🔄 Task 2 – Customer Retention & Churn Analysis

Internship: Future Interns | Data Science & Analytics Track
Track Code: DS | Repository: FUTURE_DS_02

🎯 Objective

Analyze customer data to identify churn patterns, key retention drivers, and customer lifetime trends for a subscription-based business.

🛠️ Tools Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Jupyter Notebook

📂 Project Structure

FUTURE_DS_02/
├── churn_analysis.ipynb    ← Main analysis notebook
├── data/
│   └── customer_data.csv   ← Customer dataset (800 records)
└── visuals/
    ├── churn_by_plan.png
    ├── churn_by_tenure.png
    ├── churn_heatmap.png
    └── churn_reasons.png

🔍 Key Insights

• Basic plan customers have highest churn rate (~45%)
• Customers with < 6 months tenure are at highest churn risk
• "Price too high" is the most common churn reason
• Premium plan customers have 3x higher average LTV

✅ Deliverable

Retention analysis report with cohort heatmap, churn reason breakdown, and recommendations to reduce customer loss.

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Submitted by: Subhashini | Future Interns DS Intern