100+ datasets found
  1. R

    Global Ecommerce Store Statistics 2025

    • redstagfulfillment.com
    html
    Updated Jun 20, 2025
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    Red Stag Fulfillment (2025). Global Ecommerce Store Statistics 2025 [Dataset]. https://redstagfulfillment.com/how-many-ecommerce-stores-exist/
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Jun 20, 2025
    Dataset authored and provided by
    Red Stag Fulfillment
    Time period covered
    2019 - 2025
    Area covered
    Worldwide
    Variables measured
    SKU count distribution, Industry vertical breakdown, Year-over-year growth rates, Total ecommerce stores worldwide, Geographic distribution of stores, Platform market share by merchant count
    Measurement technique
    Web crawling and platform API analysis
    Description

    Comprehensive dataset covering worldwide ecommerce store counts, platform market share, geographic distribution, industry verticals, and growth trends from 2019-2025.

  2. Ecommerce Store Data | APAC E-commerce Sector | Verified Business Profiles...

    • datarade.ai
    Updated Jan 1, 2018
    + more versions
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    Success.ai (2018). Ecommerce Store Data | APAC E-commerce Sector | Verified Business Profiles with Key Insights | Best Price Guarantee [Dataset]. https://datarade.ai/data-products/ecommerce-store-data-apac-e-commerce-sector-verified-busi-success-ai
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Jan 1, 2018
    Dataset provided by
    Area covered
    Andorra, Mexico, Malta, Lao People's Democratic Republic, Northern Mariana Islands, Austria, Canada, Fiji, Korea (Democratic People's Republic of), Italy
    Description

    Success.ai’s Ecommerce Store Data for the APAC E-commerce Sector provides a reliable and accurate dataset tailored for businesses aiming to connect with e-commerce professionals and organizations across the Asia-Pacific region. Covering roles and businesses involved in online retail, marketplace management, logistics, and digital commerce, this dataset includes verified business profiles, decision-maker contact details, and actionable insights.

    With access to continuously updated, AI-validated data and over 700 million global profiles, Success.ai ensures your outreach, market analysis, and partnership strategies are effective and data-driven. Backed by our Best Price Guarantee, this solution helps you excel in one of the world’s fastest-growing e-commerce markets.

    Why Choose Success.ai’s Ecommerce Store Data?

    1. Verified Profiles for Precision Engagement

      • Access verified profiles, business locations, employee counts, and decision-maker details for e-commerce businesses across APAC.
      • AI-driven validation ensures 99% accuracy, improving engagement rates and reducing outreach inefficiencies.
    2. Comprehensive Coverage of the APAC E-commerce Sector

      • Includes businesses from major e-commerce hubs such as China, India, Japan, South Korea, Australia, and Southeast Asia.
      • Gain insights into regional e-commerce trends, digital transformation efforts, and logistics innovations.
    3. Continuously Updated Datasets

      • Real-time updates ensure that business profiles, employee roles, and operational insights remain accurate and relevant.
      • Stay aligned with dynamic market conditions and emerging opportunities in the APAC region.
    4. Ethical and Compliant

      • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

    Data Highlights:

    • 700M+ Verified Global Profiles: Access business profiles for e-commerce professionals and organizations across APAC.
    • Firmographic Insights: Gain detailed information, including business locations, employee counts, and operational details.
    • Decision-maker Profiles: Connect with key e-commerce leaders, managers, and strategists driving online retail innovation.
    • Industry Trends: Understand emerging e-commerce trends, consumer behavior, and market dynamics in the APAC region.

    Key Features of the Dataset:

    1. Comprehensive E-commerce Business Profiles

      • Identify and connect with businesses specializing in online retail, marketplace management, and digital commerce logistics.
      • Target decision-makers involved in supply chain optimization, digital marketing, and platform development.
    2. Advanced Filters for Precision Campaigns

      • Filter businesses and professionals by industry focus (fashion, electronics, grocery), geographic location, or employee size.
      • Tailor campaigns to address specific goals, such as promoting technology adoption, enhancing customer engagement, or expanding supply chains.
    3. Regional and Sector-specific Insights

      • Leverage data on APAC’s fast-growing e-commerce markets, consumer purchasing trends, and regional challenges.
      • Refine your marketing strategies and outreach efforts to align with market priorities.
    4. AI-Driven Enrichment

      • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

    Strategic Use Cases:

    1. Marketing Campaigns and Outreach

      • Promote e-commerce solutions, logistics services, or digital commerce tools to businesses and professionals in the APAC region.
      • Use verified contact data for multi-channel outreach, including email, phone, and social media campaigns.
    2. Partnership Development and Vendor Collaboration

      • Build relationships with e-commerce marketplaces, logistics providers, and payment solution companies seeking strategic partnerships.
      • Foster collaborations that drive operational efficiency, enhance customer experiences, or expand market reach.
    3. Market Research and Competitive Analysis

      • Analyze regional e-commerce trends, consumer preferences, and logistics challenges to refine product offerings and business strategies.
      • Benchmark against competitors to identify growth opportunities and high-demand solutions.
    4. Recruitment and Talent Acquisition

      • Target HR professionals and hiring managers in the e-commerce industry recruiting for roles in operations, logistics, and digital marketing.
      • Provide workforce optimization platforms or training solutions tailored to the digital commerce sector.

    Why Choose Success.ai?

    1. Best Price Guarantee

      • Access premium-quality e-commerce store data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.
    2. Seamless Integration

      • Integrate verified e-commerce data into CRM systems, analytics platforms, or market...
  3. h

    Bitext-retail-ecommerce-llm-chatbot-training-dataset

    • huggingface.co
    Updated Aug 6, 2024
    + more versions
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    Bitext (2024). Bitext-retail-ecommerce-llm-chatbot-training-dataset [Dataset]. https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 6, 2024
    Dataset authored and provided by
    Bitext
    License

    https://choosealicense.com/licenses/cdla-sharing-1.0/https://choosealicense.com/licenses/cdla-sharing-1.0/

    Description

    Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants

      Overview
    

    This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.

  4. Linear Regression E-commerce Dataset

    • kaggle.com
    zip
    Updated Sep 16, 2019
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    Saurabh Kolawale (2019). Linear Regression E-commerce Dataset [Dataset]. https://www.kaggle.com/datasets/kolawale/focusing-on-mobile-app-or-website
    Explore at:
    zip(44169 bytes)Available download formats
    Dataset updated
    Sep 16, 2019
    Authors
    Saurabh Kolawale
    Description

    This dataset is having data of customers who buys clothes online. The store offers in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.

    The company is trying to decide whether to focus their efforts on their mobile app experience or their website.

  5. E-Commerce Retail Sales Series Data Collection

    • kaggle.com
    Updated Dec 7, 2019
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    US Census Bureau (2019). E-Commerce Retail Sales Series Data Collection [Dataset]. https://www.kaggle.com/datasets/census/e-commerce-retail-sales-series-data-collection
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 7, 2019
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    US Census Bureau
    Description

    Content

    More details about each file are in the individual file descriptions.

    Context

    This is a dataset from the U.S. Census Bureau hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according the amount of data that is brought in. Explore the U.S. Census Bureau using Kaggle and all of the data sources available through the U.S. Census Bureau organization page!

    • Update Frequency: This dataset is updated daily.

    Acknowledgements

    This dataset is maintained using FRED's API and Kaggle's API.

  6. d

    Buy eCommerce Leads | eCommerce Store Owner Database 2025 | 3M+ Contacts |...

    • datarade.ai
    .csv, .xls
    Updated Feb 20, 2022
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    Lead for Business (2022). Buy eCommerce Leads | eCommerce Store Owner Database 2025 | 3M+ Contacts | Contact Direct Email and Mobile Number [Dataset]. https://datarade.ai/data-products/buy-ecommerce-leads-ecommerce-leads-database-ecommerce-le-lead-for-business
    Explore at:
    .csv, .xlsAvailable download formats
    Dataset updated
    Feb 20, 2022
    Dataset authored and provided by
    Lead for Business
    Area covered
    Kazakhstan, Qatar, Maldives, Jordan, Guernsey, Lithuania, Canada, Finland, Argentina, United States of America
    Description

    • 3M+ Contact Profiles • 5M+ Worldwide eCommerce Brands • Direct Contact Info for Decision Makers • Contact Direct Email and Mobile Number • 15+ eCommerce Platforms • 20+ Data Points • Lifetime Support Until You 100% Satisfied

    Buy eCommerce leads from our eCommerce leads database today. Reach out to eCommerce companies to expand your business. Now is the time to buy eCommerce leads and start running a campaign to attract new customers. We provide current and accurate information that will assist you in achieving your goals.

