Ecommerce Customer Behavior Dataset

About This Dataset

This is a comprehensive Customer Engagement and Churn Analytics Dataset containing behavioral, demographic, and transactional data for 50,000 customers across a global e-commerce/subscription platform. The dataset captures 25 distinct features that provide a 360-degree view of customer interactions and engagement patterns.

Dataset Characteristics

• Records: 50,000 customers
• Features: 25 columns
• Data Types: Mixed (numerical, categorical, object)
• Geographic Coverage: Multiple countries (USA, UK, Germany, Canada, India, Japan, France, Australia)
• Time Period: Captures customer journey from signup through current status

Key Feature Categories

1. Customer Demographics (5 features)

• Age, Gender, Country, City, Membership_Years

2. Platform Engagement (8 features)

• Login_Frequency, Session_Duration_Avg, Pages_Per_Session
• Cart_Abandonment_Rate, Wishlist_Items, Email_Open_Rate
• Mobile_App_Usage, Social_Media_Engagement_Score

3. Purchase Behavior (6 features)

• Total_Purchases, Average_Order_Value, Days_Since_Last_Purchase
• Discount_Usage_Rate, Return_Rate, Payment_Method_Diversity

4. Customer Service (3 features)

• Customer_Service_Calls, Product_Reviews_Written, Lifetime_Value

5. Financial & Status (3 features)

• Credit_Balance, Churned (target variable), Signup_Quarter

Data Quality

• Contains some missing values (NaN) in certain columns
• Mix of continuous numerical values (e.g., order values, engagement scores)
• Categorical variables (Gender, Country, City, Payment methods)
• Binary indicator (Churned: 0 = Active, 1 = Churned)

Potential Applications

Machine Learning Tasks:

• Binary classification (churn prediction)
• Customer segmentation (clustering)
• Lifetime value forecasting (regression)
• Recommendation systems

Business Analytics:

• Customer behavior analysis
• Marketing campaign optimization
• Retention strategy development
• Risk assessment for customer attrition

Research Areas:

• E-commerce consumer behavior
• Digital engagement patterns
• Subscription service optimization
• Cross-channel marketing effectiveness

Target Variable

Churned: Binary indicator showing whether a customer has discontinued using the service (1) or remains active (0)

Ideal For

• Data scientists and analysts working on customer retention
• Marketing teams optimizing engagement strategies
• Business intelligence professionals
• Students learning classification and customer analytics
• Researchers studying e-commerce behavior patterns

This dataset provides rich, real-world-style data suitable for both educational purposes and practical business intelligence applications.