saurav123-ux / pizza_sales_analysis

Pizza Sales analysis project

Objective :

The primary objective of this project was to perform a comprehensive analysis of pizza sales data and leverage the insights to develop visually appealing dashboards. These dashboards are designed to assist the management in making informed decisions to enhance sales and overall business performance.

Project Description :

1) Data Import to MS SQL Server:

The project began by importing raw data into Microsoft SQL Server, ensuring data accuracy, consistency, and security. This process involved data cleansing and transformation to prepare it for analysis.

2) SQL Queries:

The power of SQL queries was harnessed to extract valuable insights from the dataset. This included data aggregation, filtering, and joining multiple tables to create a coherent dataset for visualization.

3) Data Import to Power BI:

To leverage Power BI's robust visualization capabilities, the processed data was seamlessly imported from SQL Server into Power BI, creating a strong foundation for visualizations.

4) DAX Queries:

The Data Analysis Expressions (DAX) language allowed us to perform advanced calculations and create calculated columns and measures, adding depth to our analysis. This step was crucial in uncovering hidden patterns and trends within the data.

5) Chart Building:

The project came to life with captivating charts and visualizations. The appropriate chart types (e.g., bar charts, line charts, scatter plots) were carefully selected to represent data insights effectively. Color schemes and formatting were thoughtfully chosen to enhance the overall aesthetic.

6) Dashboard Creation:

To provide a holistic view of the analysis, interactive dashboards were designed to allow users to explore data intuitively. These dashboards incorporated multiple charts, slicers, and filters, enabling users to customize their experience and gain insights tailored to their needs.

🛠 Tools used

MS SQL,POWER BI, MS WORD

SQL queries

1) Total_revenue

2) Total Orders

3) Total Pizza Sold

4) Amount Spent per Order

5) Average number of Pizza Per order

6) Total_revenue_per_months

7) Weekly Trend for total Orders

8) Monthly trend for Total Orders

9) Percentage of Sales by Pizza Category

10) Percentage sales for january month

11) Percentage Sales of Pizza by pizza_size

12) Percentage Sales of pizza by pizza_size for 1st quarter

13) Top 5 Selling pizzas based on total_revenue

14) Bottom 5 Selling pizzas based on total_revenue

15) Top 5 Selling pizzas based on total_quantity

Some of the insights

1) Daily trend for total orders

orders are highest on weekends Friday/saturday evenings

2) Monthly trend for total orders

maximum numbers of orders were placed on january & july month

3) Percentage of sales by pizza size

Large size contributes to maximum sales

4) Total pizza sold by pizza Category

Classic category contributes to maximum sales

5) Top 5 and bottom 5 pizza by revenue

The Thai chicken Pizza contributes to highest revenue and The Brie Carrie Pizza contributes to Lowest revenue

6) Top 5 and bottom 5 Pizza based on total quantity

The Classic Deluxe Pizza contributes to maximum total quanity and The Brie Carrie Pizza contributes to minimum total quanity

Dashboards

Suggestions

1) Promotion Strategies:

special promotions or discounts during the weekends, especially on Fridays and Saturdays should be given to capitalize on the higher order volume during those times

2) Pizza Size Focus:

Given that large-sized pizzas contribute significantly to sales, consider expanding the variety of large pizzas or running promotions that encourage customers to choose larger sizes.

3) Pizza Category Enhancement:

Expand the classic pizza category by introducing new classic pizza options or variations to maintain and increase sales in this popular category..

4) Low-Revenue Pizza Evaluation:

Review the performance of the lowest revenue pizzas, such as The Brie Carrie Pizza. Assess whether it's worth reimagining these items or discontinuing them from the menu.

5) Inventory Planning:

Plan inventory management strategies to meet the demand for popular pizza options during peak times, ensuring no stockouts or delays.

Challenges faced and solutions

Challenge 1:

Extracting actionable insights from a large dataset can be daunting. Crafting SQL queries that yield meaningful insights can be time-consuming.

Approach:

Embraced this challenge by employing advanced SQL techniques, including aggregations, subqueries, and joins. By systematically analyzing the data, we identified trends, correlations, and outliers to uncover valuable insights that could drive sales growth.

Challenge 2:

Selecting the most appropriate chart types in Power BI to effectively communicate insights can be complex. Using the wrong chart type may obscure the message or mislead stakeholders.

Approach:

Tackled this challenge by considering the nature of each insight. Meticulously matched the data and insights with appropriate chart types such as bar charts, line graphs, scatter plots, and heatmaps. This approach ensured that our visualizations effectively conveyed the information at hand.

Challenge 3:

Building an interactive Power BI dashboard with slicers that cater to diverse user needs can be intricate. Balancing functionality and simplicity is crucial.

Approach:

arefully designed the dashboard layout and integrated slicers strategically.