📞 Telco Customer Churn Analysis & Retention Strategy

📌 Project Overview

This project focuses on identifying why customers are leaving a telecommunications company (Churn Rate: 26.58%). By analyzing a dataset of 7,043 customers, I identified key behavioral patterns and service gaps. This repository contains the full end-to-end pipeline: from raw data cleaning in Python to an interactive Power BI dashboard.

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🛠️ Tech Stack & Tools

• Python (Pandas, Seaborn, Matplotlib): Data auditing, cleaning, and exploratory data analysis (EDA).
• Microsoft Excel: Feature engineering and pivot table summaries.
• Power BI: Interactive data visualization and DAX measures.
• Gamma AI: Executive presentation for stakeholders.

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📂 Repository Structure

• Data/: Contains the raw CSV and the final formatted Excel datasets.
• Python Sciprts/: Jupyter Notebook containing the data cleaning and correlation analysis.
• Visualizations/: Dashboard screenshots and individual charts showing key drivers of churn.
• Reports/: Full project documentation and technical notes.
• Presentation/: Executive-level slides (PDF/PPTX) summarizing the business impact.

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🔍 Key Business Insights

1. The Support Gap: 63% of churned internet users did not have Technical Support. Providing support is the #1 way to increase loyalty.
2. Fiber Optic Risk: Despite being a premium service, Fiber Optic has a 41.89% churn rate, likely due to high costs ($74.40 avg) without bundled support.
3. Contract Maturity: Customers on Month-to-Month contracts represent the highest attrition risk. Long-term (1-2 year) contracts significantly stabilize revenue.

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📊 Dashboard Preview

The dashboard allows users to filter by Payment Method, Internet Service, and Tenure to identify "at-risk" customer segments in real-time.

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🚀 Strategic Recommendations

• Contract Migration: Offer a 10% discount to transition high-risk Month-to-Month users to 1-year plans.
• Support Bundling: Include "Premium Tech Support" for all Fiber Optic plans to reduce service frustration.
• Retention Milestones: Implement automated check-ins for new customers at the 3-month and 6-month marks, as these are the peak churn windows.

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📄 How to Explore

1. Read the Reports/Telco customer analysis documentation.docx for the full methodology.
2. Open Python Sciprts/analysis.ipynb to see the data cleaning code.
3. View Presentation/Telco-Customer-Churn-Analysis.pdf for the executive summary.