Service · Beauty & personal care data

Beauty & Personal Care Data Scraping

At shade level, because a foundation with two shades left is not in stock.

Beauty and personal care data scraping is the automated collection of cosmetics, skincare and personal care retail data at shade and variant level — per-shade availability, pricing, INCI ingredient lists, published claims, sampling and gift-with-purchase mechanics, and assortment by category — so shade-level demand becomes measurable rather than hidden behind a product-level stock flag.

Beauty has the same structural problem as fashion and almost nobody treats it that way. A foundation ranges forty shades. Product-level data says it is in stock. Thirty-eight shades are gone, which is the opposite of what the data implies.

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beauty_shades.jsonl LIVE FEED

{"product_key":"aw-bty-771204", "retailer":"example-beauty.com", "brand":"Example Beauty", "product":"Luminous Serum Foundation 30ml", "category_path":["Makeup","Face","Foundation"], "price":38.00,"was_price":38.00, "unit_price_computed":1.27, "unit_basis":"per_ml", "shades_total":40,"shades_available":2, "shade_range_broken":true, "deep_shades_oos":true, "shade_curve":[{"shade":"120 Ivory","in_stock":true}, {"shade":"340 Almond","in_stock":false}, {"shade":"480 Espresso","in_stock":false}], "claims_published":["vegan","non-comedogenic","SPF 15"], "claims_verified":false, "inci_captured":true,"inci_count":34, "gwp_offer":"free 7-piece gift over 60"} {"product_key":"aw-bty-771204", "retailer":"other-beauty.com", "shades_available":31, "deep_shades_oos":false}

2 of 4,412,700 product-shade rowsshade parse 96.8% · INCI captured 89.2% · schema v3.4

Our Data Powers

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Written and reviewed by the Actowiz Solutions Data Engineering team Published 5 August 2026 Last updated 5 August 2026 Reading time 11 min

Key facts at a glance

What it is: Managed collection of beauty, skincare and personal care retail data at shade and variant level
Granularity: Product-shade, not product level, since shade is the purchasable unit
Shade curve: Per-shade availability with broken-range and deep-shade stockout flags
Ingredients: INCI lists captured as published, for formulation and compliance work
Claims: Published claims recorded verbatim with a verified flag set to false
Sampling: Gift-with-purchase, sampling and bundle mechanics captured as displayed
Refresh: Daily standard; sub-daily during launches and sale periods
Who it's for: Beauty brands, retailers, category and formulation teams, and investors

Product-shadecollection granularitynot product level

96.8%shade parse rateacross naming conventions

Deep shadesflagged separatelythe ranging signal that matters

Claimscaptured, never verifiedstated boundary

Key takeaways

What it is: Managed collection of beauty, skincare and personal care retail data at shade and variant level
Granularity: Product-shade, not product level, since shade is the purchasable unit
Shade curve: Per-shade availability with broken-range and deep-shade stockout flags
Ingredients: INCI lists captured as published, for formulation and compliance work
Claims: Published claims recorded verbatim with a verified flag set to false
Sampling: Gift-with-purchase, sampling and bundle mechanics captured as displayed

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is beauty data scraping, and why does shade level change the conclusion?

Beauty and personal care data scraping is the automated collection of cosmetics, skincare, haircare and personal care retail data: pricing and unit pricing, per-shade availability, ingredient lists, published claims, sampling mechanics, assortment and category placement.

The category has the same structural problem as fashion, and almost no dataset treats it that way: shade is the purchasable unit, not the product.

What product-level data gets wrong

Availability inverts. A forty-shade foundation with two shades left reads as in stock. It has sold out of thirty-eight, which is a very different commercial fact.
Deep-shade ranging is invisible. The most commercially and reputationally significant question in foundation — whether deep shades are ranged and replenished — cannot be answered from product-level data at all.
Launch performance is unreadable. A launch selling through its core shades in a week looks identical to one selling nothing, since both show as in stock.
Assortment depth is hidden. Two retailers carrying the same product differ enormously if one ranges forty shades and the other twelve.

