100+ datasets found
  1. United States: annual retail industry sales 2002-2024

    • statista.com
    • ai-chatbox.pro
    Updated Dec 19, 2024
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    Statista (2024). United States: annual retail industry sales 2002-2024 [Dataset]. https://www.statista.com/statistics/243448/holiday-retail-industry-sales-in-the-united-states/
    Explore at:
    Dataset updated
    Dec 19, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    Total retail sales in the United States was forecast to amount to 5.23 trillion U.S. dollars in 2024, up by 13 billion U.S. dollars in the previous year. Retail establishments come in many forms such as grocery stores, restaurants, and bookstores. There are around four million retail establishments in the United States. Leading companies in U.S. retail The domestic retail market in the United States is very competitive, with many companies recording substantial retail sales. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers. American retailers worldwide Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of American retailers doing business on a global scale. The success of U.S. retailers can also be seen through their performance in online retail. Amazon is a prime example of this, with the company’s sales revenue flourishing over the previous years.

  2. 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
    Turkmenistan, Hong Kong, Georgia, Malaysia, Kuwait, Lebanon, Jordan, Singapore, Cyprus, Bangladesh
    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.

    ...

  3. United States: physical retail store count 2015-2023

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). United States: physical retail store count 2015-2023 [Dataset]. https://www.statista.com/statistics/1079239/brick-and-mortar-retail-store-count-us/
    Explore at:
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, there were a total of **** million retail establishments throughout the United States, a number which has increased over the past couple of years. The chains with the most stores in the United States were Dollar General and Dollar Tree.

  4. T

    US Retail Sales

    • tradingeconomics.com
    • zh.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 17, 2025
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    TRADING ECONOMICS (2025). US Retail Sales [Dataset]. https://tradingeconomics.com/united-states/retail-sales
    Explore at:
    csv, xml, excel, jsonAvailable download formats
    Dataset updated
    Jun 17, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Feb 29, 1992 - May 31, 2025
    Area covered
    United States
    Description

    Retail Sales in the United States decreased 0.90 percent in May of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  5. Food & beverage retail store sales in the U.S. by format 2018-2022

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Food & beverage retail store sales in the U.S. by format 2018-2022 [Dataset]. https://www.statista.com/statistics/240907/us-food-retail-store-sales-by-format/
    Explore at:
    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2022, U.S. food and beverage retailers generated about *** billion U.S. dollars in sales. Supermarkets and other grocery stores took the lion's share of food and beverage retail store sales, while beer, wine, and liquor stores took the second highest share of sales. Between 2018 and 2022 generally the sales of all formats grew.

  6. d

    Retail Food Stores

    • catalog.data.gov
    • data.buffalony.gov
    • +3more
    Updated Sep 13, 2024
    + more versions
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    data.ny.gov (2024). Retail Food Stores [Dataset]. https://catalog.data.gov/dataset/retail-food-stores
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    Dataset updated
    Sep 13, 2024
    Dataset provided by
    data.ny.gov
    Description

    A listing of all retail food stores which are licensed by the Department of Agriculture and Markets.

  7. Big Data Analytics in Retail Market - Trends & Industry Analysis

    • mordorintelligence.com
    pdf,excel,csv,ppt
    + more versions
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    Mordor Intelligence, Big Data Analytics in Retail Market - Trends & Industry Analysis [Dataset]. https://www.mordorintelligence.com/industry-reports/big-data-analytics-in-retail-marketing-market
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset authored and provided by
    Mordor Intelligence
    License

    https://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy

    Time period covered
    2021 - 2030
    Area covered
    Global
    Description

    The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

  8. d

    Point-of-Interest (POI) Data | Shopping & Retail Store Locations in US and...

    • datarade.ai
    Updated Jun 30, 2022
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    Xtract (2022). Point-of-Interest (POI) Data | Shopping & Retail Store Locations in US and Canada | Retail Store Data | Comprehensive Data Coverage [Dataset]. https://datarade.ai/data-products/poi-data-retail-us-and-canada-xtract
    Explore at:
    .json, .xml, .csv, .xls, .txtAvailable download formats
    Dataset updated
    Jun 30, 2022
    Dataset authored and provided by
    Xtract
    Area covered
    Canada, United States
    Description

    This comprehensive retail point-of-interest (POI) dataset provides a detailed map of retail establishments across the United States and Canada. Retail strategists, market researchers, and business developers can leverage precise store location data to analyze market distribution, identify emerging trends, and develop targeted expansion strategies.

    Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive retail landscape of location intelligence.

    LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive retail store data database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail store locations -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping centers and malls, and more

    Why Choose LocationsXYZ for Your Retail POI Data Needs? At LocationsXYZ, we: -Deliver POI data with 95% accuracy for reliable store location data -Refresh POIs every 30, 60, or 90 days to ensure the most recent retail location information -Create on-demand POI datasets tailored to your specific retail data requirements -Handcraft boundaries (geofences) for shopping center locations to enhance accuracy -Provide retail POI data and polygon data in multiple file formats

    Unlock the Power of Retail Location Intelligence With our point-of-interest data for retail stores, you can: -Perform thorough market analyses using comprehensive store location data -Identify the best locations for new retail stores -Gain insights into consumer behavior and shopping patterns -Achieve an edge with competitive intelligence in retail markets

    LocationsXYZ has empowered businesses with geospatial insights and retail location data, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge retail POI data and shopping center location intelligence.

  9. World: retail sales 2021-2026

    • statista.com
    Updated Jun 24, 2025
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    Statista (2025). World: retail sales 2021-2026 [Dataset]. https://www.statista.com/statistics/443522/global-retail-sales/
    Explore at:
    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 2022
    Area covered
    Worldwide
    Description

    Global retail sales were projected to amount to around **** trillion U.S. dollars by 2026, up from approximately **** trillion U.S. dollars in 2021. The retail industry encompasses the journey of a good or service. This typically starts with the manufacturing of a product and ends with said product being purchased by a consumer from a retailer. Retail establishments come in many forms such as grocery stores, restaurants, and bookstores. American retailers worldwide As a result of globalization and various trade agreements between markets and countries, many retailers are capable of doing business on a global scale. Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of such American retailers. The success of U.S. retailers can also be seen through their performance in online retail. Retail in the U.S. The domestic retail market in the United States is a lucrative market, in which many companies compete. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers.

  10. x

    Retail Store Location Data | Retail Location Data | Xtract.io

    • xtract.io
    Updated Nov 4, 2022
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    Xtract.io Technology Solutions (2022). Retail Store Location Data | Retail Location Data | Xtract.io [Dataset]. https://www.xtract.io/cmp/poidata/retail
    Explore at:
    Dataset updated
    Nov 4, 2022
    Dataset provided by
    Xtract.Io Technology Solutions Private Limited
    Authors
    Xtract.io Technology Solutions
    License

    https://www.xtract.io/privacy-policyhttps://www.xtract.io/privacy-policy

    Area covered
    United States, Canada
    Description

    This core point of interest dataset consists of 1M location information of retail stores in the US and Canada. The POI database includes electronic stores, supermarkets and groceries, specialty retailers, home improvement and convenience stores, and apparel and accessories shops.

  11. F

    Retail Sales: Discount Department Stores (DISCONTINUED)

    • fred.stlouisfed.org
    json
    Updated Apr 16, 2025
    + more versions
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    (2025). Retail Sales: Discount Department Stores (DISCONTINUED) [Dataset]. https://fred.stlouisfed.org/series/MRTSSM452112EUSN
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Apr 16, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Retail Sales: Discount Department Stores (DISCONTINUED) (MRTSSM452112EUSN) from Jan 1992 to Feb 2025 about discount, retail trade, sales, retail, and USA.

  12. U.S. specialty retail store consumer satisfaction 2022/2023-2023/2024

    • statista.com
    • ai-chatbox.pro
    Updated Jun 25, 2025
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    Statista (2025). U.S. specialty retail store consumer satisfaction 2022/2023-2023/2024 [Dataset]. https://www.statista.com/statistics/882672/customer-satisfaction-with-selected-specialty-retail-stores-us/
    Explore at:
    Dataset updated
    Jun 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, Hobby Lobby was the leading hobby and home specialty retailer in terms of customer satisfaction in the United States. The company scored ** on a 100-point scale, overtaking TJX (HomeGoods) by one point that year.

