Global hair care market worth at USD 51.73 Billion in 2024, is expected to surpass USD 124.21 Billion by 2034, with a CAGR of 9.3% from 2025 to 2034.

The Personal Care and Beauty Products Dataset from Sephora is a comprehensive collection of data related to a wide variety of beauty and personal care products available on Sephora's platform. This dataset contains essential information such as product names, categories, prices, ratings, customer reviews, brand details, ingredients, and more. It is an invaluable resource for data scientists, researchers, and businesses looking to analyze trends, customer preferences, and product performance in the beauty industry.

Key Features:

• Product Information: Includes product names, descriptions, categories (e.g., skincare, makeup, haircare), and pricing.
• Brand Details: Information on various beauty brands available at Sephora.
• Customer Ratings and Reviews: Aggregated star ratings and detailed customer reviews for products.
• Ingredients: Lists of ingredients for skincare, makeup, and haircare products, where applicable.
• Availability: Stock status and availability of products in different regions.

Use Cases:

• Sentiment Analysis: Analyze customer reviews to determine product reception and trends in customer sentiment.
• Market Research: Gain insights into the most popular beauty products and brands in the personal care industry.
• Product Recommendation Systems: Develop recommendation algorithms based on user reviews, ratings, and preferences.
• Ingredient Analysis: Study ingredients across different beauty products for research into product formulations and trends.

Explore the "Mecca Beauty Products Dataset," a comprehensive collection of data on a wide range of beauty products featured on Mecca, a leading beauty retailer. This dataset is ideal for data analysts, e-commerce professionals, market researchers, and beauty industry experts looking to analyze product trends, conduct pricing analysis, and gain valuable insights into the beauty market.

🔗 For additional beauty product datasets, including real-time product feeds, ingredient breakdowns, and enriched metadata across top beauty retailers, visit BeautyFeeds.io.

What’s Included:

1. Product Details: Detailed information on various beauty products, including product names, categories, and brands. This data is essential for analyzing popular products and understanding market demand.
2. Pricing Information: Comprehensive pricing data for each product, crucial for developing competitive pricing strategies and performing in-depth pricing analysis to stay ahead in the market.
3. Product Descriptions: In-depth descriptions of each product, providing insights into features and benefits. This information can aid in content creation, marketing strategies, and identifying trends in skincare and cosmetics.
4. Stock Availability: Updated information on product availability, useful for inventory management and understanding consumer purchasing behavior.
5. CSV Format: User-friendly CSV format for seamless integration into various data analysis tools, making it accessible for professionals in data science, market research, and e-commerce.

Applications:

1. Market Trend Analysis: Understand beauty market trends by analyzing product categories, brand popularity, and pricing strategies. This dataset helps identify emerging trends and consumer preferences in skincare, cosmetics, and beauty products.
2. Competitive Analysis: Compare product offerings and pricing from Mecca with other beauty retailers to understand the competitive landscape and optimize market positioning.
3. Product Development: Utilize detailed product descriptions and categories to identify gaps in the market and innovate new products that cater to consumer demand.
4. Inventory Optimization: Use stock availability data to enhance inventory management strategies, ensuring that high-demand beauty products are always in stock and readily available to consumers. For more details and to access the dataset, visit Crawlfeeds - Mecca Beauty Products Data.

Why Use This Dataset?

The "Mecca Beauty Products Dataset" provides valuable insights for professionals looking to deepen their understanding of the beauty industry. Whether you're conducting a market analysis, refining a pricing strategy, or exploring new product development opportunities, this dataset offers the comprehensive data you need. Download the CSV file today from Crawlfeeds to start analyzing and uncovering trends in the beauty market!

Unlock in-depth beauty industry insights with our Ulta Beauty Dataset, featuring 33,000 records in CSV format. This beauty cosmetics dataset provides detailed product listings, pricing, ratings, reviews, brand information, and availability, making it a powerful resource for businesses, analysts, and AI developers in the cosmetics and skincare industry.

