🍕 Pizza Sales Performance & Customer Insights (SQL & Power BI)

End-to-end analysis of pizza sales using SQL and Power BI to highlight business opportunities.Showcasing strong data cleaning, visualization, and communication skills for real-world impact

📌 Table of Contents

• Overview
• Business Problem
• Dataset
• Tools & Technologies
• Project Structure
• Data Cleaning & Preparation
• Exploratory Data Analysis (EDA)
• Research Questions & Key Findings
• Dashboard
• How to Run This Project
• Final Recommendations
• Author & Contact

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📌 Overview

This project analyzes pizza sales data using SQL for querying and Power BI for visualization.
The goal is to uncover sales trends, customer behavior, and performance insights to support data-driven decision-making.

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💡 Business Problem

A pizza store collects daily sales data but struggles to interpret it effectively.
The objective is to transform raw data into actionable insights—identifying top products, sales patterns, and improvement opportunities.

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📊 Dataset

• Source: Pizza sales transaction data (orders, pizzas, quantities, prices, etc.)
• Size: ~48k rows (depending on dataset version)
• Columns include: OrderID, Date, PizzaName, Category, Size, Quantity, Price, etc.

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🛠 Tools & Technologies

• SQL → Data cleaning, transformation, and KPI calculations
• Power BI → Dashboard design and visualization
• Excel/CSV → Raw data storage

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📂 Project Structure

📁 Pizza-Sales-Performance-Customer-Insights-SQL-Power-BI
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├── PIZZA SALES.sql # SQL queries for cleaning and KPI generation
├── pizza sales analysis.pbix # Power BI dashboard file
├── images/ # Exported PNG screenshots of dashboards
└── README.md # Project documentation

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🧹 Data Cleaning & Preparation

• Removed duplicates and missing values
• Standardized pizza categories and sizes
• Created calculated fields: Total Bill, Day of Week, Hour of Sale
• Ensured consistency for time-based analysis

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🔎 Exploratory Data Analysis (EDA)

• Time Trends → Hourly & daily sales distribution
• Branch Analysis → Revenue contribution by category/size
• Product Demand → Best & worst-selling pizzas
• Customer Behavior → Average order size, repeat preferences

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❓ Research Questions & Key Findings

1. When do customers order the most pizzas?
   → Peak hours: Friday & Saturday evenings (6–8 PM)
2. Which pizzas generate the highest revenue?
   → Classic Deluxe Pizza and Large Sizes
3. Which pizzas perform poorly?
   → Certain specialty pizzas contribute <2% of sales
4. What is the average order value?
   → ~$38 per order

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📊 Dashboard

⭐ KPI Overview

📅 Sales Trends

🍕 Pizza Performance

📂 Category Insights

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🚀 How to Run This Project

1. Clone/download this repository
2. Run PIZZA SALES.sql in your SQL environment to generate KPIs & insights
3. Open pizza sales analysis.pbix in Power BI Desktop
4. Explore interactive dashboards for trends and patterns

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📝 Final Recommendations

• 📈 Staffing → Increase staff during evening peak hours
• 🍴 Menu Optimization → Promote Classic Deluxe and high-margin items
• 🎁 Marketing Strategy → Offer bundled deals for large-sized pizzas
• 📊 Future Work → Add customer demographics for deeper personalization

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👤 Author & Contact

T V Shreyas Kumar

• 💼 LinkedIn: www.linkedin.com/in/shreyas-kumar-7868ab228
• ✉️ Email: Shreyas291103@gmail.com