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https://github.com/amitbisht99/pizza-sales-report-

Its a guided project to practice tools like SSMS + Power BI & also skills like data cleaning, data exploration, data analysis, data visualization, etc.
https://github.com/amitbisht99/pizza-sales-report-

analytics data data-visualization powerbi sql-server

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Its a guided project to practice tools like SSMS + Power BI & also skills like data cleaning, data exploration, data analysis, data visualization, etc.

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README 

        
# Pizza-Sales-Report- 🍕 Pizza Sales Analysis Project

This project analyzes pizza sales data to uncover key business insights and trends. Using SQL, Power Query, and Power BI, I explored, cleaned, and visualized the data to provide actionable recommendations for improving sales performance and understanding customer preferences.



📊 Project Objectives

Calculate Key Performance Indicators (KPIs):

a. Total Revenue

b. Average Order Value

c. Total Pizzas Sold

d. Total Orders

e. Average Pizzas Per Order



Visualize sales trends and patterns through interactive dashboards:

a. Daily and Monthly Sales Trends

b. Percentage of Sales by Pizza Category and Size

c. Performance of Top and Bottom-Selling Pizzas

d. Identify actionable insights to guide business decisions.



🛠 Tools & Technologies

a. SQL Server Management Studio (SSMS): Data exploration and KPI calculations

b. Power Query: Data cleaning and transformation

c. Power BI: Data visualization and dashboard creation



📈 Key Insights

a. Revenue: Total revenue generated was $817,860, reflecting strong performance.

b. Order Volume: 21,000 orders placed, with an Average Order Value of $38.31.

c. Daily Trends: Orders peaked on Fridays and dropped significantly over the weekend.

d. Monthly Trends: Sales peaked in July, with a slight recovery in November and December.



Pizza Categories:

a. Classic Pizza was the most popular category (26.91% share).

b. Chicken Pizza drove the highest revenue.

c. Pizza Sizes: Large pizzas dominated sales, accounting for 45.89% of orders.

d. Top Performers: Thai Chicken and Barbecue Chicken pizzas were the leaders by revenue.

e. Bottom Performers: Spinach-based pizzas consistently underperformed.



📂 Project Structure

The repository contains:

a. SQL Queries: Scripts used for data exploration and KPI calculations (/sql_scripts).

b. Power BI Dashboard: Interactive dashboard file showcasing trends and insights (/dashboard).

c. Data Cleaning Steps: Documented cleaning process using Power Query (/data_cleaning).



📌 Future Recommendations

* Focus on promoting top-performing pizzas to maximize revenue.

* Address low weekend sales with targeted campaigns or discounts.

* Revisit underperforming pizzas (e.g., spinach-based pizzas) to refine or replace offerings.

* Use monthly sales trends to plan for peak periods effectively.



🚀 Conclusion

This project showcases how data analytics can drive strategic decision-making in the food industry. By combining SQL, Power Query, and Power BI, I delivered insights that can directly impact business outcomes.



💻 Contact

Feel free to reach out if you have any feedback or questions about this project!



LinkedIn: linkedin.com/in/amitbisht181299/

Email: [email protected]