Retail Customer Transaction Data

Columns

• Transaction_ID: A unique identifier for each customer transaction.
• Customer_ID: Identifies each individual customer.
• Name: The customer's full name.
• Email: The email address associated with the customer.
• Phone: The customer's telephone number.
• Address: The customer's street address.
• City: The city of the customer, including common entries such as Chicago and Portsmouth.
• State: The customer's state or region, often listed as England and Berlin.
• Zipcode: The postal or zip code for the customer's address.
• Country: The customer's country, covering the USA, UK, Canada, Australia, and Germany.
• Age: The customer's age, with a range from 18 to 70 years.
• Gender: The reported gender of the customer, primarily Male or Female.
• Income: Categorisation of customer income levels, such as Medium and Low.
• Customer_Segment: Defines the customer's segment, including Regular and New.
• Date: The specific date of the purchase transaction, spanning from March 2023 to February 2024.
• Year: The year in which the purchase took place.
• Month: The month of the purchase.
• Time: The specific time of the purchase transaction.
• Total_Purchases: The total number of purchases made by a customer, typically ranging between 1 and 10.
• Amount: The monetary value of a single transaction, generally between 10 and 500.
• Total_Amount: The overall amount spent by a customer, with a maximum value of 5,000.
• Product_Category: The broad classification of the product, such as Electronics, Grocery, Clothing, Books, and Home Decor.
• Product_Brand: The brand name of the product, with examples including Pepsi and Coca-Cola.
• Product_Type: The specific type of product, such as Water or Smartphone.
• Feedback: Customer feedback provided for a purchase, often categorised as Excellent or Good.
• Shipping_Method: The method used for product shipping, suching as Same-Day or Express.
• Payment_Method: The payment type utilised for the transaction, including Credit Card and Debit Card.
• Order_Status: The current status of the order, for example, Delivered or Shipped.
• Ratings: The rating given to a product, on a scale of 1 to 5.
• products: The specific name of the product.

Distribution

Usage

• Customer segmentation analysis: Grouping customers based on their demographics, purchasing behaviour, and feedback.
• Sales trend analysis: Identifying seasonal peaks, recurring patterns, and overall sales performance over time.
• Product performance analysis: Evaluating the popularity and success of different product categories, brands, or types.
• Geographic analysis: Understanding regional consumer preferences and market dynamics.
• Payment and shipping method analysis: Improving operational services by reviewing common payment and shipping choices.
• Customer satisfaction analysis: Measuring and enhancing customer satisfaction based on their feedback and order status.
• Development of recommendation systems: Building intelligent systems to suggest relevant products to customers, thereby improving their shopping experience.

Coverage

• Geographic Scope: The data includes customer information from several countries, specifically the USA, UK, Canada, Australia, and Germany.
• Temporal Scope: Transactional data is recorded from March 2023 to February 2024. Separate columns for year, month, date, and time enable detailed temporal pattern analysis.
• Demographic Scope: The dataset includes demographic attributes such as customer age (ranging from 18 to 70), gender (predominantly Male and Female), and classified income levels (e.g., Medium, Low). Customer segments, including Premium, Regular, and New, are also available.

License

Who Can Use It

• Data Scientists and Analysts: Ideal for statistical modelling, machine learning applications, and in-depth data exploration.
• Business Intelligence Professionals: For creating insightful dashboards and reports on retail operations and customer behaviour.
• Marketing Strategists: To refine targeting strategies, understand customer segments, and personalise campaigns.
• E-commerce Businesses: For optimising product offerings, supply chain management, and user experience.
• Academic Researchers: For studies on consumer psychology, market dynamics, and retail economics.

Dataset Name Suggestions

• Retail Customer Transaction Data
• Global Retail Sales Dataset
• Customer Purchase Dynamics
• Multi-Market Retail Insights
• Transactional Retail Analytics

Attributes