Data Description
This Synthetic dataset simulates customer behavior data for an online retail company and is designed to be useful for Exploratory Data Analysis (EDA) and various machine learning tasks such as:
Customer segmentation
Churn prediction
Recommendation systems
Customer lifetime value estimation
🔍 Dataset Overview:
Each row represents a unique customer, and the columns provide information on their demographics, shopping habits, engagement with the website, and satisfaction.
| Column | Description |
|---|---|
CustomerID |
Unique identifier for each customer |
Age |
Customer's age |
Gender |
Gender of the customer |
Annual_Income_USD |
Annual income in US dollars |
Spending_Score |
Score based on spending behavior (1–100) |
Membership_Status |
Customer loyalty level (Bronze to Platinum) |
Preferred_Payment_Method |
Payment method most often used |
Region |
Geographical region (e.g., North, South) |
Total_Purchases |
Total number of purchases made |
Avg_Purchase_Value |
Average value of each purchase |
Last_Purchase_Date |
Date of the most recent purchase |
Churn |
Whether the customer has churned (0 = No, 1 = Yes) |
Satisfaction_Score |
Satisfaction score (1–5 scale) |
Website_Visits_Last_Month |
Number of visits to the website last month |
Avg_Time_Per_Visit_Minutes |
Average time spent on website per visit |
Support_Tickets_Last_6_Months |
Number of support tickets raised |
Referred_Friends |
Number of friends referred to the platform |
✅ Use Cases:
Churn Prediction: Predict if a customer will churn based on behavior and demographics.
Segmentation: Use clustering to segment customers by behavior (e.g., income, spending, satisfaction).
Classification/Regression: Predict customer satisfaction or spending score.
Recommendation Engines: Based on purchase history and behavior patterns.
Verification Report
The following data verification reports are provided by the seller:
