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Online E-commerce Transaction Details
E-commerce & Online Transactions
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About
This dataset captures essential details of online e-commerce transactions, providing insights into the buying and selling of goods or services over the internet. It illustrates how consumers browse, select, and purchase products, and how retailers leverage online platforms for product display, transaction processing, and inventory management. The data reflects the revolution of retail, fostering global trade and empowering businesses to reach a wider audience. It is an ideal resource for understanding digital market dynamics and consumer behaviour in an online retail environment.
Columns
- Order_Number: A unique identifier for each order.
- State_Code: The two-letter code representing the state where the transaction occurred (e.g., MH, GJ).
- Customer_Name: The name of the customer who placed the order.
- Order_Date: The date when the order was placed (e.g., 03/04/2020, 03/04/2021).
- Status: The current processing status of the order (e.g., Processing, Order).
- Product: The name of the product purchased (e.g., 2GB Graphic Card, Standard ATX motherboard).
- Category: The category to which the product belongs (e.g., Monitor, CPU).
- Brand: The brand name of the product (e.g., Samsung, Dell).
- Cost: The individual cost of the product item.
- Sales: The individual sales price of the product item.
- Quantity: The number of units of the product purchased in the order.
- Total_Cost: The aggregated cost for all items within a specific order.
- Total_Sales: The aggregated sales value for all items within a specific order.
- Assigned Supervisor: The name of the supervisor associated with the order (e.g., Aarvi Gupta, Ajay Sharma).
Distribution
The dataset is provided as a CSV file named
Online-eCommerce.csv, with a size of 565.57 kB. It contains 14 distinct columns and approximately 5095 valid records of online transaction data. There are a small number of missing values across all columns (15 records).Usage
This dataset is perfectly suited for data analytics and cleaning tasks. Ideal applications include:
- Market analysis and trend identification in the e-commerce sector.
- Sales forecasting and revenue prediction.
- Customer behaviour analysis, including purchasing patterns and popular products.
- Inventory management and optimisation.
- Supply chain analysis and efficiency improvements.
- Performance tracking for products, categories, and brands.
- Studying the impact of e-commerce on global trade.
Coverage
The dataset focuses on online e-commerce transactions, with geographic indicators provided by state codes (e.g., MH, GJ) within a broader global context. The time range for orders spans at least 2020 and 2021, with a variety of order dates recorded. No specific demographic scope is detailed beyond customer and supervisor names.
License
CC0: Public Domain
Who Can Use It
This dataset is valuable for a wide array of users, including:
- E-commerce Retailers and Businesses: For market insights, strategic planning, and operational efficiency.
- Data Analysts and Scientists: For developing predictive models, segmenting customers, and extracting actionable insights.
- Academics and Researchers: For studying online retail trends, consumer psychology, and economic impacts of digital commerce.
- Marketing Professionals: For understanding product popularity, brand performance, and campaign effectiveness.
- Supply Chain Managers: For optimising logistics and inventory based on sales data.
Dataset Name Suggestions
- Online E-commerce Transaction Details
- Digital Retail Sales Records
- Global E-commerce Order Data
- Marketplace Transaction Analytics
- E-tail Sales and Customer Activity
Attributes
Original Data Source: Online E-commerce Transaction Details
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