We extract footwear listings, size and width availability, pricing signals, XC4 technology specs, and customer reviews from johnstonmurphy.com. Delivered as clean JSON, CSV, or Parquet to S3 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 johnstonmurphy.com. All fields typed and schema-versioned.
"sku": "20-12345", "title": "Melton Classic Oxford", "category": "Men's Shoes", "base_price": 179.0, "colour": "Black", "material": "Full Grain Leather", "url": "https://www.johnstonmurphy.com/melton-classic-oxford/20-12345.html"
| # | sku | title | category | sub_category | base_price | colour |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Sizing objects from johnstonmurphy.com. All fields typed and schema-versioned.
"variant_id": "20-12345-105-W", "parent_sku": "20-12345", "size": "10.5", "width": "Wide", "in_stock": true, "stock_level": 14, "price": 179.0
| # | variant_id | parent_sku | size | width | in_stock | stock_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Tech & Materials objects from johnstonmurphy.com. All fields typed and schema-versioned.
"sku": "20-98765", "upper_material": "Waterproof Leather", "sole_material": "Rubber", "waterproof": true, "xc4_enabled": true, "trufoam_enabled": false
| # | sku | upper_material | lining_material | sole_material | waterproof | xc4_enabled |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Promos objects from johnstonmurphy.com. All fields typed and schema-versioned.
"sku": "20-54321", "msrp": 159.0, "current_price": 119.99, "discount_pct": 24.5, "on_sale": true, "clearance": false, "promo_text": "Take an extra 20% off at checkout"
| # | sku | msrp | current_price | discount_pct | discount_abs | on_sale |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Fit objects from johnstonmurphy.com. All fields typed and schema-versioned.
"review_id": "rev_847392", "sku": "20-12345", "rating": 5, "title": "Perfect fit out of the box", "body": "These oxfords require zero break-in time.", "fit_rating": "True to size", "verified_buyer": true
| # | review_id | sku | rating | title | body | fit_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our scraper maps the entire Johnston & Murphy catalogue, capturing complex size-width matrices, proprietary technology specifications like XC4, and dynamic promotional pricing.
Capture product titles, descriptions, categories, and style numbers across men's and women's footwear lines.
Extract comprehensive availability grids including standard, wide, and extra-wide widths mapped against specific sizes.
Isolate specifications for XC4 waterproof technology, Trufoam soles, and smart degree temperature control features.
Monitor base MSRP, markdown prices, clearance tags, and cart-level promotional discounts across all SKUs.
Map parent products to individual colourways and material types, including full-grain leather and suede specifications.
Track out-of-stock indicators, low stock warnings, and backorder dates for specific size and width combinations.
Extract customer reviews, star ratings, and aggregated fit feedback indicating if shoes run large, small, or true to size.
Expand extraction beyond footwear to include outerwear, shirts, belts, and leather goods catalogues.
Process only changed records using hash-based diffing to track new arrivals and price drops efficiently.
Brief in. Clean data out.
Specify target categories, product lines, or specific SKUs for extraction.
We configure crawlers to handle Johnston & Murphy's size matrices and dynamic inventory endpoints.
Schema validation, null-rate checks on size availability, and price anomaly detection.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake warehouse on agreed cadence.
Footwear sites present unique scraping challenges with multi-dimensional variants and dynamic inventory states. We handle the complexity at the infrastructure level.
Footwear requires mapping a single parent product to dozens of child SKUs based on colour, size, and width. We flatten these matrices into queryable, normalised relational records.
Availability changes rapidly during sales events. We bypass cached frontend pages and poll backend inventory endpoints directly to ensure stock accuracy.
Retailers use edge protection to block automated scraping. We route requests through US-based residential proxies with realistic TLS and browser fingerprints.
E-commerce platforms frequently update their frontend frameworks. We use multiple fallback selectors and JSON-LD extraction to maintain pipeline stability during site updates.
We paginate through all review pages, extracting granular fit data, comfort ratings, and verified buyer status to build comprehensive product sentiment datasets.
Footwear brands track Johnston & Murphy pricing, promotional cadences, and clearance strategies to adjust their own retail pricing.
Retail merchandisers analyse category depth, width availability, and new product introductions to inform their own buying decisions.
Analysts track the adoption of specific technologies like waterproof leathers and hybrid dress-sneakers across product lines.
Supply chain teams monitor out-of-stock rates on specific sizes to identify manufacturing bottlenecks or high-demand categories.
Design teams mine customer reviews and fit feedback to identify design flaws or opportunities for comfort improvements.
Wholesale partners monitor direct-to-consumer pricing to ensure alignment with Minimum Advertised Price agreements.
"Footwear data is inherently multi-dimensional. A single shoe can have sixty distinct SKUs when accounting for colour, size, and width combinations. Extracting this accurately requires deep variant mapping."
Most generic scrapers fail at footwear retail because they capture the parent product but miss the granular size-level inventory data. DataFlirt traverses the entire variant matrix, executing the necessary network requests to build a complete picture of stock availability, pricing, and fit data across the entire Johnston & Murphy catalogue.
Everything supported by our johnstonmurphy.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 manages catalogue traversal and deduplication, while Playwright handles dynamic inventory endpoints and variant selection.
Requests are routed through US-based residential IPs with automated rotation to bypass edge protection and rate limiting.
Pipelines execute on AWS infrastructure with Airflow managing schedules, retries, and data delivery to your warehouse.
Data delivered to where your team already works — no new tooling required.
About johnstonmurphy.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our scraper maps the entire size and width matrix, capturing independent inventory statuses for medium, wide, and extra-wide variants of the same shoe size.
We can configure pipelines to run daily, hourly, or at custom intervals to capture flash sales, clearance markdowns, and promotional events as they happen.
Yes. We extract all structured and unstructured product specifications, isolating proprietary features like XC4 waterproofing, Trufoam soles, and specific leather types.
Absolutely. While footwear is the primary focus, our pipelines can extract data from all categories including outerwear, shirts, belts, and accessories.
Out-of-stock items are still extracted but flagged with a false boolean for availability. This allows you to track historical catalogue presence and restock patterns.
Yes. Alongside standard text reviews and star ratings, we extract aggregated fit matrices indicating if customers found the item to run small, large, or true to size.
20-minute scoping call. Pilot dataset within the week. Production within two. From complete catalogue dumps to daily inventory tracking across complex size matrices, we build and maintain the infrastructure. Provide your requirements to get started.
