We extract product listings, sizing matrices, stock availability, pricing, Sparks offers, and reviews from Marks & Spencer. 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 marksandspencer.com. All fields typed and schema-versioned.
"product_id": "P60591234", "title": "Pure Cotton Striped Oxford Shirt", "department": "Men", "category": "Shirts", "price": 35.0, "currency": "GBP", "colours_available": "['Blue Mix', 'Pink Mix', 'Green Mix']", "sizes_available": "['S', 'M', 'L', 'XL', 'XXL', '3XL']"
| # | product_id | title | brand | department | category | sub_category |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Stock objects from marksandspencer.com. All fields typed and schema-versioned.
"product_id": "P60591234", "variant_id": "V12345678", "colour": "Blue Mix", "size": "M", "price": 35.0, "in_stock": true, "promotion_text": "2 for £60 on Selected Shirts", "price_timestamp": "2026-05-12T10:15:00Z"
| # | product_id | variant_id | colour | size | price | original_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from marksandspencer.com. All fields typed and schema-versioned.
"review_id": "REV-98765432", "product_id": "P60591234", "rating": 5, "fit_rating": "True to size", "quality_rating": 5, "review_title": "Excellent quality and fit", "review_body": "Washes well and requires minimal ironing. Great value.", "submission_date": "2026-04-20"
| # | review_id | product_id | reviewer_nickname | rating | fit_rating | quality_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Food & Grocery objects from marksandspencer.com. All fields typed and schema-versioned.
"product_id": "F123456", "title": "Colin the Caterpillar Cake", "weight_volume": "625g", "price": 9.0, "price_per_unit": "£1.44 per 100g", "allergens": "['Milk', 'Wheat', 'Soya', 'Eggs']", "dietary_flags": "['Vegetarian']", "shelf_life": "3 Days"
| # | product_id | title | weight_volume | price | price_per_unit | ingredients |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Store Locator objects from marksandspencer.com. All fields typed and schema-versioned.
"store_id": "S0012", "store_name": "London Marble Arch", "postcode": "W1C 1DX", "latitude": 51.5135, "longitude": -0.1558, "click_and_collect_available": true, "food_hall_available": true, "clothing_available": true
| # | store_id | store_name | address_line_1 | city | postcode | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Marks & Spencer scraper processes the complex variant structures of apparel, the detailed metadata of food products, and dynamic pricing across thousands of categories.
Extract apparel, home, food, and beauty products across all M&S departments and sub-categories.
Map available sizes to specific colours with real-time stock status, capturing complex variant relationships.
Capture base price, markdowns, multi-buy offers, and Sparks loyalty pricing logic.
Extract ingredients, allergens, nutritional tables, and dietary flags like vegan or gluten-free for the entire food hall range.
Aggregate star ratings, fit scores, quality scores, and full text reviews across the product catalogue.
Extract store locations, opening hours, facilities, and click-and-collect availability nationwide.
Preserve the M&S taxonomy from top-level department down to specific sub-categories.
Bypass Akamai and Datadome protections using residential proxies and humanised request patterns.
Run daily catalogue sweeps or hourly stock checks depending on your required data freshness.
Brief in. Clean data out.
Provide M&S category URLs, search terms, or specific product codes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for marksandspencer.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.
Marks & Spencer relies on dynamic frontend frameworks and strict bot protection. Here is how we maintain pipeline stability.
M&S uses advanced bot mitigation to block scraping. Our crawlers use UK residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to bypass these filters.
Stock availability for specific size and colour combinations is loaded dynamically via JavaScript. We run full Playwright browser sessions to trigger these network requests and capture the true stock status.
M&S frequently updates its frontend for seasonal campaigns. Our selector strategy uses multiple fallback chains per field, including CSS selectors, XPath, and JSON-LD extraction, ensuring continuous data flow.
For large product catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing price fields, and coverage drops, responding before the data reaches your warehouse.
Retailers track M&S fashion and food pricing against high-street rivals to adjust their own pricing strategies.
Fashion analysts review category depth, colour trends, and new season launches to identify market shifts.
Supply chain teams monitor out-of-stock rates across sizes and categories to gauge consumer demand.
Health applications aggregate M&S food data, including ingredients and allergens, for dietary tracking features.
Pricing teams track sale events, multi-buy offers, and clearance cadences to optimise promotional calendars.
Brands process customer reviews for fit, quality, and value feedback to benchmark against M&S private label products.
"Marks & Spencer's digital catalogue spans high-street fashion, premium food, and homeware - a massive taxonomy that requires constant monitoring to track retail trends."
Scraping M&S requires handling complex variant matrices where stock status changes per size and colour combination. DataFlirt manages the JavaScript rendering, proxy rotation, and schema maintenance so you receive clean, normalised retail data without operating the infrastructure.
Everything supported by our marksandspencer.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 M&S pages.
We maintain pools of residential ISP proxies across UK regions. Rotation happens per-request with sticky sessions where required to bypass regional blocking.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About marksandspencer.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We extract full product details from the food sections, including weight, price per unit, ingredient lists, allergen warnings, and nutritional tables.
Our pipeline maps every size and colour combination. If a specific variant is out of stock, it is recorded with a boolean flag, allowing you to track inventory depth and demand.
Yes. We use UK-based residential proxies and humanised browser interaction patterns via Playwright to ensure high success rates against Akamai and other bot mitigation layers.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record for base price, promotional discounts, and Sparks offers.
Our packages typically start at a defined category list or 10,000 products with weekly delivery. For full catalogue extraction, we price based on volume and delivery frequency.
Yes. We capture paginated reviews including star ratings, fit and quality scores, review text, and helpful votes across all product categories.
Yes. We provide a sample run of up to 500 products as part of the pre-engagement scoping process so you can validate the schema and data quality.
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 thousands of products, we scope, build, and operate the pipeline. Tell us what you need.
