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E-commerce Customer and Sales Insights
E-commerce & Online Transactions
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About
A view of customer transactions across various regions, product categories, and age groups is provided here. It captures essential details about customer purchases, including shipping status and fees, offering a clear snapshot of sales performance and customer demographics. Whether you're looking to analyse sales trends, customer behaviour, or regional purchasing habits, this dataset provides a versatile foundation for exploring e-commerce insights.
Columns
- customer_id: A unique identifier for each customer to track their individual purchases.
- gender: The gender of the customer (Male or Female), used for demographic analysis.
- region: The geographical region of the customer, allowing for regional sales insights.
- age: The age of the customer, providing an opportunity for age-based segmentation.
- product_name: The name of the product purchased by the customer.
- category: The category to which the product belongs (e.g., Electronics, Accessories).
- unit_price: The price for a single unit of the product.
- quantity: The number of units of the product purchased in a single transaction.
- total_price: The total cost of the order, calculated from unit price and quantity.
- shipping_fee: The additional cost for shipping the order.
- shipping_status: The current status of the order shipment (e.g., Delivered, In Transit).
- order_date: The date on which the customer placed the order.
Distribution
The dataset is provided in a single CSV file named
realistic_e_commerce_sales_data.csv. The file size is approximately 84.1 kB and it contains 12 columns. The number of rows or records is not specified.Usage
Ideal applications for this dataset include:
- Customer Segmentation: Analyse purchasing behaviour based on demographics like age, gender, and location.
- Product Sales Analysis: Identify top-selling products and assess performance across various categories.
- Logistics and Shipping Analysis: Examine the impact of shipping fees and delivery statuses on sales.
- Sales Forecasting: Use historical data to predict future sales trends and patterns.
Coverage
The dataset covers a time range from 1st January 2023 to 3rd January 2024. Geographically, it includes customers from various regions, including West and South. The demographic scope includes male and female customers with ages ranging from 18 to 69. Some data points for 'region', 'age', and 'shipping_status' are noted as missing.
License
CC BY-SA 4.0
Who Can Use It
- Data Scientists: For building predictive models for sales forecasting and customer behaviour.
- Business Analysts: To generate insights on sales performance, product popularity, and regional market trends.
- Marketers: For creating targeted campaigns based on customer segmentation and purchase history.
Dataset Name Suggestions
- E-commerce Customer and Sales Insights
- Retail Transaction and Customer Demographics Data
- Online Sales Performance Dataset
- E-commerce Product and Shipping Analytics
Attributes
Original Data Source: E-commerce Customer and Sales Insights
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