How we structure it

Every record is a product-retailer combination carrying a full shade curve: per-shade availability, count available against total ranged, plus derived flags. shade_range_broken indicates gaps across the range. deep_shades_oos flags whether the deeper end of the range is unavailable, which is the field most brand and category teams actually came for.

Shade naming is chaotic — numeric codes, descriptive names, or both, with no cross-brand standard. We parse and retain exactly what is published rather than forcing a standardised scale that does not exist.

What we do not do

Verify claims. Published claims like "vegan", "non-comedogenic", "dermatologist tested" or an SPF figure are captured verbatim with claims_verified set to false. We are not a testing house, and presenting a captured claim as validated would be the most damaging thing we could do in this category.

What we collect

Six categories of beauty and personal care data

Shade availability is the field clients most often did not know they needed. Ingredient capture is the fastest growing.

Shade & variant availability

The layer that makes the data meaningful.

Per-shade availability with full curve
Shades available against total ranged
Broken shade range detection
Deep-shade stockout flags
Shade introduction and discontinuation

Pricing & unit pricing

Comparable across pack sizes.

Current and was price
Unit price computed per ml or gram on a consistent basis
Multi-size range pricing
Set and bundle pricing decomposed
Price change events with dates

Ingredients & formulation

INCI as structured data.

Full INCI list as published
Ingredient count and ordering
Flagged ingredient presence per your list
Fragrance and allergen declarations
Formulation change detection over time

Published claims

Captured verbatim, never verified.

Claim text exactly as published
Normalised claim categories
SPF and protection figures as stated
Certification and seal references
Claim wording change detection

Sampling & GWP mechanics

How beauty actually promotes.

Gift-with-purchase offers and thresholds
Sample and mini inclusion
Bundle and set composition
Loyalty point multipliers where displayed
Offer validity windows

Assortment & launches

Range and newness.

Range breadth by category and brand
New product and new shade detection
Exclusive and limited edition flags
Discontinuation detection
Category mix over time

Service scope

What the ecommerce data scraping service includes

A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.

✓ Included in every engagement

Product-shade granularity with full per-shade availability curves
Deep-shade stockout flagged separately, derived per brand and product line
Not-ranged distinguished from ranged-but-unavailable
INCI lists captured with ordering retained and capture rate reported
Claims captured verbatim with claims_verified permanently false
Source discovery, scoping and a written collection plan
Free pilot on your own sources before any commitment
Full pipeline build, hosting and proxy infrastructure
Schema design, validation and sampled human QA on every run
Ongoing maintenance when source layouts change — our cost, not yours
Delivery to your warehouse, bucket, SFTP or API endpoint
Documented methodology and compliance notes for your legal review

× Not included — stated upfront

Verification of any published claim, SPF figure or ingredient list
Formulation, safety or dermatological assessment or advice
Product-level stock substituted where shade availability is not published
Reviewer names, profiles or review histories
Anything behind a login, paywall or credentialed session
Personal data beyond a documented lawful basis
Licensed third-party datasets we do not hold rights to
Guarantees about fields a source simply does not publish

Schema

Beauty data fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 110+ and is agreed during scoping.

Deliverable schema — v3.4 core fields (full dictionary: 110+ fields)
Field	Type	What it captures	Refresh
`product_key / brand`	string	Cross-retailer product identity and normalised brand	Every run
`shade_curve`	array	Per-shade availability, the field that makes shade-level demand visible	Daily
`shades_available / shades_total`	int	Purchasable shades against total ranged at that retailer	Daily
`shade_range_broken / deep_shades_oos`	boolean	Derived flags for range gaps and deep-shade unavailability	Daily
`price / unit_price_computed / unit_basis`	decimal / string	Price and unit price computed by us on a consistent basis	Daily
`inci_captured / inci_list / inci_count`	boolean / array / int	Whether the ingredient list was published, and its content	Weekly
`claims_published / claims_verified`	array / boolean	Claims verbatim, with verified always false	Weekly
`gwp_offer / sample_included`	string / boolean	Gift-with-purchase mechanics and sample inclusion as displayed	Daily
`is_exclusive / is_limited_edition`	boolean	Retailer exclusivity and limited edition status where indicated	Weekly
`first_seen / discontinued_at`	date	Launch observation and discontinuation detection	Daily
`shade_added_at`	date	When a new shade entered the range, for range expansion tracking	Daily

claims_verified is a constant reading false. It exists so nobody downstream mistakes a captured claim for a validated one. We record what a brand published; whether it is substantiated is a testing question, not an extraction one.

