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E-Commerce Customer Behavior Dataset

This dataset contains 5,000 e-commerce transactions from an online retail platform in Turkey between January 2023 and March 2024.

The data includes information about customers, products, orders, and user behavior during online shopping sessions. It allows analysis of how customers interact with the platform, what they buy, and how different factors affect purchasing decisions.

This dataset is used in this project to explore customer behavior, sales patterns, and engagement metrics.

Dataset Features

The dataset contains 18 columns describing different parts of the transaction process.

Order Information

  • Order_ID – unique identifier of each order

  • Date – date when the transaction happened

Customer Information

  • Customer_ID – unique customer identifier

  • Age – customer age (18–75)

  • Gender – customer gender

  • City – customer location (major cities in Turkey)

Product Information

  • Product_Category – category of the purchased product

  • Unit_Price – price per product unit (TRY)

  • Quantity – number of items purchased

Transaction Details

  • Discount_Amount – discount applied to the order

  • Total_Amount – final amount paid after discount

  • Payment_Method – payment method used by the customer

User Behavior

  • Device_Type – device used during the purchase (mobile, desktop, tablet)

  • Session_Duration_Minutes – time spent on the website

  • Pages_Viewed – number of pages visited during the session

  • Is_Returning_Customer – indicates whether the customer purchased before

Post-Purchase Metrics

  • Delivery_Time_Days – delivery duration

  • Customer_Rating – rating given by the customer (1–5)

Dataset Statistics

  • Total transactions: 5,000

  • Time period: January 2023 – March 2024

  • Average transaction value: ~450 TRY

  • Average customer rating: 3.9 / 5

  • Returning customers: ~60%

  • Mobile purchases: ~55%

Possible Analysis

This dataset can be used for several types of analysis, for example:

  • Customer segmentation

  • Sales trend analysis

  • Product category performance

  • Customer behavior analysis by device type

  • Predictive modeling for purchase amount or ratings

  • Market comparison between cities

Technical Information

Format: CSV

Encoding: UTF-8

File size: ~500 KB

Delimiter: comma

Source

Dataset originally published on Kaggle and used here for educational and portfolio analysis purposes.