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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