    Our database is made up of highly valuable and interested leads who are ready to make online purchases. You can always filter our data and choose the database that best meets your needs if you need eCommerce leads based on industry.

    We have millions of eCommerce data ready to go no matter where you are. We’ve acquired hundreds of clients from all over the world over the years and delivered data that they’re happy with.

    We were able to do so by obtaining data from various locations around the world. As a result, our database is widely accessible, and anyone can use it from any location on the planet. Please contact us if you want the best eCommerce leads .

    We sell eCommerce leads that can be filtered by industry. We know what you’re going through and what you’ll need for your next project. As a result, we’ve compiled a list of eCommerce leads that are exactly what you require. With the most potential data we provide, you can grow your business and achieve your business goals. All of our eCommerce leads are generated professionally, with real people – not bots – entering data.

    We’re a leading brand in the industry because we source data from the most well-known platforms, ensuring that the information you receive from us is accurate and reliable. That’s especially true because we verify each and every piece of information in order to provide you with yet another benefit in your life.

    The majority of our customers have had success with the information we’ve provided. That is why they keep contacting us for our services. You can count on our business-to-business eCommerce sales leads. Contact us to work with one of the most effective lead generation companies in the industry, which has already helped thousands of potential members achieve success.

    Every month, we update our eCommerce store sales leads in order to provide our clients with the most accurate data possible. We have a team of professionals who strive for excellence when it comes to gathering the right leads to ensure you get the number of sales you need. Our experts also double-check that all of the sales data we receive is genuine and accurate.

    The accuracy of our eCommerce database is why the majority of our clients choose us. Furthermore, we offer round-the-clock support to provide on-demand solutions. We take care of everything so you can spend less time evaluating our product database and more time becoming one of them.

  7. Zoro Product Data Sample – Structured E-commerce Dataset

    • crawlfeeds.com
    csv, zip
    Updated Jun 27, 2025
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    Crawl Feeds (2025). Zoro Product Data Sample – Structured E-commerce Dataset [Dataset]. https://crawlfeeds.com/datasets/zoro-product-data-sample-structured-e-commerce-dataset
    Explore at:
    zip, csvAvailable download formats
    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

    https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy

    Description

    Zoro.com Product Data Sample – Explore Structured E-commerce Product Listings

    This dataset is a sample extraction of product listings from Zoro.com, a leading industrial supply e-commerce platform. It provides structured product-level data that can be used for market research, price comparison engines, product matching models, and e-commerce analytics.

    The sample includes a variety of products across tools, hardware, safety equipment, and industrial supplies — with clean, structured fields suitable for both analysis and model training.

    Also available: Grainger Product Datasets – structured data from a top industrial supplier.

    Submit your custom data requests via the Zoro products page or contact us directly at contact@crawlfeeds.com.

    Ideal for previewing before requesting larger or full Zoro datasets

    Use Cases:

    • Building product comparison or search engines

    • Price intelligence and competitor monitoring

    • Product classification and attribute extraction

    • Training data for e-commerce AI models

    Want More?

    This is a sample of a much larger dataset extracted from Zoro.com.
    👉 Contact us to access full datasets or request custom category extractions.

  8. Data from: E-commerce and ICT activity

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Feb 5, 2021
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    Office for National Statistics (2021). E-commerce and ICT activity [Dataset]. https://www.ons.gov.uk/businessindustryandtrade/itandinternetindustry/datasets/ictactivityofukbusinessesecommerceandictactivity
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Feb 5, 2021
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Use of information and communication technology (ICT) and e-commerce activity by UK businesses. Annual data on e-commerce sales and how businesses are using the internet.

