SuperStore Sales Analysis in Power Bi

This visualization project aims to analyze a fictitious superstore’s sales performance between 2016 and 2019 using Microsoft Power BI.

AYN superstore is located in the United States of America where the shops are distributed in the US and the products are shipped to consumers, corporate and home office items. These items include furniture, technology, and office supplies.

Dashboard of Profit in Sales

The three key steps involved in this project include:

1. Data Collection
2. Data Preparation/Cleaning
3. Analysis and Visualization

Data Collection

The Dataset used in this Dashboard is picked up from Kaggle as a fresher I don’t have the set skills to collect data so I took the quickest and easiest way.

Data Preparation/Cleaning

The Data Cleaning involved tasks like handling missing values, removing duplicates, correcting data types, and addressing inconsistencies in columns, ensuring a more accurate and usable dataset for analysis. This phase enabled me to improve the quality of data, and have better insights and decision-making from the Sample SuperStore Dataset.

Power Query Editing/Cleaning

Analysis and Visualization

As I mentioned before the goal of this project is to carry out a sales performance analysis for the store. A sales performance analysis should answer the following questions:

1. What are the total sales accrued by the states?
2. What is the revenue accrued annually by the states?
3. What is the average revenue accrued by the states?
4. Which category generates the most revenue for the country?
5. What Region, Category, and Segment yields more revenue at different periods?

1. What is the total sales accrued by the state?

The Sales for the store can be obtained by selecting the “Sales” column and checking the sum option from the dropdown in the field section. We can see the total sales, quantity, and profit made by the store throughout the years between 2017 to 2019 as $2,300,000 (2 million and 3 hundred thousand dollars only)

2. What is the revenue accrued annually by the country?

Analyzing the sales data from 2017 to 2019 through a yearly line graph provides a comprehensive visual narrative of the Superstore’s performance over these years. The graph reveals trends, patterns, and fluctuations in sales across the three years, allowing for a clear understanding of the company’s growth trajectory or potential challenges. This visualization aids in identifying seasonal variations, year-on-year growth, or any noteworthy changes in sales patterns, offering valuable insights for strategic planning, resource allocation, and decision-making within the organization.

Line Graph of Sales Annually and Quarterly

3. What is the average sales accrued by the state?

This can easily be answered by referring to the screenshots in question 1. The average annual Sales can easily be obtained by adjusting the ‘Sales’ Column measures to the average and selecting a particular State of choice.

4. Which category and state generates the most revenue for the country?

This data cut across 49 distinct states in the USA. Displaying the revenue per state for all 49 states would not look nice; for that reason, this was limited to the top 5 states by revenue generated. The top 5 states and profits made by sub-categories that generated the most revenue between 2017 and 2019 are shown in the screenshot below:

5. What Region, Category, and Segment yields more revenue at different periods?

If you own a store (or have ever worked in a store), you would understand the importance of identifying products that contribute greatly to sales. This will help you save money on stock-ups, provide insight to the company’s marketing team for targeted advertisements, and a lot more.

This has been shown in the form of a donut chart which in my eyes is the best representation of understanding different sections of sales procured by different categories:

Between 2017 and 2019, the West region yielded exactly $108K which accounted for 37.85% of the total revenue generated by the store during that period.

Technology yielded $145K and Consumers yielded exactly $134K which shows where the next target should be for the corporations if they trust the given trends.

Conclusion

There are a lot more insights that can be discovered from this analysis. The Power BI report is available for viewing here. Comments and improvement actions are welcome.

I hope you enjoyed this article. Did you learn something new? Please leave a comment below, share, and commend.

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