Coverage

Retailers and markets we collect from

Beauty retail is a mix of specialists, department stores, pharmacy and brand D2C. Coverage is built to your competitive set.

SephoraUltaBootsSuperdrugDouglasMarionnaudNotinoNykaaPurplleTiraAmazon beautyLook FantasticCult BeautySpace NKHarrods beautyEl Corte Inglés beautyWatsonsSasaNamshi beautyFacesBrand D2C sitesPharmacy beauty aislesGrocery beauty aislesMarketplace beauty sellers

Shade-level availability is published by most beauty specialists and by fewer general retailers. We confirm per retailer which expose shade availability publicly before build rather than substituting product-level stock. Request a source we don't list →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why demand concentrates there
Market	Why demand concentrates here
United Kingdom & France	The deepest beauty specialist retail with widespread public shade-level availability, which makes shade curve analysis most complete.
United States	Largest prestige beauty market with the most scrutinised deep-shade ranging, and heavy launch cadence.
India	Fast-growing beauty ecommerce on Nykaa, Purplle and Tira with aggressive sampling and bundle mechanics.
United Arab Emirates & Southeast Asia	High prestige beauty penetration with distinct shade range requirements and strong travel retail overlap.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

Beauty theses need observable launch performance and range signals ahead of reported results.

What we deliver

Longitudinal shade availability, launch cadence and assortment panels by brand and retailer.

Metric that moves

Signal lead time

Use cases

How beauty data gets used in practice

Four patterns, with the outcome each is judged on.

Shade-level sell-through inference

Per-shade availability is tracked daily, so products losing core shades while remaining listed are identified as strong sellers, and shades returning to stock indicate replenishment.

Outcome: Launch and range performance read from shade movement rather than from a product-level stock flag.

Deep-shade ranging and availability audit

The deeper end of every shade range is tracked separately with its own stockout flag, per retailer.

Outcome: Deep-shade ranging and replenishment measured across the market rather than asserted.

Formulation and ingredient trend tracking

INCI lists are captured with change detection, revealing reformulations and the adoption or removal of specific ingredients across competitor ranges.

Outcome: Competitor formulation shifts detected from published ingredient lists rather than from press coverage.

Claims monitoring for substantiation review

Published claims are captured verbatim with first-seen dates and wording change detection, including quiet withdrawals.

Outcome: A dated claim inventory across the market, which is what a substantiation or regulatory review starts from.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

Beauty brand · UK

Core shades were out at retail partners and nobody noticed until sales dipped

Situation

Availability monitoring ran at product level, so a foundation with two of forty shades remaining reported as in stock.

What we ran

Shade-level collection with per-shade curves, deep-shade stockout flags and replenishment detection across retail partners.

Result

Core and deep shade stockouts became visible per retailer within a collection cycle.

Retailer · EU

Competitor formulation shifts were learned from press coverage

Situation

The category team had no systematic view of competitor ingredient changes, so reformulations surfaced weeks late.

What we ran

INCI list capture with formulation change detection across the competitive range, refreshed weekly.

Result

Reformulations and ingredient adoption became measurable from published lists rather than from announcements.

Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →

The 48-hour sample — run on your sources, not ours

Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.

Real extraction from your actual sources
Returned inside two business days
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Build vs buy

Should you build beauty data collection in-house or hire it as a service?

Shade parsing across inconsistent naming conventions is the part in-house builds consistently underestimate.

