Retail Sales and Feedback Dataset

Columns

• Customer Information:
  • Customer ID: A unique identifier for each customer.
  • Name: The customer’s full name.
  • Email: The customer’s email address for communication.
  • Phone: The customer’s contact number.
  • Address: The customer’s physical address.
  • City: Geographic detail of the customer's city (e.g., Chicago, Portsmouth).
  • State: Geographic detail of the customer's state (e.g., England, Berlin).
  • Zipcode: The customer's postal code.
  • Country: The customer's country of residence (e.g., USA, UK).
  • Age: The customer’s age.
  • Gender: The customer’s gender.
  • Income: The customer’s income bracket or level.
  • Customer Segment: Classification of customers based on their behaviour or demographics.
• Transaction Details:
  • Transaction_ID: A unique identifier for each transaction.
  • Last Purchase Date: The date of the customer’s most recent purchase.
  • Total Purchases: The total number of purchases made by the customer.
  • Amount: The monetary amount of a single purchase.
  • Total_Amount: The total monetary amount spent by the customer across multiple transactions.
• Product Information:
  • Product Category: The category to which the purchased product belongs (e.g., electronics, clothing, groceries).
  • Product Brand: The brand name of the product (e.g., Pepsi, Coca-Cola).
  • Product Type: The specific type or model of the product purchased (e.g., Water, Smartphone).
  • products: Unique values representing individual products (e.g., Spring water).
• Feedback:
  • Feedback: The customer’s textual feedback or rating related to the product or service received (e.g., Excellent, Good).
  • Ratings: Numerical ratings provided by customers (on a scale of 1 to 5).
• Transaction Logistics:
  • Shipping Method: The method used to deliver the purchased products (e.g., Same-Day, Express).
  • Payment Method: The method of payment chosen by the customer (e.g., Credit Card, Debit Card).
  • Order Status: The current status of the order (e.g., shipped, delivered, cancelled).
• Time-related Attributes:
  • Date: The date of the transaction.
  • Year: The year of the transaction.
  • Month: The month of the transaction.
  • Time: The time of the transaction.

Distribution

Usage

• Analysing customer preferences and buying habits.
• Identifying demographic trends in retail.
• Assessing customer satisfaction levels.
• Optimising marketing campaigns and strategies.
• Improving customer service operations.
• Personalising promotional offers.
• Managing inventory more efficiently.
• Enhancing the overall customer experience.
• Performing exploratory data analysis.
• Conducting customer segmentation and clustering.

Coverage

License

Who Can Use It

• Retail businesses: To optimise their operations, marketing, and customer engagement strategies.
• Data analysts and scientists: For in-depth consumer behaviour studies, predictive modelling, and market research.
• Marketing teams: To create targeted campaigns and personalise customer interactions.
• Operations managers: To refine inventory management and supply chain logistics.
• Academic researchers: For studies on retail economics and consumer psychology.

Dataset Name Suggestions

• Retail Transaction Records
• Consumer Purchase Data
• E-commerce Transaction Analysis
• Customer Behaviour Insights
• Retail Sales and Feedback Dataset

Attributes