E- Commerce Project - PIZZA SALES REPORT

Problem Statement

The pizza business has been thriving, but to sustain and enhance our growth, I need a deeper understanding of our sales data. I aim to address the following questions:

1. What is my total revenue over a specific period?
2. How many pizzas have I sold in total?
3. What is the average order amount?
4. Which pizza names are the most and least popular by order?
5. Which pizza names have the highest and lowest quantity sold?
6. Which pizza sizes are most preferred by our customers?
7. Which pizza categories generate the most sales?
8. What are the daily and monthly sales trends?
9. How do sales vary across pizza categories and sizes?

Challenges While the analysis has been insightful, some challenges may arise:

• The dataset may require continuous updates for real-time insights.
• Customer behavior can change, affecting my sales trends.
• External factors like the economy may influence pizza sales.

Data Exploration

To kickstart my analysis, I first take a look at the dataset. It provides information about pizza orders, including the total price, quantity sold, order date, pizza name, size, and category.

• Total Revenue: I calculate the total revenue, which gives me an overview of my earnings.
• Total Pizza Sold: This metric tells me the total number of pizzas I have sold.
• Total Orders: It provides the count of distinct orders, giving me an idea of my order volume.
• Average Order: The average order amount helps me understand the spending patterns of our customers.

Results

The following are some of the key findings from the analysis:

• The total number of pizza orders was approximately 21,000.
• The total sales of pizza were approximately 50,000.
• The total revenue from pizza sales was $817,860.05.
• The average order value was approximately $38.
• Analysis Processes

1. Top and Bottom Performers: I identify the top and bottom 5 pizza names by the number of orders. This helps me understand customer preferences.

2. Top and Bottom Performers by Quantity: I identify the top and bottom 5 pizza names by the quantity sold. This reveals which pizzas are most and least popular.

3. Sales by Pizza Size and Category: I determine which pizza sizes and categories are most sold, helping me optimize my inventory.

4. Sales Trends: I analyze daily and monthly sales trends to spot patterns in customer ordering behavior.

5. Percentage Sales by Pizza Category and Size: I calculate the percentage of sales for each pizza category and size, giving me insights into product performance.

Key Findings

The following are some of the key findings from the data analysis:

The top 5 pizzas by order and quantity sold were:

1. The Classic Deluxe Pizza
2. The Hawaiian Pizza
3. The Pepperoni Pizza
4. The Barbecue Chicken Pizza
5. The Thai Chicken Pizza

The bottom 5 pizzas by order and quantity sold were:

1. The Brie Carre Pizza
2. The Mediterranean Pizza
3. The Spinach Supreme Pizza
4. The Calabrese Pizza
5. The Chicken Pesto Pizza

The total amount sold by size was:

• L- 18526
• M- 15385
• S- 14137
• XL- 544
• XXL- 28

The total amount sold by categories was:

• Classic- 14579
• Supreme- 11777
• Veggie- 11449
• Chicken- 10815

The trend pizza by the day was:

• Friday: 3538
• Thursday: 3239
• Saturday: 3158
• Wednesday: 3024
• Tuesday: 2973
• Monday: 2794
• Sunday: 2624

The trend pizza by month was:

• July
• May
• January

The percent sales by pizza category were:

• Chicken- 23.96%
• Supreme- 25.46%
• Classic- 26.91%
• Veggie- 23.68%

The percent sales by pizza size were:

• L- 45.89%
• M- 30.39%
• S- 21.77%
• XL- 1.72%
• XXL- 0.12%

Recommendations

1. The total revenue for the specified period is $817,860.05. To sustain growth, I should focus on maintaining or increasing this figure.
2. They have sold a total of 50,000. pizzas. This data can guide inventory and production planning.
3. The average order amount is $38, indicating the customers’ spending habits. This could inform pricing and promotions.
4. The Classic Deluxe Pizza are customer favorites, and they should consider marketing them more prominently.
5. The Classic Deluxe Pizza are top performers in terms of quantity, showing strong demand.
6. Large Size and Classic dominate sales, suggesting they should prioritize these sizes and categories.
7. Sales show a noticeable Trend by Day (Friday) and Trend by Month (July), which could help with staffing and marketing efforts.
8. Percentage Sales by Pizza Category and Percentage Sales by Pizza Size provide insights into the contribution of different products.

Conclusion This analysis has unveiled critical insights that can guide my business decisions. By understanding my sales data and customer preferences, I can optimize my offerings and enhance my growth.