Customer Churn Analytics Dashboard

Project Overview

This project analyzes customer churn behavior, customer segments, revenue contribution, and revenue risk using SQL, Python, and Power BI.

The objective is to identify:

• Why customers churn
• Which customer groups generate the most revenue
• Which segments are at the highest risk
• Key business drivers influencing customer retention

The project follows a complete analytics workflow:

SQL → Data Analysis → Business Insights → Power BI Dashboard

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Dataset Information

Metric	Value
Total Customers	5,000
Total Features	15
Churned Customers	2,077
Churn Rate	41.54%
Total Revenue	₹141.72 Million

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Tools & Technologies

• SQL
• Python (Pandas)
• Power BI
• DAX
• Git & GitHub

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Project Structure

Customer-Churn-Analytics-PowerBI/

│
├── Dashboard/
│   └── Customer-Churn-Analytics.pbix
│
├── Dataset/
│   └── customers.csv
│
├── Screenshots/
│   ├── Page1_Executive_Overview.png
│   ├── Page2_Churn_Intelligence.png
│   ├── Page3_Customer_Segmentation.png
│   └── Page4_Advanced_Churn_Intelligence.png
│
├── SQL/
│   ├── 01_database_setup.sql
│   ├── 02_executive_overview.sql
│   ├── 03_churn_analytics.sql
│   ├── 04_customer_segmentation.sql
│   └── 05_advanced_churn_intelligence.sql
│
└── README.md

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SQL Analysis

The analysis was divided into five business-focused modules:

1. Executive Overview

• Total Customers
• Churned Customers
• Churn Rate
• Total Revenue
• Revenue At Risk
• Average Customer Lifetime Value (CLV)

2. Churn Analytics

• Churn by Plan Type
• Churn by Contract Type
• Churn by Gender
• Churn by Payment Method
• Churn by Age Group

3. Customer Segmentation

• Regular Customers
• High Value Customers
• VIP Customers
• Segment Revenue Contribution
• Segment-wise Churn Analysis

4. Revenue Analysis

• Revenue by City
• Revenue by Plan Type
• Top Revenue Generating Customers
• Revenue Distribution Analysis

5. Advanced Churn Intelligence

• Revenue At Risk Analysis
• Satisfaction vs Churn
• Support Tickets vs Churn
• High-Risk Customer Identification

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Power BI Dashboard

The dashboard consists of four interactive pages.

Page 1 — Executive Overview

• KPI Cards
• Churn Distribution
• Revenue by Plan Type
• Customers by Contract Type
• Top Cities by Revenue

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Page 2 — Churn Intelligence

• Churn Rate by Contract Type
• Churn by Plan Type
• Satisfaction vs Churn
• Support Tickets vs Churn
• Churn by Payment Method

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Page 3 — Customer Segmentation & Revenue Analysis

• Revenue by Segment
• Segment Distribution
• Revenue by City
• Segment-wise Churn
• Top Customers Analysis

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Page 4 — Advanced Churn Intelligence

• Revenue At Risk
• Churn by Age Group
• Satisfaction Segment Analysis
• Revenue Risk by Plan Type
• Top Revenue Risk Customers

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Key Business Insights

• Monthly contract customers exhibit the highest churn rate.
• Customers with lower satisfaction scores are significantly more likely to churn.
• Higher support ticket volumes correlate strongly with customer churn.
• High Value customers generate the largest share of revenue.
• VIP customers show the lowest churn rate.
• Revenue is concentrated within a small group of high-value customers.

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Skills Demonstrated

SQL

• Aggregations
• CASE Statements
• Window Functions
• Ranking Functions
• Business Analysis Queries

Python

• Data Cleaning
• Data Exploration
• Exploratory Data Analysis (EDA)

Power BI

• Data Modeling
• DAX Measures
• Interactive Dashboards
• KPI Design
• Business Storytelling

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Author

Saurav18K

Aspiring Data Analyst | SQL | Python | Power BI | Data Visualization