  9. E-commerce - Users of a French C2C fashion store

    • kaggle.com
    zip
    Updated Mar 17, 2020
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    Jeffrey Mvutu Mabilama (2020). E-commerce - Users of a French C2C fashion store [Dataset]. https://www.kaggle.com/jmmvutu/ecommerce-users-of-a-french-c2c-fashion-store
    Explore at:
    zip(1906187 bytes)Available download formats
    Dataset updated
    Mar 17, 2020
    Authors
    Jeffrey Mvutu Mabilama
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    Context

    There are a lot of unknowns when running an E-commerce store, even when you have analytics to guide your decisions.

    Users are an important factor in an e-commerce business. This is especially true in a C2C-oriented store, since they are both the suppliers (by uploading their products) AND the customers (by purchasing other user's articles).

    This dataset aims to serve as a benchmark for an e-commerce fashion store. Using this dataset, you may want to try and understand what you can expect of your users and determine in advance how your grows may be.

    • For instance, if you see that most of your users are not very active, you may look into this dataset to compare your store's performance.

    If you think this kind of dataset may be useful or if you liked it, don't forget to show your support or appreciation with an upvote/comment. You may even include how you think this dataset might be of use to you. This way, I will be more aware of specific needs and be able to adapt my datasets to suits more your needs.

    This dataset is part of a preview of a much larger dataset. Please contact me for more.

    Content

    What is inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

    The data was scraped from a successful online C2C fashion store with over 9M registered users. The store was first launched in Europe around 2009 then expanded worldwide.

    Visitors vs Users: Visitors do not appear in this dataset. Only registered users are included. "Visitors" cannot purchase an article but can view the catalog.

    Acknowledgements

    We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

    Inspiration

    Questions you might want to answer using this dataset:

    • Are e-commerce users interested in social network feature ?
    • Are my users active enough (compared to those of this dataset) ?
    • How likely are people from other countries to sign up in a C2C website ?
    • How many users are likely to drop off after years of using my service ?

    License

    CC-BY-NC-SA 4.0

    For other licensing options, contact me.

  10. Online Retail E-Commerce Data

    • kaggle.com
    Updated Mar 12, 2025
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    Shravan Kanamadi (2025). Online Retail E-Commerce Data [Dataset]. https://www.kaggle.com/datasets/shravankanamadi/online-retail-e-commerce-data/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 12, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Shravan Kanamadi
    Description

    Online Retail E-Commerce Data Hey everyone! 👋

    This dataset contains real e-commerce transaction data from 2009 to 2011. It comes from a UK-based online store that sells a variety of products. The data includes details like invoices, product codes, descriptions, prices, and even customer IDs.

    What’s Inside? Each row represents a transaction, and the dataset has the following key columns: 🛒 Invoice – Unique order ID 📦 StockCode – Product code 📝 Description – Name of the product 📊 Quantity – Number of units sold ⏳ InvoiceDate – When the purchase happened 💰 Price – Unit price of the product 👤 Customer ID – Unique identifier for each customer 🌍 Country – Where the customer is from

    Why is this dataset useful? This dataset is great for exploring: Customer Segmentation (Find high-value customers) Customer Lifetime Value (LTV) Analysis Sales & Revenue Trends Market Basket Analysis (Which products are bought together?) Predicting Churn & Retention Strategies

    How Can You Use It? If you're into data science, machine learning, or business analytics, this dataset is perfect for hands-on projects. You can analyze customer behavior, predict sales, or even build recommendation systems.

    Hope this dataset helps with your projects! Let me know if you find something interesting.

  11. d

    Ecommerce Data | Store Location Data | Global Coverage | 60M+ Contacts |...

    • datarade.ai
    Updated Jan 24, 2024
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    Exellius Systems (2024). Ecommerce Data | Store Location Data | Global Coverage | 60M+ Contacts | (Verified E-mail, Direct Dails)| Decision Makers Contacts| 20+ Attributes [Dataset]. https://datarade.ai/data-products/ecommerce-data-ecommerce-store-data-global-coverage-200-exellius-systems
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Jan 24, 2024
    Dataset authored and provided by
    Exellius Systems
    Area covered
    Saint Vincent and the Grenadines, Jersey, Lithuania, Heard Island and McDonald Islands, Namibia, Gabon, Congo (Democratic Republic of the), Spain, Iran (Islamic Republic of), Seychelles
    Description

    Revolutionize Customer Engagement with Our Comprehensive Ecommerce Data

    Our Ecommerce Data is designed to elevate your customer engagement strategies, providing you with unparalleled insights and precision targeting capabilities. With over 61 million global contacts, this dataset goes beyond conventional data, offering a unique blend of shopping cart links, business emails, phone numbers, and LinkedIn profiles. This comprehensive approach ensures that your marketing strategies are not just effective but also highly personalized, enabling you to connect with your audience on a deeper level.

