Customer Segmentation Analysis

Using machine learning to identify distinct customer segments

Project Overview

In this project, I performed customer segmentation analysis for an e-commerce company using unsupervised machine learning techniques. By analyzing customer behavior, purchase history, and demographic data, I identified distinct customer segments that helped the company tailor their marketing strategies and product recommendations.

Technologies Used

  • Python (Pandas, NumPy, Scikit-learn)
  • K-means clustering
  • Principal Component Analysis (PCA) for dimension reduction
  • Advanced data visualization (D3.js, Matplotlib, Seaborn)
  • RFM (Recency, Frequency, Monetary) analysis

Project Goals

The main objectives of this customer segmentation project were:

  • Identify distinct customer segments based on purchasing behavior
  • Determine the optimal number of customer segments
  • Profile each segment with actionable insights
  • Create visualizations that help stakeholders understand the segments
  • Provide recommendations for targeted marketing campaigns

Interactive customer segment visualization.
(This would be an actual D3.js visualization in a real implementation)

Customer Segment Profiles

Segment 1: High-Value Loyalists (22% of customers)

These customers make frequent purchases with high average order values. They have been customers for a long time and respond well to loyalty programs and exclusive offers.

  • Average order value: $120
  • Purchase frequency: 2.3 times per month
  • Customer lifetime value: $4,800

Segment 2: Potential Loyalists (35% of customers)

These customers show promising signs with moderate purchase frequency and growing average order values. They respond well to personalized recommendations and special offers.

  • Average order value: $85
  • Purchase frequency: 1.5 times per month
  • Customer lifetime value: $2,200

Segment 3: New Customers (18% of customers)

Recent first-time buyers who haven't established a clear purchase pattern yet. They need encouragement to make a second purchase and begin forming a relationship with the brand.

  • Average order value: $65
  • Purchase frequency: 1 time
  • Customer lifetime value: TBD

Segment 4: At-Risk Customers (15% of customers)

Previously active customers who haven't made a purchase in over 3 months. They require re-engagement campaigns to prevent churn.

  • Average order value: $95
  • Purchase frequency: Previously 1.8 times per month
  • Customer lifetime value: $1,800

Segment 5: One-Time Bargain Hunters (10% of customers)

These customers made a single purchase, typically during a major sale or with a significant discount. They are price-sensitive and respond primarily to deep discounts.

  • Average order value: $45
  • Purchase frequency: 1 time
  • Customer lifetime value: $45

Key Features for Segmentation

Conclusion and Business Impact

The customer segmentation analysis provided valuable insights that led to several business improvements:

  • 42% increase in marketing ROI through targeted campaigns
  • 28% improvement in customer retention rate
  • 35% increase in repeat purchases from at-risk customers
  • 18% growth in average order value through personalized recommendations

By understanding the distinct needs and behaviors of each customer segment, the company was able to develop tailored marketing strategies, optimize product recommendations, and improve overall customer satisfaction.