  13. Online Retail Transaction Data

    • kaggle.com
    Updated Dec 21, 2023
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    The Devastator (2023). Online Retail Transaction Data [Dataset]. https://www.kaggle.com/datasets/thedevastator/online-retail-transaction-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 21, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    The Devastator
    Description

    Online Retail Transaction Data

    UK Online Retail Sales and Customer Transaction Data

    By UCI [source]

    About this dataset

    Comprehensive Dataset on Online Retail Sales and Customer Data

    Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

    This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

    The available attributes within this dataset offer valuable pieces of information:

    • InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

    • StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

    • Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

    • Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

    • InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

    • UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

    Finally,

    • Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

    This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

    Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

    How to use the dataset

    1. Sales Analysis:

    Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

    2. Product Analysis:

    Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

    3. Customer Segmentation:

    If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

    4. Geographical Analysis:

    The Country column enables analysts to study purchase patterns across different geographical locations.

    Practical applications

    Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

  14. Retail Inventory Optimization

    • kaggle.com
    Updated Feb 28, 2024
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    BALUSAMI (2024). Retail Inventory Optimization [Dataset]. https://www.kaggle.com/datasets/balusami/retail-inventory-optimization
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 28, 2024
    Dataset provided by
    Kaggle
    Authors
    BALUSAMI
    License

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

    Description

    The dataset is about a retail sales dataset containing information about store sales for various products over time.

    The specific variables include: Store: Unique identifier for the store location Date: Calendar date of the sales data Product: Name of the product being sold Weekly Sales: Total number of units sold for the product in a week Inventory Level: Number of units of the product currently in stock at the store Temperature: Average temperature for the week at the store location Past Promotion of Product (in lac): Total value (in lakhs) of any past promotions for the product during the week (1 lac = 100,000) Demand Forecast: Predicted number of units to be sold for the product in the next week (provided for baseline model comparison)

    This dataset can be used for various analytical purposes related to retail sales and inventory management, including:

    Demand forecasting: By analyzing historical sales data, temperature, past promotions, and other relevant factors, you can build models to predict future demand for products. This information can be used to optimize inventory levels and prevent stock outs or overstocking. Promotion analysis: You can compare sales data during promotional periods with non-promotional periods to assess the effectiveness of different promotions and identify products that respond well to promotions. Product analysis: By analyzing sales data across different stores and time periods, you can identify which products are most popular and in which locations. This information can be used to inform product placement, marketing strategies, and assortment planning. Store performance analysis: You can compare sales performance across different stores to identify top-performing stores and understand factors contributing to their success. This information can be used to identify areas for improvement in underperforming stores.

    By utilizing this dataset for these analytical purposes, retail organizations can gain valuable insights into their sales patterns, customer behavior, and inventory management practices. This information can be used to make data-driven decisions that improve sales performance, profitability, and customer satisfaction.

  15. Retail store financial estimates, inactive

    • www150.statcan.gc.ca
    • open.canada.ca
    • +2more
    Updated Mar 13, 2017
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    Government of Canada, Statistics Canada (2017). Retail store financial estimates, inactive [Dataset]. http://doi.org/10.25318/2010001001-eng
    Explore at:
    Dataset updated
    Mar 13, 2017
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Estimates of financial variables for store retailers, by type of store, based on the North American Industry Classification System (NAICS), annual.

  16. F

    Retailers Sales

    • fred.stlouisfed.org
    json
    Updated Jun 17, 2025
    + more versions
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    (2025). Retailers Sales [Dataset]. https://fred.stlouisfed.org/series/RETAILSMSA
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 17, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Retailers Sales (RETAILSMSA) from Jan 1992 to Apr 2025 about retail trade, sales, retail, and USA.

  17. Global Fashion Retail Sales

    • kaggle.com
    Updated Mar 19, 2025
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    Ric. G. (2025). Global Fashion Retail Sales [Dataset]. https://www.kaggle.com/datasets/ricgomes/global-fashion-retail-stores-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 19, 2025
    Dataset provided by
    Kaggle
    Authors
    Ric. G.
    License

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

    Description

    Global Fashion Retail Analytics Dataset

    📊 Dataset Overview

    This synthetic dataset simulates two years of transactional data for a multinational fashion retailer, featuring:
    - 📈 4+ million sales records
    - 🏪 35 stores across 7 countries:
    🇺🇸 United States | 🇨🇳 China | 🇩🇪 Germany | 🇬🇧 United Kingdom | 🇫🇷 France | 🇪🇸 Spain | 🇵🇹 Portugal

    Currencies Covered: Each transaction includes detailed currency information, covering multiple currencies:
    💵 USD (United States) | 💶 EUR (Eurozone) | 💴 CNY (China) | 💷 GBP (United Kingdom)

    Designed for Detailed and Multifaceted Analysis

    🌐 Geographic Sales Comparison
    Gain insights into how sales performance varies between regions and countries, and identify trends that drive success in different markets.