With structured and up-to-date data, brands can analyze consumer trends, competitor pricing, product performance, and customer sentiment to optimize their marketing strategies and product offerings. Ideal for AI/ML model training, recommendation engines, price tracking, and trend forecasting, this dataset helps you stay ahead in the dynamic beauty sector.

📩 Get in touch today to access this exclusive Ulta dataset and elevate your data-driven decision-making!

The revenue ranking in the beauty & personal care market is led by the United States with 101.29 billion U.S. dollars, while China is following with 70.57 billion U.S. dollars. In contrast, Egypt is at the bottom of the ranking with 7.06 billion U.S. dollars, showing a difference of 94.23 billion U.S. dollars to the United States. Find other insights concerning similar markets and segments, such as a ranking by country regarding revenue in the hair care segment of the beauty & personal care market and a ranking of subsegments in Asia regarding revenue in the Beauty & Personal Care market as a whole. The Statista Market Insights cover a broad range of additional markets.

Description:

Ultra Beauty Products Dataset offers detailed insights into Ulta Beauty's wide range of beauty products. This dataset includes product URLs, titles, SKUs, high-quality images, pricing, availability, and customer reviews, making it an essential resource for e-commerce analytics, competitor research, and digital marketing.

Leverage this dataset to optimize product listings, analyze market trends, and enhance customer engagement.

For access to more beauty and cosmetics datasets, explore the Beauty and Cosmetics Data Collection and power your data-driven strategies today.

Fields:

• url: Product URL on the Ultra Beauty website.
• title: Name of the beauty product.
• sku: Stock Keeping Unit identifier.
• productID: Unique identifier for each product.
• main_image: URL of the main product image.
• price: Retail price of the product.
• currency: Currency used for the price (e.g., USD).
• product_variant: Variant of the product (e.g., size, color).
• summary: Short description or summary of the product.
• raw_summary: Raw, unprocessed summary text of the product.
• availability: Availability status (e.g., in stock, out of stock).
• primary_category: Main category of the product.
• category1: Primary sub-category.
• category2: Secondary sub-category (if applicable).
• breads: Breadth of product categories (if applicable).
• images: Additional product images (URLs).
• description: Detailed description of the product.
• ingredients: Ingredients used in the product.
• raw_ingredients: Raw, unprocessed ingredients list.
• details: Additional product details.
• raw_details: Raw, unprocessed details.
• how_to_use: Instructions on how to use the product.
• raw_how_to_use: Raw, unprocessed usage instructions.
• avg_rating: Average customer rating (out of 5 stars).
• review_count: Number of customer reviews.
• reviews: Customer reviews (text).
• uniq_id: Unique identifier for the record.
• scraped_at: Timestamp when the data was scraped.
  

Source:

The dataset is sourced from Ultra Beauty's product catalog.

Usage:

Ideal for analyzing product trends, pricing strategies, customer preferences, and market research in the beauty industry.

This dataset provides comprehensive information extracted from Dermstore, featuring over 100 skincare products with a focus on ingredients. It is ideal for researchers, data scientists, and beauty enthusiasts looking to analyze skincare ingredients, assess product compositions, and gain insights into trends in the beauty and skincare industry. From serums and moisturizers to cleansers and sunscreens, this dataset covers a wide range of products, each detailed with its ingredients—making it a valuable asset for ingredient analysis, product comparison, and skincare recommendation systems.

🔗 For more structured beauty product data, including enriched skincare datasets, ingredients, product reviews, and real-time updates from global retailers, check out BeautyFeeds.io.

Dataset Highlights:

• Product Information: Each entry includes essential details such as product name, brand, category, and target skincare concerns.
• Ingredients List: One of the most comprehensive features of this dataset is its detailed ingredients list for each product, ideal for ingredient trend analysis, research on active components, and identifying commonly used skincare compounds.
• Skin Concerns: For each product, the dataset includes specific skin concerns it addresses (e.g., acne, aging, dryness), allowing users to analyze which ingredients are frequently associated with particular skin conditions.
• Ratings and Reviews: Some records may include user ratings, providing insights into consumer preferences and product effectiveness based on real user feedback.

