We extract product specifications, pricing signals, colour variants, dimensions, and warranty details from American Tourister. 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 Specifications objects from americantourister.com. All fields typed and schema-versioned.
"sku": "146513-1041", "title": "Curio Spinner 69/25 EXP", "collection": "Curio", "material": "Polypropylene", "dimensions": "69.0 x 49.0 x 30.0 cm", "weight": "3.9 kg", "volume": "82 L", "tsa_lock": true, "expandable": true
| # | sku | title | collection | category | material | dimensions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Variants objects from americantourister.com. All fields typed and schema-versioned.
"sku": "146513-1041", "colour_name": "Black", "colour_hex": "#000000", "price": 149.0, "list_price": 199.0, "currency": "USD", "discount_pct": 25, "in_stock": true, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | parent_id | colour_name | colour_hex | price | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from americantourister.com. All fields typed and schema-versioned.
"review_id": "REV-98234", "sku": "146513-1041", "star_rating": 5, "review_title": "Excellent travel companion", "review_body": "Survived multiple international flights with barely a scratch.", "review_date": "2026-04-18", "verified_buyer": true, "durability_rating": 5
| # | review_id | sku | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Collections & Hierarchy objects from americantourister.com. All fields typed and schema-versioned.
"collection_name": "Curio", "category_path": "Luggage > Hard Side Luggage", "breadcrumb": "Home / Luggage / Hard Side Luggage / Curio", "product_count": 12, "price_range_min": 129.0, "price_range_max": 249.0, "hero_image_url": "https://americantourister.com/images/curio-hero.jpg"
| # | collection_id | collection_name | description | category_path | breadcrumb | product_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Warranty & Care objects from americantourister.com. All fields typed and schema-versioned.
"sku": "146513-1041", "warranty_duration": "10 Years Global Warranty", "warranty_terms": "Covers manufacturing defects in material and workmanship.", "care_instructions": "Wipe clean with a damp cloth.", "airline_compatibility": "Check-in size", "cabin_size_approved": false, "spare_parts_available": true
| # | sku | warranty_duration | warranty_terms | care_instructions | repair_info | spare_parts_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
American Tourister's catalogue requires precision extraction of physical dimensions, material properties, and dynamic colour inventory. We handle the complex DOM structures and deliver normalised schemas.
Extract and normalise physical specifications: height, width, depth, weight, and volume across metric and imperial systems.
Map parent product IDs to all child colour variations, capturing specific hex codes, swatch images, and variant-specific pricing.
Capture base price, promotional discounts, clearance tags, and currency formats across different geographic storefronts.
Track in-stock, out-of-stock, and low-stock indicators at the variant level to monitor supply chain movements.
Parse warranty durations (e.g., 3-year, 10-year global) and specific terms linked to individual product collections.
Extract cabin-size approval flags and specific airline compatibility tags for carry-on luggage.
Paginate through customer reviews to extract text, star ratings, and sub-ratings for durability and design.
Extract data from US, EU, and APAC American Tourister storefronts with region-specific catalogue normalisation.
Run scheduled pipelines with hash-based diffing. Receive only records that have changed since the last extraction.
Brief in. Clean data out.
Provide target categories, regions, or specific collections. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, handle regional redirects, and map the product variant structures.
Schema validation, null-rate checks, dimension normalisation, and variant completeness testing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting accurate luggage data requires handling dynamic swatches, regional pricing, and nested product specifications. Here is how we maintain data integrity.
Colour and size variants on americantourister.com load dynamically via JavaScript. We deploy Playwright to execute full browser sessions, triggering swatch clicks to expose variant-specific pricing, SKUs, and stock states.
Luggage specifications are often formatted inconsistently (e.g., '69 x 49 x 30 cm' vs '27.1 x 19.2 x 11.8 in'). Our pipeline parses these raw strings into strict, queryable numeric fields for height, width, depth, and weight.
The site uses IP-based geolocation to force redirects to regional storefronts. We utilise precision-targeted residential proxies to anchor sessions in specific countries, ensuring accurate capture of local pricing and availability.
Retail sites frequently overhaul layouts for seasonal campaigns. We use multiple fallback chains—CSS selectors, XPath, and JSON-LD extraction—to ensure continuous data flow during site redesigns.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs—reducing compute cost, storage bloat, and downstream processing load for your engineering team.
Direct-to-consumer luggage brands track American Tourister's pricing tiers, promotional cadences, and discount depths to optimise their own pricing strategies.
Product development teams analyse dimension, volume, and material trends across collections to identify underserved market segments.
Distributors monitor authorised retail prices across different regional sites to ensure compliance with Minimum Advertised Price agreements.
Retail analysts track the introduction of new materials (e.g., Polypropylene vs Polycarbonate) and features (e.g., TSA locks, dual wheels) across product lines.
Consumer insights teams aggregate review data to evaluate customer satisfaction regarding durability, wheel performance, and handle mechanics.
Analysts monitor out-of-stock rates across specific colour variants and collections to estimate demand and supply chain constraints.
"Luggage specifications require strict schema adherence. Parsing '20-inch spinner' into queryable dimensions, weight, and volume is the difference between raw HTML and warehouse-ready data."
Extracting retail data is trivial; maintaining a clean, normalised database of physical product specifications is hard. We handle the JavaScript rendering, proxy rotation, and string parsing required to turn American Tourister's catalogue into a strictly typed, analytics-ready dataset. Your engineers get clean Parquet files, not regex maintenance tasks.
Everything supported by our americantourister.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 for dynamic swatches.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request to bypass geo-blocks and ensure accurate regional pricing.
Pipelines run on AWS Lambda and ECS. 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 americantourister.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We use geo-targeted residential proxies to bypass forced IP redirects, allowing us to scrape the US, EU, UK, or APAC storefronts exactly as a local user would see them.
Our pipeline identifies the parent product and systematically triggers the JavaScript events for every child swatch. We capture the unique SKU, price, and stock status for every specific colour and size combination.
Yes. We parse raw text strings like 'Dimensions: 69 x 49 x 30 cm' into distinct numeric fields (height: 69.0, width: 49.0, depth: 30.0, unit: 'cm') to ensure the data is immediately queryable in your warehouse.
We can configure pipelines to run at daily, weekly, or custom cadences. For price monitoring, daily extraction of the complete catalogue typically completes within a 2-hour window.
Yes. We track stock indicators at the variant level. Combined with our change detection system, we can emit a webhook or delta record the moment a specific SKU changes from 'in stock' to 'out of stock'.
We scope engagements based on extraction frequency and the number of regional storefronts required. Contact us with your target regions and update cadence for a precise quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across multiple regions — we scope, build, and operate the pipeline. Tell us what you need.
