📊 Telecom Customer Churn Analysis

📌 Project Overview

Customer churn is one of the biggest challenges faced by subscription-based businesses. Retaining existing customers is significantly more cost-effective than acquiring new ones.

In this project, I performed an end-to-end data analytics workflow to identify the major factors influencing customer churn in a telecom company. The project includes data cleaning, exploratory data analysis (EDA), SQL-based analysis, and an interactive Power BI dashboard to provide business insights and recommendations.

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🎯 Business Problem

The telecom company is experiencing customer churn and wants to understand:

• Why are customers leaving?
• Which customers are most likely to churn?
• What factors contribute the most to churn?
• What strategies can improve customer retention?

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🛠 Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• SQL
• Power BI
• Jupyter Notebook

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📂 Dataset

Dataset: Telco Customer Churn Dataset

The dataset contains customer demographics, subscription details, services, billing information, tenure, and churn status.

Key columns include:

• Customer ID
• Gender
• Senior Citizen
• Partner
• Dependents
• Tenure
• Phone Service
• Internet Service
• Online Security
• Tech Support
• Contract
• Payment Method
• Monthly Charges
• Total Charges
• Churn

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📈 Project Workflow

1. Data Cleaning

• Loaded the dataset
• Identified missing values
• Removed records with missing Total Charges
• Converted Total Charges to numeric
• Checked duplicates
• Saved cleaned dataset

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2. Exploratory Data Analysis (EDA)

Performed analysis on:

• Customer churn distribution
• Gender vs churn
• Contract type vs churn
• Monthly charges vs churn
• Tenure vs churn
• Internet service vs churn
• Online security vs churn
• Tech support vs churn
• Payment method vs churn
• Correlation analysis

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3. SQL Analysis

Used SQL to answer business questions including:

• Total customers
• Churned customers
• Churn rate
• Average monthly charges

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4. Power BI Dashboard

Designed an executive dashboard to visualize:

• Total customers
• Churned customers
• Churn rate
• Average monthly charges
• Average tenure
• Churn by contract type
• Customer churn distribution
• Business insights

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📊 Dashboard

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🔍 Key Insights

• Month-to-month customers have the highest churn rate.
• Customers with shorter tenure are significantly more likely to leave.
• Customers paying higher monthly charges are more likely to churn.
• Customers without Tech Support or Online Security show higher churn.
• Gender has minimal impact on customer churn.

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💡 Business Recommendations

• Encourage customers to switch to long-term contracts through discounts and loyalty programs.
• Improve customer onboarding during the early months of service.
• Bundle Tech Support and Online Security with internet plans.
• Review pricing strategies for customers paying high monthly charges.
• Develop targeted retention campaigns for high-risk customers.

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📁 Project Structure

Customer-Churn-Analysis-Telecom
│
├── data
├── notebooks
├── dashboard
├── images
├── README.md
├── requirements.txt

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🚀 How to Run the Project

1. Clone the repository

git clone https://github.com/DataWithHamza/Customer-Churn-Analysis-Telecom.git

2. Install dependencies

pip install -r requirements.txt

3. Open the Jupyter notebooks.

4. Open the Power BI dashboard (.pbix).

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📚 Skills Demonstrated

• Data Cleaning
• Data Analysis
• Exploratory Data Analysis
• SQL Queries
• Data Visualization
• Business Analytics
• Dashboard Design
• Business Storytelling

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👨‍💻 Author

Hamza

Data Analyst | Python | SQL | Power BI