Name: Customer Purchase Behavior - Electronic Sales Data
Creator: Kaggle
License: https://opensource.org/licenses/MIT

About this Dataset

Customer Purchase Behavior - Electronic Sales Data

Description:
This dataset contains sales transaction records for an electronics company over a one-year period, spanning from September 2023 to September 2024. It includes detailed information about customer demographics, product types, and purchase behaviors.

Key Features:

Customer ID: Unique identifier for each customer.
Age: Age of the customer (numeric)
Gender: Gender of the customer (Male or Female)
Loyalty Member: (Yes/No) (Values change by time, so pay attention to who cancelled and who signed up)
Product Type: Type of electronic product sold (e.g., Smartphone, Laptop, Tablet)
SKU: a unique code for each product.
Rating: Customer rating of the product (1-5 stars) (Should have no Null Ratings)
Order Status: Status of the order (Completed, Cancelled)
Payment Method: Method used for payment (e.g., Cash, Credit Card, Paypal)
Total Price: Total price of the transaction (numeric)
Unit Price: Price per unit of the product (numeric)
Quantity: Number of units purchased (numeric)
Purchase Date: Date of the purchase (format: YYYY-MM-DD)
Shipping Type: Type of shipping chosen (e.g., Standard, Overnight, Express)
Add-ons Purchased: List of any additional items purchased (e.g., Accessories, Extended Warranty)
Add-on Total: Total price of add-ons purchased (numeric)

NOTE: The original generated data had exactly 50% counts for both gender and loyalty. I have adjusted this for a more realistic distribution. Additionally, I ensured that all customer information is consistent across all their orders.

Data Stats:

Total Rows: 20,000
Time Period: September 2023 to September 2024
Product Types: Includes various electronics such as Smartphones, Laptops, Tablets, and Smartwatches.

Disclaimer:
This dataset is generated. It does not represent real-world transactions or actual customer data.
All values, including customer IDs, ages, product types, and ratings, are fictitious and created for the purpose of analysis and modeling.

Tables

Electronic Sales Sep2023 Sep2024

@kaggle.cameronseamons_electronic_sales_sep2023_sep2024.electronic_sales_sep2023_sep2024

325.79 kB
20,000 rows
16 columns

CREATE TABLE electronic_sales_sep2023_sep2024 (
  "customer_id" BIGINT,
  "age" BIGINT,
  "gender" VARCHAR,
  "loyalty_member" VARCHAR,
  "product_type" VARCHAR,
  "sku" VARCHAR,
  "rating" BIGINT,
  "order_status" VARCHAR,
  "payment_method" VARCHAR,
  "total_price" DOUBLE,
  "unit_price" DOUBLE,
  "quantity" BIGINT,
  "purchase_date" TIMESTAMP,
  "shipping_type" VARCHAR,
  "add_ons_purchased" VARCHAR,
  "add_on_total" DOUBLE
);