📊 Customer Churn Analysis Dashboard — Power BI Project
📌 Project Overview
This project focuses on analyzing customer churn for a telecommunications company to understand the key drivers behind customer attrition and identify actionable strategies to reduce revenue loss.
The goal was not only to visualize churn metrics but to transform raw data into meaningful business insights that support decision-making.
🎯 Business Objectives
The project was designed to answer three critical business questions:
1️⃣ What is the current scale and financial impact of customer churn?
2️⃣ Which customer segments contribute most to revenue loss?
3️⃣ What actions can the business take to reduce churn effectively?
🧠 Key Analytical Approach
The analysis was structured into three main stages:
1️⃣ Executive Overview
Providing a high-level snapshot of:
Total customers
Churn rate
Churned customers
Monthly revenue loss
This stage focuses on understanding the overall problem and its business impact.
2️⃣ Risk Segmentation
Customers were segmented into:
High Risk
Medium Risk
Low Risk
This segmentation helps identify which customer groups contribute disproportionately to revenue loss.
3️⃣ Drivers & Actions
This stage explores:
Key churn drivers
Customer behavior patterns
Potential retention strategies
It also includes scenario analysis to estimate the financial impact of reducing churn.
⭐ Advanced DAX Measures (What Makes This Project Unique)
Unlike traditional churn dashboards, this project incorporates advanced analytical metrics, including:
✔ Risk Segmentation Logic
Customers were categorized based on churn likelihood and business impact to prioritize retention efforts.
✔ Churn Lift Analysis
Measured how much higher the churn risk is for specific segments compared to the overall churn rate.
✔ Priority Scoring Model
A scoring system was developed to identify high-value customers based on:
Contract type
Tenure duration
Monthly charges
This enables targeted retention campaigns.
✔ What-If Scenario Analysis
An interactive model was implemented to simulate retention improvements and estimate:
Number of customers that can be saved
Potential revenue retained
This transforms the dashboard from descriptive analytics into a decision-support tool.
📊 Key Insights
The analysis revealed that:
Churn is highly concentrated among short-tenure customers on month-to-month contracts.
A relatively small high-risk segment drives a disproportionate share of revenue loss.
Customers without technical support are significantly more likely to churn.
🛠️ Tools & Technologies
Power BI
DAX (Data Analysis Expressions)
Data Modeling
Business Intelligence Techniques
🎯 Business Value
This project demonstrates how data analytics can move beyond reporting to:
Identify critical risk segments
Prioritize business interventions
Quantify potential financial impact
Support strategic decision-making
📷 Dashboard Preview
Executive Overview
Risk Segmentation
Actions & Drivers
📌 Author
Mahmoud Lotfi Data Analyst | Business Intelligence Enthusiast