We extract running shoe catalogues, apparel pricing, local store inventory, and product reviews from Fleet Feet. 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 fleetfeet.com. All fields typed and schema-versioned.
"product_id": "FF-HOKA-CLIF9-M", "name": "HOKA Clifton 9", "brand": "HOKA", "price": 145.0, "colours": "['Black', 'White', 'Blue Glass']", "sizes": "['8', '8.5', '9', '9.5', '10', '11']", "drop": "5mm", "support_type": "Neutral"
| # | product_id | url | name | brand | category | price |
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Complete list of extractable fields for Store Inventory objects from fleetfeet.com. All fields typed and schema-versioned.
"store_id": "102", "store_name": "Fleet Feet Chicago", "zip_code": "60614", "product_id": "FF-HOKA-CLIF9-M", "size": "10", "width": "D", "stock_status": "In Stock", "last_updated": "2026-05-12T09:14:00Z"
| # | store_id | store_name | address | zip_code | product_id | size |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Promos objects from fleetfeet.com. All fields typed and schema-versioned.
"product_id": "FF-BRKS-GHOST15-W", "base_price": 140.0, "sale_price": 109.95, "discount_pct": 21, "currency": "USD", "clearance_flag": true, "price_timestamp": "2026-05-12T09:14:00Z"
| # | product_id | base_price | sale_price | discount_pct | currency | clearance_flag |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Product Reviews objects from fleetfeet.com. All fields typed and schema-versioned.
"review_id": "REV-9928341", "product_id": "FF-HOKA-CLIF9-M", "author": "MarathonMike", "rating": 5, "title": "Great daily trainer", "body": "The foam is slightly softer than the 8.", "verified_buyer": true, "date": "2026-04-18"
| # | review_id | product_id | author | rating | title | body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Taxonomy & Brands objects from fleetfeet.com. All fields typed and schema-versioned.
"brand_id": "BR-HOKA", "brand_name": "HOKA", "category_path": "Men > Running Shoes", "subcategory": "Trail", "product_count": 42, "url": "https://www.fleetfeet.com/brands/hoka", "scraped_at": "2026-05-12T09:14:33Z"
| # | brand_id | brand_name | category_path | subcategory | product_count | url |
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Our Fleet Feet scraper handles the entire product catalogue, capturing complex size grids, local store inventory via zip code permutation, and technical shoe specifications.
Extract every running shoe, track spike, and trail shoe with associated metadata, images, and technical specifications.
Map local availability across all Fleet Feet franchise locations by iterating through zip codes and store IDs.
Capture the exact stock status for every combination of shoe size, width (Narrow, Regular, Wide), and colourway.
Extract running specific data points including heel-to-toe drop, stack height, weight, and support type (Neutral vs Stability).
Track base prices, markdown events, and clearance flags across previous season models.
Paginate through customer reviews to extract sentiment, fit feedback, and durability reports.
Map the exact category structures for brands like Brooks, HOKA, On, Nike, and Saucony.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Link parent product URLs to all child variants to ensure no colourway or size combination is missed.
Brief in. Clean data out.
Provide categories, brand URLs, or store locations. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for fleetfeet.com.
Schema validation, null-rate checks, and sample data review before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting accurate local inventory requires more than simple HTTP requests. Here is how we maintain data integrity.
Fleet Feet relies on franchise-level inventory. We interact directly with the store locator APIs, iterating through geographic coordinates and zip codes to compile a national view of stock levels.
Running shoes have multidimensional variants: colour, size, and width. Our pipeline traverses the entire matrix, executing JavaScript where necessary to reveal stock status for edge-case sizes.
We utilise US-based residential proxies to prevent rate limiting when querying the inventory APIs at scale, ensuring continuous data flow without IP bans.
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.
Every run emits structured logs. We alert on null-rate spikes, missing fields, and coverage drops, responding before you notice.
Running specialty retailers monitor Fleet Feet pricing, markdowns, and clearance events to adjust their own pricing strategies.
Brands track stock depth across franchise locations to understand sell-through rates and optimise replenishment.
Manufacturers audit product listings to ensure MAP compliance and verify that technical specifications are displayed correctly.
Analysts track brand representation and shelf share within the premier running specialty channel.
Retailers analyse size and width availability to understand which edge sizes sell out fastest.
Product teams mine review data to understand how runners react to foam updates and upper redesigns.
"Fleet Feet holds the most accurate local inventory and fit profile data for running gear in North America, but extracting it requires a dedicated pipeline."
Most teams underestimate the investment required: reliable Fleet Feet scraping requires bypassing basic bot protection, rendering local store locators via JavaScript, and maintaining selectors for complex size and width grids. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our fleetfeet.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 for store locators and variant grids.
We maintain pools of US residential proxies. Rotation happens per request to prevent API blocking.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About fleetfeet.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product and inventory data is generally permissible. DataFlirt targets only public, non-authenticated sections of the site. We do not extract personal Fit ID data or circumvent authentication walls.
We interact with the underlying store locator APIs, passing geographic coordinates or zip codes to return stock status across all franchise locations for a given product.
Yes. Running shoes frequently have complex matrices involving multiple widths (B, D, 2E, 4E) and sizes. Our pipeline maps the entire grid to determine availability for every specific SKU.
Pipelines can be configured to run daily or at custom intervals. The data reflects the stock status as reported by Fleet Feet's systems at the time of extraction.
Yes. We capture drop, stack height, weight, and support categories (Neutral vs Stability) directly from the product detail pages.
Yes. We provide a sample run of up to 100 products as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off product catalogue dump or a continuous local inventory feed. Tell us what you need.
