High-Value Customer Prediction

Overview

In this project, I performed a complete data analysis and machine learning pipeline using real e-commerce transaction data. The goal was to understand customer behavior and predict high-value customers. The dataset consists of 392,692 purchases from customers across multiple countries.

Data Processing Pipeline

Problem Statement

The goal was to build a predictive model to identify high-value customers—those who generate significant revenue and show strong engagement. This helps businesses focus retention efforts and optimize marketing strategies.

Key Questions:

• Which customers are most valuable to the business?
• What behavioral patterns distinguish high-value customers?
• How can we predict customer value early in their lifecycle?

Data & Methodology

The dataset contains 392,692 purchase records. I transformed this into a customer-level dataset with key features:

Customer Features Engineered:

• Frequency: Number of purchases per customer
• Total Products: Sum of quantities purchased
• Monetary Value: Total amount spent
• Country: Geographic distribution

PCA Results:

• PC1 explains 72.9% of variance
• PC2 explains 27.1% of variance
• Total: 100% variance explained in 2 dimensions

PCA Implementation:

# PCA for dimensionality reduction
from sklearn.decomposition import PCA

pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)

print(f"PC1 variance: {pca.explained_variance_ratio_[0]:.1%}")
print(f"PC2 variance: {pca.explained_variance_ratio_[1]:.1%}")
# Output: PC1 variance: 72.9%
# Output: PC2 variance: 27.1%

Key Insights

Top Predictors of Customer Value:

• Purchase Frequency: Number of transactions strongly correlates with value
• Average Order Value: Higher spending per transaction
• Recency: Time since last purchase indicates engagement

Model Performance

Random Forest achieved the best performance with ~90% accuracy in predicting high-value customers, demonstrating strong pattern recognition in customer behavior.

Business Impact:

• Target high-value customers with personalized marketing
• Improve retention for at-risk valuable customers
• Optimize resource allocation for customer acquisition

Conclusion

This project demonstrates a complete data science pipeline—from data cleaning and exploratory analysis to predictive modeling. The insights gained can help e-commerce companies better understand their customers and improve marketing strategies.