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Understanding the business context, exploring the dataset, and defining the critical 0-3 month churn crisis
947 out of 5,630 customers have churned, representing significant revenue loss
New customers in their first quarter show the highest risk, with nearly 50% churn rate
Immediate revenue exposure from at-risk customers without intervention
Customer acquisition costs in e-commerce typically range from $50-$200 per customer. With 947 churned customers, the company has lost between $47,350 - $189,400 in acquisition investments alone, not counting the lost lifetime value of these customers.
The 0-3 month churn crisis is particularly critical because:
Explore both the raw and cleaned versions of the dataset used in this analysis
Complete customer records
Behavioral & demographic data
Real-world transaction data
This dataset contains the original, unprocessed customer data from an e-commerce platform. It includes behavioral patterns, transaction history, satisfaction scores, and churn status for 5,630 customers. The raw data requires preprocessing to handle missing values, outliers, and prepare features for machine learning models.
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No missing values
Ready for modeling
Enhanced predictive power
The cleaned dataset has undergone comprehensive preprocessing and feature engineering. This includes handling missing values, removing outliers, encoding categorical variables, creating interaction features, and normalizing numerical features. This dataset is production-ready for machine learning model training and achieves the 89.3% accuracy reported in this analysis.
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Understanding the features used in the analysis
Unique identifier for each customer
Months as active customer (0-61 months)
Primary device used: Mobile, Computer, Phone
City classification: Tier 1, 2, or 3
Distance between warehouse and customer (km)
Preferred payment method
Average hours spent on mobile app
Total devices registered to account
Customer satisfaction rating (1-5)
Total number of orders placed
Average cashback earned per order
Percentage increase in order value
Number of coupons used in last month
Binary: Has filed complaint (Yes/No)
Days since most recent purchase
Target variable: Customer churned (1) or active (0)
Churn rate for customers in 0-3 month tenure
Churn rate for customers who filed complaints
Satisfaction score range showing highest churn
Tenure feature importance in model