In-house build vs self-serve tool vs Actowiz managed service
Consideration	In-house scraping team	Generic proxy / DIY tool	Actowiz managed feed
Time to first usable data	6–12 weeks of engineering before anything is trustworthy	Days, but output needs manual cleanup before use	Free pilot in 48 hours, production in 5–10 business days
Who fixes it when a source changes	Your engineers, at the cost of their roadmap	You do — tools report failures, they don't resolve them	We do, same business day, inside the retainer
Data quality assurance	Whatever your team has time to build	None beyond HTTP success	Schema validation plus sampled human QA on every run
Compliance documentation	Rarely produced, then requested urgently by legal	Not provided; terms risk sits with you	Sources, method and lawful basis documented for review
Accountability	Distributed across a team with other priorities	A support ticket queue	A named engineer and an account owner
True annual cost	Engineer salaries, proxies, hosting, ongoing maintenance	Low licence fee plus significant hidden analyst time	One fixed monthly retainer, quoted after scoping

Deep-shade availability is the single most requested field in this category

When beauty clients see shade-level data for the first time, the question they ask within minutes is always the same: what is happening at the deep end of the range.

Why it is a distinct question

Ranging and availability are different failures. A retailer may range only twelve of forty shades, or range all forty and never replenish the deepest. Product-level data cannot distinguish these, and they need different conversations.
It is reputationally live. Deep-shade availability is publicly scrutinised in this category in a way that few retail metrics are.
It is a genuine demand signal. Persistent deep-shade stockouts frequently indicate under-forecasting rather than low demand.
It differs sharply by retailer. The same brand can have full range availability at one retailer and a truncated range at another.

How we handle it

We flag deep_shades_oos separately from the general broken-range flag, and we distinguish not ranged from ranged but unavailable — the same distinction that matters in quick commerce between listed and in stock. A shade never ranged at a retailer is a category conversation; a ranged shade persistently out is a supply conversation.

Defining the deep end requires judgement, since shade naming has no cross-brand standard. We derive it per brand and product line from the published range ordering rather than applying a fixed global rule, and we state the method in the scope document so the flag is inspectable rather than a black box.

Claims and ingredients: we capture, we do not judge

Beauty is dense with claims, and the temptation to sell "claim verification" is obvious. We do not, and the boundary is worth stating clearly because it protects you as much as us.

What we capture

Claim text verbatim — the exact wording, because in claims work the wording is the substance.
Normalised claim categories so filtering works across differently-worded equivalents.
SPF and protection figures exactly as stated.
Certification and seal references as displayed.
Wording changes and withdrawals, with dates — a quietly removed claim is often the most informative event.
Full INCI lists as published, with ordering retained, since concentration order carries meaning.

What we will not do

Assess whether a claim is substantiated, whether an SPF figure is accurate, or whether an ingredient list is complete. Those require laboratory testing and regulatory expertise. claims_verified is a constant false so that no downstream system can mistake capture for validation.

Nor will we give formulation or dermatological advice on the data. We provide the published ingredient list; interpreting it for safety or efficacy is your regulatory and R&D function's work, and a data vendor offering that opinion would be creating liability for you.

For claim monitoring across sustainability specifically, our ESG service applies the same capture-not-verify principle.

How it works

How a beauty data engagement goes live in 5 to 10 business days

Retailers, categories and whether shade-level collection is required are scoped first, since shade curves multiply record volume substantially.

Scope the sources and fields

You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.

Ongoing monitoring and SLA support

We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.

Formats & destinations

JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.

Compliance & data ethics

We collect publicly accessible product, category and search pages. Published claims and ingredient lists are captured as published and never verified, with claims_verified constant false. We provide no formulation, safety or dermatological assessment. Reviewer names and profiles are not part of the deliverable.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
Commitment	What we hold ourselves to
Pilot turnaround	A real sample from your own sources within 48 hours of scoping, at no cost.
Go-live	Production collection running within 5–10 business days of sign-off.
Delivery punctuality	99.5% on-schedule delivery, measured monthly and reported to you.
Breakage response	Source layout changes triaged same business day; critical sources inside 4 hours.
Data quality	Schema validation on every run plus sampled human QA before any delivery leaves us.
Escalation	A named engineer and an account owner, not a shared ticket queue.
Change requests	Field additions and source changes handled inside the retainer, not re-quoted.
Exit	Your historical data exported in full on request. No lock-in, no export fee.

