We extract product catalogues, member pricing, typical retail values, ingredient lists, and reviews from Beauty Pie. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Products objects from beautypie.com. All fields typed and schema-versioned.
"product_id": "BP-SKIN-001", "name": "Super Retinol Ceramide-Boost Anti-Aging Face Serum", "category": "Skincare", "member_price": 19.0, "typical_price": 90.0, "volume_ml": 50, "stock_status": "IN_STOCK"
| # | product_id | name | category | sub_category | member_price | typical_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing objects from beautypie.com. All fields typed and schema-versioned.
"product_id": "BP-SKIN-001", "member_price": 19.0, "typical_price": 90.0, "discount_pct": 78.89, "currency": "GBP", "timestamp": "2026-05-12T09:14:00Z", "subscription_eligible": true
| # | product_id | member_price | typical_price | discount_pct | currency | timestamp |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from beautypie.com. All fields typed and schema-versioned.
"review_id": "REV-98234", "product_id": "BP-SKIN-001", "rating": 5, "review_text": "Transformed my skin texture within three weeks.", "skin_type": "Combination", "age_range": "35-44", "purchase_verified": true
| # | review_id | product_id | reviewer_name | rating | review_text | skin_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Shades objects from beautypie.com. All fields typed and schema-versioned.
"product_id": "BP-MAKEUP-042", "shade_name": "Ivory", "shade_description": "Fair with neutral undertones", "hex_code": "#F2E3D5", "stock_status": "OUT_OF_STOCK", "member_price": 12.0, "typical_price": 35.0
| # | product_id | shade_name | shade_description | hex_code | stock_status | image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ingredients objects from beautypie.com. All fields typed and schema-versioned.
"product_id": "BP-SKIN-001", "key_actives": "['Retinol', 'Ceramides', 'Hyaluronic Acid']", "vegan": true, "cruelty_free": true, "fragrance_free": false, "dermatologically_tested": true, "allergens": "['Limonene', 'Linalool']"
| # | product_id | full_list | key_actives | vegan | cruelty_free | fragrance_free |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Beauty Pie scraper maps the entire catalogue, capturing the precise spread between member pricing and typical retail value, alongside full ingredient lists and colour variants.
Extract both the Beauty Pie member price and the stated Typical Retail Price for margin and discount analysis.
Capture full ingredient lists, key active compounds, and formulation flags like vegan or cruelty-free.
Map every foundation, lipstick, and concealer shade to its respective hex code, description, and stock status.
Paginate through customer reviews to capture star ratings, text feedback, skin type profiles, and age demographics.
Monitor out-of-stock flags and restock events across the entire product catalogue at an hourly cadence.
Preserve the navigation structure from primary categories down to specific skincare routines and bundles.
Extract URLs for primary product images, texture macros, and shade swatches directly from the CDN.
Scrape localised pricing and product availability across the UK and US storefronts.
Receive only records that have changed since the last run to optimise downstream processing.
Brief in. Clean data out.
Specify categories, specific product URLs, or the entire catalogue. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for beautypie.com.
Schema validation, null-rate checks, and data type enforcement before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Beauty Pie relies heavily on dynamic rendering and regional pricing. Here is how our infrastructure processes it reliably.
The storefront uses heavy client-side rendering. We run full Playwright browser sessions to execute JavaScript, hydrate product state, and trigger lazy-loaded shade variants.
We utilise ISP-grade residential proxies to bypass automated blocking, maintaining clean request profiles that mimic standard consumer browsing patterns.
Pricing varies based on geolocation and selected currency. Our crawlers inject specific location headers and cookies to ensure the captured pricing matches your target market.
eCommerce DOM structures change frequently during promotional periods. We employ fallback selector chains to ensure data extraction continues without interruption.
Product images and texture swatches are hosted on external CDNs. We resolve and extract the highest resolution asset URLs available in the source code.
Beauty brands monitor the gap between Beauty Pie member pricing and typical retail values to adjust their own pricing strategies.
R&D teams analyse the frequency of specific active ingredients across top-rated products to inform new formulations.
Market researchers map the existing catalogue to identify missing shade ranges or underserved skincare categories.
Analysts track the expansion of categories like supplements and haircare to model brand growth trajectories.
Marketing teams process review text to understand customer priorities regarding texture, scent, and efficacy.
Procurement teams monitor out-of-stock velocity on key items to estimate production volumes and demand.
"Beauty Pie exposes the gap between manufacturing cost and retail markup. Querying that spread requires building a resilient extraction pipeline."
Extracting data from modern single page applications requires full browser rendering and proxy rotation. DataFlirt handles the infrastructure complexity so your engineering team can focus on data modelling and market analysis.
Everything supported by our beautypie.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 manages crawl orchestration and retry logic. Playwright handles JavaScript execution and dynamic DOM interaction.
ISP-grade residential IPs are rotated per request to prevent IP bans and maintain consistent access to regional storefronts.
Pipelines execute on AWS infrastructure. Airflow manages scheduling and dependencies, with all state stored in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About beautypie.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly accessible product information, pricing, and reviews is generally permissible under standard web scraping legal frameworks. DataFlirt targets only unauthenticated public data. We do not extract personal user data or circumvent authentication walls.
We configure our proxy pools and HTTP headers to match the target region (e.g., UK or US), ensuring the pricing and stock availability captured reflects the correct market.
Yes. Our Playwright integration interacts with the shade selector elements on the page, extracting the specific hex code, name, and stock status for every available variant.
We support daily, weekly, or monthly cadences. For stock monitoring, we can configure specific category pipelines to run at hourly intervals.
We build managed pipelines for defined extraction scopes. Contact us with your requirements for a precise quote based on volume and frequency.
Yes. We offer a sample run of up to 50 products during the scoping phase to ensure the schema meets your analytical requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous price and ingredient monitoring. Tell us what you need.
