Beauty Brand Performance & Retailer Coverage Dataset

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Description

Unlock comprehensive insights into the Beauty Retail sector with this extensive dataset, featuring 26,624 records on brand performance and retailer coverage. Sourced from major online beauty retailers and e-commerce marketplaces, this data provides a vital pulse on the competitive landscape. Each record delivers critical metrics, including

brand_name

,

product_count

,

avg_price

,

avg_discount_pct

,

avg_rating

,

total_reviews

, and a detailed breakdown of

retailer_count

,

retailers

, and

categories

. Analyze market share, benchmark competitor pricing strategies, identify top-performing products, optimize inventory, and discover emerging Beauty Retail trends to sharpen your strategic decisions. This dataset empowers beauty brands, market researchers, and retailers to assess online visibility, understand product-level performance across channels, and refine go-to-market strategies. Gain a competitive edge with fresh, continuously updated data reflecting current market dynamics and extensive product coverage across global markets.

Highlights

  • Covers 26,624 brand performance records across beauty retail.

  • Extensive coverage across multiple countries and beauty categories.

  • Regularly updated data ensures current brand performance insights.

  • Includes key metrics like average discount and best-seller status.

Sample Data

Preview of available data:

Version Avg Price Countries Last Seen Max Price Min Price Retailers Avg Rating Brand Name Categories First Seen Imported At Country Count Product Count Total Reviews Category Count In Stock Count Retailer Count Avg Discount Pct Best Seller Count Out Of Stock Count Size Variety Count Asin Coverage Count Gtin Coverage Count Total Product Count Avg Reviews Per Product
1788229616067 25.83 us 22/08/26 13:15 70 9.99 amazon.com 4.65 #OOTD Beauty & Personal Care 11/10/25 22:13 2026-09-01 02:26:56 1 5 917 1 4 1 16 0 1 1 5 0 396530 183.4
1788229616067 23.57 us 11/10/25 22:13 23.57 23.57 amazon.com 4.6 & Honey Beauty & Personal Care 11/10/25 22:13 2026-09-01 02:26:56 1 1 494 1 1 1 \N 0 0 0 1 0 396530 494
1788229616067 1848.67 in 10/08/26 15:41 2698 1424 nykaa.com 4.67 &Done Hair 04/08/26 00:17 2026-09-01 02:26:56 1 3 279 1 3 1 6.67 0 0 3 0 0 396530 93
1788229616067 18.65 uk, us 07/08/26 00:26 29.16 0 iherb.com 4.64 &honey Bath & Personal Care, Beauty 08/01/26 19:53 2026-09-01 02:26:56 2 30 2168 2 18 1 \N 0 12 8 0 30 396530 72.3
1788229616067 24 us 27/08/26 11:23 24 24 amazon.com 2.9 \'\'BULGARIAN ROSE\'\' KARLOVO Beauty & Personal Care 27/08/26 11:23 2026-09-01 02:26:56 1 1 2 1 1 1 \N 0 0 0 1 0 396530 2
1788229616067 38.75 us 27/08/26 11:01 48 29 amazon.com 4.2 (be)fragil Beauty & Personal Care 15/08/26 07:49 2026-09-01 02:26:56 1 4 125 1 4 1 \N 0 0 1 4 0 396530 31.3
1788229616067 111 jp 05/06/25 06:57 111 111 amazon.co.jp \N (株)IMA Health & Personal Care 05/06/25 06:57 2026-09-01 02:26:56 1 2 \N 1 2 1 \N 0 0 0 2 0 396530 \N
1788229616067 1355.5 jp 05/06/25 06:57 1464 1247 amazon.co.jp 3.75 * Health & Personal Care 05/06/25 06:57 2026-09-01 02:26:56 1 2 36 1 2 1 \N 0 0 0 2 0 396530 18
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Data Fields

This dataset includes the following data points:

Brand Name
Product Count
Avg Price
Min Price
Max Price
Avg Discount Pct
Avg Rating
Total Reviews
Avg Reviews Per Product
Retailer Count
Retailers
Country Count
Countries
Category Count
Categories
In Stock Count
Out Of Stock Count
Best Seller Count
Gtin Coverage Count
Asin Coverage Count
Size Variety Count
First Seen
Last Seen
Total Product Count
Version
Imported At

Why This Data

This beauty retail dataset from provides comprehensive market intelligence and competitive insights. Perfect for:

  • Market Research: Understand market trends and customer preferences
  • Competitive Analysis: Compare pricing, products, and strategies
  • Business Intelligence: Make data-driven decisions
  • Price Monitoring: Track price changes and optimize your pricing

Use Cases

This dataset is perfect for various applications:

Competitive Pricing & Discount Analysis: E-commerce managers analyze competitor brand pricing, average discount percentages, and minimum/maximum price points across various retailers to strategically adjust their own product pricing and promotional campaigns.

Market Trend & Brand Performance Identification: Market researchers identify top-performing beauty brands and popular product categories by examining average ratings, total reviews, and best-seller counts to uncover emerging trends and consumer preferences.

Product Assortment & Inventory Optimization: Retail buyers and merchandisers leverage insights into brand performance, in-stock counts, and retailer coverage to refine product assortments, negotiate with suppliers, and optimize inventory levels for high-demand beauty items.

Content Strategy & Topic Discovery: Content marketers and SEO specialists discover trending beauty brands and categories with high customer engagement (total reviews, average rating) to inform blog posts, buying guides, and other search-engine-optimized content.

Brand Expansion & Retailer Coverage Assessment: Business development teams evaluate potential market white spaces and strategic partnerships by assessing target brand presence, retailer count, country coverage, and total product offerings.

Get Access to This Dataset

Start using this dataset today. Available in CSV, JSON, and Excel formats with flexible access options.

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Frequently Asked Questions

The Beauty Brand Performance dataset includes comprehensive information across 26,624 records, featuring data points such as `brand_name`, `product_count`, `avg_price`, `avg_rating`, and `total_reviews`. It also details market presence through `retailer_count`, `country_count`, `category_count`, and inventory status via `in_stock_count` and `out_of_stock_count`.

`avg_price` represents the average selling price of all products associated with a specific brand, providing insight into its pricing strategy. `avg_discount_pct` indicates the average discount percentage offered across a brand's product range, useful for competitive analysis. Additionally, `total_reviews` and `avg_reviews_per_product` measure overall customer feedback and engagement levels.

The dataset includes `first_seen` and `last_seen` timestamps, along with an `_imported_at` field, which reflect the data's freshness for each record. These timestamps allow users to understand when specific brand and product information was first observed and most recently updated, ensuring timely insights.

While specific formats are not detailed, datasets of this nature are typically available in common, machine-readable formats such as CSV or JSON, suitable for various analytical tools. Delivery options often include secure direct downloads or API access, allowing for flexible integration into existing data infrastructures.

Beauty brands, market researchers, and competitive intelligence firms benefit significantly by using this dataset to analyze market share, pricing trends, and product performance. Users can build competitive benchmarking reports, identify expansion opportunities based on `category_count` and `retailer_count`, and optimize their go-to-market strategies effectively.

Providers of such specialized `Beauty Brands Intelligence` data typically offer options for dataset customization, allowing users to filter by specific `countries`, `categories`, or `retailers` of interest. Comprehensive support for data integration and analysis is also commonly available to help users maximize the dataset's value.

The data is collected through specialized `Beauty Brands Intelligence` methods, meticulously tracking `brand_name` performance and `retailer_count` across various digital touchpoints. Robust quality assurance processes are applied to ensure the accuracy, consistency, and reliability of key metrics such as `avg_price`, `avg_rating`, and `in_stock_count`.