🍕 SQL Food Delivery Insights

This SQL project is focused on analyzing food delivery data to derive actionable business insights. It simulates real-world use cases faced by growth and marketing teams in a food delivery business, like customer acquisition, retention, and promotions.

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📦 Dataset

The dataset contains a single orders table with the following columns:

• Order_id
• Customer_code
• Placed_at
• Restaurant_id
• Cuisine
• Order_status
• Promo_code_Name

All data is simulated and covers various cuisines, promo usage, and customer behaviors across multiple cities.

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📌 Business Questions Solved

#	Problem Statement
1️⃣	Top outlets by cuisine without using LIMIT or TOP
2️⃣	Daily new customer count from the launch date
3️⃣	Count of users acquired in Jan 2025 who placed only one order in Jan and none after
4️⃣	Customers with no orders in the last 7 days but were acquired a month ago using a promo
5️⃣	Trigger target customers after every third order
6️⃣	List customers who only ordered using promos and ordered more than once
7️⃣	% of users organically acquired in Jan 2025 (without a promo code)

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🧰 Tools Used

• SQL Server (T-SQL syntax)
• SSMS (SQL Server Management Studio)

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🧠 Key Learnings

• Use of CTE, ROW_NUMBER(), WINDOW FUNCTIONS
• Conditional Aggregation
• Temporal Filtering (based on GETDATE())
• Real-world business thinking in SQL

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🔗 Links

• 🧑‍💻 GitHub
• 💼 LinkedIn

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