📊 Telco Customer Churn Analysis | Power BI Business Analytics Project

🚀 Project Overview

Customer churn is one of the biggest revenue leakages for telecom companies.

This project analyzes customer behavior and identifies:

• Why customers leave
• Which segments are at highest risk
• What actions can reduce churn

The goal is to help business teams take data-driven retention decisions.

---

🎯 Business Problem

Telecom company is facing high customer churn.

Questions to solve:

• What is the overall churn rate?
• Which contract types churn the most?
• Does tenure impact churn?
• Which services increase churn risk?
• What actionable strategies can reduce churn?

---

🛠 Tools & Skills Used

• Power BI
• SQL (data preparation)
• DAX measures
• Data modeling
• Business analytics
• KPI design
• Dashboard storytelling

---

📂 Dataset

Telco Customer Churn dataset
~7,000 customers with:

• Demographics
• Services
• Contract details
• Charges
• Churn status

---

📈 Dashboard Features

KPIs

• Total Customers
• Total Churned Customers
• Churn %

Analysis Views

• Churn by Contract Type
• Churn by Tenure
• Churn by Internet Service
• Churn by Payment Method
• Churn by Tech Support
• Interactive Filters (Gender, Service, Contract)

Interactivity

• Dynamic filtering
• Drill-down analysis
• Business-focused visuals

---

🔍 Key Insights

• Month-to-month contracts show highest churn (~42%)
• Fiber optic customers churn more than DSL users
• Electronic check users show highest churn risk
• Customers without tech support churn significantly more
• Short tenure customers are more likely to leave

---

💡 Recommended Business Actions

• Promote annual/long-term contracts with discounts
• Bundle tech support with internet plans
• Target high-risk customers with retention offers
• Improve onboarding for first 3 months
• Incentivize automatic payments

---

📊 Business Impact (Expected)

If actions are implemented:

• Reduce churn by 5–10%
• Increase customer lifetime value
• Improve retention revenue
• Lower acquisition cost

---

📁 Repository Structure

Folder	Description
📂 01_DATA	Raw and processed CSV datasets.
📂 02_SQL_SCRIPTS	Data cleaning and transformation scripts.
📂 03_SQL_REPORTS	Queries for business logic and margin calculations.
📂 04_DASHBOARD	Power BI (.pbix) files and visual exports.
📂 05_RECOMMENDATION	Final business insights and recovery strategies.