We extract product listings, ingredient decks, pricing signals, shade and size variants, bestseller rankings, brand intelligence, and reviews from Sephora. 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 sephora.com. All fields typed and schema-versioned.
"product_id": "P512901", "brand_name": "Charlotte Tilbury", "product_name": "Pillow Talk Matte Revolution Lipstick", "category": "Lips", "price": 34.00, "currency": "USD", "rating": 4.5, "review_count": 12048, "is_bestseller": true, "is_clean": false, "in_stock": true
| # | product_id | sku_id | brand_name | product_name | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from sephora.com. All fields typed and schema-versioned.
"product_id": "P512901", "price": 34.00, "sale_price": 27.20, "discount_pct": 20, "sale_event_name": "Sephora Savings Event", "sale_event_ends_at": "2026-05-19T23:59:00Z", "rouge_price": 25.50, "gift_with_purchase": true
| # | product_id | sku_id | price | sale_price | discount_pct | sale_event_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from sephora.com. All fields typed and schema-versioned.
"review_id": "rv_sep_7391820", "product_id": "P512901", "star_rating": 5, "verified_purchase": true, "review_title": "My forever shade — nothing compares", "skin_type": "combination", "skin_tone": "light", "shade_purchased": "Pillow Talk", "review_date": "2026-04-22"
| # | review_id | product_id | sku_id | reviewer_name | verified_purchase | star_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brand & Category Intel objects from sephora.com. All fields typed and schema-versioned.
"brand_id": "B2807", "brand_name": "Charlotte Tilbury", "total_products": 284, "avg_price": 42.80, "avg_rating": 4.4, "new_arrivals_count": 18, "clean_product_pct": 22, "scraped_at": "2026-05-12T09:10:00Z"
| # | brand_id | brand_name | brand_url | total_products | avg_price | avg_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Sephora scraper handles every layer of the platform: product catalogues, ingredient decks, shade variant mapping, savings event pricing, brand intelligence, and the review corpus — with skin-type and shade metadata that makes beauty data uniquely actionable.
Product name, brand, category, how-to-use, full ingredient deck, shade name and hex codes, size options, clean beauty flags — scraped at SKU level with complete variant mapping.
Capture full price, sale price, Beauty Insider tier pricing, Rouge-exclusive prices, gift-with-purchase eligibility, and bundle offers — timestamped per crawl.
Extract bestseller badges, new arrival flags, love counts, and category rank positions — track what's rising across every beauty category in real time.
Full review text, star ratings, skin type, skin tone, shade purchased, helpful votes, and incentivised review flags — paginated across all review pages.
Full INCI ingredient list per SKU — parsed and structured for formulation analysis, clean beauty classification, allergen screening, and regulatory compliance workflows.
All products per brand with average pricing, average rating, new arrival velocity, bestseller SKUs, and clean-product share — track brand health at a glance.
sephora.com, sephora.co.uk, sephora.fr, sephora.de, sephora.com.au, sephora.sg and regional storefronts — from a unified schema with localised pricing.
Sephora Clean, cruelty-free, vegan, and Sephora Clean Planet Positive badges captured per product for sustainability and compliance datasets.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide brand lists, category URLs, or product ID sets. We design the extraction schema — including which ingredient and shade fields you need.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and anti-bot handling for sephora.com.
Schema validation, null-rate checks, ingredient field completeness checks, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Sephora uses dynamic rendering and bot detection across its product and review pages. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Sephora's bot detection operates on TLS fingerprints, browser headers, and behavioural signals. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing — appearing as genuine consumer traffic across US and UK IP pools.
Sephora product pages, shade selectors, and review feeds are heavily JavaScript-rendered. We run full Playwright sessions with scroll simulation and tab-panel interaction — capturing ingredient decks and shade variant data that HTTP clients miss entirely.
Sephora updates its frontend regularly across markets. Our selector strategy uses CSS, XPath, text-pattern matching, and structured data extraction as fallback layers — so DOM changes don't break your ingredient or review data feed.
For large product catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and storage. Savings event price changes and new shade additions generate targeted updates rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, ingredient field gaps, price outliers, schema drift, and coverage drops — and respond before you notice. SLA uptime is contractual, not aspirational.
Beauty brands and DTC founders benchmark Sephora pricing, discount depth during savings events, and Beauty Insider tier offers to optimise their own retail and DTC pricing strategy.
R&D teams and formulation consultants mine ingredient decks at scale to benchmark competitor formulations, screen for allergens, and monitor clean beauty certification trends.
Analysts track new arrival velocity, bestseller rank movements, love count growth, and review sentiment across categories to identify emerging trends and whitespace.
ML teams use Sephora datasets to train beauty recommendation engines, shade-matching models, skin-type classifiers, and sentiment analysis pipelines.
Retail buyers and compliance teams use ingredient and certification data to audit clean beauty standards, verify certifications, and track regulatory alignment across brand portfolios.
PE firms and analysts track brand portfolio growth, average selling prices, review velocity, and bestseller turnover to evaluate prestige beauty platform dynamics.
"Sephora is the most trusted beauty retailer in the world — and its review corpus, ingredient data, and shade variant catalogue represent an unmatched signal set for beauty intelligence."
Extracting that signal reliably requires residential proxies, full JavaScript rendering, shade-panel interaction, and ingredient deck parsing logic that goes far beyond standard scraping. DataFlirt absorbs that complexity so your formulation scientists and brand analysts can focus on the insights — not the crawlers.
Everything supported by our sephora.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, shade-panel tab interaction, and review load-more events. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US/UK/FR 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 sephora.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Sephora is generally permissible under applicable law in India, the US, and the EU — consistent with the hiQ v. LinkedIn ruling and similar precedents. DataFlirt targets only public, non-authenticated product, pricing, ingredient, and review data. We do not extract personal data or circumvent authentication walls. We recommend clients review Sephora's ToS independently and consult legal counsel for specific use cases.
We use residential ISP proxies that appear as real consumer traffic, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes don't break the pipeline. We monitor for block-rate spikes in real time and trigger pool rotation or solver queues automatically.
We support sephora.com, sephora.co.uk, sephora.fr, sephora.de, sephora.com.au, sephora.sg, sephora.com.br, and additional regional storefronts — all from a unified schema with market-normalised pricing.
Yes. We extract the full INCI ingredient list per SKU, parsed into a structured array and available as a flat field or nested object. This is one of the most requested fields for formulation benchmarking, allergen screening, and clean beauty classification workflows.
Yes. We map all shade names, hex codes, and size options per product — with per-variant pricing, availability status, and swatch image URLs. This is particularly valuable for shade gap analysis and visual recommendation model training.
We capture sale price, event name, event end date, Beauty Insider VIB pricing, Rouge-exclusive pricing, and gift-with-purchase eligibility per product on each crawl. Savings event monitoring is available at configurable cadences to catch price changes as they go live.
Yes — including reviewer skin type, skin tone, shade purchased, incentivised review flags, and reviewer-submitted images. This makes Sephora review data uniquely powerful for training skin-type-aware recommendation and formulation models.
Absolutely. We provide a sample run of up to 500 products — including ingredient decks and review data — as part of the pre-engagement scoping process, so you can validate schema fit and field completeness before signing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off ingredient catalogue dump or a continuous savings-event monitoring feed across 30,000 SKUs — we scope, build, and operate the pipeline. Tell us what you need.
