Customer Behaviour And Shopping Analysis Dataset

Dataset Description

This dataset captures detailed shopping behavior of retail customers, focusing on their demographics, purchasing patterns, preferences, and transaction-related attributes. It provides a comprehensive view of how customers interact with products across different categories, seasons, and locations, along with their spending behavior, loyalty indicators, and use of discounts or promotional offers.
The dataset is well-suited for customer segmentation, exploratory data analysis, predictive modeling, recommendation systems, and business decision-making related to marketing, pricing, and inventory management.

Column Descriptions

Customer ID: Unique identifier assigned to each customer.

Age: Age of the customer at the time of purchase.

Gender: Gender of the customer.

Item Purchased: Specific product purchased by the customer.

Category: Product category to which the purchased item belongs (e.g., Clothing, Footwear).

Purchase Amount (USD): Monetary value of the transaction in US dollars.

Location: Geographic location (state) of the customer.

Size: Size of the purchased item.

Color: Color variant of the purchased item.

Season: Season during which the purchase was made.

Review Rating: Customer’s rating of the purchased item.

Subscription Status: Indicates whether the customer is subscribed to a membership or service (Yes/No).

Shipping Type: Mode of shipping selected for the order.

Discount Applied: Indicates whether a discount was applied to the purchase (Yes/No).

Promo Code Used: Indicates whether a promotional code was used (Yes/No).

Previous Purchases: Number of purchases made by the customer prior to the current transaction.

Payment Method: Payment method used to complete the transaction.

Frequency of Purchases: How often the customer makes purchases (e.g., Weekly, Fortnightly, Annually).

Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.