🍕 Pizza_Runner – Delivery & Sales Analytics

SQL-Based Pizza Delivery Analytics Project

The Pizza_Runner project analyzes order patterns, delivery efficiency, and customer preferences using SQL analytics. By using advanced queries, joins, and window functions, the project identifies peak order times, popular pizza types, delivery delays, and customer behavior trends, helping the business optimize operations, improve delivery times, and enhance customer satisfaction.

Pizza_Runner SQL Project

Project Highlights

  • Order Volume Analysis

    Analyzed daily, weekly, and monthly pizza orders using SQL aggregation and window functions for trend detection.

  • Delivery Performance

    Measured average delivery times and delays by joining orders and delivery tables, highlighting bottlenecks.

  • Customer Insights

    Segmented customers by order frequency, preferences, and location using SQL ranking and grouping techniques.

  • Pizza Popularity Trends

    Identified top-selling pizzas and combos using aggregate and ranking queries, including seasonal trends.

  • Revenue & Regional Analysis

    Compared revenue across delivery zones and customer segments to prioritize high-value areas.

About the Project

Pizza_Runner Overview

Overview

The Pizza_Runner project provides insights into pizza delivery operations and customer ordering patterns. It integrates orders, deliveries, and customer data to analyze performance, optimize delivery times, and identify top-selling products. SQL analytics help make data-driven decisions for marketing, menu optimization, and operational efficiency.

SQL Process & Data Modeling

  • 📥 Extract: Combined order, delivery, customer, and menu item tables.
  • 🧹 Transform: Cleaned timestamps, standardized pizza names, and handled missing values.
  • 🔍 Analyze: Applied window functions, ranking, and joins to assess orders, delivery times, and customer segments.
  • 📊 Model: Designed a data mart connecting orders, customers, and deliveries for detailed analytics.
Pizza_Runner SQL Model
Pizza_Runner Insights

Key Insights

  • 🍕 Top 5 pizzas contributed to 60% of total revenue.
  • 📈 Peak order times were between 7 PM – 9 PM, suggesting optimal delivery staffing schedules.
  • 👥 Repeat customers had higher average order values than new customers.
  • 🌍 Delivery zones with faster times showed higher customer satisfaction scores.

When:
2025

Mode:
SQL Data Analysis

Dataset:
Orders, Customers & Delivery Logs

Focus:
Delivery Efficiency & Customer Insights

Project Snapshots

Business Impact

📊 Optimized delivery routes and reduced late deliveries based on SQL-driven insights.

💬 Enhanced customer satisfaction through insights on peak order times and repeat orders.

🚀 Increased revenue by highlighting top-selling pizzas and high-value customer segments.

Challenges & Learnings

⚙️ Handling multiple tables for orders, customers, and deliveries efficiently using SQL.

🧩 Identifying trends and performance metrics for operational and marketing improvements.

💡 Strengthened skills in SQL analytics, operational data modeling, and customer-focused insights.

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