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Customer Churn Analysis Project

🎯 Project Goal

The goal of this project is to analyze customer behavior and identify why customers stop using a company’s product or service.

This project is extremely important because businesses lose huge amounts of money when customers leave.

Customer Churn Analysis helps companies:

• Reduce customer loss

• Improve customer satisfaction

• ncrease retention

- Improve revenue

This is one of the MOST important real-world analytics projects used in:

- Telecom

- Banking

- Telco Customer Churn Dataset

- Customer Retention Dataset

- Telecom Churn Dataset

📂 STEP 2: Understand the Dataset

Common Columns in Churn Data

Customer ID : Unique customer identifier

Gender : Male/Female

Age : Customer age

Tenure : Time with company

Monthly Charges : Monthly bill

Total Charges : Total spending

Contract Type : Monthly/Yearly

Internet Service : Fiber/DSL

Payment Method : Payment type

Support Tickets : Complaints/issues

Churn : Customer left or not

🧹 STEP 3: Data Cleaning

Customer data usually contains:

- Missing values

- Duplicate customers

- Incorrect numeric formats

- Inconsistent categories

✔️ Cleaning Tasks

Remove Duplicate Customers

Check:

- Duplicate Customer IDs

Handle Missing Values

Common missing columns:

- Total Charges

- Contract Type

- Payment Method

Methods:

- Remove rows

- Replace values

- Use averages/defaults

Standardize Categories

Example:

- “Month-to-month”

- “Monthly”

- “month to month”

Convert into:

- “Monthly”

Correct Data Types

Examples:

- Monthly Charges → Decimal

- Tenure → Integer

- Churn → Yes/No

📊 STEP 4: Define Customer Churn KPIs

Essential KPIs

✔️ Total Customers

COUNT(Customer_ID)

✔️ Churned Customers

COUNT(CASE WHEN Churn = 'Yes' THEN 1 END)

✔️ Churn Rate

(Churned_Customers / Total_Customers) * 100

Purpose:

Measures percentage of customers leaving.

✔️ Average Revenue Per User (ARPU)

AVG(Monthly_Charges)

✔️ Customer Lifetime Value (Basic)

AVG(Total_Charges)

🗄️ STEP 5: Analyze Churn Data Using SQL

📌 SQL Query Examples

1. Churn by Contract Type

SELECT Contract_Type,

COUNT(*) AS Churn_Count

FROM Customers

WHERE Churn = 'Yes'

GROUP BY Contract_Type

ORDER BY Churn_Count DESC;

2. Average Monthly Charges of Churned Customers

SELECT AVG(Monthly_Charges) AS Avg_Monthly_Charges

FROM Customers

WHERE Churn = 'Yes';

3. Churn by Internet Service

SELECT Internet_Service,

COUNT(*) AS Customers_Left

FROM Customers

WHERE Churn = 'Yes'

GROUP BY Internet_Service;

4. Customers with Highest Tenure

SELECT Customer_ID,

Tenure,

Total_Charges

FROM Customers

ORDER BY Tenure DESC

LIMIT 10;

📈 STEP 6: Build Customer Churn Dashboard

Use:

- Power BI

- Tableau

🎨 Dashboard Layout

Section 1: KPI Cards

Display:

- Total Customers

- Churn Rate

- Monthly Revenue

- Average Tenure

Section 2: Visualizations

✔️ Line Chart

Use for: Monthly Churn Trend

✔️ Bar Chart

Use for: Churn by Contract Type

✔️ Pie Chart

Use for: Payment Method Distribution

✔️ Heatmap

Use for: Churn Correlation Analysis

✔️ Funnel Chart

Use for: Customer Lifecycle Stages

🎛️ STEP 7: Add Filters/Slicers

Add:

✔️ Contract Type

✔️ Internet Service

✔️ Gender

✔️ Payment Method

✔️ Tenure Group

Interactive dashboards improve user experience.

🎨 STEP 8: Improve Dashboard Design

Design Tips

✔️ Highlight churn KPIs in red/orange

✔️ Use clear labels

✔️ Keep visuals simple

✔️ Avoid overcrowding

✔️ Use consistent fonts

📖 STEP 9: Add Business Insights

Insights are the MOST important part.

Example Insights

✔️ Customers with monthly contracts churn more frequently.

✔️ Customers with high support tickets are more likely to leave.