🛍️ Retail Customer Analytics Project

This project is an end-to-end analytics solution that uncovers deep insights into customer behavior, churn risk, and marketing performance for a retail business. It combines powerful data analysis in Python with a dynamic Power BI dashboard, making it perfect for data storytelling, stakeholder reporting, and portfolio display.

---

📁 Folder Structure

retail_analytics_project/
├── data/                      # Raw and processed data files
├── notebooks/                # Python notebooks for EDA, ML
│   ├── 01_eda.ipynb
│   ├── 02_churn_model.ipynb
│   └── 03_customer_segmentation.ipynb
├── retail_dashboard.pbix     # Power BI dashboard file
├── requirements.txt          # Python dependencies
└── README.md                 # Project documentation

---

📊 Features

Python (Jupyter Notebooks)

• ✅ EDA: Income, Age, Spending, Recency trends
• ✅ Feature Engineering: Customer tenure, total spend, total purchases
• ✅ Customer Segmentation: KMeans clustering into behavioral groups
• ✅ Churn Prediction: Random Forest model + Partial Dependence Plots
• ✅ Export to CSV for BI tools

Power BI Dashboard

• 📌 Multi-tab layout:
  • Overview: KPIs, churn rate, customer count
  • Customer Segmentation: Avg spend, segment size, age distribution
  • Churn Analysis: Risk trends by tenure, income, complaints
  • Campaign Effectiveness: Response rates and behavior
• 🎛️ Interactive slicers by segment, churn status, demographics
• 📈 Visual storytelling for business decisions

---

📦 Setup & Run

🔧 1. Clone Repo & Install Requirements

git clone https://github.com/your-username/retail-analytics-project.git
cd retail-analytics-project
pip install -r requirements.txt

💡 2. Run Python Notebooks

Use Jupyter or VS Code to run:

• 01_eda.ipynb
• 02_churn_model.ipynb

Output: outputs/customer_churn_segments.csv

📈 3. Open Power BI Dashboard

• File: retail_dashboard.pbix
• Make sure customer_churn_segments.csv is loaded as a table

---

📚 Tech Stack

• Python: Pandas, Scikit-learn, Matplotlib, Seaborn
• Machine Learning: Random Forest, KMeans
• Power BI: Visual Analytics, DAX, Transform Data

---

🧠 Use Cases

• 🔍 Analyze churn risk by demographics and behavior
• 🎯 Segment customers for targeted marketing
• 📊 Present business-ready dashboards to stakeholders

---

📌 License

This project is for educational and portfolio use.

---

🙋‍♂️ Author

Rahul Ghantasala
Passionate about solving real-world problems with data. Let’s connect on LinkedIn or check out more projects on GitHub.