    What Makes Our Ecommerce Data Stand Out?

    • Unique Features for Enhanced Targeting
      Our Ecommerce Data is distinguished by its depth and precision. Unlike many other datasets, it includes shopping cart links—a rare and valuable feature that provides you with direct insights into consumer behavior and purchasing intent. This information allows you to tailor your marketing efforts with unprecedented accuracy. Additionally, the integration of business emails, phone numbers, and LinkedIn profiles adds multiple layers to traditional contact data, enriching your understanding of clients and enabling more personalized engagement.

    • Robust and Reliable Data Sourcing
      We pride ourselves on our dual-sourcing strategy that ensures the highest levels of data accuracy and relevance:

      • Real-Time Information from 10 Active Publication Sites: Our databases are continuously updated with the latest information, sourced from ten active publication sites that provide real-time data.
      • Dedicated Contact Discovery Team: Complementing our automated sources, our dedicated Contact Discovery Team conducts thorough research and investigations, ensuring that every piece of data is accurate and reliable. This two-pronged approach guarantees that our Ecommerce Data is both up-to-date and relevant, providing you with a solid foundation for your business strategies.

      Primary Use Cases Across Industries

    Our Ecommerce Data is versatile and can be leveraged across various industries for multiple applications: - Precision Targeting in Marketing: Create personalized marketing campaigns based on detailed shopping cart activities, ensuring that your outreach resonates with individual customer preferences. - Sales Enrichment: Sales teams can benefit from enriched client profiles that include comprehensive contact information, enabling them to connect with key decision-makers more effectively. - Market Research and Analytics: Research and analytics departments can use this data for in-depth market studies and trend analyses, gaining valuable insights into consumer behavior and market dynamics.

    Global Coverage for Comprehensive Engagement

    Our Ecommerce Data spans across the globe, providing you with extensive reach and the ability to engage with customers in diverse regions: - North America: United States, Canada, Mexico - Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Sweden, and more - Asia: China, Japan, India, South Korea, Singapore, Malaysia, and more - South America: Brazil, Argentina, Chile, Colombia, and more - Africa: South Africa, Nigeria, Kenya, Egypt, and more - Australia and Oceania: Australia, New Zealand - Middle East: United Arab Emirates, Saudi Arabia, Israel, Qatar, and more

    Comprehensive Employee and Revenue Size Information

    Our dataset also includes detailed information on: - Employee Size: Whether you’re targeting small businesses or large corporations, our data covers all employee sizes, from startups to global enterprises. - Revenue Size: Gain insights into companies across various revenue brackets, enabling you to segment the market more effectively and target your efforts where they will have the most impact.

    Seamless Integration into Broader Data Offerings

    Our Ecommerce Data is not just a standalone product; it is a critical piece of our broader data ecosystem. It seamlessly integrates with our comprehensive suite of business and consumer datasets, offering you a holistic approach to data-driven decision-making: - Tailored Packages: Choose customized data packages that meet your specific business needs, combining Ecommerce Data with other relevant datasets for a complete view of your market. - Holistic Insights: Whether you are looking for industry-specific details or a broader market overview, our integrated data solutions provide you with the insights necessary to stay ahead of the competition and make informed business decisions.

    Elevate Your Business Decisions with Our Ecommerce Data

    In essence, our Ecommerce Data is more than just a collection of contacts—it’s a strategic tool designed to give you a competitive edge in understanding and engaging your target audience. By leveraging the power of this comprehensive dataset, you can elevate your business decisions, enhance customer interactions, and navigate the digital landscape with confidence and insight.

  12. E-commerce Products Image Dataset

    • kaggle.com
    Updated Jun 14, 2022
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    Sunny Kusawa (2022). E-commerce Products Image Dataset [Dataset]. https://www.kaggle.com/datasets/sunnykusawa/ecommerce-products-image-dataset/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 14, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sunny Kusawa
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This dataset contains images of Television, Sofas, Jeans and T-shirt. It Actual raw and unstructured image data extracted from online sites.

