🧠 Customer Segmentation Analysis for Retail

🎯 Business Objective

Reduce marketing costs by 20% by identifying high-value customer segments using RFM analysis and K-means clustering.

This project uses Recency, Frequency, and Monetary (RFM) analysis to segment customers based on their purchase behavior. It's designed to help retail businesses:

• Identify their most valuable customers
• Optimize marketing strategies
• Maximize customer lifetime value

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📦 Problem Statement

Retail businesses often struggle to:

• 🎯 Target the right customers with marketing campaigns
• 📉 Reduce churn while optimizing budget
• 💰 Maximize customer lifetime value

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📂 Dataset Description

The dataset contains anonymized retail transaction records with the following fields:

• CustomerID: Unique customer identifier
• TransactionDate: Date of the transaction
• Quantity, Price: Number of items and unit price
• TotalAmount: Amount spent after discount
• ProductCategory, StoreLocation, PaymentMethod

Files included:

• Retail_Transaction_Dataset.csv – Raw data
• Retai_Transactions_Dataset_cleaned.csv – Cleaned data
• rfm_table.csv – Final RFM segmented data
• customer_segmentation_analysis.ipynb – Jupyter notebook with full code

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💡 Solution Strategy

We performed customer segmentation using RFM scoring and K-means clustering to group customers based on behavior. This enables:

• Personalized targeting
• Smarter budget allocation
• Data-driven customer relationship management

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🛠️ Tools & Technologies

• Python: pandas, numpy, datetime, sklearn.preprocessing, sklearn.cluster, sklearn.metrics
• Power BI: Interactive visualization dashboard
• SQL: Data cleaning and aggregation
• Git & GitHub: Version control and project sharing

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🔎 Methodology

1. Data Cleaning & Preparation

   • Handled missing values, date formats, and data types
   • Calculated TotalAmount per transaction

2. RFM Feature Engineering

   • Recency: Days since last transaction
   • Frequency: Number of transactions
   • Monetary: Total spend

3. Scoring & Segmentation

   • Assigned scores (1–5) to each RFM feature
   • Combined them into an RFM_Score
   • Applied K-means to group customers into segments

4. Segments Identified

   • 🏆 Champions
   • 🔁 Loyal Customers
   • ⚠️ At Risk
   • 💤 Hibernating
   • 🌱 New Customers

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📊 Key Results & Insights

Through RFM segmentation and K-means clustering, we identified six actionable customer segments:

Segment	Description
🏆 Champions	Recent (1–2 months), high frequency, high spenders. Top revenue drivers.
🔁 Loyal Customers	Recent (1–3 months), moderate to high frequency and spend. Engaged/stable.
⚠️ At Risk	Recent (2–6 months), low frequency, low spenders. Show signs of churn.
🌱 New Customers	Very recent (0–1 month), low frequency/spend. Potential to nurture.
💸 Big Spenders	Medium frequency, very high spend. Could be retained with special care.
❓ Others	Irregular, infrequent, low/average spenders. Need nurturing or reactivation.

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✅ Recommendations

Segment	Actions
🏆 Champions	Offer exclusive deals, loyalty rewards, and personalized offers
🔁 Loyal Customers	Provide incentives, gather feedback, and engage regularly
⚠️ At Risk	Run win-back campaigns and offer limited-time discounts
🌱 New Customers	Send welcome offers and introductory discounts
💸 Big Spenders	Give VIP treatment, loyalty programs, and priority access
❓ Others	Track and nurture toward higher segments with targeted messaging

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📈 Projected Impact:

• 15% reduction in churn
• 20% decrease in marketing waste
• Higher marketing ROI through personalization

🔗 View Power BI Dashboard | 🧾 Explore Full Code 🤝 Let's Connect 📧 Email: hudaelbasheer15@gmail.com 💼 LinkedIn