Massive eBay Marketplace Data Collection for E-commerce Intelligence

Unlock the power of online marketplace analytics with our comprehensive eBay products dataset. This premium collection contains 1.29 million products from eBay's global marketplace, providing extensive insights into one of the world's largest e-commerce platforms. Perfect for competitive analysis, pricing strategies, market research, and machine learning applications in e-commerce.

Dataset Overview

• Total Products: 1,290,000+ marketplace listings
• Source: eBay Global Marketplace
• Format: CSV, ZIP compressed
• File Size: Optimized compressed format
• Coverage: Multi-category product listings across eBay

Complete Data Fields Included

Product Identification

• id: Unique eBay product identifiers
• name: Complete product titles and names
• url: Direct eBay listing page links
• epid: eBay Product ID for catalog matching
• source: Data source identification

Product Details

• raw_product_description: Original unprocessed product descriptions
• product_description: Cleaned and formatted product descriptions
• brand: Brand names and manufacturer information
• mpn: Manufacturer Part Numbers
• gtin13: Global Trade Item Numbers (barcodes)

Pricing and Availability

• price: Current listing prices
• currency: Currency information for international listings
• in_stock: Stock availability status
• breadcrumbs: Category navigation paths

Visual and Technical Data

• images: Product image URLs and references
• crawled_at: Data collection timestamps

Key Use Cases

E-commerce Market Research

• Analyze eBay marketplace trends and patterns
• Study product category performance
• Monitor pricing strategies across sellers
• Identify high-demand product categories

Competitive Intelligence

• Benchmark pricing against eBay marketplace
• Analyze product positioning strategies
• Study seller competition and market share
• Monitor inventory levels and availability

Price Optimization

• Develop dynamic pricing algorithms
• Analyze price elasticity across categories
• Compare marketplace pricing trends
• Optimize listing prices for maximum visibility

Machine Learning Applications

• Train recommendation systems
• Develop price prediction models
• Create product categorization algorithms
• Build inventory forecasting systems

Target Industries

E-commerce Retailers

• Marketplace Strategy: Optimize eBay selling strategies
• Pricing Intelligence: Competitive price monitoring
• Product Research: Identify profitable product opportunities
• Inventory Planning: Demand forecasting and stock optimization

Technology Companies

• AI Training Data: Machine learning model development
• Analytics Platforms: E-commerce intelligence tools
• Price Comparison: Marketplace comparison services
• Search Enhancement: Product discovery optimization

Market Research Firms

• Industry Reports: E-commerce marketplace analysis
• Consumer Behavior: Online shopping pattern studies
• Brand Monitoring: Brand performance tracking
• Trend Analysis: Market trend identification

Academic Research

• E-commerce Studies: Online marketplace research
• Business Intelligence: Retail analytics case studies
• Data Science Projects: Large-scale dataset analysis
• Economic Research: Digital marketplace economics

Data Quality Features

• Comprehensive Coverage: 1.29 million unique products
• Rich Metadata: Complete product information included
• Validated Data: Quality-checked and processed
• Structured Format: Ready-to-use CSV format
• Global Scope: International marketplace coverage

Dataset Overview: This dataset compares three major e-commerce companies: Amazon, Alibaba, and eBay, over the decade 2010-2020. It provides insights into sales trends, product availability, user engagement, and other performance metrics. Researchers, analysts, and data enthusiasts can use this dataset for time-series analysis, market comparison, and predictive modeling.

Context: E-commerce has witnessed exponential growth, especially during the last decade. Amazon, Alibaba, and eBay have been key players in shaping the online retail landscape. This dataset serves as a resource to explore how these companies performed in various domains, such as revenue, user base, and product offerings.

Data Description: The dataset contains 12 columns and 6 rows (3 companies across 2 time points: 2010 and 2020).

Column Name Description Year The year the data refers to (2010 or 2020). Company The name of the company (Amazon, Alibaba, or eBay). Total_Sales (USD Billion) Total annual sales/revenue in billion USD. Number_of_Products (Million) The total number of products listed on the platform in millions. Active_Users (Million) Number of active users on the platform (in millions). Market_Share (%) Percentage of the global e-commerce market held by the company. Gross_Margin (%) Gross margin as a percentage of revenue. Operating_Income (%) Operating income as a percentage of revenue. Region_with_Highest_Sales The geographic region where the company had the highest sales (e.g., North America, Asia, Europe). Average_Order_Value (USD) Average monetary value of an order placed on the platform (in USD). Mobile_Transactions (%) Percentage of transactions completed via mobile devices. Number_of_Sellers (Million) Total number of sellers active on the platform (in millions).

