The Beauty & Personal Care market is rapidly evolving — fragmented across private-label drugstores, prestige specialists, and fast-scaling e-commerce platforms. With premiumization, digital discovery, and social commerce driving growth, companies need reliable ecommerce datasets to compete effectively. 

A structured beauty products dataset, cosmetic dataset, or makeup dataset enables brands, retailers, and investors to benchmark performance and uncover growth opportunities across global markets. 

That’s where web scraping services and advanced data extraction solutions come in. Using a site scraping service or web scraping company, brands can collect and analyze thousands of data points — from prices to reviews — across retailers, marketplaces, and regions. In short, web data extraction transforms raw retail signals into a powerful ecommerce product dataset — the difference between reacting late and leading your category. 

Why are datasets critical for Beauty & Personal Care companies?

Organizations rely on structured, up-to-date data, often gathered through web data scraping services to: 

  • Quantify performance: Benchmark market share, track category momentum, and size opportunities by region and channel.
  • Shape portfolios: Identify whitespace in assortments, price tiers, pack sizes, and formats.
  • Optimize channels: Balance offline, e-commerce, and marketplace exposure based on demand and margin.
  • Monitor competition: Follow promotions, launches, pricing ladders, and availability in near real time using a web crawling service.
  • Elevate customer insight: Mine reviews, ratings, and search signals with data scraping services to guide innovation and messaging.
  • Improve operations: Measure delivery SLAs, pickup coverage, and returns using datasets enriched by a web scraping service provider. 

For companies seeking to buy ecommerce data or download ecommerce datasets, structured and analysis-ready delivery is essential. 

What should a high-quality beauty products dataset include?

High-quality datasets, typically created via a data extraction services company, provide objective, repeatable analysis across markets and channels. Key components include: 

  • Assortment intelligence: Normalized product catalogs, variants, and pricing ladders.
  • Merchandising & promo tracking: Discounts, bundles, gifting windows, and loyalty mechanics.
  • Demand signals: Rankings, search terms, and reviews (ideal for NLP-ready fields).
  • Operational signals: Delivery times, pickup/return policies, and store coverage.
  • Competitive mapping: Cross-border availability and marketplace share-of-shelf from web data scraping companies. 

A well-structured ecommerce product dataset supports both BI dashboards and AI forecasting pipelines. 

What does this guide provide?

Below, you’ll find a practical, ready-to-execute guide outlining which data sources to monitor — by region and retailer, so your teams or web scraping service providers can plug them straight into your BI dashboards or AI/ML pipelines. 

For teams that want to accelerate execution, Datasets.store provides analysis-ready BI & AI ecommerce datasets covering products, prices, promos, availability, and reviews across target retailers and regions.  

If you’re looking to buy ecommerce datasets or download ecommerce datasets without building infrastructure from scratch, ready-made solutions significantly reduce time to insight. 

Western Europe

dm-drogerie markt (Germany, Austria, CEE) 

  • Type: Drugstore chain
  • Sells: Cosmetics, skincare, baby care, haircare, health, household
  • Presence: ~4,100+ stores across Europe
  • Model: Strong offline network + integrated ecommerce
  • Distinctives: Affordability, sustainability, deep private label 

ROSSMANN (Germany, Poland, CEE) 

  • Type: Drugstore
  • Sells: Beauty, hygiene, baby, household
  • Presence: ~4,900 stores across Europe
  • Model: Omnichannel (offline + online)
  • Distinctives: Aggressive promo and loyalty; eastward expansion 

Sephora (France & Pan-Europe) 

  • Type: Prestige beauty specialist
  • Sells: Makeup, skincare, fragrance, haircare, accessories
  • Presence: ~2,700+ stores globally; extensive EU footprint
  • Model: Offline + online (own stores + shop-in-shops)
  • Distinctives: Trend leadership, exclusives, strong digital community 

Müller  

  • Type: Mixed drugstore/perfumery
  • Sells: Beauty, household, toys, etc.
  • Presence: Significant DE/AT presence
  • Model: Offline + online
  • Distinctives: Direct competitor to dm/ROSSMANN in many locales 

Boots (UK, Ireland) 

  • Type: Pharmacy & beauty chain
  • Sells: Cosmetics, skincare, pharmacy, wellness, baby, healthcare
  • Presence: ~2,200 UK stores
  • Model: Omnichannel
  • Distinctives: Health-beauty integration, NHS-linked services, promotions 

KIKO Milano  

  • Type: Makeup specialist 
  • Sells: Color cosmetics, skincare, accessories
  • Presence: ~900 stores globally
  • Model: Offline + online
  • Distinctives: Affordable premium positioning, Italian-made quality, high turnover of seasonal collections 

Western Europe tendencies: 
Prestige focus, sophisticated loyalty ecosystems, and strong omnichannel execution. Private labels thrive in drugstores, while exclusivity drives specialist performance.

