We extract apparel listings, sizing matrices, fabric details, and pricing signals from Topshop. 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 topshop.com. All fields typed and schema-versioned.
"product_id": "TS984210", "title": "Jamie High Waisted Skinny Jeans", "brand": "Topshop", "category": "Clothing", "sub_category": "Jeans", "fit_type": "Petite", "fabric_composition": "92% Cotton, 6% Polyester, 2% Elastane", "care_instructions": "Machine wash at 30 degrees"
| # | product_id | title | brand | category | sub_category | fit_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from topshop.com. All fields typed and schema-versioned.
"product_id": "TS984210", "price": 35.0, "original_price": 45.0, "currency": "GBP", "discount_pct": 22, "promo_code_eligible": false, "markdown_tier": "Mid-Season Sale", "price_timestamp": "2026-10-12T08:14:00Z"
| # | product_id | price | original_price | currency | discount_pct | promo_code_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sizing & Inventory objects from topshop.com. All fields typed and schema-versioned.
"product_id": "TS984210", "colour": "Mid Blue", "size_uk": "W28 L30", "size_eu": "36", "size_us": "4", "in_stock": true, "low_stock_warning": true, "stock_timestamp": "2026-10-12T08:14:05Z"
| # | product_id | colour | size_uk | size_eu | size_us | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Colours objects from topshop.com. All fields typed and schema-versioned.
"product_id": "TS984210", "parent_id": "TS984000", "colour_name": "Washed Black", "colour_hex": "#2b2b2b", "model_height": "5'9" / 175cm", "model_wearing_size": "W26 L32", "variant_url": "https://www.topshop.com/..."
| # | product_id | parent_id | colour_name | colour_hex | swatch_url | model_height |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Categories objects from topshop.com. All fields typed and schema-versioned.
"keyword": "leather jacket", "category_path": "Women > Coats & Jackets", "position": 3, "product_id": "TS554192", "title": "Oversized Faux Leather Biker", "price": 65.0, "badges": "['Selling Fast', 'New In']", "scraped_at": "2026-10-12T08:15:33Z"
| # | keyword | category_path | position | product_id | title | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Topshop's catalogue relies on heavy front-end hydration for sizing and stock states. We parse the underlying data structures to deliver flat, relational inventory metrics without the noise.
Extract titles, descriptions, categories, and high-resolution image arrays for every SKU in the catalogue.
Track current price, original RRP, and discount percentages. Monitor promotional tags and markdown events.
Capture availability across all size variants (UK, US, EU) and specific dimensions like waist/leg length combinations.
Isolate data for specific fits including Petite, Tall, and Maternity to analyse assortment breadth.
Extract material composition percentages and care instructions for compliance and sustainability tracking.
Map parent-child relationships across colourways, including exact colour names and swatch image URLs.
Monitor 'Low Stock' warnings and out-of-stock states at the individual size and colour level.
Extract pricing and availability specific to UK, US, and EU storefronts using targeted residential exit nodes.
Run pipelines daily for catalogue syncs or hourly for fast-moving inventory and markdown monitoring.
Brief in. Clean data out.
Provide category URLs, search terms, or specific product IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for topshop.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.
Topshop relies on complex JavaScript frameworks to render stock and pricing. Here is how we extract clean data from dynamic payloads.
E-commerce sites block datacentre IPs to prevent competitor scraping. We route requests through UK-based residential proxies to mimic legitimate shopper traffic and bypass rate limits.
Instead of relying solely on DOM selectors, we intercept and parse the underlying JSON payloads used by the front-end framework. This guarantees accurate, structured data for complex sizing matrices.
Front-end redesigns break fragile scrapers. We build fallback chains combining API interception, JSON-LD extraction, and CSS selectors to ensure continuous data flow.
We hash product records and only emit downstream events when price, stock, or metadata changes. This reduces your ingest costs and database bloat.
Automated checks monitor null rates for critical fields like price and stock. If the site structure shifts, our on-call engineers are alerted immediately.
High-street retailers monitor Topshop's pricing tiers and markdown velocity to optimise their own promotional calendars.
Fashion analysts aggregate colourway frequency, fabric adoption, and silhouette changes to predict upcoming seasonal trends.
Merchandisers track the ratio of standard fits to Petite/Tall ranges to benchmark their own inclusive sizing strategies.
Pricing teams analyse how quickly specific categories hit clearance to adjust their own inventory purchasing models.
Brands track new-in velocity and category depth to understand Topshop's supply chain responsiveness.
Machine learning teams use structured apparel descriptions and high-resolution imagery to train visual search and recommendation engines.
"Topshop's catalogue represents critical high-street fashion signals, but extracting normalised sizing and stock data requires bypassing complex front-end hydration."
Apparel scraping is notoriously difficult due to matrixed variants. Every SKU has multiple colours, and every colour has multiple sizes with independent stock states. DataFlirt unwinds this complexity, flattening nested JSON hydration states into clean, relational database rows so your analysts can query inventory depth immediately.
Everything supported by our topshop.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 UK and US 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 topshop.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and product data is generally permissible. DataFlirt extracts only public, non-authenticated catalogue data. We do not extract personal user data or circumvent authentication walls. Clients should review site ToS and consult legal counsel.
We intercept the front-end state payloads to extract the exact stock status for every colour and size combination. This is flattened into a relational schema, allowing you to query stock depth accurately.
Yes. We route requests through region-specific residential proxies (e.g., UK, US) to capture localised pricing, currency, and availability.
Pipelines can be configured to run daily for full catalogue syncs, or hourly for specific high-priority categories to monitor fast-moving stock and markdowns.
Yes. We extract the source URLs for all product imagery, bypassing thumbnail compression to provide the highest resolution assets available.
Our minimum engagement typically starts at weekly deliveries for a defined category set. We scope pricing based on volume, frequency, and schema complexity. Contact us for a quote.
Yes. We provide a sample run of up to 500 SKUs during the scoping phase to validate schema fit and data quality before contract signature.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or a continuous pricing feed to inform your merchandising strategy — we build and operate the pipeline. Tell us what you need.
