This repository contains the SQL queries used to uncover key operational insights for a meal delivery service — including scaling performance, improving efficiency, and analyzing the impact of promotional campaigns.
data_cleaning.sql– Queries used to clean and prepare the dataset.exploratory_analysis.sql– Queries used to uncover insights on meal demand, center performance, and promotional impact.dashboard_screenshot.png– Visual snapshot of Excel dashboard.
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Rice Bowls dominate in revenue, with one meal alone generating over ₦2.4B. Beverages drive the highest total revenue due to consistent volume across orders.
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Promotions increased weekly orders nearly 3x, but unregulated discounting suggests revenue leakage. Targeted, margin-aware strategies are needed.
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Center Type A is the most scalable — balancing high order volume and coverage with strong operational efficiency.
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Order spikes in Weeks 5, 48, 53, and 60 hint at seasonal or event-driven demand. This calls for smarter forecasting and promotional alignment.
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Italian meals and Beverages show sustained appeal, supporting a strategy that balances flagship items with volume drivers.
👉 Read the full analysis and recommendations on Medium
(Includes detailed thought process, reasoning behind key insights, and strategic takeaways)
This analysis was conducted with a few key constraints that limit certain conclusions:
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Missing Year Information in Weekly Data
Week numbers spanned over 145 values, suggesting multiple years, but no year data was included.
🔍 Impact: Prevents accurate trend or seasonality analysis across years. -
No Cost or Profit Margin Data Per Meal
The dataset lacked true unit costs, waste data, or profit margin metrics for individual meals.
🔍 Impact: Limits financial depth and confidence in promotion/profitability recommendations. -
Absence of Delivery Timing or Delay Metrics
No timestamps for dispatch or delivery were available.
🔍 Impact: Restricts analysis on logistics, timeliness, and customer satisfaction.
- SQL (Data Cleaning & Analysis)
- Excel (Dashboard & Visualizations)
Siva Satya Varaprasad Vasamsetti
Data Analyst | Solving problems using data and code
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