    All images are of different sites. You may also find some junk images in data for example in television dataset you will find the television remote images.

    This dataset is not refined intentionally to make sure practitioners should get taste of What kind of data ML/Data Science Engineer get when they start working on any project in industry.

  13. u

    E-commerce Industry Statistics 2025

    • upmetrics.co
    webpage
    Updated Oct 25, 2023
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    Upmetrics (2023). E-commerce Industry Statistics 2025 [Dataset]. https://upmetrics.co/blog/ecommerce-statistics
    Explore at:
    webpageAvailable download formats
    Dataset updated
    Oct 25, 2023
    Dataset authored and provided by
    Upmetrics
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2023
    Description

    A comprehensive dataset providing key insights into the eCommerce industry, including global retail online sales projections, number of eCommerce stores, digital buyer statistics, revenue growth in the United States, sector-wise revenue details with a focus on consumer electronics, average conversion rates, and mobile commerce sales forecasts.

  14. G

    Retail e-commerce sales, inactive

    • open.canada.ca
    • ouvert.canada.ca
    • +1more
    csv, html, xml
    Updated Mar 24, 2023
    + more versions
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    Statistics Canada (2023). Retail e-commerce sales, inactive [Dataset]. https://open.canada.ca/data/en/dataset/0ffbe1ee-7fa7-4369-ac78-a01c8175e1a6
    Explore at:
    html, csv, xmlAvailable download formats
    Dataset updated
    Mar 24, 2023
    Dataset provided by
    Statistics Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    This table contains 3 series, with data for years 2016 - 2017 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Sales (3 items: Retail trade; Electronic shopping and mail-order houses; Retail E-commerce sales).

  15. Online Sales Dataset - Popular Marketplace Data

    • kaggle.com
    Updated May 25, 2024
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    ShreyanshVerma27 (2024). Online Sales Dataset - Popular Marketplace Data [Dataset]. https://www.kaggle.com/datasets/shreyanshverma27/online-sales-dataset-popular-marketplace-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 25, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    ShreyanshVerma27
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

    Columns:

    • Order ID: Unique identifier for each sales order.
    • Date:Date of the sales transaction.
    • Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
    • Product Name:Specific name or model of the product sold.
    • Quantity:Number of units of the product sold in the transaction.
    • Unit Price:Price of one unit of the product.
    • Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
    • Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
    • Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

    Insights:

    • 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
    • 2. Explore the popularity of different product categories across regions.
    • 3. Investigate the impact of payment methods on sales volume or revenue.
    • 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
    • 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.
  16. Purchase Real-Time eCommerce Leads List | Gain Direct Access to Store Owners...

    • datacaptive.com
    Updated May 23, 2022
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    DataCaptive™ (2022). Purchase Real-Time eCommerce Leads List | Gain Direct Access to Store Owners | 40+ Data Points | Lifetime Access | DataCaptive [Dataset]. https://www.datacaptive.com/technology-users-email-list/ecommerce-company-data/
    Explore at:
    Dataset updated
    May 23, 2022
    Dataset provided by
    DataCaptive
    Authors
    DataCaptive™
    Area covered
    Singapore, Canada, Bahrain, United Kingdom, Jordan, France, Finland, Georgia, Sweden, Spain
    Description

    Unlock the door to business expansion by investing in our real-time eCommerce leads list. Gain direct access to store owners and make informed decisions with data fields including Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.

    Ensure a lifetime of access for continuous growth and tailor your campaigns with accurate and reliable information, initiating targeted efforts that align with your marketing goals. Whether you're targeting specific industries or global locations, our database provides up-to-date and valuable insights to support your business journey.

    • 4M+ eCommerce Companies • 40M+ Worldwide eCommerce Leads • Direct Contact Info for Shop Owners • 47+ eCommerce Platforms • 40+ Data Points • Lifetime Access • 10+ Data Segmentations • Sample Data

  17. Global retail e-commerce sales 2022-2028

    • statista.com
    Updated Jun 24, 2025
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    Statista (2025). Global retail e-commerce sales 2022-2028 [Dataset]. https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/
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    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 2025
    Area covered
    Worldwide
    Description

    In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.

