victorugbedeojo/Telecom-Customer-and-Revenue-Analytics---Nigeria

End-to-end telecom analytics project (Excel → MySQL → Power BI) analyzing churn, ARPU, and revenue for a simulated Nigerian telecom customer base, benchmarked against real NCC complaint data across MTN, Airtel, Glo, and 9mobile # Telecom Customer & Revenue Analytics — Nigeria An end-to-end data analytics project combining **real NCC (Nigerian Communications Commission) complaint data** with a simulated customer base to answer six real-world telecom business questions — built across Excel/Google Sheets, MySQL, and Power BI. ## Business Questions Answered 1. What % of revenue comes from data vs. voice vs. SMS? 2. Which regions have the highest ARPU (Average Revenue Per User)? 3. Are prepaid or postpaid customers more profitable? 4. Churn signal: which customers haven't recharged in 30+ days? 5. Which recharge channel is most popular, and does channel correlate with customer value? 6. How do MTN, Airtel, Glo, and 9mobile compare on real NCC complaint volume and resolution rate? ## Dashboard Built in Power BI, connected live to a MySQL database of 6 analytical views. ## Tech Stack - *Google Sheets / Excel* — data joining (VLOOKUP/XLOOKUP) and initial cleaning - *MySQL* — relational schema (5 tables, foreign-key constraints) and 6 analytical views - *Power BI* — dashboard with 5 KPI cards and 6 visuals, connected live to MySQL ## Data Sources - *Real data:* NCC Consumer Complaint Statistics, pulled directly from the NCC Market Data & Reports portal (February 2026 figures, plus a full-year 2025 trend). - *Simulated data:* customer base (300 customers), recharge transactions (2,100+), and usage/revenue records (5,400+) — no real telecom company publishes customer-level data, so this layer is simulated for portfolio purposes, following standard practice for this type of project. ## Repository Contents | File | Description | |---|---| | telecom analytics.sql | Full schema — 5 tables + 6 analytical views (one per business question) | | dim_customers.csv | Raw customer master data (300 customers) | | dim_pricing.csv | Reference bundle pricing by network | | fact_recharges.csv | Raw recharge transaction data (2,100+ rows) | | fact_usage_revenue.csv | Raw monthly usage/revenue data by serv …