4 datasets found
  1. 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
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    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.
  2. Survey of Consumer Finances

    • federalreserve.gov
    Updated Oct 18, 2023
    + more versions
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    Board of Governors of the Federal Reserve Board (2023). Survey of Consumer Finances [Dataset]. http://doi.org/10.17016/8799
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    Dataset updated
    Oct 18, 2023
    Dataset provided by
    Federal Reserve Board of Governors
    Federal Reserve Systemhttp://www.federalreserve.gov/
    Authors
    Board of Governors of the Federal Reserve Board
    Time period covered
    1962 - 2023
    Description

    The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

  3. Alternative Data Market Analysis North America, Europe, APAC, South America,...

    • technavio.com
    pdf
    Updated Jan 17, 2025
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    Technavio (2025). Alternative Data Market Analysis North America, Europe, APAC, South America, Middle East and Africa - US, Canada, China, UK, Mexico, Germany, Japan, India, Italy, France - Size and Forecast 2025-2029 [Dataset]. https://www.technavio.com/report/alternative-data-market-industry-analysis
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    pdfAvailable download formats
    Dataset updated
    Jan 17, 2025
    Dataset provided by
    TechNavio
    Authors
    Technavio
    License

    https://www.technavio.com/content/privacy-noticehttps://www.technavio.com/content/privacy-notice

    Time period covered
    2025 - 2029
    Area covered
    United Kingdom, Mexico, Canada, United States
    Description

    Snapshot img

    Alternative Data Market Size 2025-2029

    The alternative data market size is valued to increase USD 60.32 billion, at a CAGR of 52.5% from 2024 to 2029. Increased availability and diversity of data sources will drive the alternative data market.

    Major Market Trends & Insights

    North America dominated the market and accounted for a 56% growth during the forecast period.
    By Type - Credit and debit card transactions segment was valued at USD 228.40 billion in 2023
    By End-user - BFSI segment accounted for the largest market revenue share in 2023
    

    Market Size & Forecast

    Market Opportunities: USD 6.00 million
    Market Future Opportunities: USD 60318.00 million
    CAGR from 2024 to 2029 : 52.5%
    

    Market Summary

    The market represents a dynamic and rapidly expanding landscape, driven by the increasing availability and diversity of data sources. With the rise of alternative data-driven investment strategies, businesses and investors are increasingly relying on non-traditional data to gain a competitive edge. Core technologies, such as machine learning and natural language processing, are transforming the way alternative data is collected, analyzed, and utilized. Despite its potential, the market faces challenges related to data quality and standardization. According to a recent study, alternative data accounts for only 10% of the total data used in financial services, yet 45% of firms surveyed reported issues with data quality.
    Service types, including data providers, data aggregators, and data analytics firms, are addressing these challenges by offering solutions to ensure data accuracy and reliability. Regional mentions, such as North America and Europe, are leading the adoption of alternative data, with Europe projected to grow at a significant rate due to increasing regulatory support for alternative data usage. The market's continuous evolution is influenced by various factors, including technological advancements, changing regulations, and emerging trends in data usage.
    

    What will be the Size of the Alternative Data Market during the forecast period?

    Get Key Insights on Market Forecast (PDF) Request Free Sample

    How is the Alternative Data Market Segmented ?

    The alternative data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

    Type
    
      Credit and debit card transactions
      Social media
      Mobile application usage
      Web scrapped data
      Others
    
    
    End-user
    
      BFSI
      IT and telecommunication
      Retail
      Others
    
    
    Geography
    
      North America
    
        US
        Canada
        Mexico
    
    
      Europe
    
        France
        Germany
        Italy
        UK
    
    
      APAC
    
        China
        India
        Japan
    
    
      Rest of World (ROW)
    

    By Type Insights

    The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.

    Alternative data derived from credit and debit card transactions plays a significant role in offering valuable insights for market analysts, financial institutions, and businesses. This data category is segmented into credit card and debit card transactions. Credit card transactions serve as a rich source of information on consumers' discretionary spending, revealing their luxury spending tendencies and credit management skills. Debit card transactions, on the other hand, shed light on essential spending habits, budgeting strategies, and daily expenses, providing insights into consumers' practical needs and lifestyle choices. Market analysts and financial institutions utilize this data to enhance their strategies and customer experiences.

    Natural language processing (NLP) and sentiment analysis tools help extract valuable insights from this data. Anomaly detection systems enable the identification of unusual spending patterns, while data validation techniques ensure data accuracy. Risk management frameworks and hypothesis testing methods are employed to assess potential risks and opportunities. Data visualization dashboards and machine learning models facilitate data exploration and trend analysis. Data quality metrics and signal processing methods ensure data reliability and accuracy. Data governance policies and real-time data streams enable timely access to data. Time series forecasting, clustering techniques, and high-frequency data analysis provide insights into trends and patterns.

    Model training datasets and model evaluation metrics are essential for model development and performance assessment. Data security protocols are crucial to protect sensitive financial information. Economic indicators and compliance regulations play a role in the context of this market. Unstructured data analysis, data cleansing pipelines, and statistical significance are essential for deriving meaningful insights from this data. New

  4. Annual number of AmEx issued credit cards in the U.S. and worldwide...

    • statista.com
    Updated Jun 30, 2025
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    Statista (2025). Annual number of AmEx issued credit cards in the U.S. and worldwide 2015-2024 [Dataset]. https://www.statista.com/statistics/618147/number-of-american-express-credit-cards-worldwide-by-region/
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    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The United States was a big market for American Express in 2021 in terms of credit cards, making up nearly **** of all issued cards that year. This was a slight increase from the previous year, as the number of issued credit cards outside of North American country decreased over time. The source stopped reporting this regional split since 2022, however.

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    Learn how you can add new datasets to our index.

Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
ShreyanshVerma27 (2024). Online Sales Dataset - Popular Marketplace Data [Dataset]. https://www.kaggle.com/datasets/shreyanshverma27/online-sales-dataset-popular-marketplace-data
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Online Sales Dataset - Popular Marketplace Data

Global Transactions Across Various Product Categories

Explore at:
4 scholarly articles cite this dataset (View in Google Scholar)
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.
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