Executive Summary
- Problem & Objective: Assess the economic viability of working as an on-demand driver and determine the sustainability of the model.
- Data & Approach: This analysis combines manual daily records of income, expenses, and mileage; data cleaning and transformation in Power Query; star schema modeling and visualization in Power BI to evaluate the gig model from an operational financial perspective.
- Key Findings: Average net income per km: S/ 1.40; Net Margin: -14.08%; Operational Margin: 6.34%; profitability depends more on working during peak hours than on the total number of rides.
- Limitations & Next Steps: Improve distinction between personal and operational expenses, and expand the analysis with segmentation by app, driving time, and cost sensitivity.
Recommendation: Optimize time allocation to peak hours and define minimum income thresholds per hour and per km based on your costs. If the threshold is not consistently met, avoid operating during those time slots.
🗓️This exercise was conducted in 2022 while I was driving and learning Data Analysis. It stands as a practical case of end-to-end learning and application. Absolute values may not reflect current market conditions, but the approach, modeling, and decision-making remain valid.
Objective
Evaluate the economic viability of working as an on-demand driver by analyzing associated income and expenses to determine profitability and model sustainability. This approach can be replicated to assess similar models across other gig platforms.
Tools Used
- Excel: Manual daily logging of income, expenses, and kilometers driven.
- Power Query: Data cleaning and consolidation of daily records.
- Power BI: Dashboard with profitability KPIs, segmentations, and interactive visualizations.
Process
1. Data Logging
- Daily income (earnings from rides, incentives, and other sources outside the apps)
- Daily expenses (fuel, maintenance, personal and family-related costs)
- Daily mileage (total kilometers driven, regardless of app usage)
2. Transformation
- Record consolidation in Power Query.