🧠 Customer Segmentation Using Cluster Analysis (SPSS)

This project demonstrates unsupervised clustering techniques applied to a UK bank's customer dataset. The primary aim is to segment customers into distinct groups based on demographic and financial behavior, enabling data-driven marketing and service personalization.

---

📊 Project Summary

We applied Hierarchical Clustering and Two-Step Clustering using SPSS to identify natural customer segments. Key objectives:

• Discover distinct customer profiles.
• Understand variable contributions to cluster formation.
• Use clustering results to recommend marketing strategies.

---

📁 Dataset Description

Source: Simulated UK bank customer dataset (425 records)

Variables Used for Clustering (standardized via Z-scores):

• Age
• Current Account Balance
• Savings Account Balance
• Months as Customer
• Months Employed

---

🧮 Clustering Techniques

1. Hierarchical Clustering

• Method: Ward’s Method
• Distance Metric: Squared Euclidean
• Output: Dendrogram

📌 Result:

• 4 clusters identified
• Visual splits observed in dendrogram at sharp linkage jumps

🔢 Proximity Matrix (Top View)
(Excerpt from squared Euclidean distance between select cases)

---

2. Two-Step Clustering

• Distance: Euclidean
• Auto-clustering: Enabled
• Output: Cluster Quality, Model Summary

📈 Cluster Quality:

• Silhouette Score: ~0.4 (Fair)

📊 Model Summary:

---

🧠 Cluster Insights

🔍 Cluster Profiles:

Cluster	Description
1	Younger customers, low balances, short job tenure
2	Mature, high-income customers with long tenure
3	Mid-aged customers with moderate finances
4	High current balance, but low savings and tenure

📋 Case Processing Summary
All 425 records included and processed:

---

🧩 Strategic Recommendations

• Cluster 2: Target with premium and wealth management services
• Cluster 1: Offer beginner-friendly digital banking tools
• Cluster 3: Promote long-term savings and credit plans
• Cluster 4: Improve retention through loyalty and engagement offers

---

📂 Files

• data.xlsx – Raw customer data
• cluster.docx – Detailed report
• images/ – SPSS chart exports

---

💡 Conclusion

This analysis highlights the value of unsupervised machine learning in marketing strategy. By combining Hierarchical Clustering for visual insights and Two-Step Clustering for profiling strength, we provide actionable segmentation that supports tailored customer engagement.

---

🚀 Built with SPSS, Excel, and GitHub to showcase real-world data segmentation in financial services.