Pizza Sales Analysis .

Project Overview

This project analyzes pizza store sales data to uncover valuable insights about revenue, customer behavior, and product performance. The analysis helps management make data-driven decisions in sales, marketing, operations, and inventory planning.

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Business Objectives

• Calculate total revenue, total pizzas sold, and total orders
• Identify best-selling and least-selling pizzas
• Analyze sales by category and size
• Explore daily, hourly, and monthly sales trends
• Measure customer behavior with KPIs like:
  • Average Order Value (AOV)
  • Average Pizzas per Order
• Provide visual dashboards for quick decision-making

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Dataset.

File: pizza_sales.csv

Key Fields:

• order_id → Unique order identifier
• pizza_id → Unique pizza identifier
• pizza_name → Pizza sold
• quantity → Number of pizzas per order
• total_price → Revenue per transaction
• date, time → Order timestamp
• pizza_category, pizza_size → Classification details

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Analysis & Visualizations

🔹 Daily & Hourly Trends

🔹 Monthly Trends

🔹 Sales by Category & Size

🔹 Best & Worst Performers

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Key Insights

• Large (L) pizzas drive the highest revenue.
• Classic pizzas are the most popular category, while Veggie pizzas sell less.
• Peak orders occur during evenings and weekends.
• Some low-performing pizzas may need redesign or removal.
• Marketing campaigns during summer months increase sales.

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Conclusion & Recommendations

• Focus promotions on high-selling categories and sizes.
• Reconsider or optimize least-selling pizzas.
• Use sales trends to plan staffing and inventory.
• Regularly monitor KPIs through automated dashboards.

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Sample Output

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✨ This project demonstrates how Exploratory Data Analysis (EDA) can transform raw sales data into actionable business insights.