Dark Mode
Synthetic E-commerce Analytics Data
Synthetic Data Generation
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This synthetic e-commerce dataset offers a detailed view of transactions, customers, products, and advertising within a simulated marketplace. It is designed to mirror real-world scenarios by incorporating seasonal effects, regional variations, and diverse customer purchasing behaviours. The data includes transaction-level details, customer demographics, product categories, and advertising metrics, making it a valuable resource for e-commerce analytics, exploring marketing effectiveness, customer segmentation, and product performance analysis.
Columns
- Transaction_ID: A unique identifier for each transaction.
- Customer_ID: A unique identifier for each customer.
- Product_ID: A unique identifier for each product.
- Transaction_Date: The date and time when the transaction occurred.
- Units_Sold: The number of units sold in the transaction.
- Discount_Applied: The discount percentage applied to the transaction.
- Revenue: The total revenue generated from the transaction.
- Clicks: The number of clicks on an associated advertisement.
- Impressions: The number of times an associated advertisement was shown.
- Conversion_Rate: The rate at which impressions converted into transactions.
- Category: The product category (e.g., Electronics, Toys).
- Region: The geographic region of the transaction (e.g., Asia, Europe).
- Ad_CTR: The Click-Through Rate for the associated advertisement.
- Ad_CPC: The Cost Per Click for the associated advertisement.
- Ad_Spend: The amount spent on the associated advertisement.
Distribution
The dataset is provided in a single CSV file named
synthetic_ecommerce_data.csv with a size of 13.77 MB. It contains 100,000 records.Usage
This dataset is ideal for a wide range of analytical and data science applications. Potential uses include:
- Customer Insights: Performing customer segmentation based on demographics, lifetime value, and purchasing habits.
- Product Performance Analysis: Identifying top-performing products and evaluating the impact of discounts on sales.
- Marketing Analytics: Measuring the effectiveness of advertising campaigns by analysing metrics like CTR, CPC, and conversion rates.
- Seasonal Trend Analysis: Investigating how sales volumes and revenue fluctuate during different times of the year, such as holiday periods.
- Regional Performance Analysis: Comparing customer preferences and sales trends across different geographical regions.
- Data Science Modelling: Building predictive models for sales forecasting, creating customer segmentation clusters, and developing optimisation strategies for ad spend or inventory.
Coverage
The dataset covers a time range from 7 December 2023 to 6 December 2024. Geographically, it includes data from regions such as Asia and Europe. The data simulates a general e-commerce marketplace without specific demographic limitations.
License
CC BY-SA 3.0
Who Can Use It
- Data Analysts: Can explore sales patterns, customer behaviour, and marketing campaign performance to generate business insights.
- Data Scientists: Can use the data to build and train machine learning models for sales forecasting, customer clustering, and ad spend optimisation.
- Marketing Professionals: Can analyse advertising metrics to assess campaign effectiveness and ROI.
- Business Intelligence Developers: Can create dashboards and reports to visualise key performance indicators for an e-commerce business.
- Students and Researchers: Can use this dataset as a practical tool for projects and research in e-commerce, analytics, and data science.
Dataset Name Suggestions
- Synthetic E-commerce Analytics Data
- Simulated E-commerce Transactions and Marketing Dataset
- E-commerce Customer and Sales Analytics
- Advanced E-commerce Performance Metrics
- E-commerce Sales and Advertising Simulation
Attributes
Original Data Source: Synthetic E-commerce Analytics Data
QUALITY