Key Insights: Amazon dominates in total sales and market share, with steady growth in the user base and product offerings. Alibaba leads in mobile transactions and seller count, reflecting its focus on mobile-first markets like Asia. eBay maintains strong gross margins but lags in user growth compared to Amazon and Alibaba.

**Potential Uses: **Trend Analysis: Understand how the e-commerce industry evolved during 2010-2020. Market Insights: Compare the performance of Amazon, Alibaba, and eBay. Predictive Modeling: Forecast future trends in e-commerce using regression or machine learning. Visualization: Create graphs and dashboards showcasing the metrics over time.

The B2C E-commerce Market size was valued at USD 6.23 trillion in 2023 and is projected to reach USD 21.18 trillion by 2032, exhibiting a CAGR of 19.1 % during the forecasts period. Recent developments include: In March 2024, Blink, an Amazon company, launched the Blink Mini 2 camera. The new compact plug-in camera offers enhanced features such as person detection, a broader field of view, a built-in LED spotlight for night view in color, and improved image quality. The Blink Mini 2 is designed to work indoors and outdoors, with the option to purchase the Blink Weather Resistant Power Adapter for outdoor use. , In October 2023, Flipkart.com introduced the 'Flipkart Commerce Cloud,' a customized suite of AI-driven retail technology solutions for global retailers and e-commerce businesses. This extensive offering includes marketplace technology, retail media solutions, pricing, and inventory management features rigorously assessed by Flipkart.com. The company aims to equip international sellers with reliable and secure tools to enhance business expansion and efficiency within the competitive global market. , In August 2023, Shopify and Amazon.com, Inc. announced a strategic partnership that will allow Shopify merchants to seamlessly implement Amazon's "Buy with Prime" option on their sites. As a result of the agreement, Amazon.com, Inc. Prime customers will enjoy a more efficient checkout process on various platforms. This collaboration allows Amazon Prime members to utilize their existing Amazon payment options, while Shopify will handle the transaction processing through its system, showcasing a partnership between the two leading companies. , In February 2023, eBay acquired 3PM Shield, a developer of AI-powered online retail solutions. 3PM Shield uses machine learning and artificial intelligence to analyze extensive data sets, enhancing marketplace compliance and user experience. This acquisition aligns with eBay's goal to offer a "safe and reliable" platform by boosting its ability to block the sale of counterfeit and prohibited items. By incorporating 3PM Shield's sophisticated monitoring technologies, eBay seeks to enhance its capability to address problematic seller behavior and spot problematic listings, fostering a safer e-commerce space for its worldwide community of sellers and buyers. .

Dataset Overview

The Perfume E-Commerce Dataset 2024 comprises detailed information on 2000 perfume listings sourced from eBay, split into two separate CSV files for men's and women's perfumes, each containing 1000 entries. This dataset provides a comprehensive view of the current market trends, pricing, availability, and geographical distribution of perfumes in the e-commerce space.

Data Science Applications

This dataset offers numerous opportunities for data science applications, including but not limited to:

1. Market Analysis: Understand market trends and consumer preferences by analyzing the pricing and availability of different perfume brands and types.
2. Sales Prediction: Develop models to predict future sales based on historical data, aiding in inventory management and sales strategy optimization.
3. Customer Insights: Gain insights into customer preferences and behaviors by examining the data on sold quantities and item locations.
4. Price Optimization: Analyze price distributions and identify optimal pricing strategies to maximize revenue.
5. Geographical Distribution: Study the geographical distribution of listings to understand regional market dynamics and demand variations.

Column Descriptors

• brand: The brand of the perfume.
• title: The title of the listing.
• type: The type of perfume (e.g., Eau de Parfum, Eau de Toilette).
• price: The price of the perfume.
• priceWithCurrency: The price with currency notation.
• available: The number of items available.
• availableText: Text description of availability.
• sold: The number of items sold.
• lastUpdated: The last updated timestamp of the listing.
• itemLocation: The location of the item.

Ethically Mined Data

This dataset has been ethically mined from eBay using the Apify API, ensuring compliance with ethical data collection practices. The data is intended solely for educational and learning purposes. Users are encouraged to use the dataset responsibly and in accordance with applicable data usage policies.

Acknowledgements

• Apify: For providing the API used to scrape eBay data.
• DALL·E 3: For generating the thumbnail image used for this dataset.

This dataset is a valuable resource for students, educators, and data science enthusiasts looking to explore and analyze e-commerce trends in the perfume industry.