We extract cycling apparel, bike components, pricing signals, stock depth across sizes, and technical specifications from Wiggle. 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 wiggle.com. All fields typed and schema-versioned.
"sku": "wig1234567", "title": "Castelli Perfetto RoS Long Sleeve Jacket", "brand": "Castelli", "price": 180.0, "currency": "GBP", "discount_pct": 20, "rating": 4.8, "review_count": 142, "in_stock": true
| # | sku | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sizing & Stock objects from wiggle.com. All fields typed and schema-versioned.
"variant_sku": "wig1234567-L-RED", "parent_sku": "wig1234567", "size": "Large", "colour": "Fiery Red", "stock_status": "In Stock", "stock_quantity": 12, "price": 180.0, "dispatch_time": "Usually dispatched within 24 hours"
| # | variant_sku | parent_sku | size | colour | stock_status | stock_quantity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Technical Specs objects from wiggle.com. All fields typed and schema-versioned.
"sku": "wig9876543", "frame_material": "Carbon Fibre", "groupset": "Shimano Ultegra Di2", "wheel_size": "700c", "weight": "7.8kg", "fork": "Full Carbon", "brakes": "Hydraulic Disc", "gears": "22 Speed"
| # | sku | frame_material | groupset | wheel_size | weight | fork |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from wiggle.com. All fields typed and schema-versioned.
"review_id": "rev_884729", "sku": "wig1234567", "rating": 5, "review_title": "Perfect for autumn riding", "helpful_votes": 14, "review_date": "2026-03-12", "verified_buyer": true, "size_purchased": "Large"
| # | review_id | sku | reviewer_name | rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Category Intelligence objects from wiggle.com. All fields typed and schema-versioned.
"category_path": "Cycling > Clothing > Jackets", "brand": "Castelli", "product_count": 48, "min_price": 85.0, "max_price": 320.0, "avg_discount": 15.5, "top_rated_sku": "wig1234567", "scraped_at": "2026-05-12T09:14:33Z"
| # | category_path | brand | product_count | min_price | max_price | avg_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Wiggle scraper navigates complex product variant matrices, extracting precise stock levels, pricing tiers, and technical geometries without missing a single SKU.
Title, description, brand, and category taxonomy scraped across all cycling, running, swimming, and outdoor departments.
Extract every combination of size and colour. Map child SKUs to parent products with distinct pricing and stock availability.
Capture detailed bike geometries, groupset details, frame materials, and component lists structured into clean JSON.
Track base prices, RRPs, discount percentages, and clearance markdowns across multiple geographic regions.
Monitor exact stock availability per size variant to predict demand and track competitor inventory depletion.
Extract customer ratings, detailed review text, pros/cons lists, and sizing feedback (e.g., 'runs small').
Scrape localised pricing and availability for Wiggle UK, US, EU, and AUS storefronts using regional proxies.
Track which brands are expanding or shrinking their product lines within specific endurance categories.
Run daily or hourly pipelines that only output records when a price drops or a size goes out of stock.
Brief in. Clean data out.
Provide category URLs, brand names, or specific product lines. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and variant mapping for wiggle.com.
Schema validation, null-rate checks, price-outlier detection, and variant completeness checks before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Endurance sports retail sites use complex variation matrices for sizes and colours. Here is how we ensure data completeness.
A single cycling jersey on Wiggle might have 6 sizes and 4 colours. Our scrapers iterate through the frontend state to capture the specific price, stock status, and SKU for all 24 variants, rather than just the default selected option.
Wiggle alters pricing and brand availability based on the user's IP and selected shipping destination. We route requests through region-specific residential proxies and set exact session cookies to capture accurate local data.
Retailers aggressively block datacentre IPs. We utilise ISP-grade residential proxies and spoof TLS fingerprints to ensure uninterrupted access to Wiggle's catalogue during high-frequency price monitoring runs.
For large catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load when tracking daily price fluctuations.
When Wiggle updates their frontend framework or alters how technical specs are displayed, our observability stack flags the null-rate spike immediately, allowing our engineers to deploy a fix before your daily delivery.
Competing sports retailers monitor Wiggle's discount strategies and clearance events to adjust their own pricing algorithms.
Cycling brands audit Wiggle to ensure their products are not being sold below Minimum Advertised Price agreements.
Merchandisers analyse Wiggle's brand mix, size availability, and category depth to inform their own purchasing decisions.
Analysts track review volume and sentiment across endurance categories to identify emerging brands and consumer preferences.
Supply chain teams correlate stock-out rates on specific sizes and colours to predict seasonal demand for apparel.
Direct-to-consumer cycling brands track Wiggle's promotional calendars and bundle offers to position their own campaigns.
"Wiggle holds the definitive catalogue for cycling and endurance sports — but matching exact components and size variants requires a precision extraction pipeline."
Most teams fail at scraping Wiggle because they miss the multi-dimensional variant matrix: a single bike jacket has 15 size and colour combinations, each with distinct stock levels and pricing. DataFlirt maps these relationships perfectly, handling the anti-bot friction so your engineers can focus on analysis.
Everything supported by our wiggle.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 deduplication. Playwright handles JavaScript rendering, cookie sessions, and interaction flows for complex variant matrices.
We maintain pools of residential ISP proxies across UK/US/EU regions to bypass retail firewalls and access localised pricing.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About wiggle.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Wiggle is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls. Clients should review Wiggle's ToS and consult legal counsel for specific use cases.
Our Playwright integration interacts with the frontend state to expose every valid combination of size and colour. We map these child variants back to the parent product, capturing the unique price, stock level, and SKU for each specific combination.
Yes. We configure our proxy routing and session headers to simulate traffic from the UK, US, EU, or Australia, ensuring you receive the correct localised pricing, currency, and stock availability.
For price and stock monitoring on targeted SKUs, we can configure hourly pipelines. Full category or brand refreshes typically run on a daily cadence, completing within a 4-8 hour window.
Yes. We parse the technical specification tables and geometry charts on bike product pages, structuring them into clean, queryable JSON fields rather than raw HTML blocks.
Absolutely. We provide a sample run of up to 500 SKUs or specific brand categories as part of the pre-engagement scoping process — so you can validate schema fit and variant completeness.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full catalogue extract of cycling components or continuous price monitoring across top endurance brands — we build and operate the pipeline. Tell us what you need.
