brand equity across six Southeast Asian markets simultaneously.
Analysts track category growth, new entrant launches, and sold-count velocity to identify whitespace and investment opportunities across SEA.
ML teams use Lazada datasets to train recommendation engines, NLP classifiers, and multilingual sentiment models across SEA languages.
Teams correlate mega-campaign (11.11, 12.12) price movements, flash sale participation, and sold-count spikes with revenue outcomes.
PE firms and analysts track category leaders, seller growth curves, and review velocity to evaluate marketplace performance across SEA.
"Lazada is Southeast Asia's most data-rich marketplace — but its six-country structure means most teams get partial coverage at best."
Reliable Lazada scraping requires country-specific proxy pools, Playwright rendering for flash sale widgets, and per-market selector maintenance. DataFlirt operates unified pipelines across all six Lazada storefronts — delivering a single, normalised schema regardless of which market you need.
Everything supported by our lazada.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 dedicated pools of residential ISP proxies for all six Lazada markets. Rotation happens per-request with country-appropriate fingerprints and sticky sessions where required.
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 lazada.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Lazada is generally permissible under applicable law across Southeast Asia — reinforced by precedents such as hiQ v. LinkedIn. 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 Lazada's ToS independently and consult legal counsel for specific use cases.
We support all six Lazada storefronts: lazada.co.th (Thailand), lazada.com.my (Malaysia), lazada.co.id (Indonesia), lazada.com.ph (Philippines), lazada.sg (Singapore), and lazada.vn (Vietnam) — all delivered via a unified, market-normalised schema.
We use country-specific residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on real user 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.
Yes. We run elevated-frequency crawls during Lazada mega-campaigns (11.11, 12.12, Mid-Year Sale) to capture flash sale prices, countdown windows, claim rates, and stock depth at the item level — with pre/during/post campaign snapshots for analysis.
Standard pipelines deliver daily refreshes. For price and availability monitoring on a defined item set, we offer sub-60-minute latency. Campaign windows can be monitored at 15-minute intervals on request.
Yes. We provide a sample run of up to 500 items or 50 search result pages as part of pre-engagement scoping — 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 product catalogue dump or a continuous price-monitoring feed across six SEA markets — we scope, build, and operate the pipeline. Tell us what you need.