Structured retail product datasets in this region are especially valuable for tracking premiumization and promotional intensity.

Eastern & Central Europe 

Rossmann Poland  

  • Type: Drugstore
  • Sells: Beauty, personal care, household, fragrances
  • Presence: ~1,900 stores in Poland
  • Model: Omnichannel
  • Distinctives: Deep local reach, fast delivery/pickup, promo intensity 

Notino (Czech Republic; EU-wide) 

  • Type: Ecommerce marketplace for beauty
  • Sells: Fragrance, skincare, makeup, haircare, accessories, beauty tech
  • Presence: 28+ European countries; select physical stores
  • Model: Online-led, limited showrooms
  • Distinctives: Vast catalog, cross-border logistics, strong SEO/digital 

dm.cz (Czech Republic) 

  • Type: Drugstore
  • Sells: Cosmetics, hygiene, baby care, cleaning
  • Presence: Nationwide stores + e-shop
  • Model: Omnichannel
  • Distinctives: Community orientation, sustainability ethos 

EVA (Ukraine) 

  • Type: Beauty & personal care chain
  • Sells: Cosmetics, skincare, hygiene, fragrance, household
  • Presence: ~1,100+ stores; ~8M loyalty users
  • Model: Omnichannel
  • Distinctives: Massive retail footprint; broad range (drugstore to premium) 

Prostor (Ukraine) 

  • Type: Beauty & household retailer
  • Sells: Decorative cosmetics, skincare, fragrance, daily care
  • Presence: 400+ stores across 130+ cities
  • Model: Omnichannel
  • Distinctives: Fast-growing, promo-driven, high engagement 

MakeUp (Ukraine + Europe)  

  • Type: Pure-play online beauty
  • Sells: Makeup, skincare, fragrance, hair, personal care
  • Presence: Multiple European markets (UA, CZ, PL, RO, etc.)
  • Model: Online-only
  • Distinctives: Cross-border operation, localized sites, aggressive pricing 

Douglas (Bulgaria / Romania / Poland) 

  • Type: Prestige perfumery & cosmetics
  • Sells: Skincare, makeup, fragrance
  • Presence: Dozens of CEE stores + local e-shops
  • Model: Omnichannel
  • Distinctives: Premium positioning, Western-European brand equity 

Farmec (Romania) 

  • Type: Local manufacturer & retailer
  • Sells: Skincare, haircare, personal care (Gerovital, Aslavital)
  • Presence: National + exports across EU/Balkans
  • Model: Omnichannel
  • Distinctives: Heritage, “dermo-cosmetic” credibility 

Eastern/Central Europe tendencies: 
Price sensitivity, omnichannel dominance, strong local champions, and cross-border ecommerce expansion (Notino/MakeUp). Logistics and pickup coverage are decisive. A localized beauty products dataset or makeup dataset helps quantify competitive positioning in fast-growing digital markets.

North America (USA + Canada) 

Sephora (USA, Canada) 

  • Type: Prestige beauty specialist
  • Sells: Makeup, skincare, fragrance, haircare; in-store services
  • Presence: ~1,700+ US stores incl. large shop-in-shop network
  • Model: Offline + online
  • Distinctives: Exclusive brands, services, loyalty scale 

Ulta Beauty (USA) 

  • Type: Beauty & salon superstore
  • Sells: Mass + prestige beauty; salon services
  • Presence: ~1,450–1,500+ standalone stores
  • Model: Offline + online
  • Distinctives: Broad price ladder, salons, powerful loyalty 

Sally Beauty (USA, Canada) 

  • Type: Professional beauty supply retailer
  • Sells: Hair color, tools, nails, pro supplies
  • Presence: Large US footprint
  • Model: Offline + online
  • Distinctives: Professional heritage; B2C + prosumer appeal 

Kenvue (Global; North American hub) 

  • Type: Consumer health & beauty manufacturer and D2C retailer
  • Sells: Skincare (Neutrogena, Aveeno), sun care, baby care (Johnson’s), oral care (Listerine), OTC/wellness
  • Presence: Global CPG distribution across mass retail & pharmacies; US brand sites/D2C hub via kenvue.com
  • Model: Primarily retail distribution complemented by D2C/brand websites
  • Distinctives: Science-backed portfolios, dermatologist-recommended lines, strong claims & reviews data useful for market and sentiment analysis 

Shoppers Drug Mart (Canada) 

  • Type: Pharmacy & beauty chain
  • Sells: Health, beauty, skincare, wellness
  • Presence: ~1,300+ stores nationwide
  • Model: Omnichannel (offline + online)
  • Distinctives: Luxury beauty zones, strong loyalty via PC Optimum, cross-category integration 

North America tendencies: 
Integrated beauty-health ecosystems, loyalty-driven cross-category sales, and rapid DTC data integration. An Amazon beauty products dataset or amazon cosmetic dataset is particularly relevant due to Amazon’s dominant marketplace position. Ecommerce datasets in this region are critical for AI-based demand forecasting and pricing analytics.

Africa 

Dis-Chem (South Africa) 

  • Type: Pharmacy & beauty retail chain
  • Sells: Skincare, cosmetics, health, and wellness products
  • Presence: 270+ stores across South Africa
  • Model: Omnichannel
  • Distinctives: Competitive pricing, strong loyalty program, extensive private-label range 

Clicks (South Africa) 

  • Type: Health & beauty retailer
  • Sells: Beauty, personal care, pharmacy, and wellness items
  • Presence: 850+ stores and 600+ pharmacies nationwide
  • Model: Omnichannel (offline + online)
  • Distinctives: Dominant loyalty ecosystem (ClubCard), integration with healthcare services 

Edgars Beauty (South Africa, Namibia, Botswana) 

  • Type: Beauty specialty retailer 
  • Sells: Makeup, fragrance, skincare, and grooming products
  • Presence: 200+ stores in Southern Africa
  • Model: Offline + online
  • Distinctives: Prestige and mass-market mix, strong brand partnerships 

Africa tendencies: 
Rapid modernization and digital integration led by pharmacy chains and digital-first entrants. Mobile-driven ecommerce expansion increases demand for structured ecommerce product datasets and retail product datasets to track emerging online penetration.

Asia 

Watsons (Pan-Asia; local country sites) 

  • Type: Health & beauty retail leader
  • Sells: Skincare, cosmetics, personal care, health
  • Presence: 8,000+ stores worldwide (flagship Watsons)
  • Model: Omnichannel
  • Distinctives: Heavy store investments, regional breadth

Guardian (SE Asia) 

  • Type: Health & beauty chain
  • Sells: Skincare, pharmacy, personal care
  • Presence: 1,100+ stores across MY/ID/SG/VN/BN/KH
  • Model: Omnichannel
  • Distinctives: Drugstore-pharmacy hybrid strength 

Nykaa (India) 

  • Type: Beauty marketplace + private label
  • Sells: Beauty across price tiers; expanding into fashion
  • Presence: India-wide; growing offline stores
  • Model: Online-led, growing omnichannel
  • Distinctives: Premium momentum, content-commerce engine 

Asia tendencies: 
Omnichannel scale via Watsons and Guardian; India’s digital-first growth led by Nykaa; rapid K-beauty and dermocosmetics adoption. Ecommerce datasets in Asia are essential for tracking cross-border dynamics and marketplace expansion. 

Australia, Oceania & Japan 

Chemist Warehouse (Australia, New Zealand) 

  • Type: Discount pharmacy & beauty megastore
  • Sells: Cosmetics, fragrance, health, wellness
  • Presence: 550+ stores across AU & NZ
  • Model: Omnichannel
  • Distinctives: Value-driven, aggressive promotions, marketplace expansion 

Priceline Pharmacy (Australia) 

  • Type: Beauty & pharmacy chain
  • Sells: Beauty, skincare, wellness
  • Presence: 450+ stores
  • Model: Omnichannel
  • Distinctives: Female-focused branding, strong loyalty program (Sister Club) 

Mecca (Australia, NZ) 

  • Type: Prestige beauty specialist
  •  Sells: Makeup, skincare, fragrance
  • Presence: 100+ stores
  • Model: Offline + online
  • Distinctives: Exclusive global brands, editorial tone, experiential retail 

Ainz & Tulpe (Japan) 

  • Type: Beauty retailer
  • Sells: Skincare, cosmetics, health & wellness
  • Presence: Nationwide + airports
  • Model: Offline + online
  • Distinctives: Curated Japanese and global assortments 

Matsumoto Kiyoshi (Japan) 

  • Type: Drugstore chain
  • Sells: Beauty, personal care, health products
  • Presence: 1,700+ stores
  • Model: Omnichannel
  • Distinctives: Everyday pricing, tourist-friendly, data-rich loyalty system 

Oceania & Japan tendencies: 
A blend of value-driven pharmacy chains and experience-driven prestige retailers. Japan leads in data-linked loyalty ecosystems and cross-border ecommerce integration. Structured cosmetic datasets help quantify cross-channel pricing and assortment strategies. 

South America (LatAm) 

O Boticário / Grupo Boticário (Brazil) 

  • Type: Multi-brand ecosystem
  • Sells: Fragrance, skincare, makeup
  • Presence: Extensive Brazil retail + LatAm expansion
  • Model: Offline + online (including marketplaces & social)
  • Distinctives: Ecosystem synergies; strong DTC and retail 

Natura (Brazil) 

  • Type: Heritage DTC + omnichannel
  • Sells: Fragrance, skincare, body & hair
  • Presence: Leading Brazil e-commerce presence; global reach
  • Model: Omnichannel, social selling roots
  • Distinctives: Sustainability and community brand equity 

Beleza na Web (Brazil) 

  • Type: Multi-brand e-commerce
  • Sells: Broad beauty assortment
  • Presence: National online leader; part of Grupo Boticário
  • Model: Online
  • Distinctives: Marketplace-like breadth, editorial content

Farmacity (Argentina) 

  • Type: Drugstore chain
  • Sells: Beauty & personal care + pharmacy
  • Presence: Leading national chain + e-commerce
  • Model: Omnichannel
  • Distinctives: Strong city coverage; health–beauty mix 

LatAm tendencies: 
Powerful brand ecosystems (Boticário, Natura), strong DTC heritage, and fast-maturing online channels. Ecommerce datasets in this region support social-commerce analytics and marketplace expansion strategies.

Why choose Datasets.store for Beauty & Personal Care ecommerce data?

Datasets.store provides production-grade, analysis-ready ecommerce datasets created through advanced web data extraction services and web crawling technologies. 

You can choose from existing datasets — including beauty products datasets, cosmetic datasets, makeup datasets, amazon beauty products datasets, and amazon cosmetic datasets — with continuous refresh schedules (monthly, quarterly, or bi-annual). 

Data delivery supports common formats such as CSV or Parquet for seamless BI and AI integration. 

If you want to buy ecommerce data, buy ecommerce datasets, or download ecommerce datasets that are structured, normalized, and analysis-ready, Datasets.store provides scalable solutions. 

What do you get?

  • Structured, normalized data across products, prices, and availability — powered by a trusted web scraping service.
  • Flexible packaging: зre-scraped ecommerce product datasets mapped to your KPIs.
  • Operational reliability: managed refresh, accuracy, completeness 

How does it power BI & AI?

  • Business Intelligence: Integrate datasets into Power BI, Looker, or Tableau dashboards for share-of-shelf, pricing analytics, and assortment gap analysis using a retail product dataset.
  • Artificial Intelligence: Feed clean ecommerce datasets into AI models for demand forecasting, promotional uplift prediction, or sentiment analysis — including insights derived from an amazon beauty products dataset. 

How can you turn market noise into actionable advantage?

The global Beauty & Personal Care market is data-driven and fast-moving. To stay ahead, companies must rely on accurate, consistent, and current ecommerce datasets — powered by expert web scraping service providers and advanced data crawling services. 

Whether you’re monitoring category shifts, mapping competitors, or forecasting demand, Datasets.store delivers the clarity you need — combining website data scraping services with market expertise to transform raw web signals into measurable business advantage. 

If you’re ready to buy ecommerce data or download ecommerce datasets that accelerate decision-making, structured beauty and retail datasets are your competitive edge.