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Synthetic Consumer Behaviour Dataset
Retail & Consumer Behavior
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£79.99
About
This synthetic customer purchase dataset has been created as an educational resource for data science, machine learning, and retail analytics applications. The data focuses on key consumer purchase behaviours, including demographic information, product details, purchase history, and payment methods. It is designed to help users practice data manipulation, analysis, and predictive modelling in the context of retail and e-commerce.
Dataset Features:
- Customer ID: Unique identifier for each customer.
- Age: Age of the customer (in years).
- Gender: Gender of the customer (e.g., "Male," "Female").
- Item Purchased: Item that was purchased (e.g., "Blouse," "Sandals").
- Category: Category of the item purchased (e.g., "Accessories," "Clothing").
- Purchase Amount (USD): The amount spent on the purchase (in USD).
- Location: Geographical location of the customer (e.g., "Wyoming," "Hawaii").
- Size: Size of the purchased item (e.g., "M," "S," "L").
- Color: Color of the purchased item (e.g., "Red," "White").
- Season: Season during which the item was purchased (e.g., "Winter," "Summer").
- Review Rating: Rating given by the customer to the purchased item (on a scale from 1 to 5).
- Subscription Status: Whether the customer is subscribed to a loyalty program or subscription service (e.g., "Yes," "No").
- Shipping Type: Shipping method used for the purchase (e.g., "Free Shipping," "Standard").
- Discount Applied: Whether a discount was applied to the purchase (e.g., "Yes," "No").
- Promo Code Used: Whether a promotional code was used during the purchase (e.g., "Yes," "No").
- Previous Purchases: Number of previous purchases made by the customer.
- Payment Method: Method of payment used (e.g., "Bank Transfer," "PayPal," "Venmo").
- Frequency of Purchases: How often the customer makes purchases (e.g., "Annually," "Bi-Weekly," "Monthly").
Sample:
Usage:
This dataset is useful for a variety of applications, including:
- Customer Behavior Analysis: To explore trends in customer demographics, purchase behaviours, and preferences.
- Retail Analytics: To understand how different factors (like season, location, and payment method) influence purchasing decisions.
- Predictive Modeling: To develop models that predict customer behaviours such as purchase frequency or subscription status.
- Marketing Strategy: To analyze the effectiveness of promotions, discounts, and shipping methods in driving purchases.
Coverage:
This dataset is synthetic and anonymized, making it a safe tool for experimentation and learning without compromising any real customer data.
License:
CCO (Public Domain)
Who can use it:
Data science enthusiasts: For learning and practising retail data analysis, customer segmentation, and predictive modelling.
Researchers and educators: For academic studies or teaching purposes in retail analytics and consumer behaviour.
Marketing professionals: For analyzing purchasing patterns and designing targeted promotional campaigns.
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