We extract apparel listings, SKU-level pricing, stock depth, size variants, and promotional signals from Kiabi. 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 kiabi.com. All fields typed and schema-versioned.
"product_id": "ZI245", "title": "Eco-design cotton t-shirt", "department": "Women", "category": "T-shirts", "base_price": 5.0, "current_price": 4.0, "discount_pct": 20, "colour_name": "Navy Blue", "eco_conception_badge": true
| # | product_id | title | brand | department | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Promos objects from kiabi.com. All fields typed and schema-versioned.
"sku": "ZI245-NVY-M", "product_id": "ZI245", "base_price": 5.0, "current_price": 4.0, "discount_pct": 20, "promo_badge_text": "Web Exclusive Days", "web_exclusivity": true, "currency": "EUR"
| # | sku | product_id | base_price | current_price | discount_pct | discount_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Sizing objects from kiabi.com. All fields typed and schema-versioned.
"sku": "ZI245-NVY-M", "product_id": "ZI245", "colour": "Navy Blue", "size_label": "M", "in_stock": true, "low_stock_warning": true, "stock_quantity": 3
| # | sku | product_id | colour | size_label | in_stock | low_stock_warning |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Specs objects from kiabi.com. All fields typed and schema-versioned.
"product_id": "ZI245", "material_composition": "100% Cotton", "care_instructions": "Machine wash at 30C", "fit_type": "Regular", "pattern": "Solid", "neckline": "Crew neck", "sleeve_length": "Short sleeve"
| # | product_id | material_composition | care_instructions | fit_type | pattern | neckline |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category Results objects from kiabi.com. All fields typed and schema-versioned.
"category_path": "Women > Clothing > Dresses", "position": 12, "product_id": "XJ892", "title": "Floral midi dress", "current_price": 18.0, "promo_tag": "New Collection", "colour_count": 3
| # | category_path | page_number | position | product_id | title | current_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Kiabi scraper navigates fast-fashion category trees, dynamic size grids, and promotional pricing logic - handling region-specific storefronts and anti-bot measures.
Title, description, material composition, and high-resolution image URLs scraped at the parent product level.
Iterate through every colour and size combination to capture true SKU-level inventory status and low-stock warnings.
Capture base price, markdown price, discount percentages, and web-exclusive promotional badges.
Identify and extract Kiabi's eco-conception tags and sustainable material claims for ESG compliance monitoring.
Extract 'Shop the Look' and recommended product mappings to understand merchandising strategies.
Target kiabi.fr, kiabi.es, kiabi.it, and other regional domains with localised pricing and language extraction.
Extract clean URLs for all product gallery images, including detail shots and model variations.
Crawl complete department hierarchies to map product placement across primary and secondary categories.
Run continuous pipelines at daily cadences with hash-based diffing to track price drops and out-of-stock events.
Brief in. Clean data out.
Provide category URLs, specific regions, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and size-grid iteration logic for kiabi.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Fast-fashion sites use complex frontend frameworks to manage vast SKU matrices. Here is how we ensure accurate data extraction.
Kiabi loads size availability and colour-specific pricing via JavaScript. We use Playwright to execute frontend logic, ensuring we capture the exact stock state for a 'Medium Navy Blue' rather than generic parent-level placeholders.
Kiabi routes traffic and displays pricing based on IP geolocation. We deploy residential proxies matching the target storefront (e.g., French IPs for kiabi.fr) to prevent currency redirects and catalogue masking.
Aggressive crawling triggers WAF blocks. We distribute requests across thousands of residential IPs with randomised delays and strict concurrency limits to maintain high throughput without burning proxy subnets.
Retailers frequently update promotional badge markup and layout structures. Our extraction logic uses multi-layered CSS and XPath fallbacks, alongside embedded JSON-LD parsing, to survive frontend deployments.
By hashing the state of the size grid on every run, we isolate stock-out events and price markdowns, delivering clean delta files that highlight exact inventory movements.
Retailers track Kiabi's base prices and promotional markdowns to optimise their own pricing strategies in the value fashion segment.
Merchandising teams analyse Kiabi's category depth, colour distributions, and sizing curves to identify gaps in their own product lines.
Analysts monitor stock-out velocities across specific sizes and categories to estimate demand patterns and sales volume.
Market researchers quantify the percentage of the catalogue carrying eco-conception tags to benchmark sustainability initiatives.
Fashion intelligence platforms aggregate fabric, pattern, and silhouette data to track macro trends in the European mass market.
Licensing partners monitor the presentation, pricing, and discounting of licensed character apparel across Kiabi's regional sites.
"Kiabi's catalogue represents a massive node in European fast-fashion, but extracting accurate size-level inventory requires navigating complex dynamic frontend states."
Most teams fail at scraping apparel sites because they capture the parent product but miss the SKU-level matrix of size, colour, and stock availability. DataFlirt executes full browser rendering to hydrate Kiabi's dynamic grids, ensuring your pricing and inventory models ingest reality, not cached placeholders.
Everything supported by our kiabi.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 and retry logic. Playwright handles JavaScript rendering and interaction flows for size grid expansion.
We maintain pools of residential ISP proxies across European regions to ensure accurate localised pricing and bypass geographic blocks.
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 kiabi.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Kiabi is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and inventory data. We do not extract personal data, circumvent authentication walls, or violate GDPR.
We use regional residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and strict concurrency limits. Our selectors have multi-layer fallback chains so DOM changes do not break the pipeline.
We support all public regional storefronts including kiabi.fr, kiabi.es, kiabi.it, kiabi.be, and kiabi.pt. We deploy region-specific proxies to ensure accurate local pricing and currency extraction.
Both. We extract parent-level metadata (description, material) and iterate through every colour and size variant to capture SKU-level pricing, promotional badges, and inventory availability.
Pipelines can be configured for daily or weekly full-catalogue refreshes. We maintain state to deliver delta files containing only price changes or stock movements since the previous run.
Our smallest packages start at a defined category list (e.g., all Women's dresses) with weekly delivery. For full-site extraction across multiple regions, we price based on compute volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process so you can validate schema fit, field completeness, and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full European catalogue sync or targeted competitor price monitoring - we scope, build, and operate the pipeline. Tell us what you need.
