⚒ Tools:

✔Skill:

☰ Short Description

📊 PowerBI

• Data Cleaning

• Data Analytics

• Data Visualization

• Analyzed telecom customer churn with Power BI to uncover drivers of attrition.

• Built interactive dashboards with DAX, highlighting key churn factors such as contracts, service usage, demographics, and extra charges.

• Provided insights and recommendations to improve retention and reduce churn.

Overview

This project showcases a comprehensive analysis of customer churn for a fictional telecom company called Databel. Customer churn; when customers stop using a company's service is a critical metric for any business aiming to grow sustainably. Understanding why customers leave and identifying patterns in their behavior allows organizations to take proactive steps to reduce churn and improve customer retention.

Using Power BI, I have created an interactive dashboard that visualizes key metrics such as churn rate, reasons for churn, customer demographics, and service usage patterns. This analysis leverages various data visualizations including bar charts, pie charts, and geographical maps and leverage DAX formula to derive insight to uncover insights that help pinpoint the main drivers behind customer churn.

What you will find in this project:

• Calculated measures created with DAX for dynamic reporting
• Interactive filters and slicers for deeper data exploration
• Combined visualizations to present a clear overview of customer behavior
• Insights into customer service interactions, contract types, and extra charges impacting churn

This project highlights how data visualization and analytics can empower decision-makers to improve customer satisfaction and reduce churn in a competitive marketplace.

Analysis Process

1. Imported raw customer and churn data from CSV files.
2. Created calculated columns and DAX measures to capture key metrics, including churn rates, contract details, and service usage patterns.
3. Developed interactive dashboards to analyze churn across demographics, contract types, payment methods, and customer service behaviors.
4. Investigated relationships between churn and factors such as contract length, payment methods, and additional charges.

Key Insights