Global Retail Transaction and Demographic Dataset

Columns

• SessionStart: Timestamp marking the beginning of the shopping session.
• CustomerID: Unique identifier for each customer.
• FullName: Customer’s full name.
• Gender: Customer’s gender (Male/Female).
• Age: Customer’s age in years.
• CreditScore: Customer’s credit score, ranging from 600 to 780.
• MonthlyIncome: Customer’s monthly income in USD.
• Country: Customer’s country of residence.
• State: Customer’s state of residence within the country.
• City: Customer’s city of residence within the state.
• Category: Product category (e.g., electronics, fashion).
• Product: Specific product name.
• Cost: Cost price of the product.
• Price: Selling price of the product.
• Quantity: Number of products purchased in the transaction.
• CampaignSchema: Marketing campaign associated with the transaction.
• CartAdditionTime: Timestamp when the product was added to the cart.
• OrderConfirmation: Boolean indicating whether the order was confirmed.
• OrderConfirmationTime: Timestamp when the order was confirmed.
• PaymentMethod: Method used for payment (e.g., Paypal, Credit Card).
• SessionEnd: Timestamp marking the end of the shopping session.
• OrderReturn: Boolean indicating whether the order was returned.
• ReturnReason: Reason for returning the product.

Distribution

Usage

• Descriptive Analytics: Understanding basic metrics like average income, common product categories, and credit scores.
• Predictive Analytics: Utilising machine learning to predict credit risk or purchase likelihood based on session activity.
• Customer Segmentation: Grouping customers by purchasing behaviour or demographics for targeted marketing.
• Geospatial Analysis: Optimising logistics and examining sales distribution across different regions.
• Time Series Analysis: Studying seasonality in purchases and session activities.
• Funnel Analysis: Evaluating the journey from session start to order confirmation to identify drop-off points.
• Cohort Analysis: Tracking retention and repeat purchase patterns over time.
• Market Basket Analysis: Discovering product affinities for cross-selling strategies.

Coverage

• Geographic Scope: Includes customers from various countries, with specific data points for Canada, India, and others, drilled down to State and City levels.
• Time Range: The transactional data spans from 1st January 2019 to 31st December 2023.
• Demographic Scope: Covers a diverse range of customers with varying ages (18-72), income levels, and credit scores.

License

Who Can Use It

• Data Enthusiasts: Ideally suited for practicing data cleaning, transformation, and visualisation skills.
• Marketing Analysts: Useful for conducting A/B testing on campaigns and understanding customer segments.
• Data Scientists: Applicable for building predictive models and forecasting sales performance.

Dataset Name Suggestions

• Customer360Insights E-Commerce Analytics
• Synthetic E-Commerce Customer Journey Data
• Global Retail Transaction and Demographic Dataset

Attributes