  18. Premium eCommerce Leads | Target Shopify, Amazon, eBay Stores | Verified...

    • datacaptive.com
    Updated May 23, 2022
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    DataCaptive™ (2022). Premium eCommerce Leads | Target Shopify, Amazon, eBay Stores | Verified Owner Contacts | DataCaptive [Dataset]. https://www.datacaptive.com/technology-users-email-list/ecommerce-company-data/
    Explore at:
    Dataset updated
    May 23, 2022
    Dataset provided by
    DataCaptive
    Authors
    DataCaptive™
    Area covered
    Germany, Belgium, Norway, Romania, Spain, Mexico, Bahrain, United Arab Emirates, Netherlands, Switzerland
    Description

    Discover the unparalleled potential of our comprehensive eCommerce leads database, featuring essential data fields such as Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.

    With a focus on Shopify, Amazon, eBay, and other global retail stores, this database equips you with accurate information for successful marketing campaigns. Supercharge your marketing efforts with our enriched contact and company database, providing real-time, verified data insights for strategic market assessments and effective buyer engagement across digital and traditional channels.

    • 4M+ eCommerce Companies • 40M+ Worldwide eCommerce Leads • Direct Contact Info for Shop Owners • 47+ eCommerce Platforms • 40+ Data Points • Lifetime Access • 10+ Data Segmentations • Sample Data"

  19. Retail Store Data | Retail & E-commerce Sector in Asia | Verified Business...

    • datarade.ai
    Updated Feb 12, 2018
    + more versions
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    Success.ai (2018). Retail Store Data | Retail & E-commerce Sector in Asia | Verified Business Profiles & eCommerce Professionals | Best Price Guaranteed [Dataset]. https://datarade.ai/data-products/retail-store-data-retail-e-commerce-sector-in-asia-veri-success-ai
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Feb 12, 2018
    Dataset provided by
    Area covered
    Georgia, Kuwait, Malaysia, Cyprus, Singapore, Turkmenistan, Lebanon, Bangladesh, Jordan, Hong Kong
    Description

    Success.ai delivers unparalleled access to Retail Store Data for Asia’s retail and e-commerce sectors, encompassing subcategories such as ecommerce data, ecommerce merchant data, ecommerce market data, and company data. Whether you’re targeting emerging markets or established players, our solutions provide the tools to connect with decision-makers, analyze market trends, and drive strategic growth. With continuously updated datasets and AI-validated accuracy, Success.ai ensures your data is always relevant and reliable.

    Key Features of Success.ai's Retail Store Data for Retail & E-commerce in Asia:

    Extensive Business Profiles: Access detailed profiles for 70M+ companies across Asia’s retail and e-commerce sectors. Profiles include firmographic data, revenue insights, employee counts, and operational scope.

    Ecommerce Data: Gain insights into online marketplaces, customer demographics, and digital transaction patterns to refine your strategies.

    Ecommerce Merchant Data: Understand vendor performance, supply chain metrics, and operational details to optimize partnerships.

    Ecommerce Market Data: Analyze purchasing trends, regional preferences, and market demands to identify growth opportunities.

    Contact Data for Decision-Makers: Reach key stakeholders, such as CEOs, marketing executives, and procurement managers. Verified contact details include work emails, phone numbers, and business addresses.

    Real-Time Accuracy: AI-powered validation ensures a 99% accuracy rate, keeping your outreach efforts efficient and impactful.

    Compliance and Ethics: All data is ethically sourced and fully compliant with GDPR and other regional data protection regulations.

    Why Choose Success.ai for Retail Store Data?

    Best Price Guarantee: We deliver industry-leading value with the most competitive pricing for comprehensive retail store data.

    Customizable Solutions: Tailor your data to meet specific needs, such as targeting particular regions, industries, or company sizes.

    Scalable Access: Our data solutions are built to grow with your business, supporting small startups to large-scale enterprises.

    Seamless Integration: Effortlessly incorporate our data into your existing CRM, marketing, or analytics platforms.

    Comprehensive Use Cases for Retail Store Data:

    1. Market Entry and Expansion:

    Identify potential partners, distributors, and clients to expand your footprint in Asia’s dynamic retail and e-commerce markets. Use detailed profiles to assess market opportunities and risks.

