anegbe37/MTN-Nigeria-Customer-Churn-Analysis-Q1-2025

📱 MTN Customer Churn Analysis Dashboard Interactive Data-Driven Platform for analyzing and visualizing MTN Nigeria's customer churn dynamics. Built with Python, Pandas, Streamlit, and Plotly, it enables hands-on exploration of churn trends, root causes, and strategic action planning. 🚀 Project Overview MTN Nigeria's churn rate was found to be ~29%, with behaviour varying significantly across demographics, devices, regions, and subscription plans This project: Identifies churn drivers through satisfaction scores, device types, and data plans Visualizes churn hotspots across states Pinpoints high-risk and high-value segments Offers actionable recommendations for retention 📂 Repository Structure ├── mtn_churn_model.py # Data cleaning, KPI & churn analysis logic ├── mtn_streamlit_dashboard.py # Streamlit app displaying interactive dashboards ├── data/ # Raw csv data file ├── assets/ # Images for README and dashboard UI ├── requirements.txt └── README.md # This file 📎 Dataset ➡️ Download the dataset here: [kaggle.com] 🎯 Key Insights & Visualizations Overall Churn Rate: ~29%, with variations by plan, device, and region High-Risk Segments: Pre-paid mobile SIM users and 65GB monthly data subscribers churn most Device Effect: Advanced devices (e.g., 5G routers) correlate with lower churn Regional Patterns: Urban states like Lagos and Abuja show elevated churn — ripe for targeted retention strategies Revenue-Value Paradox: High-revenue, satisfied customers display stronger loyalty, indicating premium service retention potential 🏗️ Features Key Metrics Dashboard: Total size, churn rate, revenue loss, average satisfaction Satisfaction Insights: Correlations between satisfaction scores and churn Device & Plan Breakdown: Visuals on churn by device type and plan tier Geospatial View: Heatmaps of churn across states …