🎯 Marketing Segmentation Analysis

Turning 100 Customers into 4 Actionable Segments with K-Means Clustering

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📌 Project at a Glance

🎯 Goal	Group customers so marketing spend targets the right people
🧠 Method	K-Means Clustering (4 segments)
📊 Data	100 customers — age, income, gender, region, behavior
💡 Outcome	4 clear personas, each with its own marketing strategy

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🧩 Business Problem

Marketing budgets are limited — spending the same way on every customer wastes money.

Which customers should we invest in, and what message works for each group?

By segmenting customers into distinct groups, the business can spend where the return is highest and speak to each group in the way that actually converts.

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🗂️ Dataset

Attribute	Detail
Size	100 customers
Features	Age, Income, Gender, Region, purchasing patterns, promotion engagement
Prep	Cleaned in Excel, analyzed & visualized in Python

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🔬 Methodology

1. Data Cleaning    →  Fix missing values, standardize fields (Excel)
2. Feature Scaling  →  Normalize age & income for fair distance
3. K-Means          →  Cluster into 4 optimal segments
4. Profiling        →  Describe each segment's traits
5. Strategy         →  Map a tailored marketing plan per segment

Why K-Means? It's fast, interpretable, and naturally groups customers by similarity — perfect for building marketing personas.

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📊 Segmentation Dashboard

Four customer segments by age and income, each mapped to a marketing priority.

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📈 Key Insights — The 4 Segments

Segment	Profile	Marketing Strategy	Priority
🔵 Young Professionals	Younger, mid-income, digital-first	Affordable quality, personalized online promos	🔴 High
🟢 Wealthy Seniors	Older, high-income	Premium products, loyalty rewards, hybrid retail	🔴 High
🟡 Moderately Engaged Pros	Mid age & income	Mid-range practical items, value offers	🟠 Medium
🔴 Low-Income Retirees	Older, budget-conscious	Basic goods, local & community campaigns	🟡 Nurture

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💼 Business Impact

Benefit	How the Analysis Delivers
💰 Higher ROI	Focus budget on high-value segments (Young Pros & Wealthy Seniors)
🎯 Right Message	Each segment gets messaging that fits its behavior
🤝 Better Retention	Community partnerships keep retirees engaged at low cost
⚙️ Scalable	Framework supports real-time updates & AI personalization

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🛠️ Technologies Used

Category	Tools
Language	Python
ML	scikit-learn (K-Means)
Data	Pandas, NumPy, Excel
Visualization	Matplotlib, Seaborn

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📁 Repository Contents

Marketing-Segmentation-Analysis/
├── 📁 assets/
│   ├── 🎨 banner.svg                                                     # Repository banner
│   └── 📊 dashboard.svg                                                  # Segmentation dashboard
├── 📁 code/
│   ├── 📄 Group12_PythonCodesSegmentation.py                             # Clustering code
│   └── 📄 app.py                                                         # Application script
├── 📁 data/
│   └── 📊 Group12Presentation_market_segmentation_data_Charts_Analysis.xlsx  # Data, charts & analysis
├── 📁 docs/
│   └── 📄 Marketing Analytics Segmentation.pdf                           # Findings & strategy
└── 📝 README.md                                                          # Project overview

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Heta Chavda — Data Analytics | Machine Learning | Business Intelligence

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