Saraiemmanuelz/Abuja-E-Hailing-Market-Analysis

📊 End-to-end analysis of the e-hailing market in Abuja, Nigeria. Featuring a Power BI dashboard exploring driver profitability, operational bottlenecks, and the "sweet spot" for daily earnings. Includes Python data cleaning and actionable business recommendations. # Abuja-E-Hailing-Market-Analysis 📊 End-to-end analysis of the e-hailing market in Abuja, Nigeria. Featuring a Power BI dashboard exploring driver profitability, operational bottlenecks, and the "sweet spot" for daily earnings. Includes Python data cleaning and actionable business recommendations. # 📊 Dashboard Overview ## 📌 Project Overview The e-hailing ecosystem in Abuja operates within a highly competitive and cost-sensitive environment, where drivers continuously balance rising operational expenses against platform commission structures. This project analyzes survey responses from 72 e-hailing drivers to identify the primary drivers of profitability and operational efficiency. The objective is to generate data-driven insights that support drivers in optimizing earnings while providing stakeholders with a clearer understanding of the structural economic pressures within the gig economy. ## 👥 Driver Demographics & Experience Understanding the workforce behind the apps. ## 🎯 Objectives of the Analysis 1. Identify key factors influencing driver profitability in Abuja’s e-hailing market 2. Evaluate the relationship between working hours and earnings efficiency 3. Assess the impact of multi-platform usage (multi-homing) on income stability 4. Quantify the effect of major cost drivers such as fuel and platform commissions 5. Provide actionable recommendations to improve driver earnings and sustainability ## 📊 Key Insights & Findings 1. Profitability Reality Gap While the average profit margin stands at 41.7%, actual daily net earnings average approximately ₦9.42K, highlighting a significant gap between percentage margins and absolute take-home income after operational expenses. 2. Optimal Productivity Window Driver efficiency peaks between 6–11 working hours daily. Beyond 11 hours, marginal earnings decline due to fatigue, higher fuel consumption, and reduced ride efficiency. 3. Multi-Homing Advantage Drivers operating across multiple platforms (Bolt, Uber, …