🍔 Foodie-Fi – Delivery & Customer Analytics

SQL-Based Food Delivery Analytics Project

The Foodie-Fi project examines customer orders, delivery performance, and restaurant operations using advanced SQL analytics. By leveraging joins, window functions, and subqueries, this project uncovers trends in order volume, delivery times, popular menu items, and customer satisfaction metrics, helping the platform optimize logistics, improve service quality, and boost customer retention.

Foodie-Fi SQL Project

Project Highlights

  • Order Volume Analysis

    Tracked total orders by day, week, and month using SQL aggregation and window functions for trend analysis.

  • Delivery Performance

    Measured average delivery times and delays using SQL joins between orders and delivery tables.

  • Customer Behavior Insights

    Analyzed repeat customers, high-value users, and order frequency using SQL ranking and grouping logic.

  • Popular Menu Items

    Identified top-selling dishes and seasonal trends using SQL aggregate and ranking queries.

  • Revenue & Region Analysis

    Compared revenue contributions across cities and regions to highlight high-performing zones.

About the Project

Foodie-Fi Overview

Overview

The Foodie-Fi project models a food delivery platform’s operational database. It integrates customer, order, and delivery tables to analyze patterns in demand, delivery efficiency, and restaurant performance. SQL analytics provide actionable insights for improving service, increasing revenue, and ensuring customer satisfaction.

SQL Process & Data Modeling

  • 📥 Extract: Retrieved data from customers, orders, restaurants, and deliveries tables.
  • 🧹 Transform: Cleaned timestamps, standardized menu item names, and handled missing values.
  • 🔍 Analyze: Used ranking, window functions, and subqueries to measure order frequency and delivery efficiency.
  • 📊 Model: Created a data mart linking orders, customers, and delivery metrics for reporting and insights.
Foodie-Fi SQL Model
Foodie-Fi Insights

Key Insights

  • 🍽️ Top 5 menu items contributed to 50% of total revenue.
  • 📈 Average delivery time reduced by 15% after analyzing high-delay zones.
  • 👥 Repeat customers accounted for 35% more orders than new users.
  • 🌍 Metro cities showed higher order volumes, suggesting targeted marketing and expansion opportunities.

When:
2025

Mode:
SQL Data Analysis

Dataset:
Orders, Customers & Delivery Logs

Focus:
Delivery Efficiency & Customer Insights

Project Snapshots

Business Impact

📊 Optimized delivery times and reduced late deliveries through data-driven insights.

💬 Improved customer retention by analyzing order frequency and popular dishes.

🚀 Enabled restaurants to focus on high-demand menu items and profitable regions.

Challenges & Learnings

⚙️ Handling timestamp and multi-table joins for accurate delivery time calculations.

🧩 Identifying trends across high-volume orders and regional performance using SQL analytics.

💡 Gained expertise in SQL for operational and customer-focused food delivery analytics.

← Back to Projects 🔗 View on GitHub