    👥 Analyze Staffing and Performance
    Evaluate store staffing ratios and analyze the impact of employee performance on store success.

    🛍️ Customer Behavior and Segmentation
    Understand regional customer preferences, analyze demographic factors such as age and occupation, and segment customers based on their purchasing habits.

    💱 Multi-Currency Analysis
    Explore how transactions in different currencies (USD, EUR, CNY, GBP) are handled, analyze currency exchange effects, and compare sales across regions using multiple currencies.

    👗 Product Trends
    Assess how product categories (e.g., Feminine, Masculine, Children) and specific product attributes (size, color) perform across different regions.

    🎯 Pricing and Discount Analysis
    Study how different pricing models and discounts affect sales and customer decisions across diverse geographies.

    📊 Advanced Cross-Country & Currency Analysis
    Conduct complex, multi-dimensional analytics that interconnect countries, currencies, and sales data, identifying hidden correlations between economic factors, regional demand, and financial performance.

    Synthetic Data Advantages

    Generated using algorithms, it simulates real-world retail dynamics while ensuring privacy.

    • Privacy-Safe: All customer and employee data is artificially generated to ensure privacy and compliance with data protection regulations. Personal details, such as emails and phone numbers, are anonymized.
    • Scalable Patterns: The data replicates real-world retail dynamics, ensuring scalability of patterns for testing algorithms and analytics models.
    • Controlled Complexity: The dataset introduces intentional complexities (e.g., missing job titles, inconsistent phone number formats) to offer a more realistic and challenging exploration experience for exploratory data analysis.
    • Customizable for Various Use Cases: Whether you're performing sales forecasting, employee performance analysis, or customer segmentation, this dataset offers a flexible foundation for diverse analytical tasks.

    This dataset is an ideal resource for retail analysts, data scientists, and business intelligence professionals aiming to explore multinational retail data, optimize operations, and uncover new insights into customer behavior, sales trends, and employee efficiency.

  18. 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
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    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.

  19. B

    Annual Retail Store Data, 2000 [Canada] [Excel]

    • borealisdata.ca
    • dataverse.scholarsportal.info
    • +1more
    Updated Sep 28, 2023
    + more versions
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    Statistics Canada (2023). Annual Retail Store Data, 2000 [Canada] [Excel] [Dataset]. http://doi.org/10.5683/SP3/TUQXW4
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 28, 2023
    Dataset provided by
    Borealis
    Authors
    Statistics Canada
    License

    https://borealisdata.ca/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.5683/SP3/TUQXW4https://borealisdata.ca/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.5683/SP3/TUQXW4

    Area covered
    Canada
    Description

    The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

  20. F

    E-Commerce Retail Sales as a Percent of Total Sales

    • fred.stlouisfed.org
    json
    Updated May 19, 2025
    + more versions
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    (2025). E-Commerce Retail Sales as a Percent of Total Sales [Dataset]. https://fred.stlouisfed.org/series/ECOMPCTSA
    Explore at:
    jsonAvailable download formats
    Dataset updated
    May 19, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA) from Q4 1999 to Q1 2025 about e-commerce, retail trade, percent, sales, retail, and USA.

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Statista (2024). United States: annual retail industry sales 2002-2024 [Dataset]. https://www.statista.com/statistics/243448/holiday-retail-industry-sales-in-the-united-states/
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United States: annual retail industry sales 2002-2024

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Dataset updated
Dec 19, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
United States
Description

Total retail sales in the United States was forecast to amount to 5.23 trillion U.S. dollars in 2024, up by 13 billion U.S. dollars in the previous year. Retail establishments come in many forms such as grocery stores, restaurants, and bookstores. There are around four million retail establishments in the United States. Leading companies in U.S. retail The domestic retail market in the United States is very competitive, with many companies recording substantial retail sales. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers. American retailers worldwide Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of American retailers doing business on a global scale. The success of U.S. retailers can also be seen through their performance in online retail. Amazon is a prime example of this, with the company’s sales revenue flourishing over the previous years.

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