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Retail Shopping Trends Data
Retail & Consumer Behavior
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About
This dataset provides synthetic data for e-commerce shopping, designed to facilitate deductions and analyses related to customer behaviour and sales trends. It offers insights into customer demographics and preferences, allowing for analyses such as identifying the most sold products, understanding changes in sales and revenue over time, and segmenting customer bases. The data was generated for a course at Carnegie Mellon University.
Columns
- customer_id: A unique identifier for each customer, ranging from 1 to 1000.
- customer_name: The name of the customer, with 1000 unique values.
- gender: The stated gender of the customer, including categories like Male, Non-binary, and other specific values. There are 8 unique gender categories recorded.
- age: The age of the customer, with values ranging from 20 to 80 years.
- home_address: The complete home address of the customer, with 1000 unique addresses.
- zip_code: The postal code for the customer's address, ranging from 2 to 9998.
- city: The city of residence for the customer, with 961 unique city names.
- state: The state of residence for the customer, primarily including South Australia and Queensland, with 8 unique state values in total.
- country: The country of residence, with all entries recorded as Australia.
Distribution
This dataset is provided as a
customers.csv file, with a size of 102.07 kB. It is a tabular dataset comprising 9 columns and 1000 records. Specific numbers for rows or records are available for each column, all showing 1000 valid entries.Usage
This dataset is ideal for a variety of analytical applications, including:
- Sales Performance Analysis: Determine which products have sold the most within specific periods, such as the last month.
- Revenue Tracking: Analyse how sales and revenue metrics have evolved over past quarters.
- Customer Segmentation: Understand customer demographics and their purchasing preferences to create targeted marketing strategies.
- Academic Research: Useful for educational purposes and data analytics coursework, given its synthetic origin.
Coverage
The dataset's geographic scope is primarily Australia, with specific details on states such as South Australia and Queensland. The demographic scope includes customer gender (Male, Non-binary, Other categories) and age, which ranges from 20 to 80 years. There is no specific time range mentioned for the data collection, as it is synthetic.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This dataset is suitable for:
- Data Analysts: To explore customer behaviours and sales trends.
- Business Intelligence Professionals: For reporting on sales performance and customer demographics.
- Students and Researchers: Ideal for academic projects focusing on e-commerce, customer analytics, or data science.
- E-commerce Businesses: To gain insights into synthetic customer data for strategic planning and understanding potential market segments.
Dataset Name Suggestions
- E-commerce Customer Demographics
- Retail Shopping Trends Data
- Synthetic Customer Sales Dataset
- Online Shopping Behaviour Data
Attributes
Original Data Source: Retail Shopping Trends Data
QUALITY