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Case Study: Pizza Sales Data Analysis

By: Ahmed Ali

1. Project Overview

This project aims to analyze pizza sales data to understand customer behavior, identify peak times, and determine top-selling products. By transforming raw data into an interactive dashboard, this analysis empowers the restaurant's management to make data-driven decisions that maximize revenue and optimize inventory management.

2. Business Problem & Objectives

The management was facing challenges in determining the following:

• What are the busiest days and months that experience the highest customer traffic?

• What are the most profitable pizza categories and sizes?

• How can staff schedules and ingredient inventory be optimized to prevent waste and stockouts?

3. Tools Used

• SQL: Utilized for data cleaning, preprocessing and extracting key metrics using complex queries.

• Power BI: Employed for data modeling writing DAX measures for KPIs and designing an interactive data visualization dashboard.

4. Key Insights

The data analysis revealed the following key performance indicators:

• Overall Performance: Total Revenue reached approximately 329.33K, with an Average Order Value AOV of 38.40.

• Time Trend Analysis: Thursday and Friday are the best-selling days of the week. Additionally, January and July recorded the highest volume of orders.

• Product Analysis:

o

o The Large size accounted for the majority of sales at 46.18%, followed by the Medium size.

o The Classic Pizza category topped the sales chart as the most preferred choice among customers, closely followed by the Supreme category.

Insert your Dashboard Screenshot here

5. Business Recommendations

• Staff Management: Increase the number of staff during Thursday and Friday shifts, as well as throughout January and July, to ensure fast order fulfillment and avoid bottlenecks.

• Inventory Management: Maintain higher stock levels for "Classic Pizza" ingredients and "Large" size boxes to prevent stockouts during peak hours.

• Marketing Strategy: Launch promotional offers on smaller sizes and the "Chicken" category during mid-week days (e.g., Monday and Tuesday) to boost sales during slower periods.