We extract footwear catalogues, pricing signals, stock depth across size matrices, and product reviews from Campus Shoes. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
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 campusshoes.com. All fields typed and schema-versioned.
"article_code": "CG-284", "title": "Campus Nitrofly Running Shoes", "category": "Running", "gender": "Men", "price": 1499.0, "mrp": 1999.0, "discount_pct": 25, "color": "Navy Blue"
| # | article_code | title | category | gender | price | mrp |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Inventory objects from campusshoes.com. All fields typed and schema-versioned.
"sku": "CG-284-NVY-8", "size_uk": "8", "price": 1499.0, "mrp": 1999.0, "in_stock": true, "stock_qty": 42, "flash_sale": false, "discount_abs": 500.0
| # | sku | article_code | size_uk | price | mrp | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Variants & Colours objects from campusshoes.com. All fields typed and schema-versioned.
"parent_article_code": "CG-284", "variant_sku": "CG-284-BLK", "color_name": "Core Black", "hex_code": "#000000", "size_range": "['6', '7', '8', '9', '10']", "default_variant": false, "is_active": true
| # | parent_article_code | variant_sku | color_name | hex_code | image_urls | size_range |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Reviews & Ratings objects from campusshoes.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "article_code": "CG-284", "reviewer_name": "Rahul S.", "rating": 5, "review_title": "Excellent comfort", "review_text": "The Nitrofly sole is very responsive for daily runs.", "date": "2023-10-14", "verified_buyer": true
| # | review_id | article_code | reviewer_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Categories & Collections objects from campusshoes.com. All fields typed and schema-versioned.
"collection_name": "Men Running Shoes", "url": "/collections/men-running-shoes", "product_count": 342, "breadcrumbs": "['Home', 'Men', 'Running']", "sort_order": "bestselling", "seo_title": "Buy Men Running Shoes Online | Campus Shoes", "seo_desc": "Shop the latest collection of running shoes for men."
| # | collection_name | url | product_count | breadcrumbs | banner_image | sort_order |
|---|---|---|---|---|---|---|
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Our pipeline handles the dynamic frontend state of campusshoes.com. We extract article codes, size matrices, pricing updates, and product technology tags with high precision.
Title, description, article codes, materials, and technology tags like Nitrofly or Yoga Max scraped across all categories.
Track availability and stock status across the entire UK/IND size range for every individual colourway.
Capture MRP, current selling price, discount percentages, and flash sale indicators timestamped per run.
Map parent article codes to all available colour variants, including high-resolution image URLs for each.
Extract customer ratings, review text, and verified buyer badges across the entire product portfolio.
Preserve breadcrumbs and collection mappings to understand how products are positioned in the navigation tree.
Identify out-of-stock sizes and monitor restock patterns to understand inventory velocity.
Track default sorting algorithms and product visibility for specific keyword queries on the site.
Run continuous pipelines that output only changed records for pricing and inventory updates.
Brief in. Clean data out.
Provide category URLs, specific article codes, or request a full site crawl. We design the schema.
We configure Playwright crawlers, handle dynamic inventory APIs, and manage rate limits for campusshoes.com.
Schema validation, null-rate checks, and size-matrix completeness verification before launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Campus Shoes relies on dynamic frontend frameworks and CDN caching. We build resilient pipelines that bypass these extraction barriers.
Frontend product pages often hide exact stock quantities. Our Playwright scripts intercept the underlying XHR requests to the inventory APIs, extracting precise stock depth rather than simple boolean flags.
Colourways and size availability are hydrated dynamically via JavaScript when a user clicks. We execute full browser sessions to trigger these state changes and capture the complete variant matrix.
eCommerce firewalls block aggressive crawlers. We distribute requests across Indian residential IP pools and normalise request velocity to mimic legitimate browsing patterns.
Category pages use infinite scroll or dynamic load-more buttons. Our crawlers programmatically trigger these events until the complete product list is rendered and captured.
Product descriptions contain mixed HTML and inconsistent formatting. We parse and normalise this text into clean, structured key-value pairs for features like sole material and closure type.
Footwear brands track Campus Shoes pricing, discounts, and flash sales to optimise their own promotional calendars.
Retailers analyse size availability and colourway depth to understand which variants drive volume.
Analysts track product launches and technology tags to map the Indian athletic footwear landscape.
Supply chain teams monitor out-of-stock rates across specific sizes to estimate demand patterns.
Product teams mine review data to identify common complaints about fit, durability, or comfort.
Distributors verify that direct-to-consumer pricing aligns with broader marketplace pricing strategies.
"Campus Shoes represents a massive segment of the Indian footwear market. Tracking their SKU lifecycle and pricing strategies requires precision data pipelines."
Extracting accurate stock depth across UK/IND size matrices and tracking flash sale pricing requires handling dynamic frontend state and CDN caching. DataFlirt manages the extraction infrastructure so your analysts can focus on assortment strategy and price intelligence.
Everything supported by our campusshoes.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 the crawl orchestration and deduplication. Playwright executes JavaScript to trigger variant hydration and intercept inventory API responses.
We route requests through Indian residential proxy pools to bypass regional rate limits and firewall blocks.
Pipelines run on AWS infrastructure. Airflow handles scheduling, retries, and delivery to your final data sink.
Data delivered to where your team already works — no new tooling required.
About campusshoes.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We can scope the pipeline to specific URLs, such as Men's Running Shoes or Women's Sneakers, rather than crawling the entire site.
Our schema captures the complete size matrix for every colourway. Sizes that are currently unavailable are explicitly marked with an in_stock boolean set to false.
For pricing and inventory monitoring, we typically run daily or hourly pipelines. Full catalogue refreshes including descriptions and images are usually scheduled weekly.
We extract the high-resolution image URLs for every product and colour variant. We do not download the physical image files to your storage by default, but this can be configured.
Yes. We standardise price formats, size conventions, and strip HTML from product descriptions to provide clean, queryable data.
Yes. We capture the MRP, the current selling price, and calculate the absolute and percentage discount for every SKU during each run.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily price feed or a complete catalogue extraction, we build and maintain the infrastructure. Define your requirements today.
