Sales Analysis for Plato's Pizza

Welcome to the "Sales Analysis repository for Plato's Pizza." This project showcases a rich array of skills, including data cleaning, data modeling, and application of date functions, aggregation functions, and advanced data manipulation techniques such as SELECT statements, advanced joins, CTEs, GROUP BY clauses, and WHERE clauses.

Key Insights

1. Maximum Orders and Busiest Day

• Peak Orders: On November 27, 2015, our restaurant experienced a peak with a staggering 115 orders, making it the busiest day in the dataset.
• Busiest Time: The busiest time was at 12:25:12, with a notable quantity of 28 pieces sold on November 18, 2015 (Wednesday).

2. Classic Category Analysis

• Best Performers:
  • Classic (S) with 6139 items sold, generating $67,966 in sales.
  • Classic (L) with 4057 items sold, accumulating $73,269 in revenue.
• Worst Performer: Classic (XXL) with a mere 28 items sold.

3. Veggie, Supreme, and Chicken Categories

• Top Performers:
  • Veggie (L) with $101,552 in sales (5403 items sold).
  • Supreme (L) with $92,463 in sales (4564 items sold).
  • Chicken (L) with $99,579 in sales (4932 items sold).

4. Size as a Decisive Factor

• Large Pizzas: Commanded 46% of total sales.
• Veggie Category: Large pizzas accounted for 44% of total sales.
• Chicken Category: Large pizzas accounted for 38% of total sales.
• Classic Category: The small-sized "Big Meat Pizza" stole the spotlight.

5. The Enigma of Greek Pizza

• Low Sales: The "Greek Pizza" within the "Classic" category registered unexpectedly low sales, inviting contemplation on customer preferences.

6. Triumph of "The Classic Deluxe Pizza"

• Best-Seller: "The Classic Deluxe Pizza" emerged as our best-selling culinary masterpiece.

7. Busiest Month and Day

• Most Lucrative Month: July, with a total quantity of 4392 items sold.
• Busiest Day: Fridays, with a remarkable 8242 items sold.

8. Average Order Value

• Average Order Value: $37.56

Conclusion

This project, a testament to analytical prowess, dives deep into pizza sales dynamics. It reveals crucial insights, identifying best and worst-performing categories and sizes. From pinpointing peak sales times to uncovering the most profitable days and months, this project equips us with actionable knowledge.

In the realm of data, there is always more to explore. For deeper insights, consider expanding the dataset to include factors like cost prices, multi-year sales data, and customer details like addresses. These additions could elevate our analysis, allowing us to refine strategies and make data-driven decisions to improve the business.

Feel free to explore the SQL scripts for an in-depth understanding of the analyses conducted and take a look at the dashboard. Happy data diving! 🍕📊