We extract skincare catalogues, ingredient lists, pricing variants, auto-replenish rates, and customer reviews from kiehls.com. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your schedule.
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 kiehls.com. All fields typed and schema-versioned.
"product_id": "KHL234", "name": "Ultra Facial Cream", "category": "Moisturisers", "skin_type": "All Skin Types", "rating": 4.7, "review_count": 5432, "bestseller_badge": true
| # | product_id | url | name | category | skin_type | concern |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Variants objects from kiehls.com. All fields typed and schema-versioned.
"sku": "3605970358823", "product_id": "KHL234", "size_ml": "50ml", "price": 38.0, "auto_replenish_price": 34.2, "in_stock": true, "currency": "USD"
| # | sku | product_id | size_ml | size_oz | price | auto_replenish_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ingredients & Efficacy objects from kiehls.com. All fields typed and schema-versioned.
"product_id": "KHL234", "key_ingredients": "['Glacial Glycoprotein', 'Squalane']", "paraben_free": true, "fragrance_free": true, "texture": "Lightweight Cream", "benefits": "['24-Hour Hydration', 'Barrier Repair']"
| # | product_id | key_ingredients | full_ingredient_list | paraben_free | fragrance_free | clinical_results |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Customer Reviews objects from kiehls.com. All fields typed and schema-versioned.
"review_id": "REV98765", "product_id": "KHL234", "rating": 5, "skin_type": "Dry", "age_range": "35-44", "verified_buyer": true, "helpful_votes": 12
| # | review_id | product_id | reviewer_name | rating | skin_type | age_range |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Routines objects from kiehls.com. All fields typed and schema-versioned.
"category_name": "Anti-Aging Routine", "parent_category": "Skincare Routines", "routine_step": "Step 3: Moisturise", "best_seller_rank": 2, "new_arrival": false, "limited_edition": false
| # | category_id | category_name | parent_category | product_list | routine_step | routine_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Kiehls scraper navigates dynamic size variants, parses complex ingredient lists, and extracts auto-replenish pricing models using automated browser sessions and JavaScript execution.
Extract every product across all categories, including moisturisers, serums, cleansers, and men's grooming lines.
Capture pricing for every size variant (ml/oz), including travel sizes, standard jars, and value refills.
Separate key active ingredients from full INCI lists. Extract clinical results, texture descriptions, and usage directions.
Collect full review text, star ratings, helpful votes, and reviewer metadata like skin type and age range.
Monitor subscription pricing tiers and discount percentages for auto-replenish orders versus one-time purchases.
Map products to specific skincare routines and target concerns (e.g., acne, anti-aging, hydration).
Track inventory status across all variants to analyse supply chain gaps and high-demand items.
Identify products tagged as bestsellers, new arrivals, or limited editions across different sub-categories.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences with change-detection.
Brief in. Clean data out.
Provide category URLs, specific product lines, or full catalogue requirements. We map the extraction schema.
We configure Playwright crawlers, handle dynamic size selections, and bypass bot protection on kiehls.com.
Schema validation, null-rate checks, and variant price accuracy verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting beauty catalogues requires rendering dynamic size selectors and parsing unstructured ingredient text. Here is how we build resilient pipelines.
Kiehls product pages use JavaScript to update prices, SKUs, and stock status when a user selects a different size (e.g., 50ml vs 125ml). We use headless browser automation to click through every variant and extract accurate data.
eCommerce platforms deploy bot mitigation. We route requests through residential ISP proxies and rotate TLS fingerprints to maintain access without triggering blocks.
Marketing pages often have custom layouts. We build fallback extraction chains using CSS, XPath, and JSON-LD structured data to ensure high extraction success rates regardless of template changes.
Instead of delivering full dumps every day, our pipeline hashes product records and only delivers rows where price, stock status, or reviews have changed since the last run.
If Kiehls updates their ingredient layout and our parsing fails, our observability stack triggers an alert on null-rate spikes. We fix the selector before it impacts your downstream tables.
Beauty brands track Kiehls pricing, promotional discounts, and auto-replenish rates to inform their own pricing strategies.
Formulators and cosmetic chemists analyse key active ingredients across bestsellers to identify emerging skincare trends.
Retailers monitor category depth, size variations, and new product launches to optimise their own merchandising.
Marketing teams scrape review text and correlate ratings with skin types to understand consumer pain points and product efficacy.
Machine learning teams use structured routine data and skin concern tags to train beauty recommendation algorithms.
Analysts track out-of-stock frequency and review velocity to estimate demand for specific product lines.
"Kiehls maintains one of the most structured skincare catalogues online, but accessing ingredient and variant data at scale requires dedicated extraction infrastructure."
Extracting beauty data requires handling complex variant structures, dynamic pricing for subscription models, and heavy JavaScript rendering. DataFlirt manages the proxies, retries, and schema maintenance so your data engineering team receives normalised records ready for analysis.
Everything supported by our kiehls.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, variant selection, and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to bypass bot mitigation.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About kiehls.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, ingredient, and pricing information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated data. We do not extract personal user data or circumvent authentication walls.
We use Playwright to simulate browser interactions, clicking through each size option (e.g., 28ml, 50ml, 125ml) to capture the specific SKU, price, and stock status associated with that variant.
Yes. We extract both the highlighted key ingredients and the full INCI list, formatting them as structured arrays in the final JSON output.
Full catalogue refreshes run on your required schedule. Daily cadences complete within a 2-4 hour window. Stock and price monitoring can be configured to run at higher frequencies.
Yes. We paginate through all customer reviews, extracting the text, star rating, and reviewer metadata such as skin type, concern, and age range.
Our packages start with full catalogue extraction delivered weekly. For continuous price monitoring or multi-region scraping, we price based on volume and frequency. Contact us for a scoped quote.
Yes. We provide a sample run of up to 50 products as part of the scoping process so you can validate the schema and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across all variants, we build and operate the pipeline. Tell us what you need.