Why teams pick Actowiz for this work

Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
QA is part of the service. Schema validation and sampled human review run before delivery, every run.
Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.

Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

Shade curve: The pattern of which shades in a range remain available. Product-level stock data conceals it entirely, so a forty-shade foundation with two shades left reads as in stock.
Deep-shade stockout: Unavailability at the deeper end of a shade range, flagged separately because it is the most commercially and reputationally scrutinised question in the category.
Claim capture: Recording a published claim verbatim without assessing it. Verification requires laboratory testing, so claims_verified is permanently false.

FAQ

Beauty and personal care data: frequently asked questions

What category, brand and regulatory teams ask during evaluation.

Because shade is the purchasable unit. A forty-shade foundation with two shades left reads as in stock at product level, when it has sold out of thirty-eight.

Product-level data also cannot answer the question this category most cares about — whether deep shades are ranged and replenished. That is invisible without a shade curve, and it is usually the first thing clients ask once they see the data.

No, categorically. We capture claims verbatim with claims_verified set permanently to false. Verification requires laboratory testing and regulatory expertise, and presenting a captured claim as validated would be the most damaging thing we could do in this category.

What we do provide is a dated claim inventory with wording change detection — including quietly withdrawn claims, which is often the most informative event. That is what a substantiation review starts from.

Yes, where published — the full INCI list with ordering retained, since concentration order carries meaning. Our capture rate is about 89%; some retailers publish partial lists or none.

We also detect formulation changes over time, which is how competitor reformulations become visible without waiting for press coverage. What we do not do is interpret the list for safety or efficacy — that is your regulatory and R&D function's work.

We parse and retain exactly what is published rather than forcing a standardised scale, because none exists across brands. Some use numeric codes, some descriptive names, some both.

Our parse rate is about 96.8%. For the deep-shade flag we derive the deeper end per brand and product line from the published range ordering rather than applying a fixed global rule, and the method is stated in the scope document so the flag is inspectable.

The same distinction that matters in quick commerce. A shade never ranged at a retailer is a category conversation; a ranged shade persistently unavailable is a supply conversation.

We keep them separate, so an availability percentage is computed against the shades actually ranged rather than against the full range the brand offers. Merging them makes a ranging decision look like a supply failure.

Yes, as structured mechanics rather than as offer text. GWP thresholds, sample and mini inclusion, bundle composition and validity windows.

These drive a large share of beauty promotion and are rarely tracked as mechanics, which means promotional intensity in this category is routinely understated by datasets that only track price.

Most beauty specialists do; general retailers and grocery beauty aisles frequently do not. We confirm per retailer during scoping.

Where shade availability is not published, we say so rather than substituting product-level stock. That single substitution would invert the availability conclusion, which is exactly the error the shade curve exists to prevent.

Yes — new product detection with first-seen dates, and separately shade_added_at for shades entering an existing range, which is how range extensions become visible.

Range extension into deeper shades is a particularly watched event in this category, and it is only detectable if shade-level history exists rather than product-level snapshots.

We quote individually. The main driver is whether shade-level collection is required — shade curves multiply record volume by the number of shades per product, which in foundation can be forty times product-level volume.

A defined category at product level sits at the lighter end; full catalogue at shade level with INCI capture and daily refresh sits higher. One scoping call, a free pilot on your own products within 48 hours, then a fixed monthly quote. Request a quote.

See real shade-level data for your own products

Send us a product list and retailers. We return shade curves with deep-shade flags, INCI capture and claims within 48 hours.

Get a free beauty data sample Book a 20-min scoping call

Free pilot, no card, no obligation. We'll confirm which retailers expose shade availability publicly.

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