SQL-backed analysis of a meal delivery service: operations, efficiency, promotions, and scaling.
| File | Role |
|---|---|
data_cleaning.sql |
Clean and prepare the dataset |
exploratory_analysis.sql |
Demand, center performance, promotional impact |
dashboard_screenshot.png |
Excel dashboard snapshot |
- Rice Bowls lead on revenue (one meal > ₦2.4B). Beverages drive highest total revenue from volume.
- Promotions lifted weekly orders ~3×; unregulated discounts suggest revenue leakage → need targeted, margin-aware campaigns.
- Center Type A is most scalable: high volume, coverage, and efficiency.
- Order spikes in Weeks 5, 48, 53, 60 → seasonal or event-driven; use for forecasting and promo planning.
- Italian and Beverages hold strong appeal → balance flagship items with volume drivers.
- No year in weekly data — week IDs span 145+ values but no year; limits cross-year trends and seasonality.
- No cost or margin per meal — no unit cost, waste, or profit; limits promotion and profitability analysis.
- No delivery timing — no dispatch/delivery timestamps; limits logistics and satisfaction analysis.
- SQL — Cleaning and analysis
- Excel — Dashboard and visuals
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