Potential Use Cases:

• Ingredient Trend Analysis: Explore which ingredients are most frequently used across different skincare categories and how they correlate with skin concerns like aging or acne.
• Predictive Modeling: Build models to predict product effectiveness or consumer satisfaction based on ingredient lists and skincare concerns.
• Product Comparison: Conduct comparative analyses to understand the differences between high-rated and low-rated products, focusing on their ingredient composition.
• Skincare Recommendation System: Develop recommendation algorithms that suggest products based on specific skin types, concerns, and preferred ingredients.
• Natural vs. Synthetic Analysis: Examine the ratio of natural to synthetic ingredients, gaining insights into consumer trends and preferences for natural skincare products.

Introduction Dive into the most extensive Makeup and Beauty Product Reviews Dataset, featuring over 1 million records. This dataset is an invaluable resource for marketers, data analysts, and researchers seeking to explore consumer insights and product performance in the beauty industry.

Key Features of the Dataset Our dataset includes a rich array of data points, allowing for detailed analysis and understanding of beauty products. Key attributes tracked include:

• Name: The name of the beauty product for easy identification.
• Description: Detailed product descriptions that provide context and usage information.
• Rating: User-generated ratings that quantify customer satisfaction and product quality.
• Likes Count: The number of likes each review has received, indicating popular opinions among users.
• Verified Purchase: A flag indicating whether the review is from a verified purchase, enhancing the credibility of the data.
• Images: Visual representations of the products, adding an extra layer of detail.
• Reviewed At: The date each review was submitted, allowing for trend analysis over time.
• _ID: Unique identifier for each record, ensuring easy data management.
• Crawled At: Timestamp of when the data was crawled, helping users understand the freshness of the data.

Why Choose Our Makeup and Beauty Product Reviews Dataset?

1. In-Depth Market Analysis: With over 1 million records, this dataset provides a comprehensive view of consumer sentiments and preferences in the beauty sector.
2. Trend Identification: Analyze the evolution of beauty trends and product popularity over time using the reviewed_at data point.
3. Enhanced Product Development: Leverage insights from customer feedback to inform product design and marketing strategies.
4. Data-Driven Decisions: Empower your business with actionable insights derived from real consumer experiences and ratings.

How to Access the Dataset Our dataset is available in CSV format, making it user-friendly for data manipulation and analysis. Easily import it into your preferred analytics tools to begin uncovering valuable insights.

Conclusion Don’t miss the opportunity to harness the power of our extensive Makeup and Beauty Product Reviews Dataset. Gain a deeper understanding of consumer behavior and enhance your strategic initiatives in the beauty industry.

Looking for a free dataset of cosmetic products? The Sephora Makeup Products Sample Dataset provides a ready-to-use CSV of beauty product data containing 340 verified Sephora makeup product records. It includes details like product name, brand, price, ingredients, availability, user reviews count, and images - perfect for e-commerce research, market analysis, price tracking, or building machine-learning and recommendation systems for the beauty industry.

Key Features

• Complete Product Metadata: Each record includes URL, product name, brand, price, SKU, ingredients, product description, usage instructions, review count, image links, availability status, and more.
• CSV Format: Ready to Use: Download instantly without any scraping or data cleaning required.
• Ideal for Beauty-Tech & ML Projects: Useful for price comparison tools, recommendation engines, product cataloging, trend analysis, sentiment analysis based on reviews/ratings.
• Free Sample Access: This sample comes at zero cost (USD $0.0) — an excellent starting point for analysts, developers, or researchers.

This dataset is perfect for market research, price tracking, sentiment analysis, and AI-based recommendation systems. Whether you're an e-commerce retailer, a data analyst, or a machine learning professional, this dataset provides valuable insights into the beauty industry.

