We extract perfume listings, dynamic pricing, sizing variants, olfactory notes, and review data from FragranceX. 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 fragrancex.com. All fields typed and schema-versioned.
"sku": "FX-84729", "title": "Creed Aventus Eau De Parfum", "brand": "Creed", "gender": "Men", "top_notes": "Pineapple, Bergamot, Black Currant", "rating": 4.8, "base_notes": "Oakmoss, Musk, Ambergris"
| # | sku | title | brand | gender | description | image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Variants objects from fragrancex.com. All fields typed and schema-versioned.
"sku": "FX-84729-33", "variant_type": "Spray", "size_oz": 3.3, "price": 315.5, "retail_price": 435.0, "in_stock": true, "condition": "New with box"
| # | sku | parent_sku | variant_type | size_oz | size_ml | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews objects from fragrancex.com. All fields typed and schema-versioned.
"review_id": "REV-993821", "sku": "FX-84729", "star_rating": 5, "review_date": "2025-11-12", "review_text": "Exceptional longevity and projection.", "verified_buyer": true
| # | review_id | sku | reviewer_name | star_rating | review_date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Brands objects from fragrancex.com. All fields typed and schema-versioned.
"brand_id": "BR-102", "brand_name": "Tom Ford", "category": "Designer", "product_count": 142, "brand_url": "/tom-ford-fragrances", "active": true
| # | brand_id | brand_name | category | product_count | description | brand_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search Results objects from fragrancex.com. All fields typed and schema-versioned.
"keyword": "oud wood", "position": 1, "sku": "FX-91022", "title": "Tom Ford Oud Wood", "price": 245.0, "rating": 4.7, "in_stock": true
| # | keyword | position | sku | title | price | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our FragranceX scraper handles the complete catalogue: tester variants, unboxed inventory, dynamic pricing, and olfactory metadata, with geo-proxying for localised pricing.
Title, brand, description, and high-resolution image URLs scraped at the parent SKU level.
Capture prices across all sizes, formulations, and conditions including testers and unboxed items.
Parse top, middle, and base notes to build comprehensive scent profiles for every fragrance.
Extract full review text, star ratings, and verified buyer flags across all paginated review endpoints.
Route requests through specific regional proxies to capture localised pricing and currency conversions.
Monitor inventory status for specific sizes and tester variants to detect restocks or sell-outs.
Crawl complete brand index pages to maintain an exhaustive list of designers and niche houses.
Track organic positions for specific fragrance notes or keywords to monitor brand visibility.
Run bulk exports or configure continuous pipelines at daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide brand URLs, keyword sets, or specific SKUs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for fragrancex.com.
Schema validation, null-rate checks, and variant mapping verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Extracting accurate fragrance data requires strict variant normalisation and geo-routing. Here is how we build resilient pipelines.
FragranceX lists multiple variants under a single product page, including retail boxes, unboxed items, and testers. Our pipeline maps these accurately to parent SKUs so your pricing models compare identical item conditions.
Pricing and availability change based on the shipping destination. We use residential proxies in your target markets to ensure the prices extracted match what your local customers actually see.
We utilise ISP-grade residential proxies with realistic browser fingerprints and request timing to avoid rate limits and IP blocks during deep catalogue crawls.
For large brand catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing downstream processing load.
Every run emits structured logs. We alert on null-rate spikes, missing variants, and layout changes, fixing selectors before you notice data drops.
Grey market retailers and discounters monitor FragranceX pricing to adjust their own margins and stay competitive.
Fragrance houses audit listings to track unauthorised discounting and grey market distribution of their products.
Analysts track top-selling brands and category movements to identify trends in niche versus designer fragrance popularity.
Machine learning teams use olfactory notes and review sentiment to train scent recommendation engines.
Supply chain teams correlate review velocity and stock depth indicators with sales velocity for procurement.
Distributors track tester and unboxed inventory levels to identify supply leaks in their wholesale networks.
"FragranceX holds the most comprehensive discount perfume catalogue globally, but extracting accurate sizing and tester pricing requires strict variant mapping."
Most teams underestimate the complexity of fragrance SKUs. Reliable FragranceX scraping requires handling infinite scroll, geo-dependent pricing, and normalising tester versus retail variants. DataFlirt handles the infrastructure so your engineers focus on data modelling.
Everything supported by our fragrancex.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 for high-speed catalogue extraction.
We maintain pools of residential proxies to ensure accurate geo-pricing and avoid IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and alerting.
Data delivered to where your team already works — no new tooling required.
About fragrancex.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline maps all available variants on a product page, specifically tagging testers, unboxed items, and standard retail boxes with their respective prices and sizes.
We route our requests through residential proxies located in your target region. This ensures the extracted prices, shipping costs, and availability match the local market conditions.
Yes. We parse the product descriptions to structure top notes, middle notes, and base notes into clean arrays, which is highly useful for machine learning and recommendation engines.
We can configure pipelines to run daily, weekly, or on a custom schedule. Daily runs typically complete within 4 hours depending on the target SKU volume.
Yes. We capture the current availability status for every specific variant, allowing you to track restock events and inventory depletion.
No. We only scrape publicly available retail data. We do not support scraping authenticated wholesale portals or user account data.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across thousands of variants, we build and operate the pipeline. Tell us what you need.
