📊 Telco Customer Churn Analysis And Revenue Forecasting

📁 Project Overview

This project analyzes customer churn in a telecommunications company to identify key drivers of churn, forecast future revenue impact, and develop data-driven retention strategies.
It combines SQL, Python, and Power BI for a full data pipeline — from raw data extraction to advanced analytics and executive-level visualization.

---

🎯 Objectives

• Understand why customers churn and identify at-risk segments.
• Build predictive and financial models to estimate churn and its revenue impact.
• Develop actionable strategies for retention and revenue optimization.
• Present insights in an interactive Power BI dashboard for business leaders.

---

🧰 Tech Stack

Tool	Purpose
Python (Pandas, NumPy, Scikit-Learn)	Data cleaning, feature engineering, churn modeling
SQL (PostgreSQL / MySQL)	Data extraction, aggregation, and KPI computation
Power BI	Executive dashboards, churn prediction visualization, forecasting
Excel	Exploratory data review and validation
GitHub	Project versioning and portfolio publication

---

🧩 Data Pipeline

Raw Telco Data → SQL Cleaning → Python Modeling → Power BI Visualization → Strategic Insights

1. Data Cleaning & Transformation

   • Removed duplicates and nulls from TotalCharges.
   • Standardized categorical fields and encoded binary attributes.
   • Calculated new metrics such as AvgMonthlyRevenue, CLV, and ChurnRate.

2. SQL Analysis

   • Aggregated churn by contract, payment method, and tenure.
   • Segmented customers by revenue and churn behavior.
   • Calculated revenue retention under various churn scenarios.

3. Predictive Modeling (Python)

   • Built a logistic regression model to predict churn probability.
   • Evaluated using accuracy, precision, recall, and ROC-AUC metrics.
   • Exported predictions for visualization in Power BI.

4. Power BI Reporting

   • Designed a 5-page interactive dashboard with executive KPIs, churn analysis, and revenue forecasting.

---

📊 Power BI Dashboard Overview

The Power BI report consists of 5 pages, each designed for specific business decisions:

1. 🏢 Executive Overview

High-level company performance snapshot with KPIs:

• Total Customers, Active vs Churned
• Churn Rate (%)
• Average Tenure
• Monthly & Total Revenue

2. 🔍 Churn Predictions

Machine learning-powered churn probability visualization:

• Filters by gender, contract, and payment type
• Top churn drivers identified using feature importance
• At-risk customer segments highlighted

3. 📈 Revenue Forecast

Predicts future revenue based on historical trends and churn rate.

• Time-series forecasting using Power BI’s analytics pane
• Comparison of baseline vs retention-improved revenue scenarios

4. 💰 Customer Lifetime Value (CLV)

Segments customers by their lifetime value contribution:

• CLV tiers (High / Medium / Low)
• Relationship between CLV, tenure, and churn probability
• Focus on high-value churners for retention targeting

5. 🧮 Revenue Strategy Simulation

Interactive “what-if” analysis to simulate revenue impact of:

• Retention improvements
• Contract upgrades
• Customer loyalty programs

---

🔍 Key Insights & Findings

• Overall churn rate: 26.5% of customers have churned.
• Contract type: Month-to-month customers are 2.4× more likely to churn.
• Payment method: Electronic check users churn 30% faster than other groups.
• Tenure: Customers with <12 months tenure represent 60% of all churns.
• CLV impact: High-value customers contribute over 60% of total revenue, making them critical for retention.
• Forecast insight: A 5% improvement in retention leads to ~12% increase in annual revenue.

These insights guide retention strategies focused on contract conversion, early engagement, and loyalty incentives.

---

📈 Results Summary

Metric	Value	Description
Churn Rate	26.5%	Percentage of customers lost
Retention Improvement Potential	+5%	From targeted contract & payment fixes
Forecasted Revenue Uplift	+12%	Annual projection with improved retention
Model Accuracy	83%	Logistic regression churn model
CLV Uplift	+18%	From focus on high-value customers

---

🧩 Project Structure

Telco-Churn-Analysis/
│
├── data/
│   ├── raw/ WA_Fn-UseC_-Telco-Customer-Churn # Original CSV dataset
│   ├── processed/cleaned_customers           # Cleaned data for modeling and Power BI
│
├── notebooks/
│   ├── 01_data_import.ipynb
│   ├── 02_feature_engineering.ipynb
│   ├── 03_chun_modelling.ipynb
│   ├── 04_revenue_forecast.ipynb
│
├── sql/
│   ├── churn_project.sql   # SQL queries for churn, CLV, revenue segmentation
│
├── scripts/
│   ├── db_config.py         # Database configuration
│   ├── db_connection.py     # Connection handler
│   ├── generate_revenue.py   # Preprocessing and feature engineering scripts
│
├── powerbi/
│   ├── Telco_Churn_Analysis.pbix   # Power BI Dashboard file
│
├── reports/
│   ├── Telco_Churn_Analysis_Report.pdf
│
├── README.md
└── requirements.txt

---

🚀 How to Run

1. Clone the repository

git clone https://github.com/yourusername/Telco-Churn-Analysis.git

2. Navigate to the folder

cd Telco-Churn-Analysis

3. Install dependencies

pip install -r requirements.txt

4. Open the notebooks in Jupyter or VSCode jupyter notebook

🧠 STEP 2: Replace Before Commit

Before you commit:

1. Replace
   • https://app.powerbi.com/your_dashboard_link → your published Power BI report link
   • yourusername, your_email@example.com, and LinkedIn link.
2. Keep the triple backticks (```) around code blocks — GitHub formats them automatically.
3. Commit using:

   git add README.md
   git commit -m "Add project documentation"
   git push
   

---

⭐ Project Highlights

✅ End-to-end data analysis: from SQL → Python → Power BI

✅ Predictive churn modeling integrated into visualization

✅ Strategic simulation of retention impact

✅ Ready for presentation to executives or recruiters

“Data without interpretation is just noise; turning churn analytics into action is what drives value.”

📘 References

Kaggle: Telco Customer Churn Dataset

Power BI Documentation – Forecasting and Analytics

Scikit-Learn Logistic Regression Reference

👤 Author

Stephen Karanja

Data Analyst | BI Developer | Automation Specialist

📧 muhurakaranja7@gmail.com

🔗 LinkedIn Profile

🌐 Portfolio / Website

🪪 License

This project is licensed under the MIT License – see the LICENSE file for details.

---