Explore the Beauty and Cosmetics Data Collection and elevate your data-driven strategies today!

Who Can Use This Dataset?

• E-commerce analysts/retailers analyzing cosmetic product catalogs and pricing.
• Data scientists / ML engineers building recommendation engines or product-based machine-learning models.
• Market researchers & beauty industry analysts tracking brand/product trends, availability, and consumer preferences.
• Students/hobby developers exploring beauty-tech projects, demo analyses, or building portfolios with real-world data.

Why This Sephora Dataset?

• Skip the hassle: no need for manual scraping or dealing with anti-scraping restrictions.
• Clean, structured data - ready for immediate integration with tools or pipelines.
• Free and accessible: great for testing, proof-of-concept or small-scale analysis.
• Beauty industry focus: concentrated on makeup and cosmetics products - ideal for niche analyses or applications.

Here are a few use cases for this project:

1. Dermatology Health Applications: This computer vision model could be used to build dermatology tools or applications. Professionals could use it to detect, identify, and track skin conditions or diseases (like melanoma or eczema) based on changes in the pattern or texture of hair distribution on human skin over time.

2. Hair Growth Research: The model could be beneficial in the research field associated with hair growth and hair health. It could support analysis on various factors like age, health, products, influencing hair growth and development.

3. Beauty and Cosmetic Industry: This model could find application in the beauty and cosmetics industry, where it can be used in the creation of personalized hair care products. It could analyze customer's hair characteristics and patterns to recommend specific product formulas.

4. Forensics: In the crime investigation process, the model could be useful to analyze hair patterns or characteristics in evidence for identification purposes.

5. Animal Studies: This model could also be beneficial in animal studies, where it can help differentiate animal species simply by analyzing fur patterns or fur health.

1) Data Introduction • The Hair Type Dataset is a computer vision image classification dataset designed to categorize various real-world hair types. It consists of five classes: Straight, Wavy, Curly, Kinky, and Dreadlocks, representing different hair textures and styles across diverse individuals.

2) Data Utilization (1) Characteristics of the Hair Type Dataset: • The dataset is composed of real human portraits, realistically reflecting a wide range of hair textures and shapes found in everyday contexts.

(2) Applications of the Hair Type Dataset: • Development of hair type recognition models: Can be used to train deep learning models that automatically identify and classify various hair types using computer vision. • Beauty and fashion industry applications: Useful for building AI-powered beauty solutions such as personalized hair recommendation systems and virtual hairstyling applications.

Other-Long-Term-Assets Time Series for Shenzhen Beauty Star Co Ltd. Shenzhen Leaguer Co., Ltd. engages in the design, manufacturing, and service provision of plastic packaging solutions for cosmetics, daily necessities, health products, and food in China and internationally. It provides services such as, transformation of results, investment incubation, industrial services, talent cultivation, innovative services, international cooperation, events, science and technology, industry new materials, and technology industry/digital economy. It also offers plastic packaging solutions for cosmetics, health products, and food; advanced building/facility digital solutions and digital governance solutions for the construction industry for new smart cities. The company was formerly known as Shenzhen Beauty Star Co., Ltd. and changed its name to Shenzhen Leaguer Co., Ltd. in January 2021. Shenzhen Leaguer Co., Ltd. was founded in 1995 is based in Shenzhen, China.

This statistic shows the brand value of the leading 10 cosmetic brands worldwide in 2024. In that year, L'Oréal was at the top of the list, with a brand value of about 13.4 billion U.S dollars. Cosmetics worldwide Since the early twentieth century, the production of cosmetics has been controlled by a handful of multi-national corporations. The global cosmetics industry is broken down into six main categories; skin care being the largest one out of them all, accounting for 41 percent of the global market in 2022. United States: a lucrative market In recent years, consumers have been spending higher levels of disposable income on cosmetics than they had in the past. Unfortunately, the global financial crisis has put a damper on the market and during those years, more affordably priced merchandise and do it yourself at home products were key in the beauty market. However, with Generation Z entering the job market, there is now a a big driver of the cosmetics market; especially in the United States. The United States is the biggest cosmetic market in the world, with an estimated total revenue of about 49 billion U.S. dollars as of 2022. A look to the future In the coming years, in order to attract new consumers, global cosmetic companies will continue to focus their efforts on product innovation and especially, on responding to the needs of consumers, such as sustainable products that do not harm the environment or animals, as well as inclusive products, so that cosmetics and beauty can be accessible to anyone.

The Ingredients Dataset (18K+ records) provides a high-quality, structured collection of product information with detailed ingredients data. Covering a wide variety of categories including beauty, pet care, groceries, and health products, this dataset is designed to power AI, NLP, and machine learning applications that require domain-specific knowledge of consumer products.

Why This Dataset Matters

In today’s data-driven economy, access to structured and clean datasets is critical for building intelligent systems. For industries like healthcare, beauty, food-tech, and retail, the ability to analyze product ingredients enables deeper insights, including:

• Identifying allergens or harmful substances

• Comparing ingredient similarities across brands

• Training LLMs and NLP models for better understanding of consumer products

• Supporting regulatory compliance and labeling standards

• Enhancing recommendation engines for personalized shopping

This dataset bridges the gap between raw, unstructured product data and actionable information by providing well-organized CSV files with fields that are easy to integrate into your workflows.

Dataset Coverage

The 18,000+ product records span several consumer categories:

• 🛍 Beauty & Personal Care – cosmetics, skincare, haircare products with full ingredient transparency

• 🐾 Pet Supplies – pet food and wellness products with detailed formulations

• 🥫 Groceries & Packaged Foods – snacks, beverages, pantry staples with structured ingredients lists

• 💊 Health & Wellness – supplements, vitamins, and healthcare products with nutritional components

By including multiple categories, this dataset allows cross-domain analysis and model training that reflects real-world product diversity.

Key Features

• 📂 18,000+ records with structured ingredient fields

• 🧾 Covers beauty, pet care, groceries, and health products

• 📊 Delivered in CSV format, ready to use for analytics or machine learning

• 🏷 Includes categories and breadcrumbs for taxonomy and classification

• 🔎 Useful for AI, NLP, LLM fine-tuning, allergen detection, and product recommendation systems

Use Cases

1. AI & NLP Training – fine-tune LLMs on structured ingredients data for food, beauty, and healthcare applications.

2. Retail Analytics – analyze consumer product composition across categories to inform pricing, positioning, and product launches.

3. Food & Health Research – detect allergens, evaluate ingredient safety, and study nutritional compositions.

4. Recommendation Engines – build smarter product recommendation systems for e-commerce platforms.

5. Regulatory & Compliance Tools – ensure products meet industry and government standards through ingredient validation.

Why Choose This Dataset

Unlike generic product feeds, this dataset emphasizes ingredient transparency across multiple categories. With 18K+ records, it strikes a balance between being comprehensive and affordable, making it suitable for startups, researchers, and enterprise teams looking to experiment with product intelligence.

Note: Each record includes a url (main page) and a buy_url (purchase page). Records are based on the buy_url to ensure unique, product-level data.

The United States is leading the ranking by revenue in the 'Skin Care' segment of the beauty & personal care market, recording 24.9 billion U.S. dollars. Following closely behind is Japan with 22.9 billion U.S. dollars, while Ethiopia is trailing the ranking with 1.5 billion U.S. dollars, resulting in a difference of 23.4 billion U.S. dollars to the ranking leader, the United States. Find other insights concerning similar markets and segments, such as a ranking by country regarding revenue in the cosmetics segment of the beauty & personal care market and a ranking by country regarding revenue in the skin care segment of the beauty & personal care market. The Statista Market Insights cover a broad range of additional markets.

This dataset provides a simulated overview of the most used beauty and cosmetics products across the world, offering insights into global trends, consumer preferences, and popular brands. Designed for market analysis, trend predictions, and recommendation system development, this dataset is a valuable resource for researchers, marketers, and beauty enthusiasts.

🔥 What’s Inside? ✔️ Wide Product Coverage – Includes skincare, makeup, haircare, fragrances, and personal care categories. ✔️ Pricing & Ratings – Simulated product prices and user ratings help in trend analysis and market research. ✔️ User Behavior Insights – Modeled usage frequency (daily, weekly, monthly) to understand product engagement. ✔️ Ideal for Data Science & AI – Perfect for building recommendation systems, sentiment analysis, and trend forecasting.

📊 Dataset Overview: This dataset has been synthetically generated and does not contain real-world product data. It is crafted for educational and analytical purposes to support studies in consumer behavior and market dynamics within the beauty industry.

💅 Categories Covered: Skincare: Moisturizers, sunscreens, cleansers, serums Makeup: Lipsticks, foundations, eyeliners, mascaras Haircare: Shampoos, conditioners, hair treatments Fragrances: Perfumes, body sprays, colognes Personal Care: Deodorants, body lotions, bath products ✨ Key Features: Product Details: Names, brands, and categories of beauty products Pricing: Simulated product price in USD User Ratings: Ratings out of 5 stars, reflecting customer satisfaction Reviews: Modeled number of reviews, indicating product popularity Usage Frequency: Indicates how often a product is used (daily, weekly, monthly) 🔍 Potential Use Cases: ✅ Market research on beauty and cosmetics trends ✅ Developing recommendation systems for beauty products ✅ Analyzing brand popularity and customer feedback patterns ✅ Creating targeted marketing strategies based on product trends

⚠️ Disclaimer: This dataset is 100% synthetic and is not based on real-world beauty industry data. It is intended for learning, research, and data analysis practice only.

💖 Use this dataset to explore global beauty trends, customer preferences, and product popularity in an engaging and insightful way! 💅📊✨

Concerning the five selected segments, the segment Face has the largest revenue with 7.67 billion U.S. dollars. Contrastingly, Nails is ranked last, with 1.72 billion U.S. dollars. Their difference, compared to Face, lies at 5.95 billion U.S. dollars. Find other insights concerning similar markets and segments, such as a ranking of subsegments in Australia regarding revenue in the Beauty & Personal Care market as a whole and a ranking of subsegments in the Philippines regarding revenue in the segment Personal Care . The Statista Market Insights cover a broad range of additional markets.

This statistic depicts the brand value of the leading personal care brands worldwide in 2024. In that year, the value of the Clinique brand amounted to about 6.7 billion U.S. dollars. The personal care industry worldwide The personal care industry envelopes a variety of products, including makeup, fragrances, hair care and coloring products, sunscreen, toothpaste, and products for bathing, nail care, and shaving. These segments are complementary and through their diversity they are able to satisfy all consumers’ needs and expectations with regard to cosmetics. The industry overlaps with other markets like chemical, health care, and petroleum. The latter is important as personal care product raw materials, such as propylene glycol, come from petroleum products. Leading players Some of the leading personal care companies in the world include L’Oréal, Procter & Gamble, Unilever, Colgate-Palmolive and Beiersdorf. Over the coming years companies will continue to invest in eco-friendly practices. Manufacturers will also respond to demand for more natural products, widening their natural and herbal product lines. Marketing efforts will likely be increasingly geared to stress environmental protection through non-toxic ingredients.

This statistic shows the net revenue of Coty Inc. worldwide from fiscal year 2012 to 2024. In the 2024 fiscal year, Coty Inc. generated approximately six billion U.S. dollars in net revenue, a slight increase from approximately 5.6 billion U.S. dollars the previous year.

In 2024, Estée Lauder generated a gross profit of about 11.18 billion U.S. dollars, an decrease in comparison with the previous year. Since 2011, the company's gross profit doubled, peaking in 2022. The fiscal year end of the company is June 30th, 2024.