We extract apparel listings, variant matrices, dynamic pricing, and inventory signals from Old Navy. 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 oldnavy.com. All fields typed and schema-versioned.
"product_id": "748392001", "title": "High-Waisted O.G. Straight Jeans for Women", "category": "Women > Jeans", "base_price": 44.99, "review_count": 4821, "rating": 4.6, "fit_type": "Straight", "colour_options": "['Medium Wash', 'Dark Wash', 'Black']"
| # | product_id | title | brand | category | sub_category | base_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variant & Inventory objects from oldnavy.com. All fields typed and schema-versioned.
"variant_id": "849201844", "sku": "123456789", "colour": "Medium Wash", "size": "8", "inseam": "Regular", "price": 39.99, "stock_status": "IN_STOCK", "low_stock_warning": false
| # | product_id | variant_id | sku | colour | size | inseam |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from oldnavy.com. All fields typed and schema-versioned.
"review_id": "RV-9948271", "product_id": "748392001", "rating": 5, "review_title": "Perfect fit and stretch", "fit_feedback": "True to size", "length_feedback": "Just right", "date": "2026-03-14", "helpful_votes": 12
| # | review_id | product_id | reviewer_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions & Pricing objects from oldnavy.com. All fields typed and schema-versioned.
"product_id": "748392001", "base_price": 44.99, "current_price": 22.49, "discount_pct": 50, "promo_text": "50% Off All Jeans", "super_cash_eligible": true, "clearance_flag": false, "price_timestamp": "2026-05-12T10:15:00Z"
| # | product_id | base_price | current_price | discount_pct | promo_text | super_cash_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Old Navy's catalogue is built on complex variant grids and dynamic promotional logic. Our pipeline resolves JavaScript pricing, maps size-and-colour matrices, and tracks inventory states — automatically.
Extract every combination of size, colour, and fit (e.g., Petite, Tall, Regular). We map parent products to all child SKUs seamlessly.
Capture JavaScript-rendered discounts, daily deals, and Super Cash eligibility banners that static HTML parsers miss entirely.
Monitor stock availability, low-stock warnings, and out-of-stock flags at the precise variant level.
Extract customer reviews along with aggregated fit, length, and quality feedback sliders to analyse sizing accuracy.
Map colour swatches to their corresponding high-resolution model and product imagery for computer vision training.
Identify shared catalogue structures and overlapping inventory logic across the broader Gap Inc. brand portfolio.
Run one-off bulk exports or configure continuous pipelines at daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, product IDs, or search terms. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for oldnavy.com.
Schema validation, null-rate checks, price-outlier detection, and sample variants before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Apparel scraping involves massive variant matrices and dynamic promotional logic. Here is how we maintain pipeline stability.
Apparel products are not flat records. A single Old Navy jean might have 5 colours, 12 sizes, and 3 inseam lengths — resulting in 180 distinct SKUs. We map the entire parent-child hierarchy to ensure no variant is missed.
Old Navy frequently uses dynamic pricing, where discounts and Super Cash banners are applied via client-side JavaScript. We execute full Playwright browser sessions to hydrate the DOM and capture the true price.
Stock levels change rapidly during sales events. We monitor specific variant nodes to detect 'Out of Stock' or 'Low Stock' flags, providing accurate inventory signals for demand forecasting.
Retailers deploy strict bot mitigation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to maintain uninterrupted access.
For large apparel catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Retailers monitor Old Navy's promotional cadence, base pricing, and markdown strategies to optimise their own pricing models.
Merchandising teams track category depth, colour availability, and new product introductions to identify market trends.
Analysts correlate stock-out rates with promotional events to estimate sales velocity and demand elasticity.
Product teams mine review text and fit-slider data to understand sizing accuracy and fabric quality issues.
Machine learning teams use high-resolution product imagery and variant metadata to train computer vision models for fashion retail.
Consultancies track SKU counts across categories to evaluate Old Navy's strategic focus and market positioning.
"Old Navy's catalogue is a masterclass in variant complexity — sizes, fits, and colours multiply into millions of individual SKUs, all with dynamic pricing."
Extracting apparel data at scale requires more than simple HTTP requests. You must resolve JavaScript-rendered promotional pricing, map complex size-and-colour matrices, and bypass edge-tier bot protection. DataFlirt handles the extraction logic so your engineers can focus on retail analytics rather than maintaining CSS selectors.
Everything supported by our oldnavy.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. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About oldnavy.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Old Navy 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.
We map the entire parent-child hierarchy. Every combination of size, colour, and fit is extracted as a distinct SKU record, ensuring you have granular visibility into inventory and pricing at the variant level.
Yes. We use full Playwright browser sessions to execute client-side JavaScript, capturing the final discounted price and any visible promotional banners, including Super Cash eligibility.
Full catalogue refreshes at daily cadence complete within a 6-12 hour window depending on size. For targeted SKU lists, real-time streaming pipelines can achieve sub-60-minute latency for stock availability.
Yes. We extract the full review text alongside the aggregated fit, length, and quality sliders that customers submit, providing deep insights into product sizing accuracy.
Our smallest packages start at a defined category list with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 500 products as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off apparel catalogue dump or a continuous price-monitoring feed across 1M+ variants — we scope, build, and operate the pipeline. Tell us what you need.