    1. Personalized Marketing Campaigns:

    Leverage ecommerce data and consumer insights to craft highly targeted campaigns. Connect directly with decision-makers for precise and effective communication.

    1. Competitive Benchmarking:

    Analyze competitors’ operations, market positioning, and consumer strategies to refine your business plans and gain a competitive edge.

    1. Supplier and Vendor Selection:

    Evaluate potential suppliers or vendors using ecommerce merchant data, including financial health, operational details, and contact data.

    1. Customer Engagement and Retention:

    Enhance customer loyalty programs and retention strategies by leveraging ecommerce market data and purchasing trends.

    APIs to Amplify Your Results:

    Enrichment API: Keep your CRM and analytics platforms up-to-date with real-time data enrichment, ensuring accurate and actionable company profiles.

    Lead Generation API: Maximize your outreach with verified contact data for retail and e-commerce decision-makers. Ideal for driving targeted marketing and sales efforts.

    Tailored Solutions for Industry Professionals:

    Retailers: Expand your supply chain, identify new markets, and connect with key partners in the e-commerce ecosystem.

    E-commerce Platforms: Optimize your vendor and partner selection with verified profiles and operational insights.

    Marketing Agencies: Deliver highly personalized campaigns by leveraging detailed consumer data and decision-maker contacts.

    Consultants: Provide data-driven recommendations to clients with access to comprehensive company data and market trends.

    What Sets Success.ai Apart?

    70M+ Business Profiles: Access an extensive and detailed database of companies across Asia’s retail and e-commerce sectors.

    Global Compliance: All data is sourced ethically and adheres to international data privacy standards, including GDPR.

    Real-Time Updates: Ensure your data remains accurate and relevant with our continuously updated datasets.

    Dedicated Support: Our team of experts is available to help you maximize the value of our data solutions.

    Empower Your Business with Success.ai:

    Success.ai’s Retail Store Data for the retail and e-commerce sectors in Asia provides the insights and connections needed to thrive in this competitive market. Whether you’re entering a new region, launching a targeted campaign, or analyzing market trends, our data solutions ensure measurable success.

    ...

  20. c

    Women's E Commerce Clothing Reviews Dataset

    • cubig.ai
    Updated Feb 25, 2025
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    CUBIG (2025). Women's E Commerce Clothing Reviews Dataset [Dataset]. https://cubig.ai/store/products/140/womens-e-commerce-clothing-reviews-dataset
    Explore at:
    Dataset updated
    Feb 25, 2025
    Dataset authored and provided by
    CUBIG
    License

    https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service

    Measurement technique
    Privacy-preserving data transformation via differential privacy, Synthetic data generation using AI techniques for model training
    Description

    1) Data introduction • Womens-ecommerce-clothing-reviews dataset is a dataset containing 23,000 customer reviews and ratings.

    2) Data utilization (1) Womens-ecommerce-clothing-reviews data has characteristics that: • We aim for high-quality NLP and multivariate analysis with a dataset consisting of 10 functional variables such as clothing, age, and review title and 23,486 rows. (2) Womens-ecommerce-clothing-reviews data can be used to: • Rating prediction: Develop machine learning models to predict the ratings customers might give based on review text and support automated review analysis. • Trend analysis: Companies can analyze data to identify trends and patterns in customer preferences and support inventory management and marketing strategies.

Share
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Link copied
Close
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Red Stag Fulfillment (2025). Global Ecommerce Store Statistics 2025 [Dataset]. https://redstagfulfillment.com/how-many-ecommerce-stores-exist/

Global Ecommerce Store Statistics 2025

Explore at:
htmlAvailable download formats
Dataset updated
Jun 20, 2025
Dataset authored and provided by
Red Stag Fulfillment
Time period covered
2019 - 2025
Area covered
Worldwide
Variables measured
SKU count distribution, Industry vertical breakdown, Year-over-year growth rates, Total ecommerce stores worldwide, Geographic distribution of stores, Platform market share by merchant count
Measurement technique
Web crawling and platform API analysis
Description

Comprehensive dataset covering worldwide ecommerce store counts, platform market share, geographic distribution, industry verticals, and growth trends from 2019-2025.

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