Customer Churn Analysis for a Telecom Company

📊 New Project: Customer Churn Analysis for a Telecom Company

One of the biggest challenges telecom companies face is customer churn. In this project, I used the IBM Telco Customer Churn dataset to understand the key factors driving customers to leave the service, and built an interactive dashboard to support data-driven decisions.

🛠 What I did:

1️⃣ Cleaned and structured the data in SQL Server

2️⃣ Resolved data type mismatches and missing values during import

3️⃣ Built DAX measures such as Churn Rate, CLTV, and Churn Score

4️⃣ Designed a multi-page Power BI dashboard covering an overview, demographic analysis, churn reasons, and customer-level detail

🔍 Key insights:

• Overall churn rate reached ~26.5%

• Customers on month-to-month contracts are significantly more likely to churn than those on yearly contracts

• The first three months of a customer's tenure are the highest-risk period

• Customers subscribed to add-on services (like Tech Support and Online Security) are less likely to churn

This project deepened my understanding of how data cleaning, DAX measures, and dashboard design come together to turn raw data into actionable business decisions that help improve customer retention.

📂 Dataset used: IBM Telco Customer Churn (Kaggle)

#PowerBI #DataAnalyst #SQL #DAX #DataVisualization #BusinessIntelligence #